Introducing Python

Introducing Python
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Introducing Python
Bill Lubanovic
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Introducing Python
by Bill Lubanovic
Copyright © 2015 Bill Lubanovic. All rights reserved.
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ISBN: 978-1-449-35936-2
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To Mary, Karin, Tom, and Roxie.
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Table of Contents
Preface. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xv
1. A Taste of Py. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
Python in the Real World
Python versus Language X
So, Why Python?
When Not to Use Python
Python 2 versus Python 3
Installing Python
Running Python
Using the Interactive Interpreter
Use Python Files
What’s Next?
Your Moment of Zen
Things to Do
5
6
9
9
10
10
11
11
12
13
13
14
2. Py Ingredients: Numbers, Strings, and Variables. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
Variables, Names, and Objects
Numbers
Integers
Precedence
Bases
Type Conversions
How Big Is an int?
Floats
Math Functions
Strings
Create with Quotes
Convert Data Types by Using str()
15
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25
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Escape with \
Combine with +
Duplicate with *
Extract a Character with []
Slice with [ start : end : step ]
Get Length with len()
Split with split()
Combine with join()
Playing with Strings
Case and Alignment
Substitute with replace()
More String Things
Things to Do
30
31
32
32
33
35
35
36
36
37
38
39
39
3. Py Filling: Lists, Tuples, Dictionaries, and Sets. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
Lists and Tuples
Lists
Create with [] or list()
Convert Other Data Types to Lists with list()
Get an Item by Using [ offset ]
Lists of Lists
Change an Item by [ offset ]
Get a Slice to Extract Items by Offset Range
Add an Item to the End with append()
Combine Lists by Using extend() or +=
Add an Item by Offset with insert()
Delete an Item by Offset with del
Delete an Item by Value with remove()
Get an Item by Offset and Delete It by Using pop()
Find an Item’s Offset by Value with index()
Test for a Value with in
Count Occurrences of a Value by Using count()
Convert to a String with join()
Reorder Items with sort()
Get Length by Using len()
Assign with =, Copy with copy()
Tuples
Create a Tuple by Using ()
Tuples versus Lists
Dictionaries
Create with {}
Convert by Using dict()
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42
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54
Add or Change an Item by [ key ]
Combine Dictionaries with update()
Delete an Item by Key with del
Delete All Items by Using clear()
Test for a Key by Using in
Get an Item by [ key ]
Get All Keys by Using keys()
Get All Values by Using values()
Get All Key-Value Pairs by Using items()
Assign with =, Copy with copy()
Sets
Create with set()
Convert from Other Data Types with set()
Test for Value by Using in
Combinations and Operators
Compare Data Structures
Make Bigger Data Structures
Things to Do
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56
57
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58
58
59
59
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60
61
61
62
63
66
66
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4. Py Crust: Code Structures. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
Comment with #
Continue Lines with \
Compare with if, elif, and else
What Is True?
Repeat with while
Cancel with break
Skip Ahead with continue
Check break Use with else
Iterate with for
Cancel with break
Skip with continue
Check break Use with else
Iterate Multiple Sequences with zip()
Generate Number Sequences with range()
Other Iterators
Comprehensions
List Comprehensions
Dictionary Comprehensions
Set Comprehensions
Generator Comprehensions
Functions
Positional Arguments
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70
71
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75
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76
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79
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Keyword Arguments
Specify Default Parameter Values
Gather Positional Arguments with *
Gather Keyword Arguments with **
Docstrings
Functions Are First-Class Citizens
Inner Functions
Closures
Anonymous Functions: the lambda() Function
Generators
Decorators
Namespaces and Scope
Uses of _ and __ in Names
Handle Errors with try and except
Make Your Own Exceptions
Things to Do
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90
91
92
92
93
95
95
96
98
99
101
103
103
105
106
5. Py Boxes: Modules, Packages, and Programs. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109
Standalone Programs
Command-Line Arguments
Modules and the import Statement
Import a Module
Import a Module with Another Name
Import Only What You Want from a Module
Module Search Path
Packages
The Python Standard Library
Handle Missing Keys with setdefault() and defaultdict()
Count Items with Counter()
Order by Key with OrderedDict()
Stack + Queue == deque
Iterate over Code Structures with itertools
Print Nicely with pprint()
More Batteries: Get Other Python Code
Things to Do
109
110
110
110
112
112
113
113
114
114
116
118
118
119
120
121
121
6. Oh Oh: Objects and Classes. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123
What Are Objects?
Define a Class with class
Inheritance
Override a Method
Add a Method
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Get Help from Your Parent with super
In self Defense
Get and Set Attribute Values with Properties
Name Mangling for Privacy
Method Types
Duck Typing
Special Methods
Composition
When to Use Classes and Objects versus Modules
Named Tuples
Things to Do
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130
131
133
134
135
137
140
141
141
143
7. Mangle Data Like a Pro. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145
Text Strings
Unicode
Format
Match with Regular Expressions
Binary Data
bytes and bytearray
Convert Binary Data with struct
Other Binary Data Tools
Convert Bytes/Strings with binascii()
Bit Operators
Things to Do
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145
152
156
163
164
165
168
169
169
170
8. Data Has to Go Somewhere. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173
File Input/Output
Write a Text File with write()
Read a Text File with read(), readline(), or readlines()
Write a Binary File with write()
Read a Binary File with read()
Close Files Automatically by Using with
Change Position with seek()
Structured Text Files
CSV
XML
HTML
JSON
YAML
A Security Note
Configuration Files
Other Interchange Formats
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Serialize by Using pickle
Structured Binary Files
Spreadsheets
HDF5
Relational Databases
SQL
DB-API
SQLite
MySQL
PostgreSQL
SQLAlchemy
NoSQL Data Stores
The dbm Family
Memcached
Redis
Other NoSQL
Full-Text Databases
Things to Do
191
192
192
192
193
194
195
196
198
198
198
204
204
205
206
214
214
215
9. The Web, Untangled. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 217
Web Clients
Test with telnet
Python’s Standard Web Libraries
Beyond the Standard Library: Requests
Web Servers
The Simplest Python Web Server
Web Server Gateway Interface
Frameworks
Bottle
Flask
Non-Python Web Servers
Other Frameworks
Web Services and Automation
The webbrowser Module
Web APIs and Representational State Transfer
JSON
Crawl and Scrape
Scrape HTML with BeautifulSoup
Things to Do
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219
220
222
223
223
224
225
226
228
232
234
236
236
236
237
237
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239
10. Systems. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 241
Files
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Create with open()
Check Existence with exists()
Check Type with isfile()
Copy with copy()
Change Name with rename()
Link with link() or symlink()
Change Permissions with chmod()
Change Ownership with chown()
Get a Pathname with abspath()
Get a symlink Pathname with realpath()
Delete a File with remove()
Directories
Create with mkdir()
Delete with rmdir()
List Contents with listdir()
Change Current Directory with chdir()
List Matching Files with glob()
Programs and Processes
Create a Process with subprocess
Create a Process with multiprocessing
Kill a Process with terminate()
Calendars and Clocks
The datetime Module
Using the time Module
Read and Write Dates and Times
Alternative Modules
Things to Do
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243
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244
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11. Concurrency and Networks. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 261
Concurrency
Queues
Processes
Threads
Green Threads and gevent
twisted
asyncio
Redis
Beyond Queues
Networks
Patterns
The Publish-Subscribe Model
TCP/IP
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263
264
265
267
270
271
271
275
276
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Sockets
ZeroMQ
Scapy
Internet Services
Web Services and APIs
Remote Processing
Big Fat Data and MapReduce
Working in the Clouds
Things to Do
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285
289
290
291
292
297
298
302
12. Be a Pythonista. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 303
About Programming
Find Python Code
Install Packages
Use pip
Use a Package Manager
Install from Source
Integrated Development Environments
IDLE
PyCharm
IPython
Name and Document
Testing Your Code
Check with pylint, pyflakes, and pep8
Test with unittest
Test with doctest
Test with nose
Other Test Frameworks
Continuous Integration
Debugging Python Code
Debug with pdb
Logging Error Messages
Optimize Your Code
Measure Timing
Algorithms and Data Structures
Cython, NumPy, and C Extensions
PyPy
Source Control
Mercurial
Git
Clone This Book
How You Can Learn More
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304
304
305
305
306
306
306
306
307
307
309
309
311
315
316
317
317
317
318
324
326
327
328
329
330
330
330
330
333
333
Books
Websites
Groups
Conferences
Coming Attractions
334
334
335
335
335
A. Py Art. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 337
B. Py at Work. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 351
C. Py Sci. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 365
D. Install Python 3. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 385
E. Answers to Exercises. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 395
F. Cheat Sheets. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 429
Index. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 433
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Preface
This book will introduce you to the Python programming language. It’s aimed at be‐
ginning programmers, but even if you’ve written programs before and just want to add
Python to your list of languages, Introducing Python will get you started.
It’s an unhurried introduction, taking small steps from the basics to more involved and
varied topics. I mix cookbook and tutorial styles to explain new terms and ideas, but
not too many at once. Real Python code is included early and often.
Even though this is an introduction, I include some topics that might seem advanced,
such as NoSQL databases and message-passing libraries. I chose these because they can
solve some problems better than standard solutions. You’ll download and install exter‐
nal Python packages, which is good to know when the “batteries included” with Python
don’t fit your application. And it’s fun to try something new.
I also include some examples of what not to do, especially if you’ve programmed in other
languages and try to adapt those styles to Python. And I won’t pretend that Python is
perfect; I’ll show you what to avoid.
Sometimes, I’ll include a note such as this when something might be
confusing or there’s a more appropriate Pythonic way to do it.
Audience
This book is for anybody interested in learning what seems to be emerging as the world’s
most popular computing language, whether or not you have learned any programming
before.
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Outline
The first seven chapters explain Python’s basics, and you should read them in order. The
later chapters show how Python is used in specific application areas such as the Web,
databases, networks, and so on; read them in any order you like. The first three appen‐
dices showcase Python in the arts, business, and science. Then, you see how to install
Python 3 if you don’t have it. Next are answers to the end-of-chapter exercises, and then
finally, a few cheat sheets of useful things.
Chapter 1
Programs are like directions for making socks or grilling potatoes. Some real Python
programs give a little demonstration of the language’s look, capabilities, and uses in the
real world. Python fares well when compared with other languages, but it has some
imperfections. An older version of Python (Python 2) is giving way to a newer one
(Python 3). If you have Python 2, install Python 3 on your computer. Use the interactive
interpreter to try examples from this book yourself.
Chapter 2
This chapter shows Python’s simplest data types: booleans, integers, floating-point
numbers, and text strings. You also learn the basic math and text operations.
Chapter 3
We step up to Python’s higher-level built-in data structures: lists, tuples, dictionaries,
and sets. You use these as you would Legos to build much more complicated structures.
You learn how to step through them by using iterators and comprehensions.
Chapter 4
In Chapter 4, you weave the data structures of the previous chapters with code structures
to compare, choose, or repeat. You see how to package code in functions and handle
errors with exceptions.
Chapter 5
This chapter demonstrates how to scale out to larger code structures: modules, packages,
and programs. You see where to put code and data, get data in and out, handle options,
tour the Python Standard Library, and take a glance at what lies beyond.
Chapter 6
If you’ve done object-oriented programming in other languages, Python is a bit more
relaxed. Chapter 6 explains when to use objects and classes, and when it’s better to use
modules or even lists and dictionaries.
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Chapter 7
Learn to manage data like a pro. This chapter is all about text and binary data, the joy
of Unicode characters, and I/O.
Chapter 8
Data needs to go somewhere. In this chapter, you begin with basic flat files, directories,
and filesystems. Then, you see how to handle common file formats such as CSV, JSON,
and XML. You also explore how to store and retrieve with relational databases, and even
some recent NoSQL data stores.
Chapter 9
The Web gets its own chapter, which covers clients, servers, scraping, APIs, and frame‐
works. In Chapter 9, you work up a real website with request parameters and templates.
Chapter 10
This is the hard-core system chapter. In this one, you learn to manage programs, pro‐
cesses, and threads; deal with dates and times; and automate some system administra‐
tion tasks.
Chapter 11
Networking is the subject here: services, protocols, and APIs. Examples range from lowlevel TCP sockets, to messaging libraries and queuing systems, to cloud deployment.
Chapter 12
This chapter contains tips for Python developers, including installing, using IDEs, test‐
ing, debugging, logging, source control, and documentation. Chapter 12 also helps you
to find and install useful third-party packages, package your own code for reuse, and
learn where to get more information. Good luck.
Appendix A
The first appendix delves into what people are doing with Python in the arts: graphics,
music, animation, and games.
Appendix B
Python has specific applications for business: data visualization (plots, graphs, and
maps), security, and regulation.
Appendix C
Python has a strong presence in science: math and statistics, physical science, bioscience,
and medicine. Appendix C features NumPy, SciPy, and Pandas.
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Appendix D
If you don’t already have Python 3 on your computer, this appendix shows you how to
install it, no matter if you’re running Windows, Mac OS/X, Linux, or Unix.
Appendix E
This has the answers to the end-of-chapter exercises. Don’t peek here until you’ve tried
the exercises yourself.
Appendix F
This appendix contains cheat sheets to use as a quick reference.
Python Versions
Computer languages change over time as developers add features and fix mistakes. The
examples in this book were written and tested while running Python version 3.3. Version
3.4 was released as this book was being edited, and I’ll talk about a few of its additions.
If you want to know what was added to Python and when, try the What’s New in
Python page. It’s a technical reference; a bit heavy when you’re just starting with Python,
but may be useful in the future if you ever have to get programs to work on computers
with different Python versions.
Conventions Used in This Book
The following typographical conventions are used in this book:
Italic
Indicates new terms, URLs, email addresses, filenames, and file extensions.
Constant width
Used for program listings, as well as within paragraphs to refer to program elements
such as variables, functions, and data types.
Constant width bold
Shows commands or other text that should be typed literally by the user.
Constant width italic
Shows text that should be replaced with user-supplied values or by values deter‐
mined by context.
This icon signifies a tip, suggestion, or general note.
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This icon indicates a warning or caution.
Using Code Examples
The substantial code examples in this book—although not the exercises, which are
challenges for the reader—are available online for you to download. This book is here
to help you get your job done. In general, you may use the code in this book in your
programs and documentation. You do not need to contact us for permission unless
you’re reproducing a significant portion of the code. For example, writing a program
that uses several chunks of code from this book does not require permission. Selling or
distributing a CD-ROM of examples from O’Reilly books does require permission. An‐
swering a question by citing this book and quoting example code does not require
permission. Incorporating a significant amount of example code from this book into
your product’s documentation does require permission.
We appreciate, but do not require, attribution. An attribution usually includes the title,
author, publisher, and ISBN. For example: “Introducing Python by Bill Lubanovic
(O’Reilly). Copyright 2015 Bill Lubanovic, 978-1-449-35936-2.”
If you feel your use of code examples falls outside fair use or the permission given here,
feel free to contact us at [email protected]
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Acknowledgments
Thanks go to the many people who read and commented on my draft. I’d like to par‐
ticularly mention the careful reviews by Eli Bessert, Henry Canival, Jeremy Elliott,
Monte Milanuk, Loïc Pefferkorn, and Steven Wayne.
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CHAPTER 1
A Taste of Py
Let’s begin with a mini-mystery and its solution. What do you think the following two
lines mean?
(Row 1): (RS) K18,ssk,k1,turn work.
(Row 2): (WS) Sl 1 pwise,p5,p2tog,p1,turn.
It looks technical, like some kind of computer program. Actually, it’s a knitting pattern;
specifically, a fragment describing how to turn the heel of a sock. This makes as much
sense to me as the New York Times crossword puzzle does to my cat, but my wife un‐
derstands it perfectly. If you’re a knitter, you do, too.
Let’s try another example. You’ll figure out its purpose right away, although you might
not know its final product.
1/2 c. butter or margarine
1/2 c. cream
2 1/2 c. flour
1 t. salt
1 T. sugar
4 c. riced potatoes (cold)
Be sure all ingredients are cold before adding flour.
Mix all ingredients.
Knead thoroughly.
Form into 20 balls. Store cold until the next step.
For each ball:
Spread flour on cloth.
Roll ball into a circle with a grooved rolling pin.
Fry on griddle until brown spots appear.
Turn over and fry other side.
Even if you don’t cook, you probably recognized that it’s a recipe: a list of food ingredients
followed by directions for preparation. But what does it make? It’s lefse, a Norwegian
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delicacy that resembles a tortilla. Slather on some butter and jam or whatever you like,
roll it up, and enjoy.
The knitting pattern and the recipe share some features:
• A fixed vocabulary of words, abbreviations, and symbols. Some might be familiar,
others mystifying.
• Rules about what can be said, and where—their syntax.
• A sequence of operations to be performed in order.
• Sometimes, a repetition of some operations (a loop), such as the method for frying
each piece of lefse.
• Sometimes, a reference to another sequence of operations (in computer terms, a
function). In the recipe, you might need to refer to another recipe for ricing potatoes.
• Assumed knowledge about the context. The recipe assumes you know what water
is and how to boil it. The knitting pattern assumes that you can knit and purl without
stabbing yourself too often.
• An expected result. In our examples, something for your feet and something for
your stomach. Just don’t mix them up.
You’ll see all of these ideas in computer programs. I used these nonprograms to demon‐
strate that programming isn’t that mysterious. It’s just a matter of learning the right
words and the rules.
Let’s leave these stand-ins and see a real program. What does this do?
for countdown in 5, 4, 3, 2, 1, "hey!":
print(countdown)
If you guessed that it’s a Python program that prints the lines:
5
4
3
2
1
hey!
then you know that Python can be easier to learn than a recipe or knitting pattern. And,
you can practice writing Python programs from the comfort and safety of your desk,
far from the harrowing dangers of hot water and pointy sticks.
The Python program has some special words and symbols—for, in, print, commas,
colons, parentheses, and so on—that are important parts of the language’s syntax. The
good news is that Python has a nicer syntax, and less of it to remember, than most
computer languages. It seems more natural—almost like a recipe.
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Here’s another tiny Python program that selects a television news cliché from a Python
list and prints it:
cliches = [
"At the end of the day",
"Having said that",
"The fact of the matter is",
"Be that as it may",
"The bottom line is",
"If you will",
]
print(cliches[3])
The program prints the fourth cliché:
Be that as it may
A Python list such as cliches is a sequence of values, accessed by their offset from the
beginning of the list. The first value is at offset 0, and the fourth value is at offset 3.
People count from 1, so it might seem weird to count from 0. It helps
to think in terms of offsets instead of positions.
Lists are very common in Python, and Chapter 3 shows how to use them.
Following is another program that also prints a quote, but this time referenced by the
person who said it rather than its position in a list:
quotes = {
"Moe": "A wise guy, huh?",
"Larry": "Ow!",
"Curly": "Nyuk nyuk!",
}
stooge = "Curly"
print(stooge, "says:", quotes[stooge])
If you were to run this little program, it would print the following:
Curly says: Nyuk nyuk!
quotes is a Python dictionary—a collection of unique keys (in this example, the name
of the Stooge) and associated values (here, a notable quote of that Stooge). Using a
dictionary, you can store and look up things by name, which is often a useful alternative
to a list. You can read much more about dictionaries in Chapter 3.
The cliché example uses square brackets ([ and ]) to make a Python list, and the stooge
example uses curly brackets ({ and }, which are no relation to Curly), to make a Python
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dictionary. These are examples of Python’s syntax, and in the next few chapters, you’ll
see much more.
And now for something completely different: Example 1-1 presents a Python program
performing a more complex series of tasks. Don’t expect to understand how the program
works yet; that’s what this book is for. The intent is to introduce you to the look and feel
of a typical nontrivial Python program. If you know other computer languages, evaluate
how Python compares.
Example 1-1 connects to a YouTube website and retrieves information on the most
highly rated videos at the moment. If it returned a normal web page full of HTMLformatted text, it would be hard to dig out the information you want (I talk about web
scraping in “Crawl and Scrape” on page 237). Instead, it returns data in JSON format, which
is meant for processing by computers. JSON, or JavaScript Object Notation, is a humanreadable text format that describes the types, values, and order of the values within it.
It’s like a little programming language and has become a popular way to exchange data
among different computer languages and systems. You read about JSON in “JSON” on
page 185.
Python programs can translate JSON text into Python data structures—the kind you’ll
see in the next two chapters—as though you wrote a program to create them yourself.
There’s a lot of data in this YouTube response, so for this example I’ll just print the titles
of the first six videos. Again, this is a complete Python program that you can run yourself.
Example 1-1. intro/youtube.py
import json
from urllib.request import urlopen
url = "https://gdata.youtube.com/feeds/api/standardfeeds/top_rated?alt=json"
response = urlopen(url)
contents = response.read()
text = contents.decode('utf8')
data = json.loads(text)
for video in data['feed']['entry'][0:6]:
print(video['title']['$t'])
The last time I ran this program I got this output:
Evolution of Dance - By Judson Laipply
Linkin Park - Numb
Potter Puppet Pals: The Mysterious Ticking Noise
"Chocolate Rain" Original Song by Tay Zonday
Charlie bit my finger - again !
The Mean Kitty Song
This little Python program did a lot in nine fairly readable lines. If you don’t know all
these terms, don’t worry; you will within the next few chapters:
• Line 1: import all the code from the Python standard library called json
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• Line 2: import only the urlopen function from the standard urllib library
• Line 3: assign a YouTube URL to the variable url
• Line 4: connect to the web server at that URL and request a particular web service
• Line 5: get the response data and assign to the variable contents
• Line 6: decode contents to a text string in JSON format, and assign to the variable
text
• Line 7: convert text to data—Python data structures about videos
• Line 8: get the information about one video at a time into the variable video
• Line 8: use a two-level Python dictionary (data['feed']['entry']) and a slice
([0:6])
• Line 9: use the print function to print only the title of the video
The video information is a combination of various Python data structures that you just
saw recently, and which are discussed in Chapter 3.
In the previous example, we used some of Python’s standard library modules (programs
that are included with Python when it’s installed), but there’s nothing sacred about them.
The code that follows shows a rewrite that uses an external Python software package
called requests:
import requests
url = "https://gdata.youtube.com/feeds/api/standardfeeds/top_rated?alt=json"
response = requests.get(url)
data = response.json()
for video in data['feed']['entry'][0:6]:
print(video['title']['$t'])
The new version is only six lines, and I’d guess it’s more readable for most people. I have
a lot more to say about requests and other externally authored Python software in
Chapter 5.
Python in the Real World
So, is learning Python worth the time and effort? Is it a fad or a toy? Actually, it’s been
around since 1991 (longer than Java), and is consistently in the top 10 most popular
computing languages. People are paid to write Python programs—serious stuff that you
use every day, such as Google, YouTube, Dropbox, Netflix, and Hulu. I’ve used it for
production applications as varied as an email search appliance and an ecommerce web‐
site. Python has a reputation for productivity that appeals to fast-moving organizations.
You’ll find Python in many computing environments, including the following:
• The command line in a monitor or terminal window
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• Graphical user interfaces, including the Web
• The Web, on the client and server sides
• Backend servers supporting large popular sites
• The cloud (servers managed by third parties)
• Mobile devices
• Embedded devices
Python programs range from one-off scripts—such as those you’ve seen so far in this
chapter—to million-line systems. We’ll look at its uses in websites, system administra‐
tion, and data manipulation. We’ll also look at specific uses of Python in the arts, science,
and business.
Python versus Language X
How does Python compare against other languages? Where and when would you choose
one over the other? In this section, I’ll show code samples from other languages, just so
you can see what the competition looks like. You are not expected to understand these
if you haven’t worked with them. (By the time you get to the final Python sample, you
might be relieved that you haven’t had to work with some of the others.) If you’re only
interested in Python, you won’t miss anything if you just skip to the next section.
Each program is supposed to print a number and say a little about the language.
If you use a terminal or terminal window, the program that reads what you type, runs
it, and displays the results is called the shell program. The Windows shell is called cmd;
it runs batch files with the suffix .bat. Linux and other Unix-like systems (including
Mac OS X) have many shell programs, the most popular is called bash or sh. The shell
has simple abilities, such as simple logic and expanding wildcard symbols such as * into
filenames. You can save commands in files called shell scripts and run them later. These
might be the first programs you encountered as a programmer. The problem is that shell
scripts don’t scale well beyond a few hundred lines, and they are much slower than the
alternative languages. The next snippet shows a little shell program:
#!/bin/sh
language=0
echo "Language $language: I am the shell. So there."
If you saved this in a file as meh.sh and ran it with sh meh.sh, you would see the following
on your display:
Language 0: I am the shell. So there.
Old stalwarts C and C++ are fairly low-level languages, used when speed is most im‐
portant. They’re harder to learn, and require you to keep track of many details, which
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can lead to crashes and problems that are hard to diagnose. Here’s a look at a little C
program:
#include <stdio.h>
int main(int argc, char *argv[]) {
int language = 1;
printf("Language %d: I am C! Behold me and tremble!\n", language);
return 0;
}
C++ has the C family resemblance but with distinctive features:
#include <iostream>
using namespace std;
int main() {
int language = 2;
cout << "Language " << language << \
": I am C++! Pay no attention to that C behind the curtain!" << \
endl;
return(0);
}
Java and C# are successors to C and C++ that avoid some of the latters’ problems, but
are somewhat verbose and restrictive. The example that follows shows some Java:
public class Overlord {
public static void main (String[] args) {
int language = 3;
System.out.format("Language %d: I am Java! Scarier than C!\n", language);
}
}
If you haven’t written programs in any of these languages, you might wonder: what is
all that stuff? Some languages carry substantial syntactic baggage. They’re sometimes
called static languages because they require you to specify some low-level details for the
computer. Let me explain.
Languages have variables—names for values that you want to use in a program. Static
languages make you declare the type of each variable: how much room it will use in
memory, and what you can do with it. The computer uses this information to compile
the program into very low-level machine language (specific to the computer’s hardware
and easier for it to understand, but harder for humans). Computer language designers
often must decide between making things easier for people or for computers. Declaring
variable types helps the computer to catch some mistakes and run faster, but it does
require more up-front human thinking and typing. Much of the code that you saw in
the C, C++, and Java examples was required to declare types. For example, in all of them
the int declaration was needed to treat the variable language as an integer. (Other types
include floating-point numbers such as 3.14159 and character or text data, which are
stored differently.)
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Then why are they called static languages? Because variables in those languages can’t
ever change their type; they’re static. An integer is an integer, forever and ever.
In contrast, dynamic languages (also called scripting languages) do not force you to de‐
clare variable types before using them. If you type something such as x = 5, a dynamic
language knows that 5 is an integer; thus, the variable x is, too. These languages let you
accomplish more with fewer lines of code. Instead of being compiled, they are inter‐
preted by a program called—surprise!—an interpreter. Dynamic languages are often
slower than compiled static languages, but their speed is improving as their interpreters
become more optimized. For a long time, dynamic languages were used mainly for short
programs (scripts), often to prepare data for processing by longer programs written in
static languages. Such programs have been called glue code. Although dynamic lan‐
guages are well suited for this purpose, today they are able to tackle most big processing
tasks as well.
The all-purpose dynamic language for many years was Perl. Perl is very powerful and
has extensive libraries. Yet, its syntax can be awkward, and the language seems to have
lost momentum in the last few years to Python and Ruby. This example regales you with
a Perl bon mot:
my $language = 4;
print "Language $language: I am Perl, the camel of languages.\n";
Ruby is a more recent language. It borrows a little from Perl, and is popular mostly
because of Ruby on Rails, a web development framework. It’s used in many of the same
areas as Python, and the choice of one or the other might boil down to a matter of taste,
or available libraries for your particular application. The code example here depicts a
Ruby snippet:
language = 5
puts "Language #{language}: I am Ruby, ready and aglow."
PHP, which you can see in the example that follows, is very popular for web development
because it makes it easy to combine HTML and code. However, the PHP language itself
has a number of gotchas, and PHP has not caught on as a general language outside of
the Web.
<?PHP
$language = 6;
echo "Language $language: I am PHP. The web is <i>mine</i>, I say.\n";
?>
The example that follows presents Python’s rebuttal:
language = 7
print("Language %s: I am Python. What's for supper?" % language)
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So, Why Python?
Python is a good general-purpose, high-level language. Its design makes it very
readable, which is more important than it sounds. Every computer program is written
only once, but read and revised many times, often by many people. Being readable also
makes it easier to learn and remember, hence more writeable. Compared with other
popular languages, Python has a gentle learning curve that makes you productive soon‐
er, yet it has depths that you can explore as you gain expertise.
Python’s relative terseness makes it possible for you to write a program that’s much
smaller than its equivalent in a static language. Studies have shown that programmers
tend to produce roughly the same number of lines of code per day—regardless of the
language—so, writing half the lines of code doubles your productivity, just like that.
Python is the not-so-secret weapon of many companies that think this is important.
Python is the most popular language for introductory computer science courses at the
top American colleges. It is also the most popular language for evaluating programming
skill by over two thousand employers.
And of course, it’s free, as in beer and speech. Write anything you want with Python,
and use it anywhere, freely. No one can read your Python program and say, “That’s a
nice little program you have there. It would be too bad if something happened to it.”
Python runs almost everywhere and has “batteries included”—a metric boatload of
useful software in its standard library.
But, maybe the best reason to use Python is an unexpected one: people generally like it.
They actually enjoy programming with it, rather than treating it as just another tool to
get stuff done. Often they’ll say that they miss some feature of Python when they need
to work in another language. And that’s what separates Python from most of its peers.
When Not to Use Python
Python isn’t the best language for every situation.
It is not installed everywhere by default. Appendix D shows you how to install Python
if you don’t already have it on your computer.
It’s fast enough for most applications, but it might not be fast enough for some of the
more demanding ones. If your program spends most of its time calculating things (the
technical term is CPU-bound), a program written in C, C++, or Java will generally run
faster than its Python equivalent. But not always!
• Sometimes a better algorithm (a stepwise solution) in Python beats an inefficient
one in C. The greater speed of development in Python gives you more time to
experiment with alternatives.
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• In many applications, a program twiddles its thumbs while awaiting a response
from some server across a network. The CPU (central processing unit, the com‐
puter’s chip that does all the calculating) is barely involved; consequently, end-toend times between static and dynamic programs will be close.
• The standard Python interpreter is written in C and can be extended with C code.
I discuss this a little in “Optimize Your Code” on page 326.
• Python interpreters are becoming faster. Java was terribly slow in its infancy, and a
lot of research and money went into speeding it up. Python is not owned by a
corporation, so its enhancements have been more gradual. In “PyPy” on page 330, I
talk about the PyPy project and its implications.
• You might have an extremely demanding application, and no matter what you do,
Python doesn’t meet your needs. Then, as Ian Holm said in the movie Alien, you
have my sympathies. The usual alternatives are C, C++, and Java, but a newer lan‐
guage called Go (which feels like Python but performs like C) could be an answer.
Python 2 versus Python 3
The biggest issue that you’ll confront at the moment is that there are two versions of
Python out there. Python 2 has been around forever and is preinstalled on Linux and
Apple computers. It has been an excellent language, but nothing’s perfect. In computer
languages, as in many other areas, some mistakes are cosmetic and easy to fix, whereas
others are hard. Hard fixes are incompatible: new programs written with them will not
work on the old Python system, and old programs written before the fix will not work
on the new system.
Python’s creator (Guido van Rossum) and others decided to bundle the hard fixes to‐
gether and call it Python 3. Python 2 is the past, and Python 3 is the future. The last
version of Python 2 is 2.7, and it will be supported for a long time, but it’s the end of the
line; there will be no Python 2.8. New development will be in Python 3.
This book features Python 3. If you’ve been using Python 2, it’s almost identical. The
most obvious change is how to call print. The most important change is the handling
of Unicode characters, which is covered in Chapter 2 and Chapter 7. Conversion of
popular Python software has been gradual, with the usual chicken-and-egg analogies.
But now, it looks like we’ve finally reached a tipping point.
Installing Python
Rather than cluttering this chapter, the details on how to install Python 3 are in Appendix
D. If you don’t have Python 3, or aren’t sure, go there and see what to do for your
computer.
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Running Python
After you have installed a working copy of Python 3, you can use it to run the Python
programs in this book as well as your own Python code. How do you actually run a
Python program? There are two main ways:
• The interactive interpreter that comes with Python gives you the capability to ex‐
periment with small programs. You type commands line by line and see the results
immediately. With the tight coupling between typing and seeing, you can experi‐
ment faster. I’ll use the interactive interpreter to demonstrate language features,
and you can type the same commands in your own Python environment.
• For everything else, store your Python programs in text files, normally with the .py
extension, and run them by typing python followed by those filenames.
Let’s try both methods now.
Using the Interactive Interpreter
Most of the code examples in this book use the interactive interpreter. When you type
the same commands as you see in the examples and get the same results, you’ll know
you’re on the right track.
You start the interpreter by typing just the name of the main Python program on your
computer: it should be python, python3, or something similar. For the rest of this book,
we’ll assume it’s called python; if yours has a different name, type that wherever you see
python in a code example.
The interactive interpreter works almost exactly the same as Python works on files, with
one exception: when you type something that has a value, the interactive interpreter
prints its value for you automatically. For example, if you start Python and type the
number 61 in the interpreter, it will be echoed to your terminal.
In the example that follows, $ is a sample system prompt for you to
type a command like python in the terminal window. We’ll use it for
the code examples in this book, although your prompt might be
different.
$ python
Python 3.3.0 (v3.3.0:bd8afb90ebf2, Sep 29 2012, 01:25:11)
[GCC 4.2.1 (Apple Inc. build 5666) (dot 3)] on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> 61
61
>>>
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This automatic printing of a value is a time-saving feature of the interactive interpreter,
not a part of the Python language.
By the way, print() also works within the interpreter whenever you want to print
something:
>>> print(61)
61
>>>
If you tried these examples with the interactive interpreter and saw the same results,
you just ran some real (though tiny) Python code. In the next few chapters, you’ll grad‐
uate from one-liners to longer Python programs.
Use Python Files
If you put 61 in a file by itself and run it through Python, it will run, but it won’t print
anything. In normal noninteractive Python programs, you need to call the print func‐
tion to print things, as is demonstrated in the code that follows:
print(61)
Let’s make a Python program file and run it:
1. Open your text editor.
2. Type the line print(61), as it appears above.
3. Save this to a file called 61.py. Make sure you save it as plain text, rather than a “rich”
format such as RTF or Word. You don’t need to use the .py suffix for your Python
program files, but it does help you remember what they are.
4. If you’re using a graphical user interface—that’s almost everyone—open a terminal
window.1
5. Run your program by typing the following:
$ python 61.py
You should see a single line of output:
61
Did that work? If it did, congratulations on running your first standalone Python pro‐
gram.
1. If you’re not sure what this means, see Appendix D for details for different operating systems.
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What’s Next?
You’ll be typing commands to an actual Python system, and they need to follow legal
Python syntax. Rather than dumping the syntax rules on you all at once, we’ll stroll
through them over the next few chapters.
The basic way to develop Python programs is by using a plain-text editor and a terminal
window. I use plain-text displays in this book, sometimes showing interactive terminal
sessions and sometimes pieces of Python files. You should know that there are also many
good integrated development environments (IDEs) for Python. These may feature graph‐
ical user interfaces with advanced text editing and help displays. You can learn about
details for some of these in Chapter 12.
Your Moment of Zen
Each computing language has its own style. In the preface, I mentioned that there is
often a Pythonic way to express yourself. Embedded in Python is a bit of free verse that
expresses the Python philosophy succinctly (as far as I know, Python is the only language
to include such an Easter egg). Just type import this into your interactive interpreter
and then press the Enter key whenever you need this moment of Zen:
>>> import this
The Zen of Python, by Tim Peters
Beautiful is better than ugly.
Explicit is better than implicit.
Simple is better than complex.
Complex is better than complicated.
Flat is better than nested.
Sparse is better than dense.
Readability counts.
Special cases aren't special enough to break the rules.
Although practicality beats purity.
Errors should never pass silently.
Unless explicitly silenced.
In the face of ambiguity, refuse the temptation to guess.
There should be one--and preferably only one--obvious way to do it.
Although that way may not be obvious at first unless you're Dutch.
Now is better than never.
Although never is often better than *right* now.
If the implementation is hard to explain, it's a bad idea.
If the implementation is easy to explain, it may be a good idea.
Namespaces are one honking great idea--let's do more of those!
I’ll bring up examples of these sentiments throughout the book.
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Things to Do
This chapter was an introduction to the Python language—what it does, how it looks,
and where it fits in the computing world. At the end of each chapter, I’ll suggest some
mini-projects to help you remember what you just read and prepare you for what’s to
come.
1.1 If you don’t already have Python 3 installed on your computer, do it now. Read
Appendix D for the details for your computer system.
1.2 Start the Python 3 interactive interpreter. Again, details are in Appendix D. It should
print a few lines about itself and then a single line starting with >>>. That’s your prompt
to type Python commands.
1.3 Play with the interpreter a little. Use it like a calculator and type this: 8 * 9. Press
the Enter key to see the result. Python should print 72.
1.4 Type the number 47 and press the Enter key. Did it print 47 for you on the next line?
1.5 Now, type print(47) and press Enter. Did that also print 47 for you on the next line?
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CHAPTER 2
Py Ingredients: Numbers, Strings,
and Variables
In this chapter we’ll begin by looking at Python’s simplest built-in data types:
• booleans (which have the value True or False)
• integers (whole numbers such as 42 and 100000000)
• floats (numbers with decimal points such as 3.14159, or sometimes exponents like
1.0e8, which means one times ten to the eighth power, or 100000000.0)
• strings (sequences of text characters)
In a way, they’re like atoms. We’ll use them individually in this chapter. Chapter 3 shows
how to combine them into larger “molecules.”
Each type has specific rules for its usage and is handled differently by the computer.
We’ll also introduce variables (names that refer to actual data; more on these in a mo‐
ment).
The code examples in this chapter are all valid Python, but they’re snippets. We’ll be
using the Python interactive interpreter, typing these snippets and seeing the results
immediately. Try running them yourself with the version of Python on your computer.
You’ll recognize these examples by the >>> prompt. In Chapter 4, we start writing Python
programs that can run on their own.
Variables, Names, and Objects
In Python, everything—booleans, integers, floats, strings, even large data structures,
functions, and programs—is implemented as an object. This gives the language a con‐
sistency (and useful features) that some other languages lack.
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An object is like a clear plastic box that contains a piece of data (Figure 2-1). The object
has a type, such as boolean or integer, that determines what can be done with the da‐
ta. A real-life box marked “Pottery” would tell you certain things (it’s probably heavy,
and don’t drop it on the floor). Similarly, in Python, if an object has the type int, you
know that you could add it to another int.
Figure 2-1. An object is like a box
The type also determines if the data value contained by the box can be changed (muta‐
ble) or is constant (immutable). Think of an immutable object as a closed box with a
clear window: you can see the value but you can’t change it. By the same analogy, a
mutable object is like an open box: not only can you see the value inside, you can also
change it; however, you can’t change its type.
Python is strongly typed, which means that the type of an object does not change, even
if its value is mutable (Figure 2-2).
Figure 2-2. Strong typing does not mean push the keys harder
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Programming languages allow you to define variables. These are names that refer to
values in the computer’s memory that you can define for use with your program. In
Python, you use = to assign a value to a variable.
We all learned in grade school math that = means equal to. So why do
many computer languages, including Python, use = for assignment?
One reason is that standard keyboards lack logical alternatives such
as a left arrow key, and = didn’t seem too confusing. Also, in comput‐
er programs you use assignment much more than you test for equality.
The following is a two-line Python program that assigns the integer value 7 to the vari‐
able named a, and then prints the value currently associated with a:
>>> a = 7
>>> print(a)
7
Now, it’s time to make a crucial point about Python variables: variables are just names.
Assignment does not copy a value; it just attaches a name to the object that contains
the data. The name is a reference to a thing rather than the thing itself. Think of a name
as a sticky note (see Figure 2-3).
Figure 2-3. Names stick to objects
Try this with the interactive interpreter:
1. As before, assign the value 7 to the name a. This creates an object box containing
the integer value 7.
2. Print the value of a.
3. Assign a to b, making b also stick to the object box containing 7.
4. Print the value of b.
>>> a = 7
>>> print(a)
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7
>>> b = a
>>> print(b)
7
In Python, if you want to know the type of anything (a variable or a literal value), use
type( thing ). Let’s try it with different literal values (58, 99.9, abc) and different vari‐
ables (a, b):
>>> type(a)
<class 'int'>
>>> type(b)
<class 'int'>
>>> type(58)
<class 'int'>
>>> type(99.9)
<class 'float'>
>>> type('abc')
<class 'str'>
A class is the definition of an object; Chapter 6 covers classes in greater detail. In Python,
“class” and “type” mean pretty much the same thing.
Variable names can only contain these characters:
• Lowercase letters (a through z)
• Uppercase letters (A through Z)
• Digits (0 through 9)
• Underscore (_)
Names cannot begin with a digit. Also, Python treats names that begin with an under‐
score in special ways (which you can read about in Chapter 4). These are valid names:
• a
• a1
• a_b_c___95
• _abc
• _1a
These names, however, are not valid:
• 1
• 1a
• 1_
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Finally, don’t use any of these for variable names, because they are Python’s reserved
words:
False
None
True
and
as
assert
break
class
continue
def
del
elif
else
except
finally
for
from
global
if
import
in
is
lambda
nonlocal
not
or
pass
raise
return
try
while
with
yield
These words, and some punctuation, are used to define Python’s syntax. You’ll see all of
them as you progress through this book.
Numbers
Python has built-in support for integers (whole numbers such as 5 and 1,000,000,000)
and floating point numbers (such as 3.1416, 14.99, and 1.87e4). You can calculate com‐
binations of numbers with the simple math operators in this table:
Operator Description
Example
Result
+
addition
5 + 8
13
-
subtraction
90 - 10 80
*
multiplication
4 * 7
/
floating point division
7 / 2
//
integer (truncating) division 7 // 2
3
%
modulus (remainder)
7 % 3
1
**
exponentiation
3 ** 4
81
28
3.5
For the next few pages, I’ll show simple examples of Python acting as a glorified
calculator.
Integers
Any sequence of digits in Python is assumed to be a literal integer:
>>> 5
5
You can use a plain zero (0):
>>> 0
0
But don’t put it in front of other digits:
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>>> 05
File "<stdin>", line 1
05
^
SyntaxError: invalid token
This is the first time you’ve seen a Python exception—a program error.
In this case, it’s a warning that 05 is an “invalid token.” I’ll explain
what this means in “Bases” on page 24. You’ll see many more exam‐
ples of exceptions in this book because they’re Python’s main error
handling mechanism.
A sequence of digits specifies a positive integer. If you put a + sign before the digits, the
number stays the same:
>>> 123
123
>>> +123
123
To specify a negative integer, insert a – before the digits:
>>> -123
-123
You can do normal arithmetic with Python, much as you would with a calculator, by
using the operators listed in the table on the previous page. Addition and subtraction
work as you’d expect:
>>> 5 + 9
14
>>> 100 - 7
93
>>> 4 - 10
-6
You can include as many numbers and operators as you’d like:
>>> 5 + 9 + 3
17
>>> 4 + 3 - 2 - 1 + 6
10
A style note: you’re not required to have a space between each number and operator:
>>> 5+9
17
+
3
It just looks better and is easier to read.
Multiplication is also straightforward:
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>>> 6 * 7
42
>>> 7 * 6
42
>>> 6 * 7 * 2 * 3
252
Division is a little more interesting, because it comes in two flavors:
• / carries out floating-point (decimal) division
• // performs integer (truncating) division
Even if you’re dividing an integer by an integer, using a / will give you a floating-point
result:
>>> 9 / 5
1.8
Truncating integer division gives you an integer answer, throwing away any remainder:
>>> 9 // 5
1
Dividing by zero with either kind of division causes a Python exception:
>>> 5 / 0
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
ZeroDivisionError: division by zero
>>> 7 // 0
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
ZeroDivisionError: integer division or modulo by z
All of the preceding examples used literal integers. You can mix literal integers and
variables that have been assigned integer values:
>>> a = 95
>>> a
95
>>> a - 3
92
Earlier, when we said a - 3, we didn’t assign the result to a, so the value of a did not
change:
>>> a
95
If you wanted to change a, you would do this:
>>> a = a - 3
>>> a
92
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This usually confuses beginning programmers, again because of our ingrained grade
school math training, we see that = sign and think of equality. In Python, the expression
on the right side of the = is calculated first, then assigned to the variable on the left side.
If it helps, think of it this way:
• Subtract 3 from a
• Assign the result of that subtraction to a temporary variable
• Assign the value of the temporary variable to a:
>>> a = 95
>>> temp = a - 3
>>> a = temp
So, when you say:
>>> a = a - 3
Python is calculating the subtraction on the righthand side, remembering the result,
and then assigning it to a on the left side of the = sign. It’s faster and neater than using
a temporary variable.
You can combine the arithmetic operators with assignment by putting the operator
before the =. Here, a -= 3 is like saying a = a - 3:
>>> a = 95
>>> a -= 3
>>> a
92
This is like a = a + 8:
>>> a += 8
>>> a
100
And this is like a = a * 2:
>>> a *= 2
>>> a
200
Here’s a floating-point division example, such as a = a / 3:
>>> a /= 3
>>> a
66.66666666666667
Let’s assign 13 to a, and then try the shorthand for a = a // 4 (truncating integer
division):
>>> a = 13
>>> a //= 4
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>>> a
3
The % character has multiple uses in Python. When it’s between two numbers, it produces
the remainder when the first number is divided by the second:
>>> 9 % 5
4
Here’s how to get both the (truncated) quotient and remainder at once:
>>> divmod(9,5)
(1, 4)
Otherwise, you could have calculated them separately:
>>> 9 // 5
1
>>> 9 % 5
4
You just saw some new things here: a function named divmod is given the integers 9 and
5 and returns a two-item result called a tuple. Tuples will take a bow in Chapter 3;
functions will make their debut in Chapter 4.
Precedence
What would you get if you typed the following?
>>> 2 + 3 * 4
If you do the addition first, 2 + 3 is 5, and 5 * 4 is 20. But if you do the multiplication
first, 3 * 4 is 12, and 2 + 12 is 14. In Python, as in most languages, multiplication has
higher precedence than addition, so the second version is what you’d see:
>>> 2 + 3 * 4
14
How do you know the precedence rules? There’s a big table in Appendix F that lists them
all, but I’ve found that in practice I never look up these rules. It’s much easier to just add
parentheses to group your code as you intend the calculation to be carried out:
>>> 2 + (3 * 4)
14
This way, anyone reading the code doesn’t need to guess its intent or look up precedence
rules.
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Bases
Integers are assumed to be decimal (base 10) unless you use a prefix to specify another
base. You might never need to use these other bases, but you’ll probably see them in
Python code somewhere, sometime.
We generally have 10 fingers or toes (one of my cats has a few more, but rarely uses them
for counting). So, we count 0, 1, 2, 3, 4, 5, 6, 7, 8, 9. Next, we run out of digits and carry
the one to the “ten’s place” and put a 0 in the one’s place: 10 means “1 ten and 0 ones”.
We don’t have a single digit that represents “ten.” Then, it’s 11, 12, up to 19, carry the
one to make 20 (2 tens and 0 ones), and so on.
A base is how many digits you can use until you need to “carry the one.” In base 2
(binary), the only digits are 0 and 1. 0 is the same as a plain old decimal 0, and 1 is the
same as a decimal 1. However, in base 2, if you add a 1 to a 1, you get 10 (1 decimal two
plus 0 decimal ones).
In Python, you can express literal integers in three bases besides decimal:
• 0b or 0B for binary (base 2).
• 0o or 0O for octal (base 8).
• 0x or 0X for hex (base 16).
The interpreter prints these for you as decimal integers. Let’s try each of these bases.
First, a plain old decimal 10, which means 1 ten and 0 ones:
>>> 10
10
Now, a binary (base two), which means 1 (decimal) two and 0 ones:
>>> 0b10
2
Octal (base 8) for 1 (decimal) eight and 0 ones:
>>> 0o10
8
Hexadecimal (base 16) for 1 (decimal) 16 and 0 ones:
>>> 0x10
16
In case you’re wondering what “digits” base 16 uses, they are: 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, a,
b, c, d, e, and f. 0xa is a decimal 10, and 0xf is a decimal 15. Add 1 to 0xf and you get
0x10 (decimal 16).
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Why use a different base from 10? It’s useful in bit-level operations, which are described
in Chapter 7, along with more details about converting numbers from one base to an‐
other.
Type Conversions
To change other Python data types to an integer, use the int() function. This will keep
the whole number and discard any fractional part.
Python’s simplest data type is the boolean, which has only the values True and False.
When converted to integers, they represent the values 1 and 0:
>>> int(True)
1
>>> int(False)
0
Converting a floating-point number to an integer just lops off everything after the dec‐
imal point:
>>> int(98.6)
98
>>> int(1.0e4)
10000
Finally, here’s an example of converting a text string (you’ll see more about strings in a
few pages) that contains only digits, possibly with + or - signs:
>>> int('99')
99
>>> int('-23')
-23
>>> int('+12')
12
Converting an integer to an integer doesn’t change anything but doesn’t hurt either:
>>> int(12345)
12345
If you try to convert something that doesn’t look like a number, you’ll get an exception:
>>> int('99 bottles of beer on the wall')
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
ValueError: invalid literal for int() with base 10: '99 bottles of beer on the wall'
>>> int('')
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
ValueError: invalid literal for int() with base 10: ''
The preceding text string started with valid digit characters (99), but it kept on going
with others that the int() function just wouldn’t stand for.
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We’ll get to exceptions in Chapter 4. For now, just know that it’s how
Python alerts you that an error occurred (rather than crashing the
program, as some languages might do). Instead of assuming that
things always go right, I’ll show many examples of exceptions
throughout this book, so you can see what Python does when they go
wrong.
int() will make integers from floats or strings of digits, but won’t handle strings con‐
taining decimal points or exponents:
>>> int('98.6')
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
ValueError: invalid literal for int() with base 10: '98.6'
>>> int('1.0e4')
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
ValueError: invalid literal for int() with base 10: '1.0e4'
If you mix numeric types, Python will sometimes try to automatically convert them for
you:
>>> 4 + 7.0
11.0
The boolean value False is treated as 0 or 0.0 when mixed with integers or floats, and
True is treated as 1 or 1.0:
>>> True + 2
3
>>> False + 5.0
5.0
How Big Is an int?
In Python 2, the size of an int was limited to 32 bits. This was enough room to store
any integer from –2,147,483,648 to 2,147,483,647.
A long had even more room: 64 bits, allowing values from –9,223,372,036,854,775,808
to 9,223,372,036,854,775,807. In Python 3, long is long gone, and an int can be any size
—even greater than 64 bits. Thus, you can say things like the following (10**100 is called
a googol, and was the original name of Google before they decided on the easier spelling):
>>>
>>> googol = 10**100
>>> googol
100000000000000000000000000000000000000000000000000000000000000000000000000000
00000000000000000000000
>>> googol * googol
100000000000000000000000000000000000000000000000000000000000000000000000000000
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000000000000000000000000000000000000000000000000000000000000000000000000000000
000000000000000000000000000000000000000000000
In many languages, trying this would cause something called integer overflow, where
the number would need more space than the computer allowed for it, causing various
bad effects. Python handles humungous integers with no problem. Score one for Python.
Floats
Integers are whole numbers, but floating-point numbers (called floats in Python) have
decimal points. Floats are handled similarly to integers: you can use the operators (+, –,
*, /, //, **, and %) and divmod() function.
To convert other types to floats, you use the float() function. As before, booleans act
like tiny integers:
>>> float(True)
1.0
>>> float(False)
0.0
Converting an integer to a float just makes it the proud possessor of a decimal point:
>>> float(98)
98.0
>>> float('99')
99.0
And, you can convert a string containing characters that would be a valid float (digits,
signs, decimal point, or an e followed by an exponent) to a real float:
>>> float('98.6')
98.6
>>> float('-1.5')
-1.5
>>> float('1.0e4')
10000.0
Math Functions
Python has the usual math functions such as square roots, cosines, and so on. We’ll save
them for Appendix C, in which we also discuss Python uses in science.
Strings
Nonprogrammers often think that programmers must be good at math because they
work with numbers. Actually, most programmers work with strings of text much more
than numbers. Logical (and creative!) thinking is often more important than math skills.
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Because of its support for the Unicode standard, Python 3 can contain characters from
any written language in the world, plus a lot of symbols. Its handling of that standard
was a big reason for its split from Python 2. It’s also a good reason to use version 3. I’ll
get into Unicode in various places, because it can be daunting at times. In the string
examples that follow, I’ll mostly use ASCII examples.
Strings are our first example of a Python sequence. In this case, they’re a sequence of
characters.
Unlike other languages, strings in Python are immutable. You can’t change a string inplace, but you can copy parts of strings to another string to get the same effect. You’ll
see how to do this shortly.
Create with Quotes
You make a Python string by enclosing characters in either single quotes or double
quotes, as demonstrated in the following:
>>> 'Snap'
'Snap'
>>> "Crackle"
'Crackle'
The interactive interpreter echoes strings with a single quote, but all are treated exactly
the same by Python.
Why have two kinds of quote characters? The main purpose is so that you can create
strings containing quote characters. You can have single quotes inside double-quoted
strings, or double quotes inside single-quoted strings:
>>> "'Nay,' said the naysayer."
"'Nay,' said the naysayer."
>>> 'The rare double quote in captivity: ".'
'The rare double quote in captivity: ".'
>>> 'A "two by four" is actually 1 1⁄2" × 3 1⁄2".'
'A "two by four is" actually 1 1⁄2" × 3 1⁄2".'
>>> "'There's the man that shot my paw!' cried the limping hound."
"'There's the man that shot my paw!' cried the limping hound."
You can also use three single quotes (''') or three double quotes ("""):
>>> '''Boom!'''
'Boom'
>>> """Eek!"""
'Eek!'
Triple quotes aren’t very useful for short strings like these. Their most common use is
to create multiline strings, like this classic poem from Edward Lear:
>>> poem = '''There was a Young Lady of Norway,
... Who casually sat in a doorway;
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... When the door squeezed her flat,
... She exclaimed, "What of that?"
... This courageous Young Lady of Norway.'''
>>>
(This was entered in the interactive interpreter, which prompted us with >>> for the first
line and ... until we entered the final triple quotes and went to the next line.)
If you tried to create that poem with single quotes, Python would make a fuss when you
went to the second line:
>>> poem = 'There was a young lady of Norway,
File "<stdin>", line 1
poem = 'There was a young lady of Norway,
^
SyntaxError: EOL while scanning string literal
>>>
If you have multiple lines within triple quotes, the line ending characters will be pre‐
served in the string. If you have leading or trailing spaces, they’ll also be kept:
>>> poem2 = '''I do not like thee, Doctor Fell.
...
The reason why, I cannot tell.
...
But this I know, and know full well:
...
I do not like thee, Doctor Fell.
... '''
>>> print(poem2)
I do not like thee, Doctor Fell.
The reason why, I cannot tell.
But this I know, and know full well:
I do not like thee, Doctor Fell.
>>>
By the way, there’s a difference between the output of print() and the automatic echoing
done by the interactive interpreter:
>>> poem2
'I do not like thee, Doctor Fell.\n
this I know, and know full well:\n
The reason why, I cannot tell.\n
But
I do not like thee, Doctor Fell.\n'
print() strips quotes from strings and prints their contents. It’s meant for human out‐
put. It helpfully adds a space between each of the things it prints, and a newline at the
end:
>>> print(99, 'bottles', 'would be enough.')
99 bottles would be enough.
If you don’t want the space or newline, you’ll see how to avoid them shortly.
The interpreter prints the string with single quotes and escape characters such as \n,
which are explained in “Escape with \” on page 30.
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Finally, there is the empty string, which has no characters at all but is perfectly valid. You
can create an empty string with any of the aforementioned quotes:
>>>
''
>>>
''
>>>
''
>>>
''
>>>
''
""
''''''
""""""
Why would you need an empty string? Sometimes you might want to build a string from
other strings, and you need to start with a blank slate.
>>> bottles = 99
>>> base = ''
>>> base += 'current inventory: '
>>> base += str(bottles)
>>> base
'current inventory: 99'
Convert Data Types by Using str()
You can convert other Python data types to strings by using the str() function:
>>> str(98.6)
'98.6'
>>> str(1.0e4)
'10000.0'
>>> str(True)
'True'
Python uses the str() function internally when you call print() with objects that are
not strings and when doing string interpolation, which you’ll see in Chapter 7.
Escape with \
Python lets you escape the meaning of some characters within strings to achieve effects
that would otherwise be hard to express. By preceding a character with a backslash (\),
you give it a special meaning. The most common escape sequence is \n, which means
to begin a new line. With this you can create multiline strings from a one-line string.
>>> palindrome = 'A man,\nA plan,\nA canal:\nPanama.'
>>> print(palindrome)
A man,
A plan,
A canal:
Panama.
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You will see the escape sequence \t (tab) used to align text:
>>> print('\tabc')
abc
>>> print('a\tbc')
a
bc
>>> print('ab\tc')
ab
c
>>> print('abc\t')
abc
(The final string has a terminating tab which, of course, you can’t see.)
You might also need \' or \" to specify a literal single or double quote inside a string
that’s quoted by the same character:
>>> testimony = "\"I did nothing!\" he said. \"Not that either! Or the other
thing.\""
>>> print(testimony)
"I did nothing!" he said. "Not that either! Or the other thing."
>>> fact = "The world's largest rubber duck was 54'2\" by 65'7\" by 105'"
>>> print(fact)
The world's largest rubber duck was 54'2" by 65'7" by 105'
And if you need a literal backslash, just type two of them:
>>> speech = 'Today we honor our friend, the backslash: \\.'
>>> print(speech)
Today we honor our friend, the backslash: \.
Combine with +
You can combine literal strings or string variables in Python by using the + operator, as
demonstrated here:
>>> 'Release the kraken! ' + 'At once!'
'Release the kraken! At once!'
You can also combine literal strings (not string variables) just by having one after the
other:
>>> "My word! " "A gentleman caller!"
'My word! A gentleman caller!'
Python does not add spaces for you when concatenating strings, so in the preceding
example, we needed to include spaces explicitly. It does add a space between each ar‐
gument to a print() statement, and a newline at the end:
>>> a = 'Duck.'
>>> b = a
>>> c = 'Grey Duck!'
>>> a + b + c
'Duck.Duck.Grey Duck!'
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>>> print(a, b, c)
Duck. Duck. Grey Duck!
Duplicate with *
You use the * operator to duplicate a string. Try typing these lines into your interactive
interpreter and see what they print:
>>>
>>>
>>>
>>>
start = 'Na ' * 4 + '\n'
middle = 'Hey ' * 3 + '\n'
end = 'Goodbye.'
print(start + start + middle + end)
Extract a Character with []
To get a single character from a string, specify its offset inside square brackets after the
string’s name. The first (leftmost) offset is 0, the next is 1, and so on. The last (rightmost)
offset can be specified with –1 so you don’t have to count; going to the left are –2, –3,
and so on.
>>>
>>>
'a'
>>>
'b'
>>>
'z'
>>>
'y'
>>>
'z'
>>>
'f'
letters = 'abcdefghijklmnopqrstuvwxyz'
letters[0]
letters[1]
letters[-1]
letters[-2]
letters[25]
letters[5]
If you specify an offset that is the length of the string or longer (remember, offsets go
from 0 to length–1), you’ll get an exception:
>>> letters[100]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
IndexError: string index out of range
Indexing works the same with the other sequence types (lists and tuples), which we
cover in Chapter 3.
Because strings are immutable, you can’t insert a character directly into one or change
the character at a specific index. Let’s try to change 'Henny' to 'Penny' and see what
happens:
>>> name = 'Henny'
>>> name[0] = 'P'
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Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: 'str' object does not support item assignment
Instead you need to use some combination of string functions such as replace() or a
slice (which you’ll see in a moment):
>>> name = 'Henny'
>>> name.replace('H', 'P')
'Penny'
>>> 'P' + name[1:]
'Penny'
Slice with [ start : end : step ]
You can extract a substring (a part of a string) from a string by using a slice. You define
a slice by using square brackets, a start offset, an end offset, and an optional step size.
Some of these can be omitted. The slice will include characters from offset start to one
before end.
• [:] extracts the entire sequence from start to end.
• [ start :] specifies from the start offset to the end.
• [: end ] specifies from the beginning to the end offset minus 1.
• [ start : end ] indicates from the start offset to the end offset minus 1.
• [ start : end : step ] extracts from the start offset to the end offset minus 1,
skipping characters by step.
As before, offsets go 0, 1, and so on from the start to the right, and –1,–2, and so forth
from the end to the left. If you don’t specify start, the slice uses 0 (the beginning). If
you don’t specify end, it uses the end of the string.
Let’s make a string of the lowercase English letters:
>>> letters = 'abcdefghijklmnopqrstuvwxyz'
Using a plain : is the same as 0: (the entire string):
>>> letters[:]
'abcdefghijklmnopqrstuvwxyz'
Here’s an example from offset 20 to the end:
>>> letters[20:]
'uvwxyz'
Now, from offset 10 to the end:
>>> letters[10:]
'klmnopqrstuvwxyz'
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And another, offset 12 to 14 (Python does not include the last offset):
>>> letters[12:15]
'mno'
The three last characters:
>>> letters[-3:]
'xyz'
In this next example, we go from offset 18 to the fourth before the end; notice the
difference from the previous example, in which starting at –3 gets the x, but ending at
–3 actually stops at –4, the w:
>>> letters[18:-3]
'stuvw'
In the following, we extract from 6 before the end to 3 before the end:
>>> letters[-6:-2]
'uvwx'
If you want a step size other than 1, specify it after a second colon, as shown in the next
series of examples.
From the start to the end, in steps of 7 characters:
>>> letters[::7]
'ahov'
From offset 4 to 19, by 3:
>>> letters[4:20:3]
'ehknqt'
From offset 19 to the end, by 4:
>>> letters[19::4]
'tx'
From the start to offset 20 by 5:
>>> letters[:21:5]
'afkpu'
(Again, the end needs to be one more than the actual offset.)
And that’s not all! Given a negative step size, this handy Python slicer can also step
backward. This starts at the end and ends at the start, skipping nothing:
>>> letters[-1::-1]
'zyxwvutsrqponmlkjihgfedcba'
It turns out that you can get the same result by using this:
>>> letters[::-1]
'zyxwvutsrqponmlkjihgfedcba'
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Slices are more forgiving of bad offsets than are single-index lookups. A slice offset
earlier than the beginning of a string is treated as 0, and one after the end is treated as
-1, as is demonstrated in this next series of examples.
From 50 before the end to the end:
>>> letters[-50:]
'abcdefghijklmnopqrstuvwxyz'
From 51 before the end to 50 before the end:
>>> letters[-51:-50]
''
From the start to 69 after the start:
>>> letters[:70]
'abcdefghijklmnopqrstuvwxyz'
From 70 after the start to 70 after the start:
>>> letters[70:71]
''
Get Length with len()
So far, we’ve used special punctuation characters such as + to manipulate strings. But
there are only so many of these. Now, we start to use some of Python’s built-in func‐
tions: named pieces of code that perform certain operations.
The len() function counts characters in a string:
>>> len(letters)
26
>>> empty = ""
>>> len(empty)
0
You can use len() with other sequence types, too, as is described in Chapter 3.
Split with split()
Unlike len(), some functions are specific to strings. To use a string function, type the
name of the string, a dot, the name of the function, and any arguments that the function
needs: string . function ( arguments ). You’ll see a longer discussion of functions in
“Functions” on page 85.
You can use the built-in string split() function to break a string into a list of smaller
strings based on some separator. You’ll see lists in the next chapter. A list is a sequence
of values, separated by commas and surrounded by square brackets.
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>>> todos = 'get gloves,get mask,give cat vitamins,call ambulance'
>>> todos.split(',')
['get gloves', 'get mask', 'give cat vitamins', 'call ambulance']
In the preceding example, the string was called todos and the string function was called
split(), with the single separator argument ','. If you don’t specify a separator,
split() uses any sequence of white space characters—newlines, spaces, and tabs.
>>> todos.split()
['get', 'gloves,get', 'mask,give', 'cat', 'vitamins,call', 'ambulance']
You still need the parentheses when calling split with no arguments—that’s how
Python knows you’re calling a function.
Combine with join()
In what may not be an earthshaking revelation, the join() function is the opposite of
split(): it collapses a list of strings into a single string. It looks a bit backward because
you specify the string that glues everything together first, and then the list of strings to
glue: string .join( list ). So, to join the list lines with separating newlines, you
would say '\n'.join(lines). In the following example, let’s join some names in a list
with a comma and a space:
>>> crypto_list = ['Yeti', 'Bigfoot', 'Loch Ness Monster']
>>> crypto_string = ', '.join(crypto_list)
>>> print('Found and signing book deals:', crypto_string)
Found and signing book deals: Yeti, Bigfoot, Loch Ness Monster
Playing with Strings
Python has a large set of string functions. Let’s explore how the most common of them
work. Our test subject is the following string containing the text of the immortal poem
“What Is Liquid?” by Margaret Cavendish, Duchess of Newcastle:
>>> poem = '''All that doth flow we cannot liquid name
Or else would fire and water be the same;
But that is liquid which is moist and wet
Fire that property can never get.
Then 'tis not cold that doth the fire put out
But 'tis the wet that makes it die, no doubt.'''
To begin, get the first 13 characters (offsets 0 to 12):
>>> poem[:13]
'All that doth'
How many characters are in this poem? (Spaces and newlines are included in the count.)
>>> len(poem)
250
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Does it start with the letters All?
>>> poem.startswith('All')
True
Does it end with That's all, folks!?
>>> poem.endswith('That\'s all, folks!')
False
Now, let’s find the offset of the first occurrence of the word the in the poem:
>>> word = 'the'
>>> poem.find(word)
73
And the offset of the last the:
>>> poem.rfind(word)
214
How many times does the three-letter sequence the occur?
>>> poem.count(word)
3
Are all of the characters in the poem either letters or numbers?
>>> poem.isalnum()
False
Nope, there were some punctuation characters.
Case and Alignment
In this section, we’ll look at some more uses of the built-in string functions. Our test
string is the following:
>>> setup = 'a duck goes into a bar...'
Remove . sequences from both ends:
>>> setup.strip('.')
'a duck goes into a bar'
Because strings are immutable, none of these examples actually
changes the setup string. Each example just takes the value of set
up, does something to it, and returns the result as a new string.
Capitalize the first word:
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>>> setup.capitalize()
'A duck goes into a bar...'
Capitalize all the words:
>>> setup.title()
'A Duck Goes Into A Bar...'
Convert all characters to uppercase:
>>> setup.upper()
'A DUCK GOES INTO A BAR...'
Convert all characters to lowercase:
>>> setup.lower()
'a duck goes into a bar...'
Swap upper- and lowercase:
>>> setup.swapcase()
'a DUCK GOES INTO A BAR...'
Now, we’ll work with some layout alignment functions. The string is aligned within the
specified total number of spaces (30 here).
Center the string within 30 spaces:
>>> setup.center(30)
' a duck goes into a bar...
'
Left justify:
>>> setup.ljust(30)
'a duck goes into a bar...
'
Right justify:
>>> setup.rjust(30)
'
a duck goes into a bar...'
I have much more to say about string formatting and conversions in Chapter 7, including
how to use % and format().
Substitute with replace()
You use replace() for simple substring substitution. You give it the old substring, the
new one, and how many instances of the old substring to replace. If you omit this final
count argument, it replaces all instances. In this example, only one string is matched
and replaced:
>>> setup.replace('duck', 'marmoset')
'a marmoset goes into a bar...'
Change up to 100 of them:
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>>> setup.replace('a ', 'a famous ', 100)
'a famous duck goes into a famous bar...'
When you know the exact substring(s) you want to change, replace() is a good choice.
But watch out. In the second example, if we had substituted for the single character
string 'a' rather than the two character string 'a ' (a followed by a space), we would
have also changed a in the middle of other words:
>>> setup.replace('a', 'a famous', 100)
'a famous duck goes into a famous ba famousr...'
Sometimes, you want to ensure that the substring is a whole word, or the beginning of
a word, and so on. In those cases, you need regular expressions, which are described in
detail in Chapter 7.
More String Things
Python has many more string functions than I’ve shown here. Some will turn up in later
chapters, but you can find all the details at the standard documentation link.
Things to Do
This chapter introduced the atoms of Python: numbers, strings, and variables. Let’s try
a few small exercises with them in the interactive interpreter.
2.1 How many seconds are in an hour? Use the interactive interpreter as a calculator
and multiply the number of seconds in a minute (60) by the number of minutes in an
hour (also 60).
2.2 Assign the result from the previous task (seconds in an hour) to a variable called
seconds_per_hour.
2.3 How many seconds are in a day? Use your seconds_per_hour variable.
2.4 Calculate seconds per day again, but this time save the result in a variable called
seconds_per_day.
2.5 Divide seconds_per_day by seconds_per_hour. Use floating-point (/) division.
2.6 Divide seconds_per_day by seconds_per_hour, using integer (//) division. Did
this number agree with the floating-point value from the previous question, aside from
the final .0?
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CHAPTER 3
Py Filling: Lists, Tuples,
Dictionaries, and Sets
In Chapter 2 we started at the bottom with Python’s basic data types: booleans, integers,
floats, and strings. If you think of those as atoms, the data structures in this chapter are
like molecules. That is, we combine those basic types in more complex ways. You will
use these every day. Much of programming consists of chopping and glueing data into
specific forms, and these are your hacksaws and glue guns.
Lists and Tuples
Most computer languages can represent a sequence of items indexed by their integer
position: first, second, and so on down to the last. You’ve already seen Python strings,
which are sequences of characters. You’ve also had a little preview of lists, which you’ll
now see are sequences of anything.
Python has two other sequence structures: tuples and lists. These contain zero or more
elements. Unlike strings, the elements can be of different types. In fact, each element
can be any Python object. This lets you create structures as deep and complex as you
like.
Why does Python contain both lists and tuples? Tuples are immutable; when you assign
elements to a tuple, they’re baked in the cake and can’t be changed. Lists are mutable,
meaning you can insert and delete elements with great enthusiasm. I’ll show many
examples of each, with an emphasis on lists.
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By the way, you might hear two different pronunciations for tuple.
Which is right? If you guess wrong, do you risk being considered a
Python poseur? No worries. Guido van Rossum, the creator of
Python, tweeted “I pronounce tuple too-pull on Mon/Wed/Fri and
tub-pull on Tue/Thu/Sat. On Sunday I don’t talk about them. :)”
Lists
Lists are good for keeping track of things by their order, especially when the order and
contents might change. Unlike strings, lists are mutable. You can change a list in-place,
add new elements, and delete or overwrite existing elements. The same value can occur
more than once in a list.
Create with [] or list()
A list is made from zero or more elements, separated by commas, and surrounded by
square brackets:
>>>
>>>
>>>
>>>
empty_list = [ ]
weekdays = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday']
big_birds = ['emu', 'ostrich', 'cassowary']
first_names = ['Graham', 'John', 'Terry', 'Terry', 'Michael']
You can also make an empty list with the list() function:
>>> another_empty_list = list()
>>> another_empty_list
[]
“Comprehensions” on page 81 shows one more way to create a list,
called a list comprehension.
The weekdays list is the only one that actually takes advantage of list order. The
first_names list shows that values do not need to be unique.
If you only want to keep track of unique values and don’t care about
order, a Python set might be a better choice than a list. In the previ‐
ous example, big_birds could have been a set. You’ll read about sets
a little later in this chapter.
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Convert Other Data Types to Lists with list()
Python’s list() function converts other data types to lists. The following example con‐
verts a string to a list of one-character strings:
>>> list('cat')
['c', 'a', 't']
This example converts a tuple (coming up after lists in this chapter) to a list:
>>> a_tuple = ('ready', 'fire', 'aim')
>>> list(a_tuple)
['ready', 'fire', 'aim']
As I mentioned earlier in “Split with split()” on page 35, use split() to chop a string
into a list by some separator string:
>>> birthday = '1/6/1952'
>>> birthday.split('/')
['1', '6', '1952']
What if you have more than one separator string in a row in your original string? Well,
you get an empty string as a list item:
>>> splitme = 'a/b//c/d///e'
>>> splitme.split('/')
['a', 'b', '', 'c', 'd', '', '', 'e']
If you had used the two-character separator string // instead, you would get this:
>>> splitme = 'a/b//c/d///e'
>>> splitme.split('//')
>>>
['a/b', 'c/d', '/e']
Get an Item by Using [ offset ]
As with strings, you can extract a single value from a list by specifying its offset:
>>> marxes = ['Groucho', 'Chico', 'Harpo']
>>> marxes[0]
'Groucho'
>>> marxes[1]
'Chico'
>>> marxes[2]
'Harpo'
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Again, as with strings, negative indexes count backward from the end:
>>> marxes[-1]
'Harpo'
>>> marxes[-2]
'Chico'
>>> marxes[-3]
'Groucho'
>>>
The offset has to be a valid one for this list—a position you have
assigned a value previously. If you specify an offset before the begin‐
ning or after the end, you’ll get an exception (error). Here’s what
happens if we try to get the sixth Marx brother (offset 5 counting from
0), or the fifth before the end:
>>> marxes = ['Groucho', 'Chico', 'Harpo']
>>> marxes[5]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
IndexError: list index out of range
>>> marxes[-5]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
IndexError: list index out of range
Lists of Lists
Lists can contain elements of different types, including other lists, as illustrated here:
>>>
>>>
>>>
>>>
small_birds = ['hummingbird', 'finch']
extinct_birds = ['dodo', 'passenger pigeon', 'Norwegian Blue']
carol_birds = [3, 'French hens', 2, 'turtledoves']
all_birds = [small_birds, extinct_birds, 'macaw', carol_birds]
So what does all_birds, a list of lists, look like?
>>> all_birds
[['hummingbird', 'finch'], ['dodo', 'passenger pigeon', 'Norwegian Blue'], 'macaw',
[3, 'French hens', 2, 'turtledoves']]
Let’s look at the first item in it:
>>> all_birds[0]
['hummingbird', 'finch']
The first item is a list: in fact, it’s small_birds, the first item we specified when creating
all_birds. You should be able to guess what the second item is:
>>> all_birds[1]
['dodo', 'passenger pigeon', 'Norwegian Blue']
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It’s the second item we specified, extinct_birds. If we want the first item of
extinct_birds, we can extract it from all_birds by specifying two indexes:
>>> all_birds[1][0]
'dodo'
The [1] refers to the list that’s the second item in all_birds, whereas the [0] refers to
the first item in that inner list.
Change an Item by [ offset ]
Just as you can get the value of a list item by its offset, you can change it:
>>> marxes = ['Groucho', 'Chico', 'Harpo']
>>> marxes[2] = 'Wanda'
>>> marxes
['Groucho', 'Chico', 'Wanda']
Again, the list offset needs to be a valid one for this list.
You can’t change a character in a string in this way, because strings are immutable. Lists
are mutable. You can change how many items a list contains, and the items themselves.
Get a Slice to Extract Items by Offset Range
You can extract a subsequence of a list by using a slice:
>>> marxes = ['Groucho', 'Chico,' 'Harpo']
>>> marxes[0:2]
['Groucho', 'Chico']
A slice of a list is also a list.
As with strings, slices can step by values other than one. The next example starts at the
beginning and goes right by 2:
>>> marxes[::2]
['Groucho', 'Harpo']
Here, we start at the end and go left by 2:
>>> marxes[::-2]
['Harpo', 'Groucho']
And finally, the trick to reverse a list:
>>> marxes[::-1]
['Harpo', 'Chico', 'Groucho']
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Add an Item to the End with append()
The traditional way of adding items to a list is to append() them one by one to the
end. In the previous examples, we forgot Zeppo, but that’s all right because the list is
mutable, so we can add him now:
>>> marxes.append('Zeppo')
>>> marxes
['Groucho', 'Chico', 'Harpo', 'Zeppo']
Combine Lists by Using extend() or +=
You can merge one list into another by using extend(). Suppose that a well-meaning
person gave us a new list of Marxes called others, and we’d like to merge them into the
main marxes list:
>>> marxes = ['Groucho', 'Chico', 'Harpo', 'Zeppo']
>>> others = ['Gummo', 'Karl']
>>> marxes.extend(others)
>>> marxes
['Groucho', 'Chico', 'Harpo', 'Zeppo', 'Gummo', 'Karl']
Alternatively, you can use +=:
>>> marxes = ['Groucho', 'Chico', 'Harpo', 'Zeppo']
>>> others = ['Gummo', 'Karl']
>>> marxes += others
>>> marxes
['Groucho', 'Chico', 'Harpo', 'Zeppo', 'Gummo', 'Karl']
If we had used append(), others would have been added as a single list item rather than
merging its items:
>>> marxes = ['Groucho', 'Chico', 'Harpo', 'Zeppo']
>>> others = ['Gummo', 'Karl']
>>> marxes.append(others)
>>> marxes
['Groucho', 'Chico', 'Harpo', 'Zeppo', ['Gummo', 'Karl']]
This again demonstrates that a list can contain elements of different types. In this case,
four strings, and a list of two strings.
Add an Item by Offset with insert()
The append() function adds items only to the end of the list. When you want to add an
item before any offset in the list, use insert(). Offset 0 inserts at the beginning. An
offset beyond the end of the list inserts at the end, like append(), so you don’t need to
worry about Python throwing an exception.
>>> marxes.insert(3, 'Gummo')
>>> marxes
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['Groucho', 'Chico', 'Harpo', 'Gummo', 'Zeppo']
>>> marxes.insert(10, 'Karl')
>>> marxes
['Groucho', 'Chico', 'Harpo', 'Gummo', 'Zeppo', 'Karl']
Delete an Item by Offset with del
Our fact checkers have just informed us that Gummo was indeed one of the Marx
Brothers, but Karl wasn’t. Let’s undo that last insertion:
>>> del marxes[-1]
>>> marxes
['Groucho', 'Chico', 'Harpo', 'Gummo', 'Zeppo']
When you delete an item by its position in the list, the items that follow it move back
to take the deleted item’s space, and the list’s length decreases by one. If we delete 'Harpo'
from the last version of the marxes list, we get this as a result:
>>> marxes = ['Groucho', 'Chico', 'Harpo', 'Gummo', 'Zeppo']
>>> marxes[2]
'Harpo'
>>> del marxes[2]
>>> marxes
['Groucho', 'Chico', 'Gummo', 'Zeppo']
>>> marxes[2]
'Gummo'
del is a Python statement, not a list method—you don’t say
marxes[-2].del(). It’s sort of the reverse of assignment (=): it de‐
taches a name from a Python object and can free up the object’s
memory if that name was the last reference to it.
Delete an Item by Value with remove()
If you’re not sure or don’t care where the item is in the list, use remove() to delete it by
value. Goodbye, Gummo:
>>> marxes = ['Groucho', 'Chico', 'Harpo', 'Gummo', 'Zeppo']
>>> marxes.remove('Gummo')
>>> marxes
['Groucho', 'Chico', 'Harpo', 'Zeppo']
Get an Item by Offset and Delete It by Using pop()
You can get an item from a list and delete it from the list at the same time by using pop().
If you call pop() with an offset, it will return the item at that offset; with no argument,
it uses -1. So, pop(0) returns the head (start) of the list, and pop() or pop(-1) returns
the tail (end), as shown here:
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>>> marxes = ['Groucho', 'Chico', 'Harpo', 'Zeppo']
>>> marxes.pop()
'Zeppo'
>>> marxes
['Groucho', 'Chico', 'Harpo']
>>> marxes.pop(1)
'Chico'
>>> marxes
['Groucho', 'Harpo']
It’s computing jargon time! Don’t worry, these won’t be on the final
exam. If you use append() to add new items to the end and pop() to
remove them from the same end, you’ve implemented a data struc‐
ture known as a LIFO (last in, first out) queue. This is more com‐
monly known as a stack. pop(0) would create a FIFO (first in, first
out) queue. These are useful when you want to collect data as they
arrive and work with either the oldest first (FIFO) or the newest first
(LIFO).
Find an Item’s Offset by Value with index()
If you want to know the offset of an item in a list by its value, use index():
>>> marxes = ['Groucho', 'Chico', 'Harpo', 'Zeppo']
>>> marxes.index('Chico')
1
Test for a Value with in
The Pythonic way to check for the existence of a value in a list is using in:
>>> marxes = ['Groucho', 'Chico', 'Harpo', 'Zeppo']
>>> 'Groucho' in marxes
True
>>> 'Bob' in marxes
False
The same value may be in more than one position in the list. As long as it’s in there at
least once, in will return True:
>>> words = ['a', 'deer', 'a' 'female', 'deer']
>>> 'deer' in words
True
If you check for the existence of some value in a list often and don’t
care about the order of items, a Python set is a more appropriate way
to store and look up unique values. We’ll talk about sets a little later
in this chapter.
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Count Occurrences of a Value by Using count()
To count how many times a particular value occurs in a list, use count():
>>> marxes = ['Groucho', 'Chico', 'Harpo']
>>> marxes.count('Harpo')
1
>>> marxes.count('Bob')
0
>>> snl_skit = ['cheeseburger', 'cheeseburger', 'cheeseburger']
>>> snl_skit.count('cheeseburger')
3
Convert to a String with join()
“Combine with join()” on page 36 discusses join() in greater detail, but here’s another
example of what you can do with it:
>>> marxes = ['Groucho', 'Chico', 'Harpo']
>>> ', '.join(marxes)
'Groucho, Chico, Harpo'
But wait: you might be thinking that this seems a little backward. join() is a string
method, not a list method. You can’t say marxes.join(', '), even though it seems more
intuitive. The argument to join() is a string or any iterable sequence of strings (in‐
cluding a list), and its output is a string. If join() were just a list method, you couldn’t
use it with other iterable objects such as tuples or strings. If you did want it to work with
any iterable type, you’d need special code for each type to handle the actual joining. It
might help to remember: join() is the opposite of split(), as demonstrated here:
>>> friends = ['Harry', 'Hermione', 'Ron']
>>> separator = ' * '
>>> joined = separator.join(friends)
>>> joined
'Harry * Hermione * Ron'
>>> separated = joined.split(separator)
>>> separated
['Harry', 'Hermione', 'Ron']
>>> separated == friends
True
Reorder Items with sort()
You’ll often need to sort the items in a list by their values rather than their offsets. Python
provides two functions:
• The list function sort() sorts the list itself, in place.
• The general function sorted() returns a sorted copy of the list.
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If the items in the list are numeric, they’re sorted by default in ascending numeric order.
If they’re strings, they’re sorted in alphabetical order:
>>> marxes = ['Groucho', 'Chico', 'Harpo']
>>> sorted_marxes = sorted(marxes)
>>> sorted_marxes
['Chico', 'Groucho', 'Harpo']
sorted_marxes is a copy, and creating it did not change the original list:
>>> marxes
['Groucho', 'Chico', 'Harpo']
But, calling the list function sort() on the marxes list does change marxes:
>>> marxes.sort()
>>> marxes
['Chico', 'Groucho', 'Harpo']
If the elements of your list are all of the same type (such as strings in marxes), sort()
will work correctly. You can sometimes even mix types—for example, integers and floats
—because they are automatically converted to one another by Python in expressions:
>>>
>>>
>>>
[1,
numbers = [2, 1, 4.0, 3]
numbers.sort()
numbers
2, 3, 4.0]
The default sort order is ascending, but you can add the argument reverse=True to set
it to descending:
>>> numbers = [2, 1, 4.0, 3]
>>> numbers.sort(reverse=True)
>>> numbers
[4.0, 3, 2, 1]
Get Length by Using len()
len() returns the number of items in a list:
>>> marxes = ['Groucho', 'Chico', 'Harpo']
>>> len(marxes)
3
Assign with =, Copy with copy()
When you assign one list to more than one variable, changing the list in one place also
changes it in the other, as illustrated here:
>>>
>>>
[1,
>>>
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2, 3]
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>>> b
[1, 2, 3]
>>> a[0] = 'surprise'
>>> a
['surprise', 2, 3]
So what’s in b now? Is it still [1, 2, 3], or ['surprise', 2, 3]? Let’s see:
>>> b
['surprise', 2, 3]
Remember the sticky note analogy in Chapter 2? b just refers to the same list object as
a; therefore, whether we change the list contents by using the name a or b, it’s reflected
in both:
>>> b
['surprise', 2, 3]
>>> b[0] = 'I hate surprises'
>>> b
['I hate surprises', 2, 3]
>>> a
['I hate surprises', 2, 3]
You can copy the values of a list to an independent, fresh list by using any of these
methods:
• The list copy() function
• The list() conversion function
• The list slice [:]
Our original list will be a again. We’ll make b with the list copy() function, c with the
list() conversion function, and d with a list slice:
>>>
>>>
>>>
>>>
a
b
c
d
=
=
=
=
[1, 2, 3]
a.copy()
list(a)
a[:]
Again, b, c, and d are copies of a: they are new objects with their own values and no
connection to the original list object [1, 2, 3] to which a refers. Changing a does not
affect the copies b, c, and d:
>>> a[0] = 'integer lists are boring'
>>> a
['integer lists are boring', 2, 3]
>>> b
[1, 2, 3]
>>> c
[1, 2, 3]
>>> d
[1, 2, 3]
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Tuples
Similar to lists, tuples are sequences of arbitrary items. Unlike lists, tuples are immut‐
able, meaning you can’t add, delete, or change items after the tuple is defined. So, a tuple
is similar to a constant list.
Create a Tuple by Using ()
The syntax to make tuples is a little inconsistent, as we’ll demonstrate in the examples
that follow.
Let’s begin by making an empty tuple using ():
>>> empty_tuple = ()
>>> empty_tuple
()
To make a tuple with one or more elements, follow each element with a comma. This
works for one-element tuples:
>>> one_marx = 'Groucho',
>>> one_marx
('Groucho',)
If you have more than one element, follow all but the last one with a comma:
>>> marx_tuple = 'Groucho', 'Chico', 'Harpo'
>>> marx_tuple
('Groucho', 'Chico', 'Harpo')
Python includes parentheses when echoing a tuple. You don’t need them—it’s the trailing
commas that really define a tuple—but using parentheses doesn’t hurt. You can use them
to enclose the values, which helps to make the tuple more visible:
>>> marx_tuple = ('Groucho', 'Chico', 'Harpo')
>>> marx_tuple
('Groucho', 'Chico', 'Harpo')
Tuples let you assign multiple variables at once:
>>> marx_tuple = ('Groucho', 'Chico', 'Harpo')
>>> a, b, c = marx_tuple
>>> a
'Groucho'
>>> b
'Chico'
>>> c
'Harpo'
This is sometimes called tuple unpacking.
You can use tuples to exchange values in one statement without using a temporary
variable:
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>>> password = 'swordfish'
>>> icecream = 'tuttifrutti'
>>> password, icecream = icecream, password
>>> password
'tuttifrutti'
>>> icecream
'swordfish'
>>>
The tuple() conversion function makes tuples from other things:
>>> marx_list = ['Groucho', 'Chico', 'Harpo']
>>> tuple(marx_list)
('Groucho', 'Chico', 'Harpo')
Tuples versus Lists
You can often use tuples in place of lists, but they have many fewer functions—there is
no append(), insert(), and so on—because they can’t be modified after creation. Why
not just use lists instead of tuples everywhere?
• Tuples use less space.
• You can’t clobber tuple items by mistake.
• You can use tuples as dictionary keys (see the next section).
• Named tuples (see “Named Tuples” on page 141) can be a simple alternative to objects.
• Function arguments are passed as tuples (see “Functions” on page 85).
I won’t go into much more detail about tuples here. In everyday programming, you’ll
use lists and dictionaries more. Which is a perfect segue to…
Dictionaries
A dictionary is similar to a list, but the order of items doesn’t matter, and they aren’t
selected by an offset such as 0 or 1. Instead, you specify a unique key to associate with
each value. This key is often a string, but it can actually be any of Python’s immutable
types: boolean, integer, float, tuple, string, and others that you’ll see in later chapters.
Dictionaries are mutable, so you can add, delete, and change their key-value elements.
If you’ve worked with languages that support only arrays or lists, you’ll love dictionaries.
In other languages, dictionaries might be called associative arrays,
hashes, or hashmaps. In Python, a dictionary is also called a dict to
save syllables.
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Create with {}
To create a dictionary, you place curly brackets ({}) around comma-separated key :
value pairs. The simplest dictionary is an empty one, containing no keys or values at
all:
>>> empty_dict = {}
>>> empty_dict
{}
Let’s make a small dictionary with quotes from Ambrose Bierce’s The Devil’s Dictionary:
>>> bierce = {
...
"day": "A period of twenty-four hours, mostly misspent",
...
"positive": "Mistaken at the top of one's voice",
...
"misfortune": "The kind of fortune that never misses",
...
}
>>>
Typing the dictionary’s name in the interactive interpreter will print its keys and values:
>>> bierce
{'misfortune': 'The kind of fortune that never misses',
'positive': "Mistaken at the top of one's voice",
'day': 'A period of twenty-four hours, mostly misspent'}
In Python, it’s okay to leave a comma after the last item of a list, tuple,
or dictionary. Also, you don’t need to indent, as I did in the preced‐
ing example, when you’re typing keys and values within the curly
braces. It just helps readability.
Convert by Using dict()
You can use the dict() function to convert two-value sequences into a dictionary. (You
might run into such key-value sequences at times, such as “Strontium, 90, Carbon, 14”,
or “Vikings, 20, Packers, 7”.) The first item in each sequence is used as the key and the
second as the value.
First, here’s a small example using lol (a list of two-item lists):
>>> lol = [ ['a', 'b'], ['c', 'd'], ['e', 'f'] ]
>>> dict(lol)
{'c': 'd', 'a': 'b', 'e': 'f'}
Remember that the order of keys in a dictionary is arbitrary, and
might differ depending on how you add items.
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We could have used any sequence containing two-item sequences. Here are other ex‐
amples.
A list of two-item tuples:
>>> lot = [ ('a', 'b'), ('c', 'd'), ('e', 'f') ]
>>> dict(lot)
{'c': 'd', 'a': 'b', 'e': 'f'}
A tuple of two-item lists:
>>> tol = ( ['a', 'b'], ['c', 'd'], ['e', 'f'] )
>>> dict(tol)
{'c': 'd', 'a': 'b', 'e': 'f'}
A list of two-character strings:
>>> los = [ 'ab', 'cd', 'ef' ]
>>> dict(los)
{'c': 'd', 'a': 'b', 'e': 'f'}
A tuple of two-character strings:
>>> tos = ( 'ab', 'cd', 'ef' )
>>> dict(tos)
{'c': 'd', 'a': 'b', 'e': 'f'}
The section “Iterate Multiple Sequences with zip()” on page 79 introduces you to a func‐
tion called zip() that makes it easy to create these two-item sequences.
Add or Change an Item by [ key ]
Adding an item to a dictionary is easy. Just refer to the item by its key and assign a
value. If the key was already present in the dictionary, the existing value is replaced by
the new one. If the key is new, it’s added to the dictionary with its value. Unlike lists,
you don’t need to worry about Python throwing an exception during assignment by
specifying an index that’s out of range.
Let’s make a dictionary of most of the members of Monty Python, using their last names
as keys, and first names as values:
>>> pythons = {
...
'Chapman': 'Graham',
...
'Cleese': 'John',
...
'Idle': 'Eric',
...
'Jones': 'Terry',
...
'Palin': 'Michael',
...
}
>>> pythons
{'Cleese': 'John', 'Jones': 'Terry', 'Palin': 'Michael',
'Chapman': 'Graham', 'Idle': 'Eric'}
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We’re missing one member: the one born in America, Terry Gilliam. Here’s an attempt
by an anonymous programmer to add him, but he’s botched the first name:
>>> pythons['Gilliam'] = 'Gerry'
>>> pythons
{'Cleese': 'John', 'Gilliam': 'Gerry', 'Palin': 'Michael',
'Chapman': 'Graham', 'Idle': 'Eric', 'Jones': 'Terry'}
And here’s some repair code by another programmer who is Pythonic in more than one
way:
>>> pythons['Gilliam'] = 'Terry'
>>> pythons
{'Cleese': 'John', 'Gilliam': 'Terry', 'Palin': 'Michael',
'Chapman': 'Graham', 'Idle': 'Eric', 'Jones': 'Terry'}
By using the same key ('Gilliam'), we replaced the original value 'Gerry' with
'Terry'.
Remember that dictionary keys must be unique. That’s why we used last names for keys
instead of first names here—two members of Monty Python have the first name
Terry! If you use a key more than once, the last value wins:
>>> some_pythons = {
...
'Graham': 'Chapman',
...
'John': 'Cleese',
...
'Eric': 'Idle',
...
'Terry': 'Gilliam',
...
'Michael': 'Palin',
...
'Terry': 'Jones',
...
}
>>> some_pythons
{'Terry': 'Jones', 'Eric': 'Idle', 'Graham': 'Chapman',
'John': 'Cleese', 'Michael': 'Palin'}
We first assigned the value 'Gilliam' to the key 'Terry' and then replaced it with the
value 'Jones'.
Combine Dictionaries with update()
You can use the update() function to copy the keys and values of one dictionary into
another.
Let’s define the pythons dictionary, with all members:
>>> pythons = {
...
'Chapman': 'Graham',
...
'Cleese': 'John',
...
'Gilliam': 'Terry',
...
'Idle': 'Eric',
...
'Jones': 'Terry',
...
'Palin': 'Michael',
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...
}
>>> pythons
{'Cleese': 'John', 'Gilliam': 'Terry', 'Palin': 'Michael',
'Chapman': 'Graham', 'Idle': 'Eric', 'Jones': 'Terry'}
We also have a dictionary of other humorous persons called others:
>>> others = { 'Marx': 'Groucho', 'Howard': 'Moe' }
Now, along comes another anonymous programmer who thinks the members of others
should be members of Monty Python:
>>> pythons.update(others)
>>> pythons
{'Cleese': 'John', 'Howard': 'Moe', 'Gilliam': 'Terry',
'Palin': 'Michael', 'Marx': 'Groucho', 'Chapman': 'Graham',
'Idle': 'Eric', 'Jones': 'Terry'}
What happens if the second dictionary has the same key as the dictionary into which
it’s being merged? The value from the second dictionary wins:
>>> first = {'a': 1, 'b': 2}
>>> second = {'b': 'platypus'}
>>> first.update(second)
>>> first
{'b': 'platypus', 'a': 1}
Delete an Item by Key with del
Our anonymous programmer’s code was correct—technically. But, he shouldn’t have
done it! The members of others, although funny and famous, were not in Monty
Python. Let’s undo those last two additions:
>>> del pythons['Marx']
>>> pythons
{'Cleese': 'John', 'Howard': 'Moe', 'Gilliam': 'Terry',
'Palin': 'Michael', 'Chapman': 'Graham', 'Idle': 'Eric',
'Jones': 'Terry'}
>>> del pythons['Howard']
>>> pythons
{'Cleese': 'John', 'Gilliam': 'Terry', 'Palin': 'Michael',
'Chapman': 'Graham', 'Idle': 'Eric', 'Jones': 'Terry'}
Delete All Items by Using clear()
To delete all keys and values from a dictionary, use clear() or just reassign an empty
dictionary ({}) to the name:
>>> pythons.clear()
>>> pythons
{}
>>> pythons = {}
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>>> pythons
{}
Test for a Key by Using in
If you want to know whether a key exists in a dictionary, use in. Let’s redefine the pythons
dictionary again, this time omitting a name or two:
>>> pythons = {'Chapman': 'Graham', 'Cleese': 'John',
'Jones': 'Terry', 'Palin': 'Michael'}
Now let’s see who’s in there:
>>> 'Chapman' in pythons
True
>>> 'Palin' in pythons
True
Did we remember to add Terry Gilliam this time?
>>> 'Gilliam' in pythons
False
Drat.
Get an Item by [ key ]
This is the most common use of a dictionary. You specify the dictionary and key to get
the corresponding value:
>>> pythons['Cleese']
'John'
If the key is not present in the dictionary, you’ll get an exception:
>>> pythons['Marx']
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
KeyError: 'Marx'
There are two good ways to avoid this. The first is to test for the key at the outset by
using in, as you saw in the previous section:
>>> 'Marx' in pythons
False
The second is to use the special dictionary get() function. You provide the dictionary,
key, and an optional value. If the key exists, you get its value:
>>> pythons.get('Cleese')
'John'
If not, you get the optional value, if you specified one:
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>>> pythons.get('Marx', 'Not a Python')
'Not a Python'
Otherwise, you get None (which displays nothing in the interactive interpreter):
>>> pythons.get('Marx')
>>>
Get All Keys by Using keys()
You can use keys() to get all the keys in a dictionary. We’ll use a different sample
dictionary for the next few examples:
>>> signals = {'green': 'go', 'yellow': 'go faster', 'red': 'smile for the camera'}
>>> signals.keys()
dict_keys(['green', 'red', 'yellow'])
In Python 2, keys() just returns a list. Python 3 returns dict_keys(),
which is an iterable view of the keys. This is handy with large dic‐
tionaries because it doesn’t use the time and memory to create and
store a list that you might not use. But often you actually do want a
list. In Python 3, you need to call list() to convert a dict_keys object
to a list.
>>> list( signals.keys() )
['green', 'red', 'yellow']
In Python 3, you also need to use the list() function to turn the
results of values() and items() into normal Python lists. I’m using
that in these examples.
Get All Values by Using values()
To obtain all the values in a dictionary, use values():
>>> list( signals.values() )
['go', 'smile for the camera', 'go faster']
Get All Key-Value Pairs by Using items()
When you want to get all the key-value pairs from a dictionary, use the items() function:
>>> list( signals.items() )
[('green', 'go'), ('red', 'smile for the camera'), ('yellow', 'go faster')]
Each key and value is returned as a tuple, such as ('green', 'go').
Assign with =, Copy with copy()
As with lists, if you make a change to a dictionary, it will be reflected in all the names
that refer to it.
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>>> signals = {'green': 'go', 'yellow': 'go faster', 'red': 'smile for the camera'}
>>> save_signals = signals
>>> signals['blue'] = 'confuse everyone'
>>> save_signals
{'blue': 'confuse everyone', 'green': 'go',
'red': 'smile for the camera', 'yellow': 'go faster'}
To actually copy keys and values from a dictionary to another dictionary and avoid this,
you can use copy():
>>> signals = {'green': 'go', 'yellow': 'go faster', 'red': 'smile for the camera'}
>>> original_signals = signals.copy()
>>> signals['blue'] = 'confuse everyone'
>>> signals
{'blue': 'confuse everyone', 'green': 'go',
'red': 'smile for the camera', 'yellow': 'go faster'}
>>> original_signals
{'green': 'go', 'red': 'smile for the camera', 'yellow': 'go faster'}
Sets
A set is like a dictionary with its values thrown away, leaving only the keys. As with a
dictionary, each key must be unique. You use a set when you only want to know that
something exists, and nothing else about it. Use a dictionary if you want to attach some
information to the key as a value.
At some bygone time, in some places, set theory was taught in elementary school along
with basic mathematics. If your school skipped it (or covered it and you were staring
out the window as I often did), Figure 3-1 shows the ideas of union and intersection.
Suppose that you take the union of two sets that have some keys in common. Because
a set must contain only one of each item, the union of two sets will contain only one of
each key. The null or empty set is a set with zero elements. In Figure 3-1, an example of
a null set would be female names beginning with X.
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Figure 3-1. Common things to do with sets
Create with set()
To create a set, you use the set() function or enclose one or more comma-separated
values in curly brackets, as shown here:
>>> empty_set = set()
>>> empty_set
set()
>>> even_numbers = {0, 2, 4, 6, 8}
>>> even_numbers
{0, 8, 2, 4, 6}
>>> odd_numbers = {1, 3, 5, 7, 9}
>>> odd_numbers
{9, 3, 1, 5, 7}
As with dictionary keys, sets are unordered.
Because [] creates an empty list, you might expect {} to create an
empty set. Instead, {} creates an empty dictionary. That’s also why the
interpreter prints an empty set as set() instead of {}. Why? Diction‐
aries were in Python first and took possession of the curly brackets.
Convert from Other Data Types with set()
You can create a set from a list, string, tuple, or dictionary, discarding any duplicate
values.
First, let’s take a look at a string with more than one occurrence of some letters:
>>> set( 'letters' )
{'l', 'e', 't', 'r', 's'}
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Notice that the set contains only one 'e' or 't', even though 'letters' contained two
of each.
Now, let’s make a set from a list:
>>> set( ['Dasher', 'Dancer', 'Prancer', 'Mason-Dixon'] )
{'Dancer', 'Dasher', 'Prancer', 'Mason-Dixon'}
This time, a set from a tuple:
>>> set( ('Ummagumma', 'Echoes', 'Atom Heart Mother') )
{'Ummagumma', 'Atom Heart Mother', 'Echoes'}
When you give set() a dictionary, it uses only the keys:
>>> set( {'apple': 'red', 'orange': 'orange', 'cherry': 'red'} )
{'apple', 'cherry', 'orange'}
Test for Value by Using in
This is the most common use of a set. We’ll make a dictionary called drinks. Each key
is the name of a mixed drink, and the corresponding value is a set of its ingredients:
>>> drinks = {
...
'martini': {'vodka', 'vermouth'},
...
'black russian': {'vodka', 'kahlua'},
...
'white russian': {'cream', 'kahlua', 'vodka'},
...
'manhattan': {'rye', 'vermouth', 'bitters'},
...
'screwdriver': {'orange juice', 'vodka'}
...
}
Even though both are enclosed by curly braces ({ and }), a set is just a sequence of values,
and a dictionary is one or more key : value pairs.
Which drinks contain vodka? (Note that I’m previewing the use of for, if, and, and or
from the next chapter for these tests.)
>>> for name, contents in drinks.items():
...
if 'vodka' in contents:
...
print(name)
...
screwdriver
martini
black russian
white russian
We want something with vodka but are lactose intolerant, and think vermouth tastes
like kerosene:
>>> for name, contents in drinks.items():
...
if 'vodka' in contents and not ('vermouth' in contents or
...
'cream' in contents):
...
print(name)
...
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screwdriver
black russian
We’ll rewrite this a bit more succinctly in the next section.
Combinations and Operators
What if you want to check for combinations of set values? Suppose that you want to
find any drink that has orange juice or vermouth? We’ll use the set intersection opera‐
tor, which is an ampersand (&):
>>> for name, contents in drinks.items():
...
if contents & {'vermouth', 'orange juice'}:
...
print(name)
...
screwdriver
martini
manhattan
The result of the & operator is a set, which contains all the items that appear in both lists
that you compare. If neither of those ingredients were in contents, the & returns an
empty set, which is considered False.
Now, let’s rewrite the example from the previous section, in which we wanted vodka but
neither cream nor vermouth:
>>> for name, contents in drinks.items():
...
if 'vodka' in contents and not contents & {'vermouth', 'cream'}:
...
print(name)
...
screwdriver
black russian
Let’s save the ingredient sets for these two drinks in variables, just to save typing in the
coming examples:
>>> bruss = drinks['black russian']
>>> wruss = drinks['white russian']
The following are examples of all the set operators. Some have special punctuation, some
have special functions, and some have both. We’ll use test sets a (contains 1 and 2) and
b (contains 2 and 3):
>>> a = {1, 2}
>>> b = {2, 3}
You get the intersection (members common to both sets) with the special punctuation
symbol & or the set intersection() function, as demonstrated here:
>>> a & b
{2}
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>>> a.intersection(b)
{2}
This snippet uses our saved drink variables:
>>> bruss & wruss
{'kahlua', 'vodka'}
In this example, you get the union (members of either set) by using | or the set union()
function:
>>>
{1,
>>>
{1,
a | b
2, 3}
a.union(b)
2, 3}
And here’s the alcoholic version:
>>> bruss | wruss
{'cream', 'kahlua', 'vodka'}
The difference (members of the first set but not the second) is obtained by using the
character - or difference():
>>> a - b
{1}
>>> a.difference(b)
{1}
>>> bruss - wruss
set()
>>> wruss - bruss
{'cream'}
By far, the most common set operations are union, intersection, and difference. I’ve
included the others for completeness in the examples that follow, but you might never
use them.
The exclusive or (items in one set or the other, but not both) uses ^ or
symmetric_difference():
>>>
{1,
>>>
{1,
a ^ b
3}
a.symmetric_difference(b)
3}
This finds the exclusive ingredient in our two russian drinks:
>>> bruss ^ wruss
{'cream'}
You can check whether one set is a subset of another (all members of the first set are
also in the second set) by using <= or issubset():
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>>> a <= b
False
>>> a.issubset(b)
False
Adding cream to a black russian makes a white russian, so wruss is a superset of bruss:
>>> bruss <= wruss
True
Is any set a subset of itself? Yup.
>>> a <= a
True
>>> a.issubset(a)
True
To be a proper subset, the second set needs to have all the members of the first and
more. Calculate it by using <:
>>> a < b
False
>>> a < a
False
>>> bruss < wruss
True
A superset is the opposite of a subset (all members of the second set are also members
of the first). This uses >= or issuperset():
>>> a >= b
False
>>> a.issuperset(b)
False
>>> wruss >= bruss
True
Any set is a superset of itself:
>>> a >= a
True
>>> a.issuperset(a)
True
And finally, you can find a proper superset (the first set has all members of the second,
and more) by using >:
>>> a > b
False
>>> wruss > bruss
True
You can’t be a proper superset of yourself:
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>>> a > a
False
Compare Data Structures
To review: you make a list by using square brackets ([]), a tuple by using commas, and
a dictionary by using curly brackets ({}). In each case, you access a single element with
square brackets:
>>> marx_list = ['Groucho', 'Chico', 'Harpo']
>>> marx_tuple = 'Groucho', 'Chico', 'Harpo'
>>> marx_dict = {'Groucho': 'banjo', 'Chico': 'piano', 'Harpo': 'harp'}
>>> marx_list[2]
'Harpo'
>>> marx_tuple[2]
'Harpo'
>>> marx_dict['Harpo']
'harp'
For the list and tuple, the value between the square brackets is an integer offset. For the
dictionary, it’s a key. For all three, the result is a value.
Make Bigger Data Structures
We worked up from simple booleans, numbers, and strings to lists, tuples, sets, and
dictionaries. You can combine these built-in data structures into bigger, more complex
structures of your own. Let’s start with three different lists:
>>> marxes = ['Groucho', 'Chico', 'Harpo']
>>> pythons = ['Chapman', 'Cleese', 'Gilliam', 'Jones', 'Palin']
>>> stooges = ['Moe', 'Curly', 'Larry']
We can make a tuple that contains each list as an element:
>>> tuple_of_lists = marxes, pythons, stooges
>>> tuple_of_lists
(['Groucho', 'Chico', 'Harpo'],
['Chapman', 'Cleese', 'Gilliam', 'Jones', 'Palin'],
['Moe', 'Curly', 'Larry'])
And, we can make a list that contains the three lists:
>>> list_of_lists = [marxes, pythons, stooges]
>>> list_of_lists
[['Groucho', 'Chico', 'Harpo'],
['Chapman', 'Cleese', 'Gilliam', 'Jones', 'Palin'],
['Moe', 'Curly', 'Larry']]
Finally, let’s create a dictionary of lists. In this example, let’s use the name of the comedy
group as the key and the list of members as the value:
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>>> dict_of_lists = {'Marxes': marxes, 'Pythons': pythons, 'Stooges': stooges}
>> dict_of_lists
{'Stooges': ['Moe', 'Curly', 'Larry'],
'Marxes': ['Groucho', 'Chico', 'Harpo'],
'Pythons': ['Chapman', 'Cleese', 'Gilliam', 'Jones', 'Palin']}
Your only limitations are those in the data types themselves. For example, dictionary
keys need to be immutable, so a list, dictionary, or set can’t be a key for another dictio‐
nary. But a tuple can be. For example, you could index sites of interest by GPS coordi‐
nates (latitude, longitude, and altitude; see “Maps” on page 358 for more mapping exam‐
ples):
>>> houses = {
(44.79, -93.14, 285): 'My House',
(38.89, -77.03, 13): 'The White House'
}
Things to Do
In this chapter, you saw more complex data structures: lists, tuples, dictionaries, and
sets. Using these and those from Chapter 2 (numbers and strings), you can represent
elements in the real world with great variety.
3.1. Create a list called years_list, starting with the year of your birth, and each year
thereafter until the year of your fifth birthday. For example, if you were born in 1980.
the list would be years_list = [1980, 1981, 1982, 1983, 1984, 1985].
If you’re less than five years old and reading this book, I don’t know what to tell you.
3.2. In which year in years_list was your third birthday? Remember, you were 0 years
of age for your first year.
3.3. In which year in years_list were you the oldest?
3.4. Make a list called things with these three strings as elements: "mozzarella", "cin
derella", "salmonella".
3.5. Capitalize the element in things that refers to a person and then print the list. Did
it change the element in the list?
3.6. Make the cheesy element of things all uppercase and then print the list.
3.7. Delete the disease element from things, collect your Nobel Prize, and print the list.
3.8. Create a list called surprise with the elements "Groucho", "Chico", and "Harpo".
3.9. Lowercase the last element of the surprise list, reverse it, and then capitalize it.
3.10. Make an English-to-French dictionary called e2f and print it. Here are your starter
words: dog is chien, cat is chat, and walrus is morse.
Things to Do
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3.11. Using your three-word dictionary e2f, print the French word for walrus.
3.12. Make a French-to-English dictionary called f2e from e2f. Use the items method.
3.13. Using f2e, print the English equivalent of the French word chien.
3.14. Make and print a set of English words from the keys in e2f.
3.15. Make a multilevel dictionary called life. Use these strings for the topmost keys:
'animals', 'plants', and 'other'. Make the 'animals' key refer to another dictionary
with the keys 'cats', 'octopi', and 'emus'. Make the 'cats' key refer to a list of strings
with the values 'Henri', 'Grumpy', and 'Lucy'. Make all the other keys refer to empty
dictionaries.
3.16. Print the top-level keys of life.
3.17. Print the keys for life['animals'].
3.18. Print the values for life['animals']['cats'].
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CHAPTER 4
Py Crust: Code Structures
In Chapters 1 through 3, you’ve seen many examples of data but have not done much
with them. Most of the code examples used the interactive interpreter and were short.
Now you’ll see how to structure Python code, not just data.
Many computer languages use characters such as curly braces ({ and }) or keywords
such as begin and end to mark off sections of code. In those languages, it’s good practice
to use consistent indentation to make your program more readable for yourself and
others. There are even tools to make your code line up nicely.
When he was designing the language that became Python, Guido van Rossum decided
that the indentation itself was enough to define a program’s structure, and avoided
typing all those parentheses and curly braces. Python is unusual in this use of white
space to define program structure. It’s one of the first aspects that newcomers notice,
and it can seem odd to those who have experience with other languages. It turns out
that after writing Python for a little while, it feels natural and you stop noticing it. You
even get used to doing more while typing less.
Comment with #
A comment is a piece of text in your program that is ignored by the Python interpret‐
er. You might use comments to clarify nearby Python code, make notes to yourself to
fix something someday, or for whatever purpose you like. You mark a comment by using
the # character; everything from that point on to the end of the current line is part of
the comment. You’ll usually see a comment on a line by itself, as shown here:
>>> # 60 sec/min * 60 min/hr * 24 hr/day
>>> seconds_per_day = 86400
Or, on the same line as the code it’s commenting:
>>> seconds_per_day = 86400 # 60 sec/min * 60 min/hr * 24 hr/day
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The # character has many names: hash, sharp, pound, or the sinister-sounding octo‐
thorpe.1 Whatever you call it,2 its effect lasts only to the end of the line on which it
appears.
Python does not have a multiline comment. You need to explicitly begin each comment
line or section with a #.
>>> # I can say anything here, even if Python doesn't like it,
... # because I'm protected by the awesome
... # octothorpe.
...
>>>
However, if it’s in a text string, the all-powerful octothorpe reverts back to its role as a
plain old # character:
>>> print("No comment: quotes make the # harmless.")
No comment: quotes make the # harmless.
Continue Lines with \
Programs are more readable when lines are reasonably short. The recommended (not
required) maximum line length is 80 characters. If you can’t say everything you want
to say in that length, you can use the continuation character: \ (backslash). Just put \ at
the end of a line, and Python will suddenly act as though you’re still on the same line.
For example, if I wanted to build a long string from smaller ones, I could do it in steps:
>>>
>>>
>>>
>>>
>>>
alphabet
alphabet
alphabet
alphabet
alphabet
= ''
+= 'abcdefg'
+= 'hijklmnop'
+= 'qrstuv'
+= 'wxyz'
Or, I could do it in one step, using the continuation character:
>>> alphabet = 'abcdefg' + \
...
'hijklmnop' + \
...
'qrstuv' + \
...
'wxyz'
1. Like that eight-legged green thing that’s right behind you.
2. Please don’t call it. It might come back.
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Line continuation is also needed if a Python expression spans multiple lines:
>>> 1 + 2 +
File "<stdin>", line 1
1 + 2 +
^
SyntaxError: invalid syntax
>>> 1 + 2 + \
... 3
6
>>>
Compare with if, elif, and else
So far in this book, we’ve talked almost entirely about data structures. Now, we finally
take our first step into the code structures that weave data into programs. (You got a little
preview of these in the previous chapter’s section on sets. I hope no lasting damage was
done.) Our first example is this tiny Python program that checks the value of the boolean
variable disaster and prints an appropriate comment:
>>> disaster = True
>>> if disaster:
...
print("Woe!")
... else:
...
print("Whee!")
...
Woe!
>>>
The if and else lines are Python statements that check whether a condition (here, the
value of disaster) is True. Remember, print() is Python’s built-in function to print
things, normally to your screen.
If you’ve programmed in other languages, note that you don’t need
parentheses for the if test. Don’t say something such as if (disas
ter == True). You do need the colon (:) at the end. If, like me, you
forget to type the colon at times, Python will display an error message.
Each print() line is indented under its test. I used four spaces to indent each subsection.
Although you can use any indentation you like, Python expects you to be consistent
with code within a section—the lines need to be indented the same amount, lined up
on the left. The recommended style, called PEP-8, is to use four spaces. Don’t use tabs,
or mix tabs and spaces; it messes up the indent count.
Compare with if, elif, and else
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We did a number of things here, which I’ll explain more fully as the chapter progresses:
• Assigned the boolean value True to the variable named disaster
• Performed a conditional comparison by using if and else, executing different code
depending on the value of disaster
• Called the print() function to print some text
You can have tests within tests, as many levels deep as needed:
>>> furry = True
>>> small = True
>>> if furry:
...
if small:
...
print("It's
...
else:
...
print("It's
... else:
...
if small:
...
print("It's
...
else:
...
print("It's
...
It's a cat.
a cat.")
a bear!")
a skink!")
a human. Or a hairless bear.")
In Python, indentation determines how the if and else sections are paired. Our first
test was to check furry. Because furry is True, Python goes to the indented if small
test. Because we had set small to True, if small is evaluated as True. This makes Python
run the next line and print It's a cat.
If there are more than two possibilities to test, use if, elif (meaning else if), and else:
>>> color = "puce"
>>> if color == "red":
...
print("It's a tomato")
... elif color == "green":
...
print("It's a green pepper")
... elif color == "bee purple":
...
print("I don't know what it is, but only bees can see it")
... else:
...
print("I've never heard of the color", color)
...
I've never heard of the color puce
In the preceding example, we tested for equality with the == operator. Python’s com‐
parison operators are:
equality
==
inequality
!=
less than
<
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less than or equal
<=
greater than
>
greater than or equal >=
membership
in …
These return the boolean values True or False. Let’s see how these all work, but first,
assign a value to x:
>>> x = 7
Now, let’s try some tests:
>>> x
False
>>> x
True
>>> 5
True
>>> x
True
== 5
== 7
< x
< 10
Note that two equals signs (==) are used to test equality; remember, a single equals sign
(=) is what you use to assign a value to a variable.
If you need to make multiple comparisons at the same time, you use the boolean oper‐
ators and, or, and not to determine the final boolean result.
Boolean operators have lower precedence than the chunks of code that they’re compar‐
ing. This means that the chunks are calculated first, then compared. In this example,
because we set x to 7, 5 < x is calculated to be True and x < 10 is also True, so we finally
end up with True and True:
>>> 5 < x and x < 10
True
As “Precedence” on page 23 points out, the easiest way to avoid confusion about prece‐
dence is to add parentheses:
>>> (5 < x) and (x < 10)
True
Here are some other tests:
>>> 5 < x or x < 10
True
>>> 5 < x and x > 10
False
>>> 5 < x and not x > 10
True
If you’re and-ing multiple comparisons with one variable, Python lets you do this:
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>>> 5 < x < 10
True
It’s the same as 5 < x and x < 10. You can also write longer comparisons:
>>> 5 < x < 10 < 999
True
What Is True?
What if the element we’re checking isn’t a boolean? What does Python consider True
and False?
A false value doesn’t necessarily need to explicitly be False. For example, these are all
considered False:
boolean
False
null
None
zero integer
0
zero float
0.0
empty string ''
empty list
[]
empty tuple ()
empty dict
{}
empty set
set()
Anything else is considered True. Python programs use this definition of “truthiness”
(or in this case, “falsiness”) to check for empty data structures as well as False
conditions:
>>> some_list = []
>>> if some_list:
...
print("There's something in here")
... else:
...
print("Hey, it's empty!")
...
Hey, it's empty!
If what you’re testing is an expression rather than a simple variable, Python evaluates
the expression and returns a boolean result. So, if you type the following:
if color == "red":
Python evaluates color == "red". In our example, we assigned the string "puce" to
color earlier, so color == "red" is False, and Python moves on to the next test:
elif color == "green":
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Repeat with while
Testing with if, elif, and else runs from top to bottom. Sometimes, we need to do
something more than once. We need a loop, and the simplest looping mechanism in
Python is while. Using the interactive interpreter, try this next example, which is a
simple loop that prints the numbers from 1 to 5:
>>> count = 1
>>> while count <= 5:
...
print(count)
...
count += 1
...
1
2
3
4
5
>>>
We first assigned the value 1 to count. The while loop compared the value of count to
5 and continued if count was less than or equal to 5. Inside the loop, we printed the
value of count and then incremented its value by one with the statement count += 1.
Python goes back to the top of the loop, and again compares count with 5. The value of
count is now 2, so the contents of the while loop are again executed, and count is
incremented to 3.
This continues until count is incremented from 5 to 6 at the bottom of the loop. On the
next trip to the top, count <= 5 is now False, and the while loop ends. Python moves
on to the next lines.
Cancel with break
If you want to loop until something occurs, but you’re not sure when that might happen,
you can use an infinite loop with a break statement. This time we’ll read a line of input
from the keyboard via Python’s input() function and then print it with the first letter
capitalized. We break out of the loop when a line containing only the letter q is typed:
>>> while True:
...
stuff = input("String to capitalize [type q to quit]: ")
...
if stuff == "q":
...
break
...
print(stuff.capitalize())
...
String to capitalize [type q to quit]: test
Test
String to capitalize [type q to quit]: hey, it works
Hey, it works
String to capitalize [type q to quit]: q
>>>
Repeat with while
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Skip Ahead with continue
Sometimes you don’t want to break out of a loop but just want to skip ahead to the next
iteration for some reason. Here’s a contrived example: let’s read an integer, print its
square if it’s odd, and skip it if it’s even. We even added a few comments. Again, we’ll
use q to stop the loop:
>>> while True:
...
value = input("Integer, please [q to quit]: ")
...
if value == 'q':
# quit
...
break
...
number = int(value)
...
if number % 2 == 0:
# an even number
...
continue
...
print(number, "squared is", number*number)
...
Integer, please [q to quit]: 1
1 squared is 1
Integer, please [q to quit]: 2
Integer, please [q to quit]: 3
3 squared is 9
Integer, please [q to quit]: 4
Integer, please [q to quit]: 5
5 squared is 25
Integer, please [q to quit]: q
>>>
Check break Use with else
If the while loop ended normally (no break call), control passes to an optional else.
You use this when you’ve coded a while loop to check for something, and breaking as
soon as it’s found. The else would be run if the while loop completed but the object
was not found:
>>> numbers = [1, 3, 5]
>>> position = 0
>>> while position < len(numbers):
...
number = numbers[position]
...
if number % 2 == 0:
...
print('Found even number', number)
...
break
...
position += 1
... else: # break not called
...
print('No even number found')
...
No even number found
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This use of else might seem nonintuitive. Consider it a break checker.
Iterate with for
Python makes frequent use of iterators, for good reason. They make it possible for you
to traverse data structures without knowing how large they are or how they are imple‐
mented. You can even iterate over data that is created on the fly, allowing processing of
data streams that would otherwise not fit in the computer’s memory all at once.
It’s legal Python to step through a sequence like this:
>>> rabbits = ['Flopsy', 'Mopsy', 'Cottontail', 'Peter']
>>> current = 0
>>> while current < len(rabbits):
...
print(rabbits[current])
...
current += 1
...
Flopsy
Mopsy
Cottontail
Peter
But there’s a better, more Pythonic way:
>>> for rabbit in rabbits:
...
print(rabbit)
...
Flopsy
Mopsy
Cottontail
Peter
Lists such as rabbits are one of Python’s iterable objects, along with strings, tuples,
dictionaries, sets, and some other elements. Tuple or list iteration produces an item at
a time. String iteration produces a character at a time, as shown here:
>>> word = 'cat'
>>> for letter in word:
...
print(letter)
...
c
a
t
Iterating over a dictionary (or its keys() function) returns the keys. In this example,
the keys are the types of cards in the board game Clue (Cluedo outside of North
America):
Iterate with for
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>>> accusation = {'room': 'ballroom', 'weapon': 'lead pipe',
'person': 'Col. Mustard'}
>>> for card in accusation: # or, for card in accusation.keys():
...
print(card)
...
room
weapon
person
To iterate over the values rather than the keys, you use the dictionary’s values() func‐
tion:
>>> for value in accusation.values():
...
print(value)
...
ballroom
lead pipe
Col. Mustard
To return both the key and value in a tuple, you can use the items() function:
>>> for item in accusation.items():
...
print(item)
...
('room', 'ballroom')
('weapon', 'lead pipe')
('person', 'Col. Mustard')
Remember that you can assign to a tuple in one step. For each tuple returned by
items(), assign the first value (the key) to card and the second (the value) to contents:
>>> for card, contents in accusation.items():
...
print('Card', card, 'has the contents', contents)
...
Card weapon has the contents lead pipe
Card person has the contents Col. Mustard
Card room has the contents ballroom
Cancel with break
A break in a for loop breaks out of the loop, as it does for a while loop.
Skip with continue
Inserting a continue in a for loop jumps to the next iteration of the loop, as it does for
a while loop.
Check break Use with else
Similar to while, for has an optional else that checks if the for completed normally.
If break was not called, the else statement is run.
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This is useful when you want to verify that the previous for loop ran to completion,
instead of being stopped early with a break. The for loop in the following example
prints the name of the cheese and breaks if any cheese is found in the cheese shop:
>>> cheeses = []
>>> for cheese in cheeses:
...
print('This shop has some lovely', cheese)
...
break
... else: # no break means no cheese
...
print('This is not much of a cheese shop, is it?')
...
This is not much of a cheese shop, is it?
As with while, the use of else with for might seem nonintuitive. It
makes more sense if you think of the for as looking for something,
and else being called if you didn’t find it. To get the same effect
without else, use some variable to indicate whether you found what
you wanted in the for loop, as demonstrated here:
>>> cheeses = []
>>> found_one = False
>>> for cheese in cheeses:
...
found_one = True
...
print('This shop has some lovely', cheese)
...
break
...
>>> if not found_one:
...
print('This is not much of a cheese shop, is it?')
...
This is not much of a cheese shop, is it?
Iterate Multiple Sequences with zip()
There’s one more nice iteration trick: iterating over multiple sequences in parallel by
using the zip() function:
>>> days = ['Monday', 'Tuesday', 'Wednesday']
>>> fruits = ['banana', 'orange', 'peach']
>>> drinks = ['coffee', 'tea', 'beer']
>>> desserts = ['tiramisu', 'ice cream', 'pie', 'pudding']
>>> for day, fruit, drink, dessert in zip(days, fruits, drinks, desserts):
...
print(day, ": drink", drink, "- eat", fruit, "- enjoy", dessert)
...
Monday : drink coffee - eat banana - enjoy tiramisu
Tuesday : drink tea - eat orange - enjoy ice cream
Wednesday : drink beer - eat peach - enjoy pie
zip() stops when the shortest sequence is done. One of the lists (desserts) was longer
than the others, so no one gets any pudding unless we extend the other lists.
Iterate with for
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“Dictionaries” on page 53 shows you how the dict() function can create dictionaries
from two-item sequences like tuples, lists, or strings. You can use zip() to walk through
multiple sequences and make tuples from items at the same offsets. Let’s make two tuples
of corresponding English and French words:
>>> english = 'Monday', 'Tuesday', 'Wednesday'
>>> french = 'Lundi', 'Mardi', 'Mercredi'
Now, use zip() to pair these tuples. The value returned by zip() is itself not a tuple or
list, but an iterable value that can be turned into one:
>>> list( zip(english, french) )
[('Monday', 'Lundi'), ('Tuesday', 'Mardi'), ('Wednesday', 'Mercredi')]
Feed the result of zip() directly to dict() and voilà: a tiny English-French dictionary!
>>> dict( zip(english, french) )
{'Monday': 'Lundi', 'Tuesday': 'Mardi', 'Wednesday': 'Mercredi'}
Generate Number Sequences with range()
The range() function returns a stream of numbers within a specified range. without
first having to create and store a large data structure such as a list or tuple. This lets you
create huge ranges without using all the memory in your computer and crashing your
program.
You use range() similar to how to you use slices: range( start, stop, step ). If you
omit start, the range begins at 0. The only required value is stop; as with slices, the
last value created will be just before stop. The default value of step is 1, but you can go
backward with -1.
Like zip(), range() returns an iterable object, so you need to step through the values
with for ... in, or convert the object to a sequence like a list. Let’s make the range 0,
1, 2:
>>> for x in range(0,3):
...
print(x)
...
0
1
2
>>> list( range(0, 3) )
[0, 1, 2]
Here’s how to make a range from 2 down to 0:
>>> for x in range(2, -1, -1):
...
print(x)
...
2
1
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0
>>> list( range(2, -1, -1) )
[2, 1, 0]
The following snippet uses a step size of 2 to get the even numbers from 0 to 10:
>>> list( range(0, 11, 2) )
[0, 2, 4, 6, 8, 10]
Other Iterators
Chapter 8 shows iteration over files. In Chapter 6, you can see how to enable iteration
over objects that you’ve defined yourself.
Comprehensions
A comprehension is a compact way of creating a Python data structure from one or more
iterators. Comprehensions make it possible for you to combine loops and conditional
tests with a less verbose syntax. Using a comprehension is sometimes taken as a sign
that you know Python at more than a beginner’s level. In other words, it’s more Pythonic.
List Comprehensions
You could build a list of integers from 1 to 5, one item at a time, like this:
>>>
>>>
>>>
>>>
>>>
>>>
>>>
[1,
number_list = []
number_list.append(1)
number_list.append(2)
number_list.append(3)
number_list.append(4)
number_list.append(5)
number_list
2, 3, 4, 5]
Or, you could also use an iterator and the range() function:
>>>
>>>
...
...
>>>
[1,
number_list = []
for number in range(1, 6):
number_list.append(number)
number_list
2, 3, 4, 5]
Or, you could just turn the output of range() into a list directly:
>>> number_list = list(range(1, 6))
>>> number_list
[1, 2, 3, 4, 5]
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All of these approaches are valid Python code and will produce the same result. However,
a more Pythonic way to build a list is by using a list comprehension. The simplest form
of list comprehension is:
[ expression for item in iterable ]
Here’s how a list comprehension would build the integer list:
>>> number_list = [number for number in range(1,6)]
>>> number_list
[1, 2, 3, 4, 5]
In the first line, you need the first number variable to produce values for the list: that is,
to put a result of the loop into number_list. The second number is part of the for loop.
To show that the first number is an expression, try this variant:
>>> number_list = [number-1 for number in range(1,6)]
>>> number_list
[0, 1, 2, 3, 4]
The list comprehension moves the loop inside the square brackets. This comprehension
example really wasn’t simpler than the previous example, but there’s more. A list com‐
prehension can include a conditional expression, looking something like this:
[ expression for item in iterable if condition ]
Let’s make a new comprehension that builds a list of only the odd numbers between 1
and 5 (remember that number % 2 is True for odd numbers and False for even
numbers):
>>> a_list = [number for number in range(1,6) if number % 2 == 1]
>>> a_list
[1, 3, 5]
Now, the comprehension is a little more compact than its traditional counterpart:
>>> a_list = []
>>> for number in range(1,6):
...
if number % 2 == 1:
...
a_list.append(number)
...
>>> a_list
[1, 3, 5]
Finally, just as there can be nested loops, there can be more than one set of for ...
clauses in the corresponding comprehension. To show this, let’s first try a plain, old
nested loop and print the results:
>>> rows = range(1,4)
>>> cols = range(1,3)
>>> for row in rows:
...
for col in cols:
...
print(row, col)
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...
1 1
1 2
2 1
2 2
3 1
3 2
Now, let’s use a comprehension and assign it to the variable cells, making it a list of
(row, col) tuples:
>>>
>>>
>>>
>>>
...
...
(1,
(1,
(2,
(2,
(3,
(3,
rows = range(1,4)
cols = range(1,3)
cells = [(row, col) for row in rows for col in cols]
for cell in cells:
print(cell)
1)
2)
1)
2)
1)
2)
By the way, you can also use tuple unpacking to yank the row and col values from each
tuple as you iterate over the cells list:
>>> for row, col in cells:
...
print(row, col)
...
1 1
1 2
2 1
2 2
3 1
3 2
The for row ... and for col ... fragments in the list comprehension could also have
had their own if tests.
Dictionary Comprehensions
Not to be outdone by mere lists, dictionaries also have comprehensions. The simplest
form looks familiar:
{ key_expression : value_expression for expression in iterable }
Similar to list comprehensions, dictionary comprehensions can also have if tests and
multiple for clauses:
>>> word = 'letters'
>>> letter_counts = {letter: word.count(letter) for letter in word}
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>>> letter_counts
{'l': 1, 'e': 2, 't': 2, 'r': 1, 's': 1}
We are running a loop over each of the seven letters in the string 'letters' and counting
how many times that letter appears. Two of our uses of word.count(letter) are a waste
of time because we have to count all the e’s twice and all the t’s twice. But, when we
count the e’s the second time, we do no harm because we just replace the entry in the
dictionary that was already there; the same goes for counting the t’s. So, the following
would have been a teeny bit more Pythonic:
>>> word = 'letters'
>>> letter_counts = {letter: word.count(letter) for letter in set(word)}
>>> letter_counts
{'t': 2, 'l': 1, 'e': 2, 'r': 1, 's': 1}
The dictionary’s keys are in a different order than the previous example, because iter‐
ating set(word) returns letters in a different order than iterating the string word.
Set Comprehensions
No one wants to be left out, so even sets have comprehensions. The simplest version
looks like the list and dictionary comprehensions that you’ve just seen:
{ expression for expression in iterable }
The longer versions (if tests, multiple for clauses) are also valid for sets:
>>> a_set = {number for number in range(1,6) if number % 3 == 1}
>>> a_set
{1, 4}
Generator Comprehensions
Tuples do not have comprehensions! You might have thought that changing the square
brackets of a list comprehension to parentheses would create a tuple comprehension.
And it would appear to work because there’s no exception if you type this:
>>> number_thing = (number for number in range(1, 6))
The thing between the parentheses is a generator comprehension, and it returns a gen‐
erator object:
>>> type(number_thing)
<class 'generator'>
I’ll get into generators in more detail in “Generators” on page 98. A generator is one way
to provide data to an iterator.
You can iterate over this generator object directly, as illustrated here:
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>>> for number in number_thing:
...
print(number)
...
1
2
3
4
5
Or, you can wrap a list() call around a generator comprehension to make it work like
a list comprehension:
>>> number_list = list(number_thing)
>>> number_list
[1, 2, 3, 4, 5]
A generator can be run only once. Lists, sets, strings, and dictionar‐
ies exist in memory, but a generator creates its values on the fly and
hands them out one at a time through an iterator. It doesn’t remem‐
ber them, so you can’t restart or back up a generator.
If you try to re-iterate this generator, you’ll find that it’s tapped out:
>>> try_again = list(number_thing)
>>> try_again
[]
You can create a generator from a generator comprehension, as we did here, or from a
generator function. We’ll talk about functions in general first, and then we’ll get to the
special case of generator functions.
Functions
So far, all our Python code examples have been little fragments. These are good for small
tasks, but no one wants to retype fragments all the time. We need some way of organizing
larger code into manageable pieces.
The first step to code reuse is the function: a named piece of code, separate from all
others. A function can take any number and type of input parameters and return any
number and type of output results.
You can do two things with a function:
• Define it
• Call it
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To define a Python function, you type def, the function name, parentheses enclosing
any input parameters to the function, and then finally, a colon (:). Function names have
the same rules as variable names (they must start with a letter or _ and contain only
letters, numbers, or _).
Let’s take things one step at a time, and first define and call a function that has no
parameters. Here’s the simplest Python function:
>>> def do_nothing():
...
pass
Even for a function with no parameters like this one, you still need the parentheses and
the colon in its definition. The next line needs to be indented, just as you would indent
code under an if statement. Python requires the pass statement to show that this func‐
tion does nothing. It’s the equivalent of This page intentionally left blank (even though
it isn’t anymore).
You call this function just by typing its name and parentheses. It works as advertised,
doing nothing very well:
>>> do_nothing()
>>>
Now, let’s define and call another function that has no parameters but prints a single
word:
>>> def make_a_sound():
...
print('quack')
...
>>> make_a_sound()
quack
When you called the make_a_sound() function, Python ran the code inside its defini‐
tion. In this case, it printed a single word and returned to the main program.
Let’s try a function that has no parameters but returns a value:
>>> def agree():
...
return True
...
You can call this function and test its returned value by using if:
>>> if agree():
...
print('Splendid!')
... else:
...
print('That was unexpected.')
...
Splendid!
You’ve just made a big step. The combination of functions with tests such as if and
loops such as while make it possible for you to do things that you could not do before.
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At this point, it’s time to put something between those parentheses. Let’s define the
function echo() with one parameter called anything. It uses the return statement to
send the value of anything back to its caller twice, with a space between:
>>> def echo(anything):
...
return anything + ' ' + anything
...
>>>
Now let’s call echo() with the string 'Rumplestiltskin':
>>> echo('Rumplestiltskin')
'Rumplestiltskin Rumplestiltskin'
The values you pass into the function when you call it are known as arguments. When
you call a function with arguments, the values of those arguments are copied to their
corresponding parameters inside the function. In the previous example, the function
echo() was called with the argument string 'Rumplestiltskin'. This value was copied
within echo() to the parameter anything, and then returned (in this case doubled, with
a space) to the caller.
These function examples were pretty basic. Let’s write a function that takes an input
argument and actually does something with it. We’ll adapt the earlier code fragment
that comments on a color. Call it commentary and have it take an input string parameter
called color. Make it return the string description to its caller, which can decide what
to do with it:
>>> def commentary(color):
...
if color == 'red':
...
return "It's a tomato."
...
elif color == "green":
...
return "It's a green pepper."
...
elif color == 'bee purple':
...
return "I don't know what it is, but only bees can see it."
...
else:
...
return "I've never heard of the color " + color + "."
...
>>>
Call the function commentary() with the string argument 'blue'.
>>> comment = commentary('blue')
The function does the following:
• Assigns the value 'blue' to the function’s internal color parameter
• Runs through the if-elif-else logic chain
• Returns a string
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• Assigns the string to the variable comment
What do we get back?
>>> print(comment)
I've never heard of the color blue.
A function can take any number of input arguments (including zero) of any type. It can
return any number of output results (also including zero) of any type. If a function
doesn’t call return explicitly, the caller gets the result None.
>>> print(do_nothing())
None
None Is Useful
None is a special Python value that holds a place when there is nothing to say. It is not
the same as the boolean value False, although it looks false when evaluated as a boolean.
Here’s an example:
>>> thing = None
>>> if thing:
...
print("It's some thing")
... else:
...
print("It's no thing")
...
It's no thing
To distinguish None from a boolean False value, use Python’s is operator:
>>> if thing is None:
...
print("It's nothing")
... else:
...
print("It's something")
...
It's nothing
This seems like a subtle distinction, but it’s important in Python. You’ll need None to
distinguish a missing value from an empty value. Remember that zero-valued integers
or floats, empty strings (''), lists ([]), tuples ((,)), dictionaries ({}), and sets(set())
are all False, but are not equal to None.
Let’s write a quick function that prints whether its argument is None:
>>> def is_none(thing):
...
if thing is None:
...
print("It's None")
...
elif thing:
...
print("It's True")
...
else:
...
print("It's False")
...
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Now, let’s run some tests:
>>> is_none(None)
It's None
>>> is_none(True)
It's True
>>> is_none(False)
It's False
>>> is_none(0)
It's False
>>> is_none(0.0)
It's False
>>> is_none(())
It's False
>>> is_none([])
It's False
>>> is_none({})
It's False
>>> is_none(set())
It's False
Positional Arguments
Python handles function arguments in a manner that’s unusually flexible, when com‐
pared to many languages. The most familiar types of arguments are positional argu‐
ments, whose values are copied to their corresponding parameters in order.
This function builds a dictionary from its positional input arguments and returns it:
>>> def menu(wine, entree, dessert):
...
return {'wine': wine, 'entree': entree, 'dessert': dessert}
...
>>> menu('chardonnay', 'chicken', 'cake')
{'dessert': 'cake', 'wine': 'chardonnay', 'entree': 'chicken'}
Although very common, a downside of positional arguments is that you need to re‐
member the meaning of each position. If we forgot and called menu() with wine as the
last argument instead of the first, the meal would be very different:
>>> menu('beef', 'bagel', 'bordeaux')
{'dessert': 'bordeaux', 'wine': 'beef', 'entree': 'bagel'}
Keyword Arguments
To avoid positional argument confusion, you can specify arguments by the names of
their corresponding parameters, even in a different order from their definition in the
function:
>>> menu(entree='beef', dessert='bagel', wine='bordeaux')
{'dessert': 'bagel', 'wine': 'bordeaux', 'entree': 'beef'}
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You can mix positional and keyword arguments. Let’s specify the wine first, but use
keyword arguments for the entree and dessert:
>>> menu('frontenac', dessert='flan', entree='fish')
{'entree': 'fish', 'dessert': 'flan', 'wine': 'frontenac'}
If you call a function with both positional and keyword arguments, the positional ar‐
guments need to come first.
Specify Default Parameter Values
You can specify default values for parameters. The default is used if the caller does not
provide a corresponding argument. This bland-sounding feature can actually be quite
useful. Using the previous example:
>>> def menu(wine, entree, dessert='pudding'):
...
return {'wine': wine, 'entree': entree, 'dessert': dessert}
This time, try calling menu() without the dessert argument:
>>> menu('chardonnay', 'chicken')
{'dessert': 'pudding', 'wine': 'chardonnay', 'entree': 'chicken'}
If you do provide an argument, it’s used instead of the default:
>>> menu('dunkelfelder', 'duck', 'doughnut')
{'dessert': 'doughnut', 'wine': 'dunkelfelder', 'entree': 'duck'}
Default argument values are calculated when the function is defined,
not when it is run. A common error with new (and sometimes notso-new) Python programmers is to use a mutable data type such as a
list or dictionary as a default argument.
In the following test, the buggy() function is expected to run each time with a fresh
empty result list, add the arg argument to it, and then print a single-item list. However,
there’s a bug: it’s empty only the first time it’s called. The second time, result still has
one item from the previous call:
>>> def buggy(arg, result=[]):
...
result.append(arg)
...
print(result)
...
>>> buggy('a')
['a']
>>> buggy('b')
# expect ['b']
['a', 'b']
It would have worked if it had been written like this:
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>>> def works(arg):
...
result = []
...
result.append(arg)
...
return result
...
>>> works('a')
['a']
>>> works('b')
['b']
The fix is to pass in something else to indicate the first call:
>>> def nonbuggy(arg, result=None):
...
if result is None:
...
result = []
...
result.append(arg)
...
print(result)
...
>>> nonbuggy('a')
['a']
>>> nonbuggy('b')
['b']
Gather Positional Arguments with *
If you’ve programmed in C or C++, you might assume that an asterisk (*) in a Python
program has something to do with a pointer. Nope, Python doesn’t have pointers.
When used inside the function with a parameter, an asterisk groups a variable number
of positional arguments into a tuple of parameter values. In the following example, args
is the parameter tuple that resulted from the arguments that were passed to the function
print_args():
>>> def print_args(*args):
...
print('Positional argument tuple:', args)
...
If you call it with no arguments, you get nothing in *args:
>>> print_args()
Positional argument tuple: ()
Whatever you give it will be printed as the args tuple:
>>> print_args(3, 2, 1, 'wait!', 'uh...')
Positional argument tuple: (3, 2, 1, 'wait!', 'uh...')
This is useful for writing functions such as print() that accept a variable number of
arguments. If your function has required positional arguments as well, *args goes at
the end and grabs all the rest:
>>> def print_more(required1, required2, *args):
...
print('Need this one:', required1)
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...
print('Need this one too:', required2)
...
print('All the rest:', args)
...
>>> print_more('cap', 'gloves', 'scarf', 'monocle', 'mustache wax')
Need this one: cap
Need this one too: gloves
All the rest: ('scarf', 'monocle', 'mustache wax')
When using *, you don’t need to call the tuple parameter args, but it’s a common idiom
in Python.
Gather Keyword Arguments with **
You can use two asterisks (**) to group keyword arguments into a dictionary, where the
argument names are the keys, and their values are the corresponding dictionary val‐
ues. The following example defines the function print_kwargs() to print its keyword
arguments:
>>> def print_kwargs(**kwargs):
...
print('Keyword arguments:', kwargs)
...
Now, try calling it with some keyword arguments:
>>> print_kwargs(wine='merlot', entree='mutton', dessert='macaroon')
Keyword arguments: {'dessert': 'macaroon', 'wine': 'merlot', 'entree': 'mutton'}
Inside the function, kwargs is a dictionary.
If you mix positional parameters with *args and **kwargs, they need to occur in that
order. As with args, you don’t need to call this keyword parameter kwargs, but it’s
common usage.
Docstrings
Readability counts, says the Zen of Python. You can attach documentation to a function
definition by including a string at the beginning of the function body. This is the func‐
tion’s docstring:
>>> def echo(anything):
...
'echo returns its input argument'
...
return anything
You can make a docstring quite long and even add rich formatting, if you want, as is
demonstrated in the following:
def print_if_true(thing, check):
'''
Prints the first argument if a second argument is true.
The operation is:
1. Check whether the *second* argument is true.
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2. If it is, print the *first* argument.
'''
if check:
print(thing)
To print a function’s docstring, call the Python help() function. Pass the function’s name
to get a listing of arguments along with the nicely formatted docstring:
>>> help(echo)
Help on function echo in module __main__:
echo(anything)
echo returns its input argument
If you want to see just the raw docstring, without the formatting:
>>> print(echo.__doc__)
echo returns its input argument
That odd-looking __doc__ is the internal name of the docstring as a variable within the
function. “Uses of _ and __ in Names” on page 103 explains the reason behind all those
underscores.
Functions Are First-Class Citizens
I’ve mentioned the Python mantra, everything is an object. This includes numbers,
strings, tuples, lists, dictionaries—and functions, as well. Functions are first-class citi‐
zens in Python. You can assign them to variables, use them as arguments to other func‐
tions, and return them from functions. This gives you the capability to do some things
in Python that are difficult-to-impossible to carry out in many other languages.
To test this, let’s define a simple function called answer() that doesn’t have any argu‐
ments; it just prints the number 42:
>>> def answer():
...
print(42)
If you run this function, you know what you’ll get:
>>> answer()
42
Now, let’s define another function named run_something. It has one argument called
func, a function to run. Once inside, it just calls the function.
>>> def run_something(func):
...
func()
If we pass answer to run_something(), we’re using a function as data, just as with any‐
thing else:
>>> run_something(answer)
42
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Notice that you passed answer, not answer(). In Python, those parentheses mean call
this function. With no parentheses, Python just treats the function like any other object.
That’s because, like everything else in Python, it is an object:
>>> type(run_something)
<class 'function'>
Let’s try running a function with arguments. Define a function add_args() that prints
the sum of its two numeric arguments, arg1 and arg2:
>>> def add_args(arg1, arg2):
...
print(arg1 + arg2)
And what is add_args()?
>>> type(add_args)
<class 'function'>
At this point, let’s define a function called run_something_with_args() that takes three
arguments:
• func—The function to run
• arg1—The first argument for func
• arg2—The second argument for func
>>> def run_something_with_args(func, arg1, arg2):
...
func(arg1, arg2)
When you call run_something_with_args(), the function passed by the caller is as‐
signed to the func parameter, whereas arg1 and arg2 get the values that follow in the
argument list. Then, running func(arg1, arg2) executes that function with those ar‐
guments because the parentheses told Python to do so.
Let’s test it by passing the function name add_args and the arguments 5 and 9 to
run_something_with_args():
>>> run_something_with_args(add_args, 5, 9)
14
Within the function run_something_with_args(), the function name argument
add_args was assigned to the parameter func, 5 to arg1, and 9 to arg2. This ended up
running:
add_args(5, 9)
You can combine this with the *args and **kwargs techniques.
Let’s define a test function that takes any number of positional arguments, calculates
their sum by using the sum() function, and then returns that sum:
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>>> def sum_args(*args):
...
return sum(args)
I haven’t mentioned sum() before. It’s a built-in Python function that calculates the sum
of the values in its iterable numeric (int or float) argument.
We’ll define the new function run_with_positional_args(), which takes a function
and any number of positional arguments to pass to it:
>>> def run_with_positional_args(func, *args):
...
return func(*args)
Now, go ahead and call it:
>>> run_with_positional_args(sum_args, 1, 2, 3, 4)
10
You can use functions as elements of lists, tuples, sets, and dictionaries. Functions are
immutable, so you can also use them as dictionary keys.
Inner Functions
You can define a function within another function:
>>> def outer(a, b):
...
def inner(c, d):
...
return c + d
...
return inner(a, b)
...
>>>
>>> outer(4, 7)
11
An inner function can be useful when performing some complex task more than once
within another function, to avoid loops or code duplication. For a string example, this
inner function adds some text to its argument:
>>> def knights(saying):
...
def inner(quote):
...
return "We are the knights who say: '%s'" % quote
...
return inner(saying)
...
>>> knights('Ni!')
"We are the knights who say: 'Ni!'"
Closures
An inner function can act as a closure. This is a function that is dynamically generated
by another function and can both change and remember the values of variables that
were created outside the function.
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The following example builds on the previous knights() example. Let’s call the new
one knights2(), because we have no imagination, and turn the inner() function into
a closure called inner2(). Here are the differences:
• inner2() uses the outer saying parameter directly instead of getting it as an ar‐
gument.
• knights2() returns the inner2 function name instead of calling it.
>>> def knights2(saying):
...
def inner2():
...
return "We are the knights who say: '%s'" % saying
...
return inner2
...
The inner2() function knows the value of saying that was passed in and remembers
it. The line return inner2 returns this specialized copy of the inner2 function (but
doesn’t call it). That’s a closure: a dynamically created function that remembers where
it came from.
Let’s call knights2() twice, with different arguments:
>>> a = knights2('Duck')
>>> b = knights2('Hasenpfeffer')
Okay, so what are a and b?
>>> type(a)
<class 'function'>
>>> type(b)
<class 'function'>
They’re functions, but they’re also closures:
>>> a
<function knights2.<locals>.inner2 at 0x10193e158>
>>> b
<function knights2.<locals>.inner2 at 0x10193e1e0>
If we call them, they remember the saying that was used when they were created by
knights2:
>>>
"We
>>>
"We
a()
are the knights who say: 'Duck'"
b()
are the knights who say: 'Hasenpfeffer'"
Anonymous Functions: the lambda() Function
In Python, a lambda function is an anonymous function expressed as a single state‐
ment. You can use it instead of a normal tiny function.
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To illustrate it, let’s first make an example that uses normal functions. To begin, we’ll
define the function edit_story(). Its arguments are the following:
• words—a list of words
• func—a function to apply to each word in words
>>> def edit_story(words, func):
...
for word in words:
...
print(func(word))
Now, we need a list of words and a function to apply to each word. For the words, here’s
a list of (hypothetical) sounds made by my cat if he (hypothetically) missed one of the
stairs:
>>> stairs = ['thud', 'meow', 'thud', 'hiss']
And for the function, this will capitalize each word and append an exclamation point,
perfect for feline tabloid newspaper headlines:
>>> def enliven(word):
# give that prose more punch
...
return word.capitalize() + '!'
Mixing our ingredients:
>>> edit_story(stairs, enliven)
Thud!
Meow!
Thud!
Hiss!
Finally, we get to the lambda. The enliven() function was so brief that we could replace
it with a lambda:
>>>
>>> edit_story(stairs, lambda word: word.capitalize() + '!')
Thud!
Meow!
Thud!
Hiss!
>>>
The lambda takes one argument, which we call word here. Everything between the colon
and the terminating parenthesis is the definition of the function.
Often, using real functions such as enliven() is much clearer than using lambdas.
Lambdas are mostly useful for cases in which you would otherwise need to define many
tiny functions and remember what you called them all. In particular, you can use lamb‐
das in graphical user interfaces to define callback functions; see Appendix A for examples.
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Generators
A generator is a Python sequence creation object. With it, you can iterate through po‐
tentially huge sequences without creating and storing the entire sequence in memory
at once. Generators are often the source of data for iterators. If you recall, we already
used one of them, range(), in earlier code examples to generate a series of integers. In
Python 2, range() returns a list, which limits it to fit in memory. Python 2 also has the
generator xrange(), which became the normal range() in Python 3. This example adds
all the integers from 1 to 100:
>>> sum(range(1, 101))
5050
Every time you iterate through a generator, it keeps track of where it was the last time
it was called and returns the next value. This is different from a normal function, which
has no memory of previous calls and always starts at its first line with the same state.
If you want to create a potentially large sequence, and the code is too large for a generator
comprehension, write a generator function. It’s a normal function, but it returns its value
with a yield statement rather than return. Let’s write our own version of range():
>>> def my_range(first=0, last=10, step=1):
...
number = first
...
while number < last:
...
yield number
...
number += step
...
It’s a normal function:
>>> my_range
<function my_range at 0x10193e268>
And it returns a generator object:
>>> ranger = my_range(1, 5)
>>> ranger
<generator object my_range at 0x101a0a168>
We can iterate over this generator object:
>>> for x in ranger:
...
print(x)
...
1
2
3
4
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Decorators
Sometimes, you want to modify an existing function without changing its source
code. A common example is adding a debugging statement to see what arguments were
passed in.
A decorator is a function that takes one function as input and returns another function.
We’ll dig into our bag of Python tricks and use the following:
• *args and **kwargs
• Inner functions
• Functions as arguments
The function document_it() defines a decorator that will do the following:
• Print the function’s name and the values of its arguments
• Run the function with the arguments
• Print the result
• Return the modified function for use
Here’s what the code looks like:
>>> def document_it(func):
...
def new_function(*args, **kwargs):
...
print('Running function:', func.__name__)
...
print('Positional arguments:', args)
...
print('Keyword arguments:', kwargs)
...
result = func(*args, **kwargs)
...
print('Result:', result)
...
return result
...
return new_function
Whatever func you pass to document_it(), you get a new function that includes the
extra statements that document_it() adds. A decorator doesn’t actually have to run any
code from func, but document_it() calls func part way through so that you get the
results of func as well as all the extras.
So, how do you use this? You can apply the decorator manually:
>>> def add_ints(a, b):
...
return a + b
...
>>> add_ints(3, 5)
8
>>> cooler_add_ints = document_it(add_ints)
>>> cooler_add_ints(3, 5)
Running function: add_ints
# manual decorator assignment
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Positional arguments: (3, 5)
Keyword arguments: {}
Result: 8
8
As an alternative to the manual decorator assignment above, just add @decorator_name
before the function that you want to decorate:
>>> @document_it
... def add_ints(a, b):
...
return a + b
...
>>> add_ints(3, 5)
Start function add_ints
Positional arguments: (3, 5)
Keyword arguments: {}
Result: 8
8
You can have more than one decorator for a function. Let’s write another decorator
called square_it() that squares the result:
>>> def square_it(func):
...
def new_function(*args, **kwargs):
...
result = func(*args, **kwargs)
...
return result * result
...
return new_function
...
The decorator that’s used closest to the function (just above the def) runs first and then
the one above it. Either order gives the same end result, but you can see how the inter‐
mediate steps change:
>>> @document_it
... @square_it
... def add_ints(a, b):
...
return a + b
...
>>> add_ints(3, 5)
Running function: new_function
Positional arguments: (3, 5)
Keyword arguments: {}
Result: 64
64
Let’s try reversing the decorator order:
>>> @square_it
... @document_it
... def add_ints(a, b):
...
return a + b
...
>>> add_ints(3, 5)
Running function: add_ints
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Positional arguments: (3, 5)
Keyword arguments: {}
Result: 8
64
Namespaces and Scope
A name can refer to different things, depending on where it’s used. Python programs
have various namespaces—sections within which a particular name is unique and un‐
related to the same name in other namespaces.
Each function defines its own namespace. If you define a variable called x in a main
program and another variable called x in a function, they refer to different things. But
the walls can be breached: if you need to, you can access names in other namespaces in
various ways.
The main part of a program defines the global namespace; thus, the variables in that
namespace are global variables.
You can get the value of a global variable from within a function:
>>> animal = 'fruitbat'
>>> def print_global():
...
print('inside print_global:', animal)
...
>>> print('at the top level:', animal)
at the top level: fruitbat
>>> print_global()
inside print_global: fruitbat
But, if you try to get the value of the global variable and change it within the function,
you get an error:
>>> def change_and_print_global():
...
print('inside change_and_print_global:', animal)
...
animal = 'wombat'
...
print('after the change:', animal)
...
>>> change_and_print_global()
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "<stdin>", line 2, in change_and_report_it
UnboundLocalError: local variable 'animal' referenced before assignment
If you just change it, it changes a different variable also named animal, but this variable
is inside the function:
>>> def change_local():
...
animal = 'wombat'
...
print('inside change_local:', animal, id(animal))
...
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>>> change_local()
inside change_local: wombat 4330406160
>>> animal
'fruitbat'
>>> id(animal)
4330390832
What happened here? The first line assigned the string 'fruitbat' to a global variable
named animal. The change_local() function also has a variable named animal, but
that’s in its local namespace.
We used the Python function id() here to print the unique value for each object and
prove that the variable animal inside change_local() is not the same as animal at the
main level of the program.
To access the global variable rather than the local one within a function, you need to be
explicit and use the global keyword (you knew this was coming: explicit is better than
implicit):
>>> animal = 'fruitbat'
>>> def change_and_print_global():
...
global animal
...
animal = 'wombat'
...
print('inside change_and_print_global:', animal)
...
>>> animal
'fruitbat'
>>> change_and_print_global()
inside change_and_print_global: wombat
>>> animal
'wombat'
If you don’t say global within a function, Python uses the local namespace and the
variable is local. It goes away after the function completes.
Python provides two functions to access the contents of your namespaces:
• locals() returns a dictionary of the contents of the local namespace.
• globals() returns a dictionary of the contents of the global namespace.
And, here they are in use:
>>> animal = 'fruitbat'
>>> def change_local():
...
animal = 'wombat' # local variable
...
print('locals:', locals())
...
>>> animal
'fruitbat'
>>> change_local()
locals: {'animal': 'wombat'}
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>>> print('globals:', globals()) # reformatted a little for presentation
globals: {'animal': 'fruitbat',
'__doc__': None,
'change_local': <function change_it at 0x1006c0170>,
'__package__': None,
'__name__': '__main__',
'__loader__': <class '_frozen_importlib.BuiltinImporter'>,
'__builtins__': <module 'builtins'>}
>>> animal
'fruitbat'
The local namespace within change_local() contained only the local variable
animal. The global namespace contained the separate global variable animal and a
number of other things.
Uses of _ and __ in Names
Names that begin and end with two underscores (__) are reserved for use within Python,
so you should not use them with your own variables. This naming pattern was chosen
because it seemed unlikely to be selected by application developers for their own
variables.
For instance, the name of a function is in the system variable function .__name__, and
its documentation string is function .__doc__:
>>> def amazing():
...
'''This is the amazing function.
...
Want to see it again?'''
...
print('This function is named:', amazing.__name__)
...
print('And its docstring is:', amazing.__doc__)
...
>>> amazing()
This function is named: amazing
And its docstring is: This is the amazing function.
Want to see it again?
As you saw in the earlier globals printout, the main program is assigned the special
name __main__.
Handle Errors with try and except
Do, or do not. There is no try.
— Yoda
In some languages, errors are indicated by special function return values. Python uses
exceptions: code that is executed when an associated error occurs.
You’ve seen some of these already, such as accessing a list or tuple with an out-of-range
position, or a dictionary with a nonexistent key. When you run code that might fail
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under some circumstances, you also need appropriate exception handlers to intercept
any potential errors.
It’s good practice to add exception handling anywhere an exception might occur to let
the user know what is happening. You might not be able to fix the problem, but at least
you can note the circumstances and shut your program down gracefully. If an exception
occurs in some function and is not caught there, it bubbles up until it is caught by a
matching handler in some calling function. If you don’t provide your own exception
handler, Python prints an error message and some information about where the error
occurred and then terminates the program, as demonstrated in the following snippet.
>>> short_list = [1, 2, 3]
>>> position = 5
>>> short_list[position]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
IndexError: list index out of range
Rather than leaving it to chance, use try to wrap your code, and except to provide the
error handling:
>>> short_list = [1, 2, 3]
>>> position = 5
>>> try:
...
short_list[position]
... except:
...
print('Need a position between 0 and', len(short_list)-1, ' but got',
...
position)
...
Need a position between 0 and 2 but got 5
The code inside the try block is run. If there is an error, an exception is raised and the
code inside the except block runs. If there are no errors, the except block is skipped.
Specifying a plain except with no arguments, as we did here, is a catchall for any ex‐
ception type. If more than one type of exception could occur, it’s best to provide a
separate exception handler for each. No one forces you to do this; you can use a bare
except to catch all exceptions, but your treatment of them would probably be generic
(something akin to printing Some error occurred). You can use any number of specific
exception handlers.
Sometimes, you want exception details beyond the type. You get the full exception object
in the variable name if you use the form:
except exceptiontype as name
The example that follows looks for an IndexError first, because that’s the exception type
raised when you provide an illegal position to a sequence. It saves an IndexError ex‐
ception in the variable err, and any other exception in the variable other. The example
prints everything stored in other to show what you get in that object.
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>>> short_list = [1, 2, 3]
>>> while True:
...
value = input('Position [q to quit]? ')
...
if value == 'q':
...
break
...
try:
...
position = int(value)
...
print(short_list[position])
...
except IndexError as err:
...
print('Bad index:', position)
...
except Exception as other:
...
print('Something else broke:', other)
...
Position [q to quit]? 1
2
Position [q to quit]? 0
1
Position [q to quit]? 2
3
Position [q to quit]? 3
Bad index: 3
Position [q to quit]? 2
3
Position [q to quit]? two
Something else broke: invalid literal for int() with base 10: 'two'
Position [q to quit]? q
Inputting position 3 raised an IndexError as expected. Entering two annoyed the int()
function, which we handled in our second, catchall except code.
Make Your Own Exceptions
The previous section discussed handling exceptions, but all of the exceptions (such as
IndexError) were predefined in Python or its standard library. You can use any of these
for your own purposes. You can also define your own exception types to handle special
situations that might arise in your own programs.
This requires defining a new object type with a class—something we
don’t get into until Chapter 6. So, if you’re unfamiliar with classes, you
might want to return to this section later.
An exception is a class. It is a child of the class Exception. Let’s make an exception called
UppercaseException and raise it when we encounter an uppercase word in a string.
>>> class UppercaseException(Exception):
...
pass
...
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>>> words = ['eeenie', 'meenie', 'miny', 'MO']
>>> for word in words:
...
if word.isupper():
...
raise UppercaseException(word)
...
Traceback (most recent call last):
File "<stdin>", line 3, in <module>
__main__.UppercaseException: MO
We didn’t even define any behavior for UppercaseException (notice we just used
pass), letting its parent class Exception figure out what to print when the exception
was raised.
You can access the exception object itself and print it:
>>> try:
...
raise OopsException('panic')
... except OopsException as exc:
...
print(exc)
...
panic
Things to Do
4.1 Assign the value 7 to the variable guess_me. Then, write the conditional tests (if,
else, and elif) to print the string 'too low' if guess_me is less than 7, 'too high' if
greater than 7, and 'just right' if equal to 7.
4.2 Assign the value 7 to the variable guess_me and the value 1 to the variable start.
Write a while loop that compares start with guess_me. Print too low if start is less
than guess me. If start equals guess_me, print 'found it!' and exit the loop. If start
is greater than guess_me, print 'oops' and exit the loop. Increment start at the end of
the loop.
4.3 Use a for loop to print the values of the list [3, 2, 1, 0].
4.4 Use a list comprehension to make a list of the even numbers in range(10).
4.5 Use a dictionary comprehension to create the dictionary squares. Use range(10)
to return the keys, and use the square of each key as its value.
4.6 Use a set comprehension to create the set odd from the odd numbers in range(10).
4.7 Use a generator comprehension to return the string 'Got ' and a number for the
numbers in range(10). Iterate through this by using a for loop.
4.8 Define a function called good that returns the list ['Harry', 'Ron', 'Hermione'].
4.9 Define a generator function called get_odds that returns the odd numbers from
range(10). Use a for loop to find and print the third value returned.
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4.10 Define a decorator called test that prints 'start' when a function is called and
'end' when it finishes.
4.11 Define an exception called OopsException. Raise this exception to see what hap‐
pens. Then write the code to catch this exception and print 'Caught an oops'.
4.12 Use zip() to make a dictionary called movies that pairs these lists: titles =
['Creature of Habit', 'Crewel Fate'] and plots = ['A nun turns into a mon
ster', 'A haunted yarn shop'].
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CHAPTER 5
Py Boxes: Modules, Packages,
and Programs
During your bottom-up climb, you’ve progressed from built-in data types to construct‐
ing ever-larger data and code structures. In this chapter, you’ll finally get down to brass
tacks and learn how to write realistic, large programs in Python.
Standalone Programs
Thus far, you’ve been writing and running code fragments such as the following within
Python’s interactive interpreter:
>>> print("This interactive snippet works.")
This interactive snippet works.
Now let’s make your first standalone program. On your computer, create a file called
test1.py containing this single line of Python code:
print("This standalone program works!")
Notice that there’s no >>> prompt, just a single line of Python code. Ensure that there is
no indentation in the line before print.
If you’re running Python in a text terminal or terminal window, type the name of your
Python program followed by the program filename:
$ python test1.py
This standalone program works!
You can save all of the interactive snippets that you’ve seen in this
book so far to files and run them directly. If you’re cutting and past‐
ing, ensure that you delete the initial >>> and … (include the final
space).
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Command-Line Arguments
On your computer, create a file called test2.py that contains these two lines:
import sys
print('Program arguments:', sys.argv)
Now, use your version of Python to run this program. Here’s how it might look in a
Linux or Mac OS X terminal window using a standard shell program:
$ python test2.py
Program arguments: ['test2.py']
$ python test2.py tra la la
Program arguments: ['test2.py', 'tra', 'la', 'la']
Modules and the import Statement
We’re going to step up another level, creating and using Python code in more than one
file. A module is just a file of Python code.
The text of this book is organized in a hierarchy: words, sentences, paragraphs, and
chapters. Otherwise, it would be unreadable after a page or two. Code has a roughly
similar bottom-up organization: data types are like words, statements are like sentences,
functions are like paragraphs, and modules are like chapters. To continue the analogy,
in this book, when I say that something will be explained in Chapter 8, in programming,
that’s like referring to code in another module.
We refer to code of other modules by using the import statement. This makes the code
and variables in the imported module available to your program.
Import a Module
The simplest use of the import statement is import module, where module is the name
of another Python file, without the .py extension. Let’s simulate a weather station and
print a weather report. One main program prints the report, and a separate module with
a single function returns the weather description used by the report.
Here’s the main program (call it weatherman.py):
import report
description = report.get_description()
print("Today's weather:", description)
And here is the module (report.py):
def get_description(): # see the docstring below?
"""Return random weather, just like the pros"""
from random import choice
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possibilities = ['rain', 'snow', 'sleet', 'fog', 'sun', 'who knows']
return choice(possibilities)
If you have these two files in the same directory and instruct Python to run weather‐
man.py as the main program, it will access the report module and run its get_descrip
tion() function. We wrote this version of get_description() to return a random result
from a list of strings, so that’s what the main program will get back and print:
$ python weatherman.py
Today's weather: who knows
$ python weatherman.py
Today's weather: sun
$ python weatherman.py
Today's weather: sleet
We used imports in two different places:
• The main program weatherman.py imported the module report.
• In the module file report.py, the get_description() function imported the choice
function from Python’s standard random module.
We also used imports in two different ways:
• The main program called import report and then ran report.get_descrip
tion().
• The get_description() function in report.py called from random import
choice and then ran choice(possibilities).
In the first case, we imported the entire report module but needed to use report. as a
prefix to get_description(). After this import statement, everything in report.py is
available to the main program, as long as we tack report. before its name. By qualify‐
ing the contents of a module with the module’s name, we avoid any nasty naming con‐
flicts. There could be a get_description() function in some other module, and we
would not call it by mistake.
In the second case, we’re within a function and know that nothing else named choice
is here, so we imported the choice() function from the random module directly. We
could have written the function like the following snippet, which returns random re‐
sults:
def get_description():
import random
possibilities = ['rain', 'snow', 'sleet', 'fog', 'sun', 'who knows']
return random.choice(possibilities)
Like many aspects of programming, pick the style that seems the most clear to you. The
module-qualified name (random.choice) is safer but requires a little more typing.
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These get_description() examples showed variations of what to import, but but not
where to do the importing—they all called import from inside the function. We could
have imported random from outside the function:
>>> import random
>>> def get_description():
...
possibilities = ['rain', 'snow', 'sleet', 'fog', 'sun', 'who knows']
...
return random.choice(possibilities)
...
>>> get_description()
'who knows'
>>> get_description()
'rain'
You should consider importing from outside the function if the imported code might
be used in more than one place, and from inside if you know its use will be limited.
Some people prefer to put all their imports at the top of the file, just to make all the
dependencies of their code explicit. Either way works.
Import a Module with Another Name
In our main weatherman.py program, we called import report. But what if you have
another module with the same name or want to use a name that is more mnemonic or
shorter? In such a situation, you can import using an alias. Let’s use the alias wr:
import report as wr
description = wr.get_description()
print("Today's weather:", description)
Import Only What You Want from a Module
With Python, you can import one or more parts of a module. Each part can keep its
original name or you can give it an alias. First, let’s import get_description() from
the report module with its original name:
from report import get_description
description = get_description()
print("Today's weather:", description)
Now, import it as do_it:
from report import get_description as do_it
description = do_it()
print("Today's weather:", description)
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Module Search Path
Where does Python look for files to import? It uses a list of directory names and ZIP
archive files stored in the standard sys module as the variable path. You can access and
modify this list. Here’s the value of sys.path for Python 3.3 on my Mac:
>>> import sys
>>> for place in sys.path:
...
print(place)
...
/Library/Frameworks/Python.framework/Versions/3.3/lib/python33.zip
/Library/Frameworks/Python.framework/Versions/3.3/lib/python3.3
/Library/Frameworks/Python.framework/Versions/3.3/lib/python3.3/plat-darwin
/Library/Frameworks/Python.framework/Versions/3.3/lib/python3.3/lib-dynload
/Library/Frameworks/Python.framework/Versions/3.3/lib/python3.3/site-packages
That initial blank output line is the empty string '', which stands for the current di‐
rectory. If '' is first in sys.path, Python looks in the current directory first when you
try to import something: import report looks for report.py.
The first match will be used. This means that if you define a module named random and
it’s in the search path before the standard library, you won’t be able to access the standard
library’s random now.
Packages
We went from single lines of code, to multiline functions, to standalone programs, to
multiple modules in the same directory. To allow Python applications to scale even more,
you can organize modules into file hierarchies called packages.
Maybe we want different types of text forecasts: one for the next day and one for the
next week. One way to structure this is to make a directory named sources, and create
two modules within it: daily.py and weekly.py. Each has a function called forecast. The
daily version returns a string, and the weekly version returns a list of seven strings.
Here’s the main program and the two modules. (The enumerate() function takes apart
a list and feeds each item of the list to the for loop, adding a number to each item as a
little bonus.)
Main program: boxes/weather.py.
from sources import daily, weekly
print("Daily forecast:", daily.forecast())
print("Weekly forecast:")
for number, outlook in enumerate(weekly.forecast(), 1):
print(number, outlook)
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Module 1: boxes/sources/daily.py.
def forecast():
'fake daily forecast'
return 'like yesterday'
Module 2: boxes/sources/weekly.py.
def forecast():
"""Fake weekly forecast"""
return ['snow', 'more snow', 'sleet',
'freezing rain', 'rain', 'fog', 'hail']
You’ll need one more thing in the sources directory: a file named __init__.py. This can
be empty, but Python needs it to treat the directory containing it as a package.
Run the main weather.py program to see what happens:
$ python weather.py
Daily forecast: like yesterday
Weekly forecast:
1 snow
2 more snow
3 sleet
4 freezing rain
5 rain
6 fog
7 hail
The Python Standard Library
One of Python’s prominent claims is that it has “batteries included”—a large standard
library of modules that perform many useful tasks, and are kept separate to avoid bloat‐
ing the core language. When you’re about to write some Python code, it’s often worth‐
while to first check whether there’s a standard module that already does what you want.
It’s surprising how often you encounter little gems in the standard library. Python also
provides authoritative documentation for the modules, along with a tutorial. Doug
Hellmann’s website Python Module of the Week and his book The Python Standard Li‐
brary by Example (Addison-Wesley Professional) are also very useful guides.
Upcoming chapters in this book feature many of the standard modules that are specific
to the Web, systems, databases, and so on. In this section, I’ll talk about some standard
modules that have generic uses.
Handle Missing Keys with setdefault() and defaultdict()
You’ve seen that trying to access a dictionary with a nonexistent key raises an excep‐
tion. Using the dictionary get() function to return a default value avoids an exception.
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The setdefault() function is like get(), but also assigns an item to the dictionary if
the key is missing:
>>> periodic_table = {'Hydrogen': 1, 'Helium': 2}
>>> print(periodic_table)
{'Helium': 2, 'Hydrogen': 1}
If the key was not already in the dictionary, the new value is used:
>>> carbon = periodic_table.setdefault('Carbon', 12)
>>> carbon
12
>>> periodic_table
{'Helium': 2, 'Carbon': 12, 'Hydrogen': 1}
If we try to assign a different default value to an existing key, the original value is returned
and nothing is changed:
>>> helium = periodic_table.setdefault('Helium', 947)
>>> helium
2
>>> periodic_table
{'Helium': 2, 'Carbon': 12, 'Hydrogen': 1}
defaultdict() is similar, but specifies the default value for any new key up front, when
the dictionary is created. Its argument is a function. In this example, we pass the function
int, which will be called as int() and return the integer 0:
>>> from collections import defaultdict
>>> periodic_table = defaultdict(int)
Now, any missing value will be an integer (int), with the value 0:
>>> periodic_table['Hydrogen'] = 1
>>> periodic_table['Lead']
0
>>> periodic_table
defaultdict(<class 'int'>, {'Lead': 0, 'Hydrogen': 1})
The argument to defaultdict() is a function that returns the value to be assigned to
a missing key. In the following example, no_idea() is executed to return a value when
needed:
>>> from collections import defaultdict
>>>
>>> def no_idea():
...
return 'Huh?'
...
>>> bestiary = defaultdict(no_idea)
>>> bestiary['A'] = 'Abominable Snowman'
>>> bestiary['B'] = 'Basilisk'
>>> bestiary['A']
'Abominable Snowman'
>>> bestiary['B']
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'Basilisk'
>>> bestiary['C']
'Huh?'
You can use the functions int(), list(), or dict() to return default empty values for
those types: int() returns 0, list() returns an empty list ([]), and dict() returns an
empty dictionary ({}). If you omit the argument, the initial value of a new key will be
set to None.
By the way, you can use lambda to define your default-making function right inside the
call:
>>> bestiary = defaultdict(lambda: 'Huh?')
>>> bestiary['E']
'Huh?'
Using int is one way to make your own counter:
>>> from collections import defaultdict
>>> food_counter = defaultdict(int)
>>> for food in ['spam', 'spam', 'eggs', 'spam']:
...
food_counter[food] += 1
...
>>> for food, count in food_counter.items():
...
print(food, count)
...
eggs 1
spam 3
In the preceding example, if food_counter had been a normal dictionary instead of a
defaultdict, Python would have raised an exception every time we tried to increment
the dictionary element food_counter[food] because it would not have been initialized.
We would have needed to do some extra work, as shown here:
>>> dict_counter = {}
>>> for food in ['spam', 'spam', 'eggs', 'spam']:
...
if not food in dict_counter:
...
dict_counter[food] = 0
...
dict_counter[food] += 1
...
>>> for food, count in dict_counter.items():
...
print(food, count)
...
spam 3
eggs 1
Count Items with Counter()
Speaking of counters, the standard library has one that does the work of the previous
example and more:
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>>> from collections import Counter
>>> breakfast = ['spam', 'spam', 'eggs', 'spam']
>>> breakfast_counter = Counter(breakfast)
>>> breakfast_counter
Counter({'spam': 3, 'eggs': 1})
The most_common() function returns all elements in descending order, or just the top
count elements if given a count:
>>> breakfast_counter.most_common()
[('spam', 3), ('eggs', 1)]
>>> breakfast_counter.most_common(1)
[('spam', 3)]
You can combine counters. First, let’s see again what’s in breakfast_counter:
>>> breakfast_counter
>>> Counter({'spam': 3, 'eggs': 1})
This time, we’ll make a new list called lunch, and a counter called lunch_counter:
>>> lunch = ['eggs', 'eggs', 'bacon']
>>> lunch_counter = Counter(lunch)
>>> lunch_counter
Counter({'eggs': 2, 'bacon': 1})
The first way we combine the two counters is by addition, using +:
>>> breakfast_counter + lunch_counter
Counter({'spam': 3, 'eggs': 3, 'bacon': 1})
As you might expect, you subtract one counter from another by using -. What’s for
breakfast but not for lunch?
>>> breakfast_counter - lunch_counter
Counter({'spam': 3})
Okay, now what can we have for lunch that we can’t have for breakfast?
>>> lunch_counter - breakfast_counter
Counter({'bacon': 1, 'eggs': 1})
Similar to sets in Chapter 4, you can get common items by using the intersection op‐
erator &:
>>> breakfast_counter & lunch_counter
Counter({'eggs': 1})
The intersection picked the common element ('eggs') with the lower count. This makes
sense: breakfast only offered one egg, so that’s the common count.
Finally, you can get all items by using the union operator |:
>>> breakfast_counter | lunch_counter
Counter({'spam': 3, 'eggs': 2, 'bacon': 1})
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The item 'eggs' was again common to both. Unlike addition, union didn’t add their
counts, but picked the one with the larger count.
Order by Key with OrderedDict()
Many of the code examples in the early chapters of this book demonstrate that the
order of keys in a dictionary is not predictable: you might add keys a, b, and c in that
order, but keys() might return c, a, b. Here’s a repurposed example from Chapter 1:
>>> quotes = {
...
'Moe': 'A wise guy, huh?',
...
'Larry': 'Ow!',
...
'Curly': 'Nyuk nyuk!',
...
}
>>> for stooge in quotes:
... print(stooge)
...
Larry
Curly
Moe
An OrderedDict() remembers the order of key addition and returns them in the same
order from an iterator. Try creating an OrderedDict from a sequence of (key, value)
tuples:
>>> from collections import OrderedDict
>>> quotes = OrderedDict([
...
('Moe', 'A wise guy, huh?'),
...
('Larry', 'Ow!'),
...
('Curly', 'Nyuk nyuk!'),
...
])
>>>
>>> for stooge in quotes:
...
print(stooge)
...
Moe
Larry
Curly
Stack + Queue == deque
A deque (pronounced deck) is a double-ended queue, which has features of both a stack
and a queue. It’s useful when you want to add and delete items from either end of a
sequence. Here, we’ll work from both ends of a word to the middle to see if it’s a palin‐
drome. The function popleft() removes the leftmost item from the deque and returns
it; pop() removes the rightmost item and returns it. Together, they work from the ends
toward the middle. As long as the end characters match, it keeps popping until it reaches
the middle:
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>>> def palindrome(word):
...
from collections import deque
...
dq = deque(word)
...
while len(dq) > 1:
...
if dq.popleft() != dq.pop():
...
return False
...
return True
...
...
>>> palindrome('a')
True
>>> palindrome('racecar')
True
>>> palindrome('')
True
>>> palindrome('radar')
True
>>> palindrome('halibut')
False
I used this as a simple illustration of deques. If you really wanted a quick palindrome
checker, it would be a lot simpler to just compare a string with its reverse. Python doesn’t
have a reverse() function for strings, but it does have a way to reverse a string with a
slice, as illustrated here:
>>> def another_palindrome(word):
...
return word == word[::-1]
...
>>> another_palindrome('radar')
True
>>> another_palindrome('halibut')
False
Iterate over Code Structures with itertools
itertools contains special-purpose iterator functions. Each returns one item at a time
when called within a for … in loop, and remembers its state between calls.
chain() runs through its arguments as though they were a single iterable:
>>> import itertools
>>> for item in itertools.chain([1, 2], ['a', 'b']):
...
print(item)
...
1
2
a
b
cycle() is an infinite iterator, cycling through its arguments:
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>>> import itertools
>>> for item in itertools.cycle([1, 2]):
...
print(item)
...
1
2
1
2
.
.
.
…and so on.
accumulate() calculates accumulated values. By default, it calculates the sum:
>>> import itertools
>>> for item in itertools.accumulate([1, 2, 3, 4]):
...
print(item)
...
1
3
6
10
You can provide a function as the second argument to accumulate(), and it will be used
instead of addition. The function should take two arguments and return a single result.
This example calculates an accumulated product:
>>> import itertools
>>> def multiply(a, b):
...
return a * b
...
>>> for item in itertools.accumulate([1, 2, 3, 4], multiply):
...
print(item)
...
1
2
6
24
The itertools module has many more functions, notably some for combinations and
permutations that can be time savers when the need arises.
Print Nicely with pprint()
All of our examples have used print() (or just the variable name, in the interactive
interpreter) to print things. Sometimes, the results are hard to read. We need a pretty
printer such as pprint():
>>> from pprint import pprint
>>> quotes = OrderedDict([
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...
...
...
...
>>>
('Moe', 'A wise guy, huh?'),
('Larry', 'Ow!'),
('Curly', 'Nyuk nyuk!'),
])
Plain old print() just dumps things out there:
>>> print(quotes)
OrderedDict([('Moe', 'A wise guy, huh?'), ('Larry', 'Ow!'), ('Curly', 'Nyuk nyuk!')])
However, pprint() tries to align elements for better readability:
>>> pprint(quotes)
{'Moe': 'A wise guy, huh?',
'Larry': 'Ow!',
'Curly': 'Nyuk nyuk!'}
More Batteries: Get Other Python Code
Sometimes, the standard library doesn’t have what you need, or doesn’t do it in quite
the right way. There’s an entire world of open-source, third-party Python software. Good
resources include:
• PyPi (also known as the Cheese Shop, after an old Monty Python skit)
• github
• readthedocs
You can find many smaller code examples at activestate.
Almost all of the Python code in this book uses the standard Python installation on your
computer, which includes all the built-ins and the standard library. External packages
are featured in some places: I mentioned requests in Chapter 1, and have more details
in “Beyond the Standard Library: Requests” on page 222. Appendix D shows how to install
third-party Python software, along with many other nuts-and-bolts development
details.
Things to Do
5.1. Create a file called zoo.py. In it, define a function called hours() that prints the
string 'Open 9-5 daily'. Then, use the interactive interpreter to import the zoo module
and call its hours() function.
5.2. In the interactive interpreter, import the zoo module as menagerie and call its
hours() function.
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5.3. Staying in the interpreter, import the hours() function from zoo directly and
call it.
5.4. Import the hours() function as info and call it.
5.5. Make a dictionary called plain with the key-value pairs 'a': 1, 'b': 2, and 'c':
3, and then print it.
5.6. Make an OrderedDict called fancy from the same pairs listed in 5.5 and print it.
Did it print in the same order as plain?
5.7. Make a defaultdict called dict_of_lists and pass it the argument list. Make
the list dict_of_lists['a'] and append the value 'something for a' to it in one
assignment. Print dict_of_lists['a'].
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CHAPTER 6
Oh Oh: Objects and Classes
No object is mysterious. The mystery is your eye.
— Elizabeth Bowen
Take an object. Do something to it. Do something else to it.
— Jasper Johns
Up to this point, you’ve seen data structures such as strings and dictionaries, and code
structures such as functions and modules. In this chapter, you’ll deal with custom data
structures: objects.
What Are Objects?
As I mention in Chapter 2, everything in Python, from numbers to modules, is an object.
However, Python hides most of the object machinery by means of special syntax. You
can type num = 7 to create a object of type integer with the value 7, and assign an object
reference to the name num. The only time you need to look inside objects is when you
want to make your own or modify the behavior of existing objects. You’ll see how to do
both in this chapter.
An object contains both data (variables, called attributes) and code (functions, called
methods). It represents a unique instance of some concrete thing. For example, the
integer object with the value 7 is an object that facilitates methods such as addition and
multiplication, as is demonstrated in “Numbers” on page 19. 8 is a different object. This
means there’s an Integer class in Python, to which both 7 and 8 belong. The strings 'cat'
and 'duck' are also objects in Python, and have string methods that you’ve seen, such
as capitalize() and replace().
When you create new objects no one has ever created before, you must create a class
that indicates what they contain.
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Think of objects as nouns and their methods as verbs. An object represents an individual
thing, and its methods define how it interacts with other things.
Unlike modules, you can have multiple objects at the same time, each one with different
values for its attributes. They’re like super data structures, with code thrown in.
Define a Class with class
In Chapter 1, I compare an object to a plastic box. A class is like the mold that makes
that box. For instance, a String is the built-in Python class that makes string objects
such as 'cat' and 'duck'. Python has many other built-in classes to create the other
standard data types, including lists, dictionaries, and so on. To create your own custom
object in Python, you first need to define a class by using the class keyword. Let’s walk
through a simple example.
Suppose that you want to define objects to represent information about people. Each
object will represent one person. You’ll first want to define a class called Person as the
mold. In the examples that follow, we’ll try more than one version of this class as we
build up from the simplest class to ones that actually do something useful.
Our first try is the simplest possible class, an empty one:
>>> class Person():
...
pass
Just as with functions, we needed to say pass to indicate that this class was empty. This
definition is the bare minimum to create an object. You create an object from a class by
calling the class name as though it were a function:
>>> someone = Person()
In this case, Person() creates an individual object from the Person class and assigns it
the name someone. But, our Person class was empty, so the someone object that we create
from it just sits there and can’t do anything else. You would never actually define such
a class, and I’m only showing it here to build up to the next example.
Let’s try again, this time including the special Python object initialization method __in
it__:
>>> class Person():
...
def __init__(self):
...
pass
This is what you’ll see in real Python class definitions. I admit that the __init__() and
self look strange. __init__() is the special Python name for a method that initializes
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an individual object from its class definition. 1 The self argument specifies that it refers
to the individual object itself.
When you define __init__() in a class definition, its first parameter should be self.
Although self is not a reserved word in Python, it’s common usage. No one reading
your code later (including you!) will need to guess what you meant if you use self.
But even that second Person definition didn’t create an object that really did anything.
The third try is the charm that really shows how to create a simple object in Python.
This time, we’ll add the parameter name to the initialization method:
>>> class Person():
...
def __init__(self, name):
...
self.name = name
...
>>>
Now, we can create an object from the Person class by passing a string for the name
parameter:
>>> hunter = Person('Elmer Fudd')
Here’s what this line of code does:
• Looks up the definition of the Person class
• Instantiates (creates) a new object in memory
• Calls the object’s __init__ method, passing this newly-created object as self and
the other argument ('Elmer Fudd') as name
• Stores the value of name in the object
• Returns the new object
• Attaches the name hunter to the object
This new object is like any other object in Python. You can use it as an element of a list,
tuple, dictionary, or set. You can pass it to a function as an argument, or return it as a
result.
What about the name value that we passed in? It was saved with the object as an attribute.
You can read and write it directly:
>>> print('The mighty hunter: ', hunter.name)
The mighty hunter: Elmer Fudd
1. You’ll see many examples of double underscores in Python names; to save syllables, some people pronounce
them as dunder.
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Remember, inside the Person class definition, you access the name attribute as
self.name. When you create an actual object such as hunter, you refer to it as
hunter.name.
It is not necessary to have an __init__ method in every class definition; it’s used to do
anything that’s needed to distinguish this object from others created from the same class.
Inheritance
When you’re trying to solve some coding problem, often you’ll find an existing class
that creates objects that do almost what you need. What can you do? You could modify
this old class, but you’ll make it more complicated, and you might break something that
used to work.
Of course, you could write a new class, cutting and pasting from the old one and merging
your new code. But this means that you have more code to maintain, and the parts of
the old and new classes that used to work the same might drift apart because they’re
now in separate places.
The solution is inheritance: creating a new class from an existing class but with some
additions or changes. It’s an excellent way to reuse code. When you use inheritance, the
new class can automatically use all the code from the old class but without copying any
of it.
You define only what you need to add or change in the new class, and this overrides the
behavior of the old class. The original class is called a parent, superclass, or base class;
the new class is called a child, subclass, or derived class. These terms are interchangeable
in object-oriented programming.
So, let’s inherit something. We’ll define an empty class called Car. Next, define a subclass
of Car called Yugo. You define a subclass by using the same class keyword but with the
parent class name inside the parentheses (class Yugo(Car) below):
>>> class Car():
...
pass
...
>>> class Yugo(Car):
...
pass
...
Next, create an object from each class:
>>> give_me_a_car = Car()
>>> give_me_a_yugo = Yugo()
A child class is a specialization of a parent class; in object-oriented lingo, Yugo is-a
Car. The object named give_me_a_yugo is an instance of class Yugo, but it also inherits
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whatever a Car can do. In this case, Car and Yugo are as useful as deckhands on a sub‐
marine, so let’s try new class definitions that actually do something:
>>> class Car():
...
def exclaim(self):
...
print("I'm a Car!")
...
>>> class Yugo(Car):
...
pass
...
Finally, make one object from each class and call the exclaim method:
>>>
>>>
>>>
I'm
>>>
I'm
give_me_a_car = Car()
give_me_a_yugo = Yugo()
give_me_a_car.exclaim()
a Car!
give_me_a_yugo.exclaim()
a Car!
Without doing anything special, Yugo inherited the exclaim() method from Car. In
fact, Yugo says that it is a Car, which might lead to an identity crisis. Let’s see what we
can do about that.
Override a Method
As you just saw, a new class initially inherits everything from its parent class. Moving
forward, you’ll see how to replace or override a parent method. Yugo should probably
be different from Car in some way; otherwise, what’s the point of defining a new class?
Let’s change how the exclaim() method works for a Yugo:
>>> class Car():
...
def exclaim(self):
...
print("I'm a Car!")
...
>>> class Yugo(Car):
...
def exclaim(self):
...
print("I'm a Yugo! Much like a Car, but more Yugo-ish.")
...
Now, make two objects from these classes:
>>> give_me_a_car = Car()
>>> give_me_a_yugo = Yugo()
What do they say?
>>>
I'm
>>>
I'm
give_me_a_car.exclaim()
a Car!
give_me_a_yugo.exclaim()
a Yugo! Much like a Car, but more Yugo-ish.
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In these examples, we overrode the exclaim() method. We can override any methods,
including __init__(). Here’s another example that uses our earlier Person class. Let’s
make subclasses that represent doctors (MDPerson) and lawyers (JDPerson):
>>> class Person():
...
def __init__(self, name):
...
self.name = name
...
>>> class MDPerson(Person):
...
def __init__(self, name):
...
self.name = "Doctor " + name
...
>>> class JDPerson(Person):
...
def __init__(self, name):
...
self.name = name + ", Esquire"
...
In these cases, the initialization method __init__() takes the same arguments as the
parent Person class but stores the value of name differently inside the object instance:
>>> person = Person('Fudd')
>>> doctor = MDPerson('Fudd')
>>> lawyer = JDPerson('Fudd')
>>> print(person.name)
Fudd
>>> print(doctor.name)
Doctor Fudd
>>> print(lawyer.name)
Fudd, Esquire
Add a Method
The child class can also add a method that was not present in its parent class. Going
back to classes Car and Yugo, we’ll define the new method need_a_push() for class Yugo
only:
>>> class Car():
...
def exclaim(self):
...
print("I'm a Car!")
...
>>> class Yugo(Car):
...
def exclaim(self):
...
print("I'm a Yugo! Much like a Car, but more Yugo-ish.")
...
def need_a_push(self):
...
print("A little help here?")
...
Next, make a Car and a Yugo:
>>> give_me_a_car = Car()
>>> give_me_a_yugo = Yugo()
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A Yugo object can react to a need_a_push() method call:
>>> give_me_a_yugo.need_a_push()
A little help here?
But a generic Car object cannot:
>>> give_me_a_car.need_a_push()
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
AttributeError: 'Car' object has no attribute 'need_a_push'
At this point, a Yugo can do something that a Car cannot, and the distinct personality
of a Yugo can emerge.
Get Help from Your Parent with super
We saw how the child class could add or override a method from the parent. What if it
wanted to call that parent method? “I’m glad you asked,” says super(). We’ll define a
new class called EmailPerson that represents a Person with an email address. First, our
familiar Person definition:
>>> class Person():
...
def __init__(self, name):
...
self.name = name
...
Notice that the __init__() call in the following subclass has an additional email pa‐
rameter:
>>> class EmailPerson(Person):
...
def __init__(self, name, email):
...
super().__init__(name)
...
self.email = email
When you define an __init__() method for your class, you’re replacing the __init__()
method of its parent class, and the latter is not called automatically anymore. As a result,
we need to call it explicitly. Here’s what’s happening:
• The super() gets the definition of the parent class, Person.
• The __init__() method calls the Person.__init__() method. It takes care of
passing the self argument to the superclass, so you just need to give it any optional
arguments. In our case, the only other argument Person() accepts is name.
• The self.email = email line is the new code that makes this EmailPerson different
from a Person.
Moving on, let’s make one of these creatures:
>>> bob = EmailPerson('Bob Frapples', '[email protected]')
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We should be able to access both the name and email attributes:
>>> bob.name
'Bob Frapples'
>>> bob.email
'[email protected]'
Why didn’t we just define our new class as follows?
>>> class EmailPerson(Person):
...
def __init__(self, name, email):
...
self.name = name
...
self.email = email
We could have done that, but it would have defeated our use of inheritance. We used
super() to make Person do its work, the same as a plain Person object would. There’s
another benefit: if the definition of Person changes in the future, using super() will
ensure that the attributes and methods that EmailPerson inherits from Person will
reflect the change.
Use super() when the child is doing something its own way but still needs something
from the parent (as in real life).
In self Defense
One criticism of Python (besides the use of whitespace) is the need to include self as
the first argument to instance methods (the kind of method you’ve seen in the previous
examples). Python uses the self argument to find the right object’s attributes and
methods. For an example, I’ll show how you would call an object’s method, and what
Python actually does behind the scenes.
Remember class Car from earlier examples? Let’s call its exclaim() method again:
>>> car = Car()
>>> car.exclaim()
I'm a Car!
Here’s what Python actually does, under the hood:
• Look up the class (Car) of the object car.
• Pass the object car to the exclaim() method of the Car class as the self parameter.
Just for fun, you can even run it this way yourself and it will work the same as the normal
(car.exclaim()) syntax:
>>> Car.exclaim(car)
I'm a Car!
However, there’s never a reason to use that lengthier style.
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Get and Set Attribute Values with Properties
Some object-oriented languages support private object attributes that can’t be accessed
directly from the outside; programmers often need to write getter and setter methods
to read and write the values of such private attributes.
Python doesn’t need getters or setters, because all attributes and methods are public,
and you’re expected to behave yourself. If direct access to attributes makes you nervous,
you can certainly write getters and setters. But be Pythonic—use properties.
In this example, we’ll define a Duck class with a single attribute called hidden_name. (In
the next section, I’ll show you a better way to name attributes that you want to keep
private.) We don’t want people to access this directly, so we’ll define two methods: a
getter (get_name()) and a setter (set_name()). I’ve added a print() statement to each
method to show when it’s being called. Finally, we define these methods as properties
of the name attribute:
>>> class Duck():
...
def __init__(self, input_name):
...
self.hidden_name = input_name
...
def get_name(self):
...
print('inside the getter')
...
return self.hidden_name
...
def set_name(self, input_name):
...
print('inside the setter')
...
self.hidden_name = input_name
...
name = property(get_name, set_name)
The new methods act as normal getters and setters until that last line; it defines the two
methods as properties of the attribute called name. The first argument to property() is
the getter method, and the second is the setter. Now, when you refer to the name of any
Duck object, it actually calls the get_name() method to return it:
>>> fowl = Duck('Howard')
>>> fowl.name
inside the getter
'Howard'
You can still call get_name() directly, too, like a normal getter method:
>>> fowl.get_name()
inside the getter
'Howard'
When you assign a value to the name attribute, the set_name() method will be called:
>>> fowl.name = 'Daffy'
inside the setter
>>> fowl.name
inside the getter
'Daffy'
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You can still call the set_name() method directly:
>>> fowl.set_name('Daffy')
inside the setter
>>> fowl.name
inside the getter
'Daffy'
Another way to define properties is with decorators. In this next example, we’ll define
two different methods, each called name() but preceded by different decorators:
• @property, which goes before the getter method
• @name.setter, which goes before the setter method
Here’s how they actually look in the code:
>>> class Duck():
...
def __init__(self, input_name):
...
self.hidden_name = input_name
...
@property
...
def name(self):
...
print('inside the getter')
...
return self.hidden_name
...
@name.setter
...
def name(self, input_name):
...
print('inside the setter')
...
self.hidden_name = input_name
You can still access name as though it were an attribute, but there are no visible
get_name() or set_name() methods:
>>> fowl = Duck('Howard')
>>> fowl.name
inside the getter
'Howard'
>>> fowl.name = 'Donald'
inside the setter
>>> fowl.name
inside the getter
'Donald'
If anyone guessed that we called our attribute hidden_name, they could
still read and write it directly as fowl.hidden_name. In the next sec‐
tion, you’ll see how Python provides a special way to name private
attributes.
In both of the previous examples, we used the name property to refer to a single attribute
(ours was called hidden_name) stored within the object. A property can refer to a com‐
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puted value, as well. Let’s define a Circle class that has a radius attribute and a computed
diameter property:
>>> class Circle():
...
def __init__(self, radius):
...
self.radius = radius
...
@property
...
def diameter(self):
...
return 2 * self.radius
...
We create a Circle object with an initial value for its radius:
>>> c = Circle(5)
>>> c.radius
5
We can refer to diameter as if it were an attribute such as radius:
>>> c.diameter
10
Here’s the fun part: we can change the radius attribute at any time, and the diameter
property will be computed from the current value of radius:
>>> c.radius = 7
>>> c.diameter
14
If you don’t specify a setter property for an attribute, you can’t set it from the outside.
This is handy for read-only attributes:
>>> c.diameter = 20
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
AttributeError: can't set attribute
There’s one more big advantage of using a property over direct attribute access: if you
ever change the definition of the attribute, you only need to fix the code within the class
definition, not in all the callers.
Name Mangling for Privacy
In the Duck class example in the previous section, we called our (not completely) hidden
attribute hidden_name. Python has a naming convention for attributes that should not
be visible outside of their class definition: begin by using with two underscores (__).
Let’s rename hidden_name to __name, as demonstrated here:
>>> class Duck():
...
def __init__(self, input_name):
...
self.__name = input_name
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...
...
...
...
...
...
...
...
...
@property
def name(self):
print('inside the getter')
return self.__name
@name.setter
def name(self, input_name):
print('inside the setter')
self.__name = input_name
Take a moment to see if everything still works:
>>> fowl = Duck('Howard')
>>> fowl.name
inside the getter
'Howard'
>>> fowl.name = 'Donald'
inside the setter
>>> fowl.name
inside the getter
'Donald'
Looks good. And, you can’t access the __name attribute:
>>> fowl.__name
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
AttributeError: 'Duck' object has no attribute '__name'
This naming convention doesn’t make it private, but Python does mangle the name to
make it unlikely for external code to stumble upon it. If you’re curious and promise not
to tell everyone, here’s what it becomes:
>>> fowl._Duck__name
'Donald'
Notice that it didn’t print inside the getter. Although this isn’t perfect protection,
name mangling discourages accidental or intentional direct access to the attribute.
Method Types
Some data (attributes) and functions (methods) are part of the class itself, and some are
part of the objects that are created from that class.
When you see an initial self argument in methods within a class definition, it’s an
instance method. These are the types of methods that you would normally write when
creating your own classes. The first parameter of an instance method is self, and Python
passes the object to the method when you call it.
In contrast, a class method affects the class as a whole. Any change you make to the class
affects all of its objects. Within a class definition, a preceding @classmethod decorator
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indicates that that following function is a class method. Also, the first parameter to the
method is the class itself. The Python tradition is to call the parameter cls, because
class is a reserved word and can’t be used here. Let’s define a class method for A that
counts how many object instances have been made from it:
>>> class A():
...
count = 0
...
def __init__(self):
...
A.count += 1
...
def exclaim(self):
...
print("I'm an A!")
...
@classmethod
...
def kids(cls):
...
print("A has", cls.count, "little objects.")
...
>>>
>>> easy_a = A()
>>> breezy_a = A()
>>> wheezy_a = A()
>>> A.kids()
A has 3 little objects.
Notice that we referred to A.count (the class attribute) rather than self.count (which
would be an object instance attribute). In the kids() method, we used cls.count, but
we could just as well have used A.count.
A third type of method in a class definition affects neither the class nor its objects; it’s
just in there for convenience instead of floating around on its own. It’s a static method,
preceded by a @staticmethod decorator, with no initial self or class parameter. Here’s
an example that serves as a commercial for the class CoyoteWeapon:
>>> class CoyoteWeapon():
...
@staticmethod
...
def commercial():
...
print('This CoyoteWeapon has been brought to you by Acme')
...
>>>
>>> CoyoteWeapon.commercial()
This CoyoteWeapon has been brought to you by Acme
Notice that we didn’t need to create an object from class CoyoteWeapon to access this
method. Very class-y.
Duck Typing
Python has a loose implementation of polymorphism; this means that it applies the same
operation to different objects, regardless of their class.
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Let’s use the same __init__() initializer for all three Quote classes now, but add two
new functions:
• who() just returns the value of the saved person string
• says() returns the saved words string with the specific punctuation
And here they are in action:
>>> class Quote():
...
def __init__(self, person, words):
...
self.person = person
...
self.words = words
...
def who(self):
...
return self.person
...
def says(self):
...
return self.words + '.'
...
>>> class QuestionQuote(Quote):
...
def says(self):
...
return self.words + '?'
...
>>> class ExclamationQuote(Quote):
...
def says(self):
...
return self.words + '!'
...
>>>
We didn’t change how QuestionQuote or ExclamationQuote were initialized, so we
didn’t override their __init__() methods. Python then automatically calls the
__init__() method of the parent class Quote to store the instance variables person and
words. That’s why we can access self.words in objects created from the subclasses
QuestionQuote and ExclamationQuote.
Next up, let’s make some objects:
>>> hunter = Quote('Elmer Fudd', "I'm hunting wabbits")
>>> print(hunter.who(), 'says:', hunter.says())
Elmer Fudd says: I'm hunting wabbits.
>>> hunted1 = QuestionQuote('Bugs Bunny', "What's up, doc")
>>> print(hunted1.who(), 'says:', hunted1.says())
Bugs Bunny says: What's up, doc?
>>> hunted2 = ExclamationQuote('Daffy Duck', "It's rabbit season")
>>> print(hunted2.who(), 'says:', hunted2.says())
Daffy Duck says: It's rabbit season!
Three different versions of the says() method provide different behavior for the three
classes. This is traditional polymorphism in object-oriented languages. Python goes a
little further and lets you run the who() and says() methods of any objects that have
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them. Let’s define a class called BabblingBrook that has no relation to our previous
woodsy hunter and huntees (descendants of the Quote class):
>>> class BabblingBrook():
...
def who(self):
...
return 'Brook'
...
def says(self):
...
return 'Babble'
...
>>> brook = BabblingBrook()
Now, run the who() and says() methods of various objects, one (brook) completely
unrelated to the others:
>>> def who_says(obj):
...
print(obj.who(), 'says', obj.says())
...
>>> who_says(hunter)
Elmer Fudd says I'm hunting wabbits.
>>> who_says(hunted1)
Bugs Bunny says What's up, doc?
>>> who_says(hunted2)
Daffy Duck says It's rabbit season!
>>> who_says(brook)
Brook says Babble
This behavior is sometimes called duck typing, after the old saying:
If it walks like a duck and quacks like a duck, it’s a duck.
— A Wise Person
Special Methods
You can now create and use basic objects, but now let’s go a bit deeper and do more.
When you type something such as a = 3 + 8, how do the integer objects with values
3 and 8 know how to implement +? Also, how does a know how to use = to get the result?
You can get at these operators by using Python’s special methods (you might also see
them called magic methods). You don’t need Gandalf to perform any magic, and they’re
not even complicated.
The names of these methods begin and end with double underscores (__). You’ve already
seen one: __init__ initializes a newly created object from its class definition and any
arguments that were passed in.
Suppose that you have a simple Word class, and you want an equals() method that
compares two words but ignores case. That is, a Word containing the value 'ha' would
be considered equal to one containing 'HA'.
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The example that follows is a first attempt, with a normal method we’re calling equals().
self.text is the text string that this Word object contains, and the equals() method
compares it with the text string of word2 (another Word object):
>>> class Word():
...
def __init__(self, text):
...
self.text = text
...
...
def equals(self, word2):
...
return self.text.lower() == word2.text.lower()
...
Then, make three Word objects from three different text strings:
>>> first = Word('ha')
>>> second = Word('HA')
>>> third = Word('eh')
When strings 'ha' and 'HA' are compared to lowercase, they should be equal:
>>> first.equals(second)
True
But the string 'eh' will not match 'ha':
>>> first.equals(third)
False
We defined the method equals() to do this lowercase conversion and comparison. It
would be nice to just say if first == second, just like Python’s built-in types. So, let’s
do that. We change the equals() method to the special name __eq__() (you’ll see why
in a moment):
>>> class Word():
...
def __init__(self, text):
...
self.text = text
...
def __eq__(self, word2):
...
return self.text.lower() == word2.text.lower()
...
Let’s see if it works:
>>> first = Word('ha')
>>> second = Word('HA')
>>> third = Word('eh')
>>> first == second
True
>>> first == third
False
Magic! All we needed was the Python’s special method name for testing equality,
__eq__(). Tables 6-1 and 6-2 list the names of the most useful magic methods.
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Table 6-1. Magic methods for comparison
__eq__( self, other ) self == other
__ne__( self, other ) self != other
__lt__( self, other ) self < other
__gt__( self, other ) self > other
__le__( self, other ) self <= other
__ge__( self, other ) self >= other
Table 6-2. Magic methods for math
__add__( self, other )
self + other
__sub__( self, other )
self - other
__mul__( self, other )
self * other
__floordiv__( self, other ) self // other
__truediv__( self, other )
self / other
__mod__( self, other )
self % other
__pow__( self, other )
self ** other
You aren’t restricted to use the math operators such as + (magic method __add__())
and - (magic method __sub__()) with numbers. For instance, Python string objects
use + for concatenation and * for duplication. There are many more, documented online
at Special method names. The most common among them are presented in Table 6-3.
Table 6-3. Other, miscellaneous magic methods
__str__( self )
str( self )
__repr__( self ) repr( self )
__len__( self )
len( self )
Besides __init__(), you might find yourself using __str__() the most in your own
methods. It’s how you print your object. It’s used by print(), str(), and the string
formatters that you can read about in Chapter 7. The interactive interpreter uses the
__repr__() function to echo variables to output. If you fail to define either __str__()
or __repr__(), you get Python’s default string version of your object:
>>> first = Word('ha')
>>> first
<__main__.Word object at 0x1006ba3d0>
>>> print(first)
<__main__.Word object at 0x1006ba3d0>
Let’s add both __str__() and __repr__() methods to the Word class to make it prettier:
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>>> class Word():
...
def __init__(self, text):
...
self.text = text
...
def __eq__(self, word2):
...
return self.text.lower() == word2.text.lower()
...
def __str__(self):
...
return self.text
...
def __repr__(self):
...
return 'Word("' self.text '")'
...
>>> first = Word('ha')
>>> first
# uses __repr__
Word("ha")
>>> print(first)
# uses __str__
ha
To explore even more special methods, check out the Python documentation.
Composition
Inheritance is a good technique to use when you want a child class to act like its parent
class most of the time (when child is-a parent). It’s tempting to build elaborate inheri‐
tance hierarchies, but sometimes composition or aggregation (when x has-a y) make
more sense. A duck is-a bird, but has-a tail. A tail is not a kind of duck, but part of a
duck. In this next example, let’s make bill and tail objects and provide them to a new
duck object:
>>> class Bill():
...
def __init__(self, description):
...
self.description = description
...
>>> class Tail():
...
def __init__(self, length):
...
self.length = length
...
>>> class Duck():
...
def __init__(self, bill, tail):
...
self.bill = bill
...
self.tail = tail
...
def about(self):
...
print('This duck has a', bill.description, 'bill and a',
tail.length, 'tail')
...
>>> tail = Tail('long')
>>> bill = Bill('wide orange')
>>> duck = Duck(bill, tail)
>>> duck.about()
This duck has a wide orange bill and a long tail
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When to Use Classes and Objects versus Modules
Here are some guidelines for deciding whether to put your code in a class or a module:
• Objects are most useful when you need a number of individual instances that have
similar behavior (methods), but differ in their internal states (attributes).
• Classes support inheritance, modules don’t.
• If you want only one of something, a module might be best. No matter how many
times a Python module is referenced in a program, only one copy is loaded. (Java
and C++ programmers: if you’re familiar with the book Design Patterns: Elements
of Reusable Object-Oriented Software by Erich Gamma, you can use a Python mod‐
ule as a singleton.)
• If you have a number of variables that contain multiple values and can be passed as
arguments to multiple functions, it might be better to define them as classes. For
example, you might use a dictionary with keys such as size and color to represent
a color image. You could create a different dictionary for each image in your pro‐
gram, and pass them as arguments to functions such as scale() or transform().
This can get messy as you add keys and functions. It’s more coherent to define an
Image class with attributes size or color and methods scale() and trans
form(). Then, all the data and methods for a color image are defined in one place.
• Use the simplest solution to the problem. A dictionary, list, or tuple is simpler,
smaller, and faster than a module, which is usually simpler than a class.
Guido’s advice:
Avoid overengineering datastructures. Tuples are better than objects (try namedtuple too
though). Prefer simple fields over getter/setter functions … Built-in datatypes are your
friends. Use more numbers, strings, tuples, lists, sets, dicts. Also check out the collections
library, esp. deque.
— Guido van Rossum
Named Tuples
Because Guido just mentioned them and I haven’t yet, this is a good place to talk about
named tuples. A named tuple is a subclass of tuples with which you can access values by
name (with .name) as well as by position (with [ offset ]).
Let’s take the example from the previous section and convert the Duck class to a named
tuple, with bill and tail as simple string attributes. We’ll call the namedtuple function
with two arguments:
• The name
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• A string of the field names, separated by spaces
Named tuples are not automatically supplied with Python, so you need to load a module
before using them. We do that in the first line of the following example:
>>> from collections import namedtuple
>>> Duck = namedtuple('Duck', 'bill tail')
>>> duck = Duck('wide orange', 'long')
>>> duck
Duck(bill='wide orange', tail='long')
>>> duck.bill
'wide orange'
>>> duck.tail
'long'
You can also make a named tuple from a dictionary:
>>> parts = {'bill': 'wide orange', 'tail': 'long'}
>>> duck2 = Duck(**parts)
>>> duck2
Duck(bill='wide orange', tail='long')
In the preceding code, take a look at **parts. This is a keyword argument. It extracts
the keys and values from the parts dictionary and supplies them as arguments to
Duck(). It has the same effect as:
>>> duck2 = Duck(bill = 'wide orange', tail = 'long')
Named tuples are immutable, but you can replace one or more fields and return another
named tuple:
>>> duck3 = duck2._replace(tail='magnificent', bill='crushing')
>>> duck3
Duck(bill='crushing', tail='magnificent')
We could have defined duck as a dictionary:
>>> duck_dict = {'bill': 'wide orange', 'tail': 'long'}
>>> duck_dict
{'tail': 'long', 'bill': 'wide orange'}
You can add fields to a dictionary:
>>> duck_dict['color'] = 'green'
>>> duck_dict
{'color': 'green', 'tail': 'long', 'bill': 'wide orange'}
But not to a named tuple:
>>> duck.color = 'green'
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
AttributeError: 'dict' object has no attribute 'color'
To recap, here are some of the pros of a named tuple:
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• It looks and acts like an immutable object.
• It is more space- and time-efficient than objects.
• You can access attributes by using dot notation instead of dictionary-style square
brackets.
• You can use it as a dictionary key.
Things to Do
6.1. Make a class called Thing with no contents and print it. Then, create an object called
example from this class and also print it. Are the printed values the same or different?
6.2. Make a new class called Thing2 and assign the value 'abc' to a class attribute called
letters. Print letters.
6.3. Make yet another class called, of course, Thing3. This time, assign the value 'xyz'
to an instance (object) attribute called letters. Print letters. Do you need to make
an object from the class to do this?
6.4. Make a class called Element, with instance attributes name, symbol, and number.
Create an object of this class with the values 'Hydrogen', 'H', and 1.
6.5. Make a dictionary with these keys and values: 'name': 'Hydrogen', 'symbol':
'H', 'number': 1. Then, create an object called hydrogen from class Element using
this dictionary.
6.6. For the Element class, define a method called dump() that prints the values of the
object’s attributes (name, symbol, and number). Create the hydrogen object from this new
definition and use dump() to print its attributes.
6.7. Call print(hydrogen). In the definition of Element, change the name of method
dump to __str__, create a new hydrogen object, and call print(hydrogen) again.
6.8. Modify Element to make the attributes name, symbol, and number private. Define a
getter property for each to return its value.
6.9. Define three classes: Bear, Rabbit, and Octothorpe. For each, define only one
method: eats(). This should return 'berries' (Bear), 'clover' (Rabbit), or
'campers' (Octothorpe). Create one object from each and print what it eats.
6.10. Define these classes: Laser, Claw, and SmartPhone. Each has only one method:
does(). This returns 'disintegrate' (Laser), 'crush' (Claw), or 'ring' (Smart
Phone). Then, define the class Robot that has one instance (object) of each of these.
Define a does() method for the Robot that prints what its component objects do.
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CHAPTER 7
Mangle Data Like a Pro
In this chapter, you’ll learn many techniques for taming data. Most of them concern
these built-in Python data types:
strings
Sequences of Unicode characters, used for text data.
bytes and bytearrays
Sequences of eight-bit integers, used for binary data.
Text Strings
Text is the most familiar type of data to most readers, so we’ll begin with some of the
powerful features of text strings in Python.
Unicode
All of the text examples in this book thus far have been plain old ASCII. ASCII was
defined in the 1960s, when computers were the size of refrigerators and only slightly
better at performing computations. The basic unit of computer storage is the byte, which
can store 256 unique values in its eight bits. For various reasons, ASCII only used 7 bits
(128 unique values): 26 uppercase letters, 26 lowercase letters, 10 digits, some punctu‐
ation symbols, some spacing characters, and some nonprinting control codes.
Unfortunately, the world has more letters than ASCII provides. You could have a hot
dog at a diner, but never a Gewürztraminer1 at a café. Many attempts have been made
to add more letters and symbols, and you’ll see them at times. Just a couple of those
include:
1. This wine has an umlaut in Germany, but loses it in France.
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• Latin-1, or ISO 8859-1
• Windows code page 1252
Each of these uses all eight bits, but even that’s not enough, especially when you need
non-European languages. Unicode is an ongoing international standard to define the
characters of all the world’s languages, plus symbols from mathematics and other fields.
Unicode provides a unique number for every character, no matter what the platform, no
matter what the program, no matter what the language.
— The Unicode Consortium
The Unicode Code Charts page has links to all the currently defined character sets with
images. The latest version (6.2) defines over 110,000 characters, each with a unique
name and identification number. The characters are divided into eight-bit sets called
planes. The first 256 planes are the basic multilingual planes. See the Wikipedia page
about Unicode planes for details.
Python 3 Unicode strings
Python 3 strings are Unicode strings, not byte arrays. This is the single largest change
from Python 2, which distinguished between normal byte strings and Unicode character
strings.
If you know the Unicode ID or name for a character, you can use it in a Python string.
Here are some examples:
• A \u followed by four hex numbers2 specifies a character in one of Unicode’s 256
basic multilingual planes. The first two are the plane number (00 to FF), and the
next two are the index of the character within the plane. Plane 00 is good old ASCII,
and the character positions within that plane are the same as ASCII.
• For characters in the higher planes, we need more bits. The Python escape sequence
for these is \U followed by eight hex characters; the leftmost ones need to be 0.
• For all characters, \N{ name } lets you specify it by its standard name. The Unicode
Character Name Index page lists these.
The Python unicodedata module has functions that translate in both directions:
• lookup()—Takes a case-insensitive name and returns a Unicode character
• name()—Takes a Unicode character and returns an uppercase name
2. Base 16, specified with characters 0-9 and A-F.
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In the following example, we’ll write a test function that takes a Python Unicode char‐
acter, looks up its name, and looks up the character again from the name (it should
match the original character):
>>> def unicode_test(value):
...
import unicodedata
...
name = unicodedata.name(value)
...
value2 = unicodedata.lookup(name)
...
print('value="%s", name="%s", value2="%s"' % (value, name, value2))
...
Let’s try some characters, beginning with a plain ASCII letter:
>>> unicode_test('A')
value="A", name="LATIN CAPITAL LETTER A", value2="A"
ASCII punctuation:
>>> unicode_test('$')
value="$", name="DOLLAR SIGN", value2="$"
A Unicode currency character:
>>> unicode_test('\u00a2')
value="¢", name="CENT SIGN", value2="¢"
Another Unicode currency character:
>>> unicode_test('\u20ac')
value="€", name="EURO SIGN", value2="€"
The only problem you could potentially run into is limitations in the font you’re using
to display text. All fonts do not have images for all Unicode characters, and might display
some placeholder character. For instance, here’s the Unicode symbol for SNOWMAN, like
symbols in dingbat fonts:
>>> unicode_test('\u2603')
value="☃", name="SNOWMAN", value2="☃"
Suppose that we want to save the word café in a Python string. One way is to copy and
paste it from a file or website and hope that it works:
>>> place = 'café'
>>> place
'café'
This worked because I copied and pasted from a source that used UTF-8 encoding
(which you’ll see in a few pages) for its text.
How can we specify that final é character? If you look at character index for E, you see
that the name E WITH ACUTE, LATIN SMALL LETTER has the value 00E9. Let’s check with
the name() and lookup() functions that we were just playing with. First give the code
to get the name:
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>>> unicodedata.name('\u00e9')
'LATIN SMALL LETTER E WITH ACUTE'
Next, give the name to look up the code:
>>> unicodedata.lookup('E WITH ACUTE, LATIN SMALL LETTER')
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
KeyError: "undefined character name 'E WITH ACUTE, LATIN SMALL LETTER'"
The names listed on the Unicode Character Name Index page were
reformatted to make them sort nicely for display. To convert them to
their real Unicode names (the ones that Python uses), remove the
comma and move the part of the name that was after the comma to
the beginning. Accordingly, change E WITH ACUTE, LATIN SMALL
LETTER to LATIN SMALL LETTER E WITH ACUTE:
>>> unicodedata.lookup('LATIN SMALL LETTER E WITH ACUTE')
'é'
Now, we can specify the string café by code or by name:
>>> place = 'caf\u00e9'
>>> place
'café'
>>> place = 'caf\N{LATIN SMALL LETTER E WITH ACUTE}'
>>> place
'café'
In the preceding snippet, we inserted the é directly in the string, but we can also build
a string by appending:
>>>
>>>
'ü'
>>>
>>>
Now
u_umlaut = '\N{LATIN SMALL LETTER U WITH DIAERESIS}'
u_umlaut
drink = 'Gew' + u_umlaut + 'rztraminer'
print('Now I can finally have my', drink, 'in a', place)
I can finally have my Gewürztraminer in a café
The string len function counts Unicode characters, not bytes:
>>> len('$')
1
>>> len('\U0001f47b')
1
Encode and decode with UTF-8
You don’t need to worry about how Python stores each Unicode character when you do
normal string processing.
However, when you exchange data with the outside world, you need a couple of things:
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• A way to encode character strings to bytes
• A way to decode bytes to character strings
If there were fewer than 64,000 characters in Unicode, we could store each Unicode
character ID in two bytes. Unfortunately, there are more. We could encode each ID in
three or four bytes, but that would increase the memory and disk storage space needs
for common text strings by three or four times.
Ken Thompson and Rob Pike, whose names will be familiar to Unix developers, de‐
signed the UTF-8 dynamic encoding scheme one night on a placemat in a New Jersey
diner. It uses one to four bytes per Unicode character:
• One byte for ASCII
• Two bytes for most Latin-derived (but not Cyrillic) languages
• Three bytes for the rest of the basic multilingual plane
• Four bytes for the rest, including some Asian languages and symbols
UTF-8 is the standard text encoding in Python, Linux, and HTML. It’s fast, complete,
and works well. If you use UTF-8 encoding throughout your code, life will be much
easier than trying to hop in and out of various encodings.
If you create a Python string by copying and pasting from another
source such as a web page, be sure the source is encoded in the UTF-8
format. It’s very common to see text that was encoded as Latin-1 or
Windows 1252 copied into a Python string, which causes an excep‐
tion later with an invalid byte sequence.
Encoding
You encode a string to bytes. The string encode() function’s first argument is the en‐
coding name. The choices include those presented in Table 7-1.
Table 7-1. Encodings
'ascii'
Good old seven-bit ASCII
'utf-8'
Eight-bit variable-length encoding, and what you almost always want to use
'latin-1'
Also known as ISO 8859-1
'cp-1252'
A common Windows encoding
'unicode-escape' Python Unicode literal format, \uxxxx or \Uxxxxxxxx
You can encode anything as UTF-8. Let’s assign the Unicode string '\u2603' to the
name snowman:
>>> snowman = '\u2603'
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snowman is a Python Unicode string with a single character, regardless of how many
bytes might be needed to store it internally:
>>> len(snowman)
1
Next let’s encode this Unicode character to a sequence of bytes:
>>> ds = snowman.encode('utf-8')
As I mentioned earlier, UTF-8 is a variable-length encoding. In this case, it used three
bytes to encode the single snowman Unicode character:
>>> len(ds)
3
>>> ds
b'\xe2\x98\x83'
Now, len() returns the number of bytes (3) because ds is a bytes variable.
You can use encodings other than UTF-8, but you’ll get errors if the Unicode string can’t
be handled by the encoding. For example, if you use the ascii encoding, it will fail
unless your Unicode characters happen to be valid ASCII characters as well:
>>> ds = snowman.encode('ascii')
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
UnicodeEncodeError: 'ascii' codec can't encode character '\u2603'
in position 0: ordinal not in range(128)
The encode() function takes a second argument to help you avoid encoding exceptions.
Its default value, which you can see in the previous example, is 'strict'; it raises a
UnicodeEncodeError if it sees a non-ASCII character. There are other encodings. Use
'ignore' to throw away anything that won’t encode:
>>> snowman.encode('ascii', 'ignore')
b''
Use 'replace' to substitute ? for unknown characters:
>>> snowman.encode('ascii', 'replace')
b'?'
Use 'backslashreplace' to produce a Python Unicode character string, like unicodeescape:
>>> snowman.encode('ascii', 'backslashreplace')
b'\\u2603'
You would use this if you needed a printable version of the Unicode escape sequence.
The following produces character entity strings that you can use in web pages:
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>>> snowman.encode('ascii', 'xmlcharrefreplace')
b'&#9731;'
Decoding
We decode byte strings to Unicode strings. Whenever we get text from some external
source (files, databases, websites, network APIs, and so on), it’s encoded as byte strings.
The tricky part is knowing which encoding was actually used, so we can run it back‐
ward and get Unicode strings.
The problem is that nothing in the byte string itself says what encoding was used. I
mentioned the perils of copying and pasting from websites earlier. You’ve probably
visited websites with odd characters where plain old ASCII characters should be.
Let’s create a Unicode string called place with the value 'café':
>>> place = 'caf\u00e9'
>>> place
'café'
>>> type(place)
<class 'str'>
Encode it in UTF-8 format in a bytes variable called place_bytes:
>>> place_bytes = place.encode('utf-8')
>>> place_bytes
b'caf\xc3\xa9'
>>> type(place_bytes)
<class 'bytes'>
Notice that place_bytes has five bytes. The first three are the same as ASCII (a strength
of UTF-8), and the final two encode the 'é'. Now, let’s decode that byte string back to
a Unicode string:
>>> place2 = place_bytes.decode('utf-8')
>>> place2
'café'
This worked because we encoded to UTF-8 and decoded from UTF-8. What if we told
it to decode from some other encoding?
>>> place3 = place_bytes.decode('ascii')
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
UnicodeDecodeError: 'ascii' codec can't decode byte 0xc3 in position 3:
ordinal not in range(128)
The ASCII decoder threw an exception because the byte value 0xc3 is illegal in ASCII.
There are some 8-bit character set encodings in which values between 128 (hex 80) and
255 (hex FF) are legal but not the same as UTF-8:
>>> place4 = place_bytes.decode('latin-1')
>>> place4
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'café'
>>> place5 = place_bytes.decode('windows-1252')
>>> place5
'café'
Urk.
The moral of this story: whenever possible, use UTF-8 encoding. It works, is supported
everywhere, can express every Unicode character, and is quickly decoded and encoded.
For more information
If you would like to learn more, these links are particularly helpful:
• Unicode HOWTO
• Pragmatic Unicode
• The Absolute Minimum Every Software Developer Absolutely, Positively Must
Know About Unicode and Character Sets (No Excuses!)
Format
We’ve pretty much ignored text formatting—until now. Chapter 2 shows a few string
alignment functions, and the code examples have used simple print() statements, or
just let the interactive interpreter display values. But it’s time we look at how to inter‐
polate data values into strings—in other words, put the values inside the strings—using
various formats. You can use this to produce reports and other outputs for which ap‐
pearances need to be just so.
Python has two ways of formatting strings, loosely called old style and new style. Both
styles are supported in Python 2 and 3 (new style in Python 2.6 and up). Old style is
simpler, so we’ll begin there.
Old style with %
The old style of string formatting has the form string % data. Inside the string are
interpolation sequences. Table 7-2 illustrates that the very simplest sequence is a %
followed by a letter indicating the data type to be formatted.
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Table 7-2. Conversion types
%s string
%d decimal integer
%x hex integer
%o octal integer
%f decimal float
%e exponential float
%g decimal or exponential float
%% a literal %
Following are some simple examples. First, an integer:
>>> '%s'
'42'
>>> '%d'
'42'
>>> '%x'
'2a'
>>> '%o'
'52'
% 42
% 42
% 42
% 42
A float:
>>> '%s' % 7.03
'7.03'
>>> '%f' % 7.03
'7.030000'
>>> '%e' % 7.03
'7.030000e+00'
>>> '%g' % 7.03
'7.03'
An integer and a literal %:
>>> '%d%%' % 100
'100%'
Some string and integer interpolation:
>>> actor = 'Richard Gere'
>>> cat = 'Chester'
>>> weight = 28
>>> "My wife's favorite actor is %s" % actor
"My wife's favorite actor is Richard Gere"
>>> "Our cat %s weighs %s pounds" % (cat, weight)
'Our cat Chester weighs 28 pounds'
That %s inside the string means to interpolate a string. The number of % appearances in
the string needs match the number of data items after the %. A single data item such as
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actor goes right after the %. Multiple data must be grouped into a tuple (bounded by
parentheses, separated by commas) such as (cat, weight).
Even though weight is an integer, the %s inside the string converted it to a string.
You can add other values between the % and the type specifier to designate minimum
and maximum widths, alignment, and character filling:
For variables, let’s define an integer, n; a float, f; and a string, s:
>>> n = 42
>>> f = 7.03
>>> s = 'string cheese'
Format them using default widths:
>>> '%d %f %s' % (n, f, s)
'42 7.030000 string cheese'
Set a minimum field width of 10 characters for each variable, and align them to the
right, filling unused spots on the left with spaces:
>>> '%10d %10f %10s' % (n, f, s)
'
42
7.030000 string cheese'
Use the same field width, but align to the left:
>>> '%-10d %-10f %-10s' % (n, f, s)
'42
7.030000
string cheese'
This time, the same field width, but a maximum character width of 4, and aligned to
the right. This setting truncates the string, and limits the float to 4 digits after the decimal
point:
>>> '%10.4d %10.4f %10.4s' % (n, f, s)
'
0042
7.0300
stri'
The same song as before, but right-aligned:
>>> '%.4d %.4f %.4s' % (n, f, s)
'0042 7.0300 stri'
Finally, get the field widths from arguments rather than hard-coding them:
>>> '%*.*d %*.*f %*.*s' % (10, 4, n, 10, 4, f, 10, 4, s)
'
0042
7.0300
stri'
New style formatting with {} and format
Old style formatting is still supported. In Python 2, which will freeze at version 2.7, it
will be supported forever. However, new style formatting is recommended if you’re using
Python 3.
The simplest usage is demonstrated here:
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>>> '{} {} {}'.format(n, f, s)
'42 7.03 string cheese'
Old-style arguments needed to be provided in the order in which their % placeholders
appeared in the string. With new-style, you can specify the order:
>>> '{2} {0} {1}'.format(f, s, n)
'42 7.03 string cheese'
The value 0 referred to the first argument, f, whereas 1 referred to the string s, and 2
referred to the last argument, the integer n.
The arguments can be a dictionary or named arguments, and the specifiers can include
their names:
>>> '{n} {f} {s}'.format(n=42, f=7.03, s='string cheese')
'42 7.03 string cheese'
In this next example, let’s try combining our three values into a dictionary, which looks
like this:
>>> d = {'n': 42, 'f': 7.03, 's': 'string cheese'}
In the following example, {0} is the entire dictionary, whereas {1} is the string 'other'
that follows the dictionary:
>>> '{0[n]} {0[f]} {0[s]} {1}'.format(d, 'other')
'42 7.03 string cheese other'
These examples all printed their arguments with default formats. Old-style allows a type
specifier after the % in the string, but new-style puts it after a :. First, with positional
arguments:
>>> '{0:d} {1:f} {2:s}'.format(n, f, s)
'42 7.030000 string cheese'
In this example, we’ll use the same values, but as named arguments:
>>> '{n:d} {f:f} {s:s}'.format(n=42, f=7.03, s='string cheese')
'42 7.030000 string cheese'
The other options (minimum field width, maximum character width, alignment, and
so on) are also supported.
Minimum field width 10, right-aligned (default):
>>> '{0:10d} {1:10f} {2:10s}'.format(n, f, s)
'
42
7.030000 string cheese'
Same as the preceding example, but the > characters make the right-alignment more
explicit:
>>> '{0:>10d} {1:>10f} {2:>10s}'.format(n, f, s)
'
42
7.030000 string cheese'
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Minimum field width 10, left-aligned:
>>> '{0:<10d} {1:<10f} {2:<10s}'.format(n, f, s)
'42
7.030000
string cheese'
Minimum field width 10, centered:
>>> '{0:^10d} {1:^10f} {2:^10s}'.format(n, f, s)
'
42
7.030000 string cheese'
There is one change from old-style: the precision value (after the decimal point) still
means the number of digits after the decimal for floats, and the maximum number of
characters for strings, but you can’t use it for integers:
>>> '{0:>10.4d} {1:>10.4f} {2:10.4s}'.format(n, f, s)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
ValueError: Precision not allowed in integer format specifier
>>> '{0:>10d} {1:>10.4f} {2:>10.4s}'.format(n, f, s)
'
42
7.0300
stri'
The final option is the fill character. If you want something other than spaces to pad
your output fields, put it right after the :, before any alignment (<, >, ^) or width speci‐
fiers:
>>> '{0:!^20s}'.format('BIG SALE')
'!!!!!!BIG SALE!!!!!!'
Match with Regular Expressions
Chapter 2 touched on simple string operations. Armed with that introductory infor‐
mation, you’ve probably used simple “wildcard” patterns on the command line, such as
ls *.py, which means list all filenames ending in .py.
It’s time to explore more complex pattern matching by using regular expressions. These
are provided in the standard module re, which we’ll import. You define a string pat‐
tern that you want to match, and the source string to match against. For simple matches,
usage looks like this:
result = re.match('You', 'Young Frankenstein')
Here, 'You' is the pattern and 'Young Frankenstein' is the source—the string you
want to check. match() checks whether the source begins with the pattern.
For more complex matches, you can compile your pattern first to speed up the match
later:
youpattern = re.compile('You')
Then, you can perform your match against the compiled pattern:
result = youpattern.match('Young Frankenstein')
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match() is not the only way to compare the pattern and source. Here are several other
methods you can use:
• search() returns the first match, if any.
• findall() returns a list of all non-overlapping matches, if any.
• split() splits source at matches with pattern and returns a list of the string pieces.
• sub() takes another replacement argument, and changes all parts of source that are
matched by pattern to replacement.
Exact match with match()
Does the string 'Young Frankenstein' begin with the word 'You'? Here’s some code
with comments:
>>>
>>>
>>>
>>>
...
...
You
>>>
>>>
...
...
You
import re
source = 'Young Frankenstein'
m = re.match('You', source) # match starts at the beginning of source
if m: # match returns an object; do this to see what matched
print(m.group())
m = re.match('^You', source) # start anchor does the same
if m:
print(m.group())
How about 'Frank'?
>>> m = re.match('Frank', source)
>>> if m:
...
print(m.group())
...
This time match() returned nothing and the if did not run the print statement. As I
said earlier, match() works only if the pattern is at the beginning of the source. But
search() works if the pattern is anywhere:
>>> m = re.search('Frank', source)
>>> if m:
...
print(m.group())
...
Frank
Let’s change the pattern:
>>> m = re.match('.*Frank', source)
>>> if m: # match returns an object
...
print(m.group())
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...
Young Frank
Following is a brief explanation of how our new pattern works:
• . means any single character.
• * means any number of the preceding thing. Together, .* mean any number of char‐
acters (even zero).
• Frank is the phrase that we wanted to match, somewhere.
match() returned the string that matched .*Frank: 'Young Frank'.
First match with search()
You can use search() to find the pattern 'Frank' anywhere in the source string 'Young
Frankenstein', without the need for the .* wildcards:
>>> m = re.search('Frank', source)
>>> if m: # search returns an object
...
print(m.group())
...
Frank
All matches with findall()
The preceding examples looked for one match only. But what if you want to know how
many instances of the single-letter string 'n' are in the string?
>>> m = re.findall('n', source)
>>> m
# findall returns a list
['n', 'n', 'n', 'n']
>>> print('Found', len(m), 'matches')
Found 4 matches
How about 'n' followed by any character?
>>> m = re.findall('n.', source)
>>> m
['ng', 'nk', 'ns']
Notice that it did not match that final 'n'. We need to say that the character after 'n'
is optional with ?:
>>> m = re.findall('n.?', source)
>>> m
['ng', 'nk', 'ns', 'n']
Split at matches with split()
The example that follows shows you how to split a string into a list by a pattern rather
than a simple string (as the normal string split() method would do):
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>>> m = re.split('n', source)
>>> m
# split returns a list
['You', 'g Fra', 'ke', 'stei', '']
Replace at matches with sub()
This is like the string replace() method, but for patterns rather than literal strings:
>>> m = re.sub('n', '?', source)
>>> m
# sub returns a string
'You?g Fra?ke?stei?'
Patterns: special characters
Many descriptions of regular expressions start with all the details of how to define
them. I think that’s a mistake. Regular expressions are a not-so-little language in their
own right, with too many details to fit in your head at once. They use so much punc‐
tuation that they look like cartoon characters swearing.
With these expressions (match(), search(), findall(), and sub()) under your belt,
let’s get into the details of building them. The patterns you make apply to any of these
functions.
You’ve seen the basics:
• Literal matches with any non-special characters
• Any single character except \n with .
• Any number (including zero) with *
• Optional (zero or one) with ?
First, special characters are shown in Table 7-3:
Table 7-3. Special characters
Pattern Matches
\d
a single digit
\D
a single non-digit
\w
an alphanumeric character
\W
a non-alphanumeric character
\s
a whitespace character
\S
a non-whitespace character
\b
a word boundary (between a \w and a \W, in either order)
\B
a non-word boundary
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The Python string module has predefined string constants that we can use for testing.
We’ll use printable, which contains 100 printable ASCII characters, including letters
in both cases, digits, space characters, and punctuation:
>>> import string
>>> printable = string.printable
>>> len(printable)
100
>>> printable[0:50]
'0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMN'
>>> printable[50:]
'OPQRSTUVWXYZ!"#$%&\'()*+,-./:;<=>[email protected][\\]^_`{|}~ \t\n\r\x0b\x0c'
Which characters in printable are digits?
>>> re.findall('\d', printable)
['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']
Which characters are digits, letters, or an underscore?
>>> re.findall('\w', printable)
['0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'a', 'b',
'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n',
'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z',
'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L',
'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X',
'Y', 'Z', '_']
Which are spaces?
>>> re.findall('\s', printable)
[' ', '\t', '\n', '\r', '\x0b', '\x0c']
Regular expressions are not confined to ASCII. A \d will match whatever Unicode calls
a digit, not just ASCII characters '0' through '9'. Let’s add two non-ASCII lowercase
letters from FileFormat.info:
In this test, we’ll throw in the following:
• Three ASCII letters
• Three punctuation symbols that should not match a \w
• A Unicode LATIN SMALL LETTER E WITH CIRCUMFLEX (\u00ea)
• A Unicode LATIN SMALL LETTER E WITH BREVE (\u0115)
>>> x = 'abc' + '-/*' + '\u00ea' + '\u0115'
As expected, this pattern found only the letters:
>>> re.findall('\w', x)
['a', 'b', 'c', 'ê', 'ĕ']
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Patterns: using specifiers
Now, let’s make “punctuation pizza,” using the main pattern specifiers for regular ex‐
pressions, which are presented in Table 7-4.
In the table, expr and the other italicized words mean any valid regular expression.
Table 7-4. Pattern specifiers
Pattern
Matches
abc
literal abc
( expr )
expr
expr1 | expr2
expr1 or expr2
.
any character except \n
^
start of source string
$
end of source string
prev ?
zero or one prev
prev *
zero or more prev, as many as possible
prev *?
zero or more prev, as few as possible
prev +
one or more prev, as many as possible
prev +?
one or more prev, as few as possible
prev { m }
m consecutive prev
prev { m, n }
m to n consecutive prev, as many as possible
prev { m, n }?
m to n consecutive prev, as few as possible
[ abc ]
a or b or c (same as a|b|c)
[^ abc ]
not (a or b or c)
prev (?= next )
prev if followed by next
prev (?! next )
prev if not followed by next
(?<= prev ) next next if preceded by prev
(?<! prev ) next next if not preceded by prev
Your eyes might cross permanently when trying to read these examples. First, let’s define
our source string:
>>> source = '''I wish I may, I wish I might
... Have a dish of fish tonight.'''
First, find wish anywhere:
>>> re.findall('wish', source)
['wish', 'wish']
Next, find wish or fish anywhere:
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>>> re.findall('wish|fish', source)
['wish', 'wish', 'fish']
Find wish at the beginning:
>>> re.findall('^wish', source)
[]
Find I wish at the beginning:
>>> re.findall('^I wish', source)
['I wish']
Find fish at the end:
>>> re.findall('fish$', source)
[]
Finally, find fish tonight. at the end:
>>> re.findall('fish tonight.$', source)
['fish tonight.']
The characters ^ and $ are called anchors: ^ anchors the search to the beginning of the
search string, and $ anchors it to the end. .$ matches any character at the end of the
line, including a period, so that worked. To be more precise, we should escape the dot
to match it literally:
>>> re.findall('fish tonight\.$', source)
['fish tonight.']
Begin by finding w or f followed by ish:
>>> re.findall('[wf]ish', source)
['wish', 'wish', 'fish']
Find one or more runs of w, s, or h:
>>> re.findall('[wsh]+', source)
['w', 'sh', 'w', 'sh', 'h', 'sh', 'sh', 'h']
Find ght followed by a non-alphanumeric:
>>> re.findall('ght\W', source)
['ght\n', 'ght.']
Find I followed by wish:
>>> re.findall('I (?=wish)', source)
['I ', 'I ']
And last, wish preceded by I:
>>> re.findall('(?<=I) wish', source)
[' wish', ' wish']
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There are a few cases in which the regular expression pattern rules conflict with the
Python string rules. The following pattern should match any word that begins with fish:
>>> re.findall('\bfish', source)
[]
Why doesn’t it? As is discussed in Chapter 2, Python employs a few special escape char‐
acters for strings. For example, \b means backspace in strings, but in the mini-language
of regular expressions it means the beginning of a word. Avoid the accidental use of
escape characters by using Python’s raw strings when you define your regular expression
string. Always put an r character before your regular expression pattern string, and
Python escape characters will be disabled, as demonstrated here:
>>> re.findall(r'\bfish', source)
['fish']
Patterns: specifying match output
When using match() or search(), all matches are returned from the result object m as
m.group(). If you enclose a pattern in parentheses, the match will be saved to its own
group, and a tuple of them will be available as m.groups(), as shown here:
>>> m = re.search(r'(. dish\b).*(\bfish)', source)
>>> m.group()
'a dish of fish'
>>> m.groups()
('a dish', 'fish')
If you use this pattern (?P< name > expr ), it will match expr, saving the match in group
name:
>>> m = re.search(r'(?P<DISH>. dish\b).*(?P<FISH>\bfish)', source)
>>> m.group()
'a dish of fish'
>>> m.groups()
('a dish', 'fish')
>>> m.group('DISH')
'a dish'
>>> m.group('FISH')
'fish'
Binary Data
Text data can be challenging, but binary data can be, well, interesting. You need to know
about concepts such as endianness (how your computer’s processor breaks data into
bytes) and sign bits for integers. You might need to delve into binary file formats or
network packets to extract or even change data. This section will show you the basics
of binary data wrangling in Python.
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bytes and bytearray
Python 3 introduced the following sequences of eight-bit integers, with possible values
from 0 to 255, in two types:
• bytes is immutable, like a tuple of bytes
• bytearray is mutable, like a list of bytes
Beginning with a list called blist, this next example creates a bytes variable called
the_bytes and a bytearray variable called the_byte_array:
>> blist = [1, 2, 3, 255]
>>> the_bytes = bytes(blist)
>>> the_bytes
b'\x01\x02\x03\xff'
>>> the_byte_array = bytearray(blist)
>>> the_byte_array
bytearray(b'\x01\x02\x03\xff')
The representation of a bytes value begins with a b and a quote
character, followed by hex sequences such as \x02 or ASCII charac‐
ters, and ends with a matching quote character. Python converts the
hex sequences or ASCII characters to little integers, but shows byte
values that are also valid ASCII encodings as ASCII characters.
>>> b'\x61'
b'a'
>>> b'\x01abc\xff'
b'\x01abc\xff'
This next example demonstrates that you can’t change a bytes variable:
>>> the_bytes[1] = 127
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: 'bytes' object does not support item assignment
But a bytearray variable is mellow and mutable:
>>> the_byte_array = bytearray(blist)
>>> the_byte_array
bytearray(b'\x01\x02\x03\xff')
>>> the_byte_array[1] = 127
>>> the_byte_array
bytearray(b'\x01\x7f\x03\xff')
Each of these would create a 256-element result, with values from 0 to 255:
>>> the_bytes = bytes(range(0, 256))
>>> the_byte_array = bytearray(range(0, 256))
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When printing bytes or bytearray data, Python uses \x xx for non-printable bytes and
their ASCII equivalents for printable ones (plus some common escape characters, such
as \n instead of \x0a). Here’s the printed representation of the_bytes (manually refor‐
matted to show 16 bytes per line):
>>> the_bytes
b'\x00\x01\x02\x03\x04\x05\x06\x07\x08\t\n\x0b\x0c\r\x0e\x0f
\x10\x11\x12\x13\x14\x15\x16\x17\x18\x19\x1a\x1b\x1c\x1d\x1e\x1f
!"#$%&\'()*+,-./
0123456789:;<=>?
@ABCDEFGHIJKLMNO
PQRSTUVWXYZ[\\]^_
`abcdefghijklmno
pqrstuvwxyz{|}~\x7f
\x80\x81\x82\x83\x84\x85\x86\x87\x88\x89\x8a\x8b\x8c\x8d\x8e\x8f
\x90\x91\x92\x93\x94\x95\x96\x97\x98\x99\x9a\x9b\x9c\x9d\x9e\x9f
\xa0\xa1\xa2\xa3\xa4\xa5\xa6\xa7\xa8\xa9\xaa\xab\xac\xad\xae\xaf
\xb0\xb1\xb2\xb3\xb4\xb5\xb6\xb7\xb8\xb9\xba\xbb\xbc\xbd\xbe\xbf
\xc0\xc1\xc2\xc3\xc4\xc5\xc6\xc7\xc8\xc9\xca\xcb\xcc\xcd\xce\xcf
\xd0\xd1\xd2\xd3\xd4\xd5\xd6\xd7\xd8\xd9\xda\xdb\xdc\xdd\xde\xdf
\xe0\xe1\xe2\xe3\xe4\xe5\xe6\xe7\xe8\xe9\xea\xeb\xec\xed\xee\xef
\xf0\xf1\xf2\xf3\xf4\xf5\xf6\xf7\xf8\xf9\xfa\xfb\xfc\xfd\xfe\xff'
This can be confusing, because they’re bytes (teeny integers), not characters.
Convert Binary Data with struct
As you’ve seen, Python has many tools for manipulating text. Tools for binary data are
much less prevalent. The standard library contains the struct module, which handles
data similar to structs in C and C++. Using struct, you can convert binary data to and
from Python data structures.
Let’s see how this works with data from a PNG file—a common image format that you’ll
see along with GIF and JPEG files. We’ll write a small program that extracts the width
and height of an image from some PNG data.
We’ll use the O’Reilly logo—the little bug-eyed tarsier shown in Figure 7-1.
Figure 7-1. The O’Reilly tarsier
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The PNG file for this image is available on Wikipedia. We don’t show how to read files
until Chapter 8, so I downloaded this file, wrote a little program to print its values as
bytes, and just typed the values of the first 30 bytes into a Python bytes variable called
data for the example that follows. (The PNG format specification stipulates that the
width and height are stored within the first 24 bytes, so we don’t need more than that
for now.)
>>> import struct
>>> valid_png_header = b'\x89PNG\r\n\x1a\n'
>>> data = b'\x89PNG\r\n\x1a\n\x00\x00\x00\rIHDR' + \
...
b'\x00\x00\x00\x9a\x00\x00\x00\x8d\x08\x02\x00\x00\x00\xc0'
>>> if data[:8] == valid_png_header:
...
width, height = struct.unpack('>LL', data[16:24])
...
print('Valid PNG, width', width, 'height', height)
... else:
...
print('Not a valid PNG')
...
Valid PNG, width 154 height 141
Here’s what this code does:
• data contains the first 30 bytes from the PNG file. To fit on the page, I joined two
byte strings with + and the continuation character (\).
• valid_png_header contains the 8-byte sequence that marks the start of a valid PNG
file.
• width is extracted from bytes 16-20, and height from bytes 21-24.
The >LL is the format string that instructs unpack() how to interpret its input byte
sequences and assemble them into Python data types. Here’s the breakdown:
• The > means that integers are stored in big-endian format.
• Each L specifies a 4-byte unsigned long integer.
You can examine each 4-byte value directly:
>>> data[16:20]
b'\x00\x00\x00\x9a'
>>> data[20:24]0x9a
b'\x00\x00\x00\x8d'
Big-endian integers have the most significant bytes to the left. Because the width and
height are each less than 255, they fit into the last byte of each sequence. You can verify
that these hex values match the expected decimal values:
>>> 0x9a
154
>>> 0x8d
141
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When you want to go in the other direction and convert Python data to bytes, use the
struct pack() function:
>>> import struct
>>> struct.pack('>L', 154)
b'\x00\x00\x00\x9a'
>>> struct.pack('>L', 141)
b'\x00\x00\x00\x8d'
Table 7-5 and Table 7-6 show the format specifiers for pack() and unpack().
The endian specifiers go first in the format string.
Table 7-5. Endian specifiers
Specifier Byte order
<
little endian
>
big endian
Table 7-6. Format specifiers
Specifier Description
Bytes
x
skip a byte
1
b
signed byte
1
B
unsigned byte
1
h
signed short integer
2
H
unsigned short integer
2
i
signed integer
4
I
unsigned integer
4
l
signed long integer
4
L
unsigned long integer
4
Q
unsigned long long integer 8
f
single precision float
4
d
double precision float
8
p
count and characters
1 + count
s
characters
count
The type specifiers follow the endian character. Any specifier may be preceded by a
number that indicates the count; 5B is the same as BBBBB.
You can use a count prefix instead of >LL:
>>> struct.unpack('>2L', data[16:24])
(154, 141)
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We used the slice data[16:24] to grab the interesting bytes directly. We could also use
the x specifier to skip the uninteresting parts:
>>> struct.unpack('>16x2L6x', data)
(154, 141)
This means:
• Use big-endian integer format (>)
• Skip 16 bytes (16x)
• Read 8 bytes—two unsigned long integers (2L)
• Skip the final 6 bytes (6x)
Other Binary Data Tools
Some third-party open source packages offer the following, more declarative ways of
defining and extracting binary data:
• bitstring
• construct
• hachoir
• binio
Appendix D has details on how to download and install external packages such as these.
For the next example, you need to install construct. Here’s all you need to do:
$ pip install construct
Here’s how to extract the PNG dimensions from our data bytestring by using
construct:
>>> from construct import Struct, Magic, UBInt32, Const, String
>>> # adapted from code at https://github.com/construct
>>> fmt = Struct('png',
...
Magic(b'\x89PNG\r\n\x1a\n'),
...
UBInt32('length'),
...
Const(String('type', 4), b'IHDR'),
...
UBInt32('width'),
...
UBInt32('height')
...
)
>>> data = b'\x89PNG\r\n\x1a\n\x00\x00\x00\rIHDR' + \
...
b'\x00\x00\x00\x9a\x00\x00\x00\x8d\x08\x02\x00\x00\x00\xc0'
>>> result = fmt.parse(data)
>>> print(result)
Container:
length = 13
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type = b'IHDR'
width = 154
height = 141
>>> print(result.width, result.height)
154, 141
Convert Bytes/Strings with binascii()
The standard binascii module has functions with which you can convert between
binary data and various string representations: hex (base 16), base 64, uuencoded, and
others. For example, in the next snippet, let’s print that 8-byte PNG header as a sequence
of hex values, instead of the mixture of ASCII and \x xx escapes that Python uses to
display bytes variables:
>>> import binascii
>>> valid_png_header = b'\x89PNG\r\n\x1a\n'
>>> print(binascii.hexlify(valid_png_header))
b'89504e470d0a1a0a'
Hey, this thing works backwards, too:
>>> print(binascii.unhexlify(b'89504e470d0a1a0a'))
b'\x89PNG\r\n\x1a\n'
Bit Operators
Python provides bit-level integer operators, similar to those in the C language. Table 7-7
summarizes them and includes examples with the integers a (decimal 5, binary
0b0101) and b (decimal 1, binary 0b0001).
Table 7-7. Bit-level integer operators
Operator Description Example Decimal result Binary result
&
and
a & b
|
or
^
~
flip bits
~a
<<
left shift
a << 1 10
0b1010
>>
right shift
a >> 1 2
0b0010
1
0b0001
a | b
5
0b0101
exclusive or a ^ b
4
0b0100
-6
binary representation depends on int size
These operators work something like the set operators in Chapter 3. The & operator
returns bits that are the same in both arguments, and | returns bits that are set in either
of them. The ^ operator returns bits that are in one or the other, but not both. The ~
operator reverses all the bits in its single argument; this also reverses the sign because
an integer’s highest bit indicates its sign (1 = negative) in two’s complement arithmetic,
used in all modern computers. The << and >> operators just move bits to the left or right.
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A left shift of one bit is the same as multiplying by two, and a right shift is the same as
dividing by two.
Things to Do
7.1. Create a Unicode string called mystery and assign it the value '\U0001f4a9'. Print
mystery. Look up the Unicode name for mystery.
7.2. Encode mystery, this time using UTF-8, into the bytes variable pop_bytes. Print
pop_bytes.
7.3. Using UTF-8, decode pop_bytes into the string variable pop_string. Print
pop_string. Is pop_string equal to mystery?
7.4. Write the following poem by using old-style formatting. Substitute the strings
'roast beef', 'ham', 'head', and 'clam' into this string:
My kitty cat likes %s,
My kitty cat likes %s,
My kitty cat fell on his %s
And now thinks he's a %s.
7.5. Write a form letter by using new-style formatting. Save the following string as
letter (you’ll use it in the next exercise):
Dear {salutation} {name},
Thank you for your letter. We are sorry that our {product} {verbed} in your
{room}. Please note that it should never be used in a {room}, especially
near any {animals}.
Send us your receipt and {amount} for shipping and handling. We will send
you another {product} that, in our tests, is {percent}% less likely to
have {verbed}.
Thank you for your support.
Sincerely,
{spokesman}
{job_title}
7.6. Make a dictionary called response with values for the string keys 'salutation',
'name', 'product', 'verbed' (past tense verb), 'room', 'animals', 'amount', 'per
cent', 'spokesman', and 'job_title'. Print letter with the values from response.
7.7. When you’re working with text, regular expressions come in very handy. We’ll apply
them in a number of ways to our featured text sample. It’s a poem titled “Ode on the
Mammoth Cheese,” written by James McIntyre in 1866 in homage to a seven-thousandpound cheese that was crafted in Ontario and sent on an international tour. If you’d
rather not type all of it, use your favorite search engine and cut and paste the words into
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your Python program. Or, just grab it from Project Gutenberg. Call the text string
mammoth.
We have seen thee, queen of cheese,
Lying quietly at your ease,
Gently fanned by evening breeze,
Thy fair form no flies dare seize.
All gaily dressed soon you'll go
To the great Provincial show,
To be admired by many a beau
In the city of Toronto.
Cows numerous as a swarm of bees,
Or as the leaves upon the trees,
It did require to make thee please,
And stand unrivalled, queen of cheese.
May you not receive a scar as
We have heard that Mr. Harris
Intends to send you off as far as
The great world's show at Paris.
Of the youth beware of these,
For some of them might rudely squeeze
And bite your cheek, then songs or glees
We could not sing, oh! queen of cheese.
We'rt
You'd
Folks
About
thou suspended from balloon,
cast a shade even at noon,
would think it was the moon
to fall and crush them soon.
7.8. Import the re module to use Python’s regular expression functions. Use
re.findall() to print all the words that begin with c.
7.9. Find all four-letter words that begin with c.
7.10. Find all the words that end with r.
7.11. Find all words that contain exactly three vowels in a row.
7.12. Use unhexlify to convert this hex string (combined from two strings to fit on a
page) to a bytes variable called gif:
'47494638396101000100800000000000ffffff21f9' +
'0401000000002c000000000100010000020144003b'
7.13. The bytes in gif define a one-pixel transparent GIF file, one of the most common
graphics file formats. A legal GIF starts with the string GIF89a. Does gif match this?
7.14. The pixel width of a GIF is a 16-bit big-endian integer beginning at byte offset 6,
and the height is the same size, starting at offset 8. Extract and print these values for
gif. Are they both 1?
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CHAPTER 8
Data Has to Go Somewhere
It is a capital mistake to theorize before one has data.
— Arthur Conan Doyle
An active program accesses data that is stored in Random Access Memory, or RAM.
RAM is very fast, but it is expensive and requires a constant supply of power; if the
power goes out, all the data in memory is lost. Disk drives are slower than RAM but
have more capacity, cost less, and retain data even after someone trips over the power
cord. Thus, a huge amount of effort in computer systems has been devoted to making
the best tradeoffs between storing data on disk and RAM. As programmers, we need
persistence: storing and retrieving data using nonvolatile media such as disks.
This chapter is all about the different flavors of data storage, each optimized for different
purposes: flat files, structured files, and databases. File operations other than input and
output are covered in “Files” on page 241.
This is also the first chapter to show examples of nonstandard Python
modules; that is, Python code apart from the standard library. You’ll
install them by using the pip command, which is painless. There are
more details on its usage in Appendix D.
File Input/Output
The simplest kind of persistence is a plain old file, sometimes called a flat file. This is
just a sequence of bytes stored under a filename. You read from a file into memory and
write from memory to a file. Python makes these jobs easy. Its file operations were
modeled on the familiar and popular Unix equivalents.
Before reading or writing a file, you need to open it:
fileobj = open( filename, mode )
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Here’s a brief explanation of the pieces of this call:
• fileobj is the file object returned by open()
• filename is the string name of the file
• mode is a string indicating the file’s type and what you want to do with it
The first letter of mode indicates the operation:
• r means read.
• w means write. If the file doesn’t exist, it’s created. If the file does exist, it’s overwritten.
• x means write, but only if the file does not already exist.
• a means append (write after the end) if the file exists.
The second letter of mode is the file’s type:
• t (or nothing) means text.
• b means binary.
After opening the file, you call functions to read or write data; these will be shown in
the examples that follow.
Last, you need to close the file.
Let’s create a file from a Python string in one program and then read it back in the next.
Write a Text File with write()
For some reason, there aren’t many limericks about special relativity. This one will just
have to do for our data source:
>>>
...
...
...
...
>>>
150
poem = '''There was a young lady named Bright,
Whose speed was far faster than light;
She started one day
In a relative way,
And returned on the previous night.'''
len(poem)
The following code writes the entire poem to the file 'relativity' in one call:
>>> fout = open('relativity', 'wt')
>>> fout.write(poem)
150
>>> fout.close()
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The write() function returns the number of bytes written. It does not add any spaces
or newlines, as print() does. You can also print() to a text file:
>>> fout = open('relativity', 'wt')
>>> print(poem, file=fout)
>>> fout.close()
This brings up the question: should I use write() or print()? By default, print() adds
a space after each argument and a newline at the end. In the previous example, it ap‐
pended a newline to the relativity file. To make print() work like write(), pass the
following two arguments:
• sep (separator, which defaults to a space, ' ')
• end (end string, which defaults to a newline, '\n')
print() uses the defaults unless you pass something else. We’ll pass empty strings to
suppress all of the fussiness normally added by print():
>>> fout = open('relativity', 'wt')
>>> print(poem, file=fout, sep='', end='')
>>> fout.close()
If you have a large source string, you can also write chunks until the source is done:
>>>
>>>
>>>
>>>
>>>
...
...
...
...
...
100
50
>>>
fout = open('relativity', 'wt')
size = len(poem)
offset = 0
chunk = 100
while True:
if offset > size:
break
fout.write(poem[offset:offset+chunk])
offset += chunk
fout.close()
This wrote 100 characters on the first try and the last 50 characters on the next.
If the relativity file is precious to us, let’s see if using mode x really protects us from
overwriting it:
>>> fout = open('relativity', 'xt')
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
FileExistsError: [Errno 17] File exists: 'relativity'
You can use this with an exception handler:
>>> try:
...
fout = open('relativity', 'xt')]
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...
fout.write('stomp stomp stomp')
... except FileExistsError:
...
print('relativity already exists!. That was a close one.')
...
relativity already exists!. That was a close one.
Read a Text File with read(), readline(), or readlines()
You can call read() with no arguments to slurp up the entire file at once, as shown in
the example that follows. Be careful when doing this with large files; a gigabyte file will
consume a gigabyte of memory.
>>>
>>>
>>>
>>>
150
fin = open('relativity', 'rt' )
poem = fin.read()
fin.close()
len(poem)
You can provide a maximum character count to limit how much read() returns at one
time. Let’s read 100 characters at a time and append each chunk to a poem string to
rebuild the original:
>>>
>>>
>>>
>>>
...
...
...
...
...
>>>
>>>
150
poem = ''
fin = open('relativity', 'rt' )
chunk = 100
while True:
fragment = fin.read(chunk)
if not fragment:
break
poem += fragment
fin.close()
len(poem)
After you’ve read all the way to the end, further calls to read() will return an empty
string (''), which is treated as False in if not fragment. This breaks out of the while
True loop.
You can also read the file a line at a time by using readline(). In this next example,
we’ll append each line to the poem string to rebuild the original:
>>>
>>>
>>>
...
...
...
...
...
>>>
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poem = ''
fin = open('relativity', 'rt' )
while True:
line = fin.readline()
if not line:
break
poem += line
fin.close()
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>>> len(poem)
150
For a text file, even a blank line has a length of one (the newline character), and is
evaluated as True. When the file has been read, readline() (like read()) also returns
an empty string, which is also evaluated as False.
The easiest way to read a text file is by using an iterator. This returns one line at a time.
It’s similar to the previous example, but with less code:
>>>
>>>
>>>
...
...
>>>
>>>
150
poem = ''
fin = open('relativity', 'rt' )
for line in fin:
poem += line
fin.close()
len(poem)
All of the preceding examples eventually built the single string poem. The read
lines() call reads a line at a time, and returns a list of one-line strings:
>>> fin = open('relativity', 'rt' )
>>> lines = fin.readlines()
>>> fin.close()
>>> print(len(lines), 'lines read')
5 lines read
>>> for line in lines:
...
print(line, end='')
...
There was a young lady named Bright,
Whose speed was far faster than light;
She started one day
In a relative way,
And returned on the previous night.>>>
We told print() to suppress the automatic newlines because the first four lines already
had them. The last line did not, causing the interactive prompt >>> to occur right after
the last line.
Write a Binary File with write()
If you include a 'b' in the mode string, the file is opened in binary mode. In this case,
you read and write bytes instead of a string.
We don’t have a binary poem lying around, so we’ll just generate the 256 byte values
from 0 to 255:
>>> bdata = bytes(range(0, 256))
>>> len(bdata)
256
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Open the file for writing in binary mode and write all the data at once:
>>> fout = open('bfile', 'wb')
>>> fout.write(bdata)
256
>>> fout.close()
Again, write() returns the number of bytes written.
As with text, you can write binary data in chunks:
>>>
>>>
>>>
>>>
>>>
...
...
...
...
...
100
100
56
>>>
fout = open('bfile', 'wb')
size = len(bdata)
offset = 0
chunk = 100
while True:
if offset > size:
break
fout.write(bdata[offset:offset+chunk])
offset += chunk
fout.close()
Read a Binary File with read()
This one is simple; all you need to do is just open with 'rb':
>>>
>>>
>>>
256
>>>
fin = open('bfile', 'rb')
bdata = fin.read()
len(bdata)
fin.close()
Close Files Automatically by Using with
If you forget to close a file that you’ve opened, it will be closed by Python after it’s no
longer referenced. This means that if you open a file within a function and don’t close
it explicitly, it will be closed automatically when the function ends. But you might have
opened the file in a long-running function or the main section of the program. The file
should be closed to force any remaining writes to be completed.
Python has context managers to clean up things such as open files. You use the form
with expression as variable:
>>> with open('relativity', 'wt') as fout:
...
fout.write(poem)
...
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That’s it. After the block of code under the context manager (in this case, one line)
completes (normally or by a raised exception), the file is closed automatically.
Change Position with seek()
As you read and write, Python keeps track of where you are in the file. The tell()
function returns your current offset from the beginning of the file, in bytes. The seek()
function lets you jump to another byte offset in the file. This means that you don’t have
to read every byte in a file to read the last one; you can seek() to the last one and just
read one byte.
For this example, use the 256-byte binary file 'bfile' that you wrote earlier:
>>> fin = open('bfile', 'rb')
>>> fin.tell()
0
Use seek() to one byte before the end of the file:
>>> fin.seek(255)
255
Read until the end of the file:
>>> bdata = fin.read()
>>> len(bdata)
1
>>> bdata[0]
255
seek() also returns the current offset.
You can call seek() with a second argument: seek( offset, origin ):
• If origin is 0 (the default), go offset bytes from the start
• If origin is 1, go offset bytes from the current position
• If origin is 2, go offset bytes relative to the end
These values are also defined in the standard os module:
>>>
>>>
0
>>>
1
>>>
2
import os
os.SEEK_SET
os.SEEK_CUR
os.SEEK_END
So, we could have read the last byte in different ways:
>>> fin = open('bfile', 'rb')
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One byte before the end of the file:
>>> fin.seek(-1, 2)
255
>>> fin.tell()
255
Read until the end of the file:
>>> bdata = fin.read()
>>> len(bdata)
1
>>> bdata[0]
255
You don’t need to call tell() for seek() to work. I just wanted to
show that they both report the same offset.
Here’s an example of seeking from the current position in the file:
>>> fin = open('bfile', 'rb')
This next example ends up two bytes before the end of the file:
>>> fin.seek(254, 0)
254
>>> fin.tell()
254
Now, go forward one byte:
>>> fin.seek(1, 1)
255
>>> fin.tell()
255
Finally, read until the end of the file:
>>> bdata = fin.read()
>>> len(bdata)
1
>>> bdata[0]
255
These functions are most useful for binary files. You can use them with text files, but
unless the file is ASCII (one byte per character), you would have a hard time calculating
offsets. These would depend on the text encoding, and the most popular encoding
(UTF-8) uses varying numbers of bytes per character.
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Structured Text Files
With simple text files, the only level of organization is the line. Sometimes, you want
more structure than that. You might want to save data for your program to use later, or
send data to another program.
There are many formats, and here’s how you can distinguish them:
• A separator, or delimiter, character like tab ('\t'), comma (','), or vertical bar
('|'). This is an example of the comma-separated values (CSV) format.
• '<' and '>' around tags. Examples include XML and HTML.
• Punctuation. An example is JavaScript Object Notation (JSON).
• Indentation. An example is YAML (which depending on the source you use means
“YAML Ain’t Markup Language;” you’ll need to research that one yourself).
• Miscellaneous, such as configuration files for programs.
Each of these structured file formats can be read and written by at least one Python
module.
CSV
Delimited files are often used as an exchange format for spreadsheets and databases.
You could read CSV files manually, a line at a time, splitting each line into fields at
comma separators, and adding the results to data structures such as lists and diction‐
aries. But it’s better to use the standard csv module, because parsing these files can get
more complicated than you think.
• Some have alternate delimiters besides a comma: '|' and '\t' (tab) are common.
• Some have escape sequences. If the delimiter character can occur within a field, the
entire field might be surrounded by quote characters or preceded by some escape
character.
• Files have different line-ending characters. Unix uses '\n', Microsoft uses '\r
\n', and Apple used to use '\r' but now uses '\n'.
• There can be column names in the first line.
First, we’ll see how to read and write a list of rows, each containing a list of columns:
>>> import csv
>>> villains = [
...
['Doctor', 'No'],
...
['Rosa', 'Klebb'],
...
['Mister', 'Big'],
...
['Auric', 'Goldfinger'],
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...
['Ernst', 'Blofeld'],
...
]
>>> with open('villains', 'wt') as fout:
...
csvout = csv.writer(fout)
...
csvout.writerows(villains)
# a context manager
This creates the file villains with these lines:
Doctor,No
Rosa,Klebb
Mister,Big
Auric,Goldfinger
Ernst,Blofeld
Now, we’ll try to read it back in:
>>> import csv
>>> with open('villains', 'rt') as fin: # context manager
...
cin = csv.reader(fin)
...
villains = [row for row in cin] # This uses a list comprehension
...
>>> print(villains)
[['Doctor', 'No'], ['Rosa', 'Klebb'], ['Mister', 'Big'],
['Auric', 'Goldfinger'], ['Ernst', 'Blofeld']]
Take a moment to think about list comprehensions (feel free to go to “Comprehen‐
sions” on page 81 and brush up on that syntax). We took advantage of the structure
created by the reader() function. It obligingly created rows in the cin object that we
can extract in a for loop.
Using reader() and writer() with their default options, the columns are separated by
commas and the rows by line feeds.
The data can be a list of dictionaries rather than a list of lists. Let’s read the villains
file again, this time using the new DictReader() function and specifying the column
names:
>>> import csv
>>> with open('villains', 'rt') as fin:
...
cin = csv.DictReader(fin, fieldnames=['first', 'last'])
...
villains = [row for row in cin]
...
>>> print(villains)
[{'last': 'No', 'first': 'Doctor'},
{'last': 'Klebb', 'first': 'Rosa'},
{'last': 'Big', 'first': 'Mister'},
{'last': 'Goldfinger', 'first': 'Auric'},
{'last': 'Blofeld', 'first': 'Ernst'}]
Let’s rewrite the CSV file by using the new DictWriter() function. We’ll also call write
header() to write an initial line of column names to the CSV file:
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import csv
villains = [
{'first': 'Doctor', 'last': 'No'},
{'first': 'Rosa', 'last': 'Klebb'},
{'first': 'Mister', 'last': 'Big'},
{'first': 'Auric', 'last': 'Goldfinger'},
{'first': 'Ernst', 'last': 'Blofeld'},
]
with open('villains', 'wt') as fout:
cout = csv.DictWriter(fout, ['first', 'last'])
cout.writeheader()
cout.writerows(villains)
That creates a villains file with a header line:
first,last
Doctor,No
Rosa,Klebb
Mister,Big
Auric,Goldfinger
Ernst,Blofeld
Now we’ll read it back. By omitting the fieldnames argument in the DictReader() call,
we instruct it to use the values in the first line of the file (first,last) as column labels
and matching dictionary keys:
>>> import csv
>>> with open('villains', 'rt') as fin:
...
cin = csv.DictReader(fin)
...
villains = [row for row in cin]
...
>>> print(villains)
[{'last': 'No', 'first': 'Doctor'},
{'last': 'Klebb', 'first': 'Rosa'},
{'last': 'Big', 'first': 'Mister'},
{'last': 'Goldfinger', 'first': 'Auric'},
{'last': 'Blofeld', 'first': 'Ernst'}]
XML
Delimited files convey only two dimensions: rows (lines) and columns (fields within a
line). If you want to exchange data structures among programs, you need a way to encode
hierarchies, sequences, sets, and other structures as text.
XML is the most prominent markup format that suits the bill. It uses tags to delimit data,
as in this sample menu.xml file:
<?xml version="1.0"?>
<menu>
<breakfast hours="7-11">
<item price="$6.00">breakfast burritos</item>
<item price="$4.00">pancakes</item>
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</breakfast>
<lunch hours="11-3">
<item price="$5.00">hamburger</item>
</lunch>
<dinner hours="3-10">
<item price="8.00">spaghetti</item>
</dinner>
</menu>
Following are a few important characteristics of XML:
• Tags begin with a < character. The tags in this sample were menu, breakfast, lunch,
dinner, and item.
• Whitespace is ignored.
• Usually a start tag such as <menu> is followed by other content and then a final
matching end tag such as </menu>.
• Tags can nest within other tags to any level. In this example, item tags are children
of the breakfast, lunch, and dinner tags; they, in turn, are children of menu.
• Optional attributes can occur within the start tag. In this example, price is an
attribute of item.
• Tags can contain values. In this example, each item has a value, such as pancakes
for the second breakfast item.
• If a tag named thing has no values or children, it can be expressed as the single tag
by including a forward slash just before the closing angle bracket, such as <thing/
>, rather than a start and end tag, like <thing></thing>.
• The choice of where to put data—attributes, values, child tags—is somewhat arbi‐
trary. For instance, we could have written the last item tag as <item price="$8.00"
food="spaghetti"/>.
XML is often used for data feeds and messages, and has subformats like RSS and Atom.
Some industries have many specialized XML formats, such as the finance field.
XML’s über-flexibility has inspired multiple Python libraries that differ in approach and
capabilities.
The simplest way to parse XML in Python is by using ElementTree. Here’s a little pro‐
gram to parse the menu.xml file and print some tags and attributes:
>>> import xml.etree.ElementTree as et
>>> tree = et.ElementTree(file='menu.xml')
>>> root = tree.getroot()
>>> root.tag
'menu'
>>> for child in root:
...
print('tag:', child.tag, 'attributes:', child.attrib)
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...
for grandchild in child:
...
print('\ttag:', grandchild.tag, 'attributes:', grandchild.attrib)
...
tag: breakfast attributes: {'hours': '7-11'}
tag: item attributes: {'price': '$6.00'}
tag: item attributes: {'price': '$4.00'}
tag: lunch attributes: {'hours': '11-3'}
tag: item attributes: {'price': '$5.00'}
tag: dinner attributes: {'hours': '3-10'}
tag: item attributes: {'price': '8.00'}
>>> len(root)
# number of menu sections
3
>>> len(root[0]) # number of breakfast items
2
For each element in the nested lists, tag is the tag string and attrib is a dictionary of
its attributes. ElementTree has many other ways of searching XML-derived data, mod‐
ifying it, and even writing XML files. The ElementTree documentation has the details.
Other standard Python XML libraries include:
xml.dom
The Document Object Model (DOM), familiar to JavaScript developers, represents
Web documents as hierarchical structures. This module loads the entire XML file
into memory and lets you access all the pieces equally.
xml.sax
Simple API for XML, or SAX, parses XML on the fly, so it does not have to load
everything into memory at once. Therefore, it can be a good choice if you need to
process very large streams of XML.
HTML
Enormous amounts of data are saved as Hypertext Markup Language (HTML), the basic
document format of the Web. The problem is so much of it doesn’t follow the HTML
rules, which can make it difficult to parse. Also, much of HTML is intended more to
format output than interchange data. Because this chapter is intended to describe fairly
well-defined data formats, I have separated out the discussion about HTML to Chap‐
ter 9.
JSON
JavaScript Object Notation (JSON) has become a very popular data interchange format,
beyond its JavaScript origins. The JSON format is a subset of JavaScript, and often legal
Python syntax as well. Its close fit to Python makes it a good choice for data interchange
among programs. You’ll see many examples of JSON for web development in Chapter 9.
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Unlike the variety of XML modules, there’s one main JSON module, with the unfor‐
gettable name json. This program encodes (dumps) data to a JSON string and decodes
(loads) a JSON string back to data. In this next example, let’s build a Python data struc‐
ture containing the data from the earlier XML example:
>>>
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
.
menu = \
{
"breakfast": {
"hours": "7-11",
"items": {
"breakfast burritos": "$6.00",
"pancakes": "$4.00"
}
},
"lunch" : {
"hours": "11-3",
"items": {
"hamburger": "$5.00"
}
},
"dinner": {
"hours": "3-10",
"items": {
"spaghetti": "$8.00"
}
}
}
Next, encode the data structure (menu) to a JSON string (menu_json) by using dumps():
>>> import json
>>> menu_json = json.dumps(menu)
>>> menu_json
'{"dinner": {"items": {"spaghetti": "$8.00"}, "hours": "3-10"},
"lunch": {"items": {"hamburger": "$5.00"}, "hours": "11-3"},
"breakfast": {"items": {"breakfast burritos": "$6.00", "pancakes":
"$4.00"}, "hours": "7-11"}}'
And now, let’s turn the JSON string menu_json back into a Python data structure
(menu2) by using loads():
>>> menu2 = json.loads(menu_json)
>>> menu2
{'breakfast': {'items': {'breakfast burritos': '$6.00', 'pancakes':
'$4.00'}, 'hours': '7-11'}, 'lunch': {'items': {'hamburger': '$5.00'},
'hours': '11-3'}, 'dinner': {'items': {'spaghetti': '$8.00'}, 'hours': '3-10'}}
menu and menu2 are both dictionaries with the same keys and values. As always with
standard dictionaries, the order in which you get the keys varies.
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You might get an exception while trying to encode or decode some objects, including
objects such as datetime (covered in detail in “Calendars and Clocks” on page 250), as
demonstrated here.
>>> import datetime
>>> now = datetime.datetime.utcnow()
>>> now
datetime.datetime(2013, 2, 22, 3, 49, 27, 483336)
>>> json.dumps(now)
Traceback (most recent call last):
# ... (deleted stack trace to save trees)
TypeError: datetime.datetime(2013, 2, 22, 3, 49, 27, 483336) is not JSON serializable
>>>
This can happen because the JSON standard does not define date or time types; it expects
you to define how to handle them. You could convert the datetime to something JSON
understands, such as a string or an epoch value (coming in Chapter 10):
>>> now_str = str(now)
>>> json.dumps(now_str)
'"2013-02-22 03:49:27.483336"'
>>> from time import mktime
>>> now_epoch = int(mktime(now.timetuple()))
>>> json.dumps(now_epoch)
'1361526567'
If the datetime value could occur in the middle of normally converted data types, it
might be annoying to make these special conversions. You can modify how JSON is
encoded by using inheritance, which is described in “Inheritance” on page 126. Python’s
JSON documentation gives an example of this for complex numbers, which also makes
JSON play dead. Let’s modify it for datetime:
>>> class DTEncoder(json.JSONEncoder):
...
def default(self, obj):
...
# isinstance() checks the type of obj
...
if isinstance(obj, datetime.datetime):
...
return int(mktime(obj.timetuple()))
...
# else it's something the normal decoder knows:
...
return json.JSONEncoder.default(self, obj)
...
>>> json.dumps(now, cls=DTEncoder)
'1361526567'
The new class DTEncoder is a subclass, or child class, of JSONEncoder. We only need to
override its default() method to add datetime handling. Inheritance ensures that
everything else will be handled by the parent class.
The isinstance() function checks whether the object obj is of the class datetime.da
tetime. Because everything in Python is an object, isinstance() works everywhere:
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>>> type(now)
<class 'datetime.datetime'>
>>> isinstance(now, datetime.datetime)
True
>>> type(234)
<class 'int'>
>>> isinstance(234, int)
True
>>> type('hey')
<class 'str'>
>>> isinstance('hey', str)
True
For JSON and other structured text formats, you can load from a file
into data structures without knowing anything about the structures
ahead of time. Then, you can walk through the structures by using
isinstance() and type-appropriate methods to examine their val‐
ues. For example, if one of the items is a dictionary, you can extract
contents through keys(), values(), and items().
YAML
Similar to JSON, YAML has keys and values, but handles more data types such as dates
and times. The standard Python library does not yet include YAML handling, so you
need to install a third-party library named yaml to manipulate it. load() converts a
YAML string to Python data, whereas dump() does the opposite.
The following YAML file, mcintyre.yaml, contains information on the Canadian poet
James McIntyre, including two of his poems:
name:
first: James
last: McIntyre
dates:
birth: 1828-05-25
death: 1906-03-31
details:
bearded: true
themes: [cheese, Canada]
books:
url: http://www.gutenberg.org/files/36068/36068-h/36068-h.htm
poems:
- title: 'Motto'
text: |
Politeness, perseverance and pluck,
To their possessor will bring good luck.
- title: 'Canadian Charms'
text: |
Here industry is not in vain,
For we have bounteous crops of grain,
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And you behold on every field
Of grass and roots abundant yield,
But after all the greatest charm
Is the snug home upon the farm,
And stone walls now keep cattle warm.
Values such as true, false, on, and off are converted to Python Booleans. Integers and
strings are converted to their Python equivalents. Other syntax creates lists and dic‐
tionaries:
>>> import yaml
>>> with open('mcintyre.yaml', 'rt') as fin:
>>>
text = fin.read()
>>> data = yaml.load(text)
>>> data['details']
{'themes': ['cheese', 'Canada'], 'bearded': True}
>>> len(data['poems'])
2
The data structures that are created match those in the YAML file, which in this case
are more than one level deep in places. You can get the title of the second poem with
this dict/list/dict reference:
>>> data['poems'][1]['title']
'Canadian Charms'
PyYAML can load Python objects from strings, and this is danger‐
ous. Use safe_load() instead of load() if you’re importing YAML
that you don’t trust. Better yet, always use safe_load(). Read war is
peace for a description of how unprotected YAML loading compro‐
mised the Ruby on Rails platform.
A Security Note
You can use all the formats described in this chapter to save objects to files and read
them back again. It’s possible to exploit this process and cause security problems.
For example, the following XML snippet from the billion laughs Wikipedia page defines
ten nested entities, each expanding the lower level ten times for a total expansion of one
billion:
<?xml version="1.0"?>
<!DOCTYPE lolz [
<!ENTITY lol "lol">
<!ENTITY lol1 "&lol;&lol;&lol;&lol;&lol;&lol;&lol;&lol;&lol;&lol;">
<!ENTITY lol2 "&lol1;&lol1;&lol1;&lol1;&lol1;&lol1;&lol1;&lol1;&lol1;&lol1;">
<!ENTITY lol3 "&lol2;&lol2;&lol2;&lol2;&lol2;&lol2;&lol2;&lol2;&lol2;&lol2;">
<!ENTITY lol4 "&lol3;&lol3;&lol3;&lol3;&lol3;&lol3;&lol3;&lol3;&lol3;&lol3;">
<!ENTITY lol5 "&lol4;&lol4;&lol4;&lol4;&lol4;&lol4;&lol4;&lol4;&lol4;&lol4;">
<!ENTITY lol6 "&lol5;&lol5;&lol5;&lol5;&lol5;&lol5;&lol5;&lol5;&lol5;&lol5;">
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<!ENTITY lol7 "&lol6;&lol6;&lol6;&lol6;&lol6;&lol6;&lol6;&lol6;&lol6;&lol6;">
<!ENTITY lol8 "&lol7;&lol7;&lol7;&lol7;&lol7;&lol7;&lol7;&lol7;&lol7;&lol7;">
<!ENTITY lol9 "&lol8;&lol8;&lol8;&lol8;&lol8;&lol8;&lol8;&lol8;&lol8;&lol8;">
]>
<lolz>&lol9;</lolz>
The bad news: billion laughs would blow up all of the XML libraries mentioned in the
previous sections. Defused XML lists this attack and others, along with the vulnerability
of Python libraries. The link shows how to change the settings for many of the libraries
to avoid these problems. Also, you can use the defusedxml library as a security frontend
for the other libraries:
>>>
>>>
>>>
>>>
>>>
>>>
# insecure:
from xml.etree.ElementTree import parse
et = parse(xmlfile)
# protected:
from defusedxml.ElementTree import parse
et = parse(xmlfile)
Configuration Files
Most programs offer various options or settings. Dynamic ones can be provided as pro‐
gram arguments, but long-lasting ones need to be kept somewhere. The temptation to
define your own quick and dirty config file format is strong—but resist it. It often turns
out to be dirty, but not so quick. You need to maintain both the writer program and the
reader program (sometimes called a parser). There are good alternatives that you can
just drop into your program, including those in the previous sections.
Here, we’ll use the standard configparser module, which handles Windows-style .ini
files. Such files have sections of key = value definitions. Here’s a minimal settings.cfg file:
[english]
greeting = Hello
[french]
greeting = Bonjour
[files]
home = /usr/local
# simple interpolation:
bin = %(home)s/bin
Here’s the code to read it into Python data structures:
>>> import configparser
>>> cfg = configparser.ConfigParser()
>>> cfg.read('settings.cfg')
['settings.cfg']
>>> cfg
<configparser.ConfigParser object at 0x1006be4d0>
>>> cfg['french']
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<Section: french>
>>> cfg['french']['greeting']
'Bonjour'
>>> cfg['files']['bin']
'/usr/local/bin'
Other options are available, including fancier interpolation. See the configparser doc‐
umentation. If you need deeper nesting than two levels, try YAML or JSON.
Other Interchange Formats
These binary data interchange formats are usually more compact and faster than XML
or JSON:
• MsgPack
• Protocol Buffers
• Avro
• Thrift
Because they are binary, none can be easily edited by a human with a text editor.
Serialize by Using pickle
Saving data structures to a file is called serializing. Formats such as JSON might require
some custom converters to serialize all the data types from a Python program. Python
provides the pickle module to save and restore any object in a special binary format.
Remember how JSON lost its mind when encountering a datetime object? Not a prob‐
lem for pickle:
>>> import pickle
>>> import datetime
>>> now1 = datetime.datetime.utcnow()
>>> pickled = pickle.dumps(now1)
>>> now2 = pickle.loads(pickled)
>>> now1
datetime.datetime(2014, 6, 22, 23, 24, 19, 195722)
>>> now2
datetime.datetime(2014, 6, 22, 23, 24, 19, 195722)
pickle works with your own classes and objects, too. We’ll define a little class called
Tiny that returns the string 'tiny' when treated as a string:
>>> import pickle
>>> class Tiny():
...
def __str__(self):
...
return 'tiny'
...
>>> obj1 = Tiny()
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>>> obj1
<__main__.Tiny object at 0x10076ed10>
>>> str(obj1)
'tiny'
>>> pickled = pickle.dumps(obj1)
>>> pickled
b'\x80\x03c__main__\nTiny\nq\x00)\x81q\x01.'
>>> obj2 = pickle.loads(pickled)
>>> obj2
<__main__.Tiny object at 0x10076e550>
>>> str(obj2)
'tiny'
pickled is the pickled binary string made from the object obj1. We converted that back
to the object obj2 to make a copy of obj1. Use dump() to pickle to a file, and load() to
unpickle from one.
Because pickle can create Python objects, the same security warn‐
ings that were discussed in earlier sections apply. Don’t unpickle
something that you don’t trust.
Structured Binary Files
Some file formats were designed to store particular data structures but are neither re‐
lational nor NoSQL databases. The sections that follow present some of them.
Spreadsheets
Spreadsheets, notably Microsoft Excel, are widespread binary data formats. If you can
save your spreadsheet to a CSV file, you can read it by using the standard csv module
that was described earlier. If you have a binary xls file, xlrd is a third-party package
for reading and writing.
HDF5
HDF5 is a binary data format for multidimensional or hierarchical numeric data. It’s
used mainly in science, where fast random access to large datasets (gigabytes to tera‐
bytes) is a common requirement. Even though HDF5 could be a good alternative to
databases in some cases, for some reason HDF5 is almost unknown in the business
world. It’s best suited to WORM (write once/read many) applications for which database
protection against conflicting writes is not needed. Here are a couple of modules that
you might find useful:
• h5py is a full-featured low-level interface. Read the documentation and code.
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• PyTables is a bit higher-level, with database-like features. Read the documenta‐
tion and code.
Both of these are discussed in terms of scientific applications of Python in Appendix
C. I’m mentioning HDF5 here in case you have a need to store and retrieve large amounts
of data and are willing to consider something outside the box, as well as the usual
database solutions. A good example is the Million Song dataset, which has downloadable
song data in HDF5 format.
Relational Databases
Relational databases are only about 40 years old but are ubiquitous in the computing
world. You’ll almost certainly have to deal with them at one time or another. When you
do, you’ll appreciate what they provide:
• Access to data by multiple simultaneous users
• Protection from corruption by those users
• Efficient methods to store and retrieve the data
• Data defined by schemas and limited by constraints
• Joins to find relationships across diverse types of data
• A declarative (rather than imperative) query language: SQL (Structured Query
Language)
These are called relational because they show relationships among different kinds of
data in the form of tables (as they are usually called nowadays). For instance, in our
menu example earlier, there is a relationship between each item and its price.
A table is a grid of rows and columns, similar to a spreadsheet. To create a table, name
it and specify the order, names, and types of its columns. Each row has the same columns,
although a column may be defined to allow missing data (called nulls). In the menu
example, you could create a table with one row for each item being sold. Each item has
the same columns, including one for the price.
A column or group of columns is usually the table’s primary key; its values must be
unique in the table. This prevents adding the same data to the table more than once.
This key is indexed for fast lookups during queries. An index works a little like a book
index, making it fast to find a particular row.
Each table lives within a parent database, like a file within a directory. Two levels of
hierarchy help keep things organized a little better.
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Yes, the word database is used in multiple ways: as the server, the table
container, and the data stored therein. If you’ll be referring to all of
them at the same time, it might help to call them database server,
database, and data.
If you want to find rows by some non-key column value, define a secondary index on
that column. Otherwise, the database server must perform a table scan—a brute-force
search of every row for matching column values.
Tables can be related to each other with foreign keys, and column values can be con‐
strained to these keys.
SQL
SQL is not an API or a protocol, but a declarative language: you say what you want rather
than how to do it. It’s the universal language of relational databases. SQL queries are text
strings, that a client sends to the database server, which figures out what to do with them.
There have been various SQL standard definitions, but all database vendors have added
their own tweaks and extensions, resulting in many SQL dialects. If you store your data
in a relational database, SQL gives you some portability. Still, dialect and operational
differences can make it difficult to move your data to another type of database.
There are two main categories of SQL statements:
DDL (data definition language)
Handles creation, deletion, constraints, and permissions for tables, databases, and
uses
DML (data manipulation language)
Handles data insertions, selects, updates, and deletions
Table 8-1 lists the basic SQL DDL commands.
Table 8-1. Basic SQL DDL commands
Operation
SQL pattern
SQL example
Create a database
CREATE DATABASE dbname
CREATE DATABASE d
Select current database
USE dbname
USE d
Delete a database and its tables DROP DATABASE dbname
Create a table
DROP DATABASE d
CREATE TABLE tbname ( coldefs ) CREATE TABLE t (id INT, count INT)
Delete a table
DROP TABLE tbname
DROP TABLE t
Remove all rows from a table
TRUNCATE TABLE tbname
TRUNCATE TABLE t
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Why all the CAPITAL LETTERS? SQL is case-insensitive, but it’s
tradition (don’t ask me why) to SHOUT its keywords in code exam‐
ples to distinguish them from column names.
The main DML operations of a relational database are often known by the acronym
CRUD:
• Create by using the SQL INSERT statement
• Read by using SELECT
• Update by using UPDATE
• Delete by using DELETE
Table 8-2 looks at the commands available for SQL DML.
Table 8-2. Basic SQL DML commands
Operation
SQL pattern
SQL example
Add a row
INSERT INTO tbname VALUES(
…)
INSERT INTO t VALUES(7, 40)
Select all rows and columns
SELECT * FROM tbname
SELECT * FROM t
Select all rows, some columns
SELECT cols FROM tbname
SELECT id, count FROM t
Select some rows, some
columns
SELECT cols FROM tbname WHERE
SELECT id, count from t WHERE
count > 5 AND id = 9
condition
Change some rows in a column UPDATE tbname SET col = value
WHERE condition
UPDATE t SET count=3 WHERE id=5
Delete some rows
DELETE FROM t WHERE count <= 10 OR
id = 16
DELETE FROM tbname WHERE
condition
DB-API
An application programming interface (API) is a set of functions that you can call to
get access to some service. DB-API is Python’s standard API for accessing relational
databases. Using it, you can write a single program that works with multiple kinds of
relational databases instead of writing a separate program for each one. It’s similar to
Java’s JDBC or Perl’s dbi.
Its main functions are the following:
connect()
Make a connection to the database; this can include arguments such as username,
password, server address, and others.
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cursor()
Create a cursor object to manage queries.
execute() and executemany()
Run one or more SQL commands against the database.
fetchone(), fetchmany(), and fetchall()
Get the results from execute.
The Python database modules in the coming sections conform to DB-API, often with
extensions and some differences in details.
SQLite
SQLite is a good, light, open source relational database. It’s implemented as a standard
Python library, and stores databases in normal files. These files are portable across ma‐
chines and operating systems, making SQLite a very portable solution for simple rela‐
tional database applications. It isn’t as full-featured as MySQL or PostgreSQL, but it does
support SQL, and it manages multiple simultaneous users. Web browsers, smart phones,
and other applications use SQLite as an embedded database.
You begin with a connect() to the local SQLite database file that you want to use or
create. This file is the equivalent of the directory-like database that parents tables in
other servers. The special string ':memory:' creates the database in memory only; this
is fast and useful for testing but will lose data when your program terminates or if your
computer goes down.
For the next example, let’s make a database called enterprise.db and the table zoo to
manage our thriving roadside petting zoo business. The table columns are as follows:
critter
A variable length string, and our primary key
count
An integer count of our current inventory for this animal
damages
The dollar amount of our current losses from animal-human interactions
>>>
>>>
>>>
>>>
import sqlite3
conn = sqlite3.connect('enterprise.db')
curs = conn.cursor()
curs.execute('''CREATE TABLE zoo
(critter VARCHAR(20) PRIMARY KEY,
count INT,
damages FLOAT)''')
<sqlite3.Cursor object at 0x1006a22d0>
Python’s triple quotes are handy when creating long strings such as SQL queries.
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Now, add some animals to the zoo:
>>> curs.execute('INSERT INTO zoo VALUES("duck", 5, 0.0)')
<sqlite3.Cursor object at 0x1006a22d0>
>>> curs.execute('INSERT INTO zoo VALUES("bear", 2, 1000.0)')
<sqlite3.Cursor object at 0x1006a22d0>
There’s a safer way to insert data, using a placeholder:
>>> ins = 'INSERT INTO zoo (critter, count, damages) VALUES(?, ?, ?)'
>>> curs.execute(ins, ('weasel', 1, 2000.0))
<sqlite3.Cursor object at 0x1006a22d0>
This time, we used three question marks in the SQL to indicate that we plan to insert
three values, and then pass those three values as a list to the execute() function. Pla‐
ceholders handle tedious details such as quoting. They protect you against SQL injection
—a kind of external attack that is common on the Web that inserts malicious SQL
commands into the system.
Now, let’s see if we can get all our animals out again:
>>> curs.execute('SELECT * FROM zoo')
<sqlite3.Cursor object at 0x1006a22d0>
>>> rows = curs.fetchall()
>>> print(rows)
[('duck', 5, 0.0), ('bear', 2, 1000.0), ('weasel', 1, 2000.0)]
Let’s get them again, but ordered by their counts:
>>> curs.execute('SELECT * from zoo ORDER BY count')
<sqlite3.Cursor object at 0x1006a22d0>
>>> curs.fetchall()
[('weasel', 1, 2000.0), ('bear', 2, 1000.0), ('duck', 5, 0.0)]
Hey, we wanted them in descending order:
>>> curs.execute('SELECT * from zoo ORDER BY count DESC')
<sqlite3.Cursor object at 0x1006a22d0>
>>> curs.fetchall()
[('duck', 5, 0.0), ('bear', 2, 1000.0), ('weasel', 1, 2000.0)]
Which type of animal is costing us the most?
>>> curs.execute('''SELECT * FROM zoo WHERE
...
damages = (SELECT MAX(damages) FROM zoo)''')
<sqlite3.Cursor object at 0x1006a22d0>
>>> curs.fetchall()
[('weasel', 1, 2000.0)]
You would have thought it was the bears. It’s always best to check the actual data.
Before we leave SQLite, we need to clean up. If we opened a connection and a cursor,
we need to close them when we’re done:
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>>> curs.close()
>>> conn.close()
MySQL
MySQL is a very popular open source relational database. Unlike SQLite, it’s an actual
server, so clients can access it from different devices across the network.
MysqlDB has been the most popular MySQL driver, but it has not yet been ported to
Python 3. Table 8-3 lists the drivers you can use to access MySQL from Python.
Table 8-3. MySQL drivers
Name
Pypi package
Import as
MySQL Connector http://bit.ly/mysql-cpdg
Link
mysql-connectorpython
mysql.connector
PYMySQL
https://github.com/petehunt/
PyMySQL/
pymysql
pymysql
oursql
http://pythonhosted.org/oursql/
oursql
oursql
Notes
Requires the
MySQL C client
libraries.
PostgreSQL
PostgreSQL is a full-featured open source relational database, in many ways more ad‐
vanced than MySQL. Table 8-4 presents the Python drivers you can use to access it.
Table 8-4. PostgreSQL drivers
Name
Link
Pypi package Import as Notes
psycopg2
http://initd.org/psycopg/
psycopg2
psycopg2
Needs pg_config from PostgreSQL
client tools
py-postgresql http://python.projects.pgfoundry.org/ py-postgresql postgresql
The most popular driver is psycopg2, but its installation requires the PostgreSQL client
libraries.
SQLAlchemy
SQL is not quite the same for all relational databases, and DB-API takes you only so far.
Each database implements a particular dialect reflecting its features and philosophy.
Many libraries try to bridge these differences in one way or another. The most popular
cross-database Python library is SQLAlchemy.
It isn’t in the standard library, but it’s well known and used by many people. You can
install it on your system by using this command:
$ pip install sqlalchemy
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You can use SQLAlchemy on several levels:
• The lowest level handles database connection pools, executing SQL commands, and
returning results. This is closest to the DB-API.
• Next up is the SQL expression language, a Pythonic SQL builder.
• Highest is the ORM (Object Relational Model) layer, which uses the SQL Expression
Language and binds application code with relational data structures.
As we go along, you’ll understand what the terms mean in those levels. SQLAlchemy
works with the database drivers documented in the previous sections. You don’t need
to import the driver; the initial connection string you provide to SQLAlchemy will
determine it. That string looks like this:
dialect + driver :// user : password @ host : port / dbname
The values you put in this string are as follows:
dialect
The database type
driver
The particular driver you want to use for that database
user and password
Your database authentication strings
host and port
The database server’s location (: port is only needed if it’s not the standard one for
this server)
dbname
The database to initially connect to on the server
Table 8-5 lists the dialects and drivers.
Table 8-5. SQLAlchemy connection
dialect
driver
sqlite
pysqlite (or omit)
mysql
mysqlconnector
mysql
pymysql
mysql
oursql
postgresql psycopg2
postgresql pypostgresql
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The engine layer
First, we’ll try the lowest level of SQLAlchemy, which does little more than the base DBAPI functions.
Let’s try it with SQLite, which is already built into Python. The connection string for
SQLite skips the host, port, user, and password. The dbname informs SQLite as to what
file to use to store your database. If you omit the dbname, SQLite builds a database in
memory. If the dbname starts with a slash (/), it’s an absolute filename on your computer
(as in Linux and OS X; for example, C:\\ on Windows). Otherwise, it’s relative to your
current directory.
The following segments are all part of one program, separated here for explanation.
To begin, you need to import what we need. The following is an example of an import
alias, which lets us use the string sa to refer to SQLAlchemy methods. I do this mainly
because sa is a lot easier to type than sqlalchemy:
>>> import sqlalchemy as sa
Connect to the database and create the storage for it in memory (the argument string
'sqlite:///:memory:' also works):
>>> conn = sa.create_engine('sqlite://')
Create a database table called zoo that comprises three columns:
>>> conn.execute('''CREATE TABLE zoo
...
(critter VARCHAR(20) PRIMARY KEY,
...
count INT,
...
damages FLOAT)''')
<sqlalchemy.engine.result.ResultProxy object at 0x1017efb10>
Running conn.execute() returns a SQLAlchemy object called a ResultProxy. You’ll
soon see what to do with it.
By the way, if you’ve never made a database table before, congratulations. Check that
one off your bucket list.
Now, insert three sets of data into your new empty table:
>>> ins = 'INSERT INTO zoo (critter, count, damages) VALUES (?, ?, ?)'
>>> conn.execute(ins, 'duck', 10, 0.0)
<sqlalchemy.engine.result.ResultProxy object at 0x1017efb50>
>>> conn.execute(ins, 'bear', 2, 1000.0)
<sqlalchemy.engine.result.ResultProxy object at 0x1017ef090>
>>> conn.execute(ins, 'weasel', 1, 2000.0)
<sqlalchemy.engine.result.ResultProxy object at 0x1017ef450>
Next, ask the database for everything that we just put in:
>>> rows = conn.execute('SELECT * FROM zoo')
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In SQLAlchemy, rows is not a list; it’s that special ResultProxy thing that we can’t print
directly:
>>> print(rows)
<sqlalchemy.engine.result.ResultProxy object at 0x1017ef9d0>
However, you can iterate over it like a list, so we can get a row at a time:
>>> for row in rows:
...
print(row)
...
('duck', 10, 0.0)
('bear', 2, 1000.0)
('weasel', 1, 2000.0)
That was almost the same as the SQLite DB-API example that you saw earlier. The one
advantage is that we didn’t need to import the database driver at the top; SQLAlchemy
figured that out from the connection string. Just changing the connection string would
make this code portable to another type of database. Another plus is SQLAlchemy’s
connection pooling, which you can read about at its documentation site.
The SQL Expression Language
The next level up is SQLAlchemy’s SQL Expression Language. It introduces functions
to create the SQL for various operations. The Expression Language handles more of the
SQL dialect differences than the lower-level engine layer does. It can be a handy middleground approach for relational database applications.
Here’s how to create and populate the zoo table. Again, these are successive fragments
of a single program.
The import and connection are the same as before:
>>> import sqlalchemy as sa
>>> conn = sa.create_engine('sqlite://')
To define the zoo table, we’ll begin using some of the Expression Language instead of
SQL:
>>> meta = sa.MetaData()
>>> zoo = sa.Table('zoo', meta,
...
sa.Column('critter', sa.String, primary_key=True),
...
sa.Column('count', sa.Integer),
...
sa.Column('damages', sa.Float)
...
)
>>> meta.create_all(conn)
Check out the parentheses in that multiline call in the preceding example. The structure
of the Table() method matches the structure of the table. Just as our table contains
three columns, there are three calls to Column() inside the parentheses of the Table()
method call.
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Meanwhile, zoo is some magic object that bridges the SQL database world and the
Python data structure world.
Insert the data with more Expression Language functions:
... conn.execute(zoo.insert(('bear', 2, 1000.0)))
<sqlalchemy.engine.result.ResultProxy object at 0x1017ea910>
>>> conn.execute(zoo.insert(('weasel', 1, 2000.0)))
<sqlalchemy.engine.result.ResultProxy object at 0x1017eab10>
>>> conn.execute(zoo.insert(('duck', 10, 0)))
<sqlalchemy.engine.result.ResultProxy object at 0x1017eac50>
Next, create the SELECT statement (zoo.select() selects everything from the table
represented by the zoo object, such as SELECT * FROM zoo would do in plain SQL):
>>> result = conn.execute(zoo.select())
Finally, get the results:
>>> rows = result.fetchall()
>>> print(rows)
[('bear', 2, 1000.0), ('weasel', 1, 2000.0), ('duck', 10, 0.0)]
The Object-Relational Mapper
In the last section, the zoo object was a mid-level connection between SQL and
Python. At the top layer of SQLAlchemy, the Object-Relational Mapper (ORM) uses the
SQL Expression Language but tries to make the actual database mechanisms invisible.
You define classes, and the ORM handles how to get their data in and out of the database.
The basic idea behind that complicated phrase, “object-relational mapper,” is that you
can refer to objects in your code, and thus stay close to the way Python likes to operate,
while still using a relational database.
We’ll define a Zoo class and hook it into the ORM. This time, we’ll make SQLite use the
file zoo.db so that we can confirm that the ORM worked.
As in the previous two sections, the snippets that follow are actually one program sep‐
arated by explanations. Don’t worry if you don’t understand some if it. The SQLAlchemy
documentation has all the details, and this stuff can get complex. I just want you to get
an idea of how much work it is to do this, so that you can decide which of the approaches
discussed in this chapter suits you.
The initial import is the same, but this time we need another something also:
>>> import sqlalchemy as sa
>>> from sqlalchemy.ext.declarative import declarative_base
Here, we make the connection:
>>> conn = sa.create_engine('sqlite:///zoo.db')
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Now, we get into SQLAlchemy’s ORM. We define the Zoo class and associate its attributes
with table columns:
>>> Base = declarative_base()
>>> class Zoo(Base):
...
__tablename__ = 'zoo'
...
critter = sa.Column('critter', sa.String, primary_key=True)
...
count = sa.Column('count', sa.Integer)
...
damages = sa.Column('damages', sa.Float)
...
def __init__(self, critter, count, damages):
...
self.critter = critter
...
self.count = count
...
self.damages = damages
...
def __repr__(self):
...
return "<Zoo({}, {}, {})>".format(self.critter, self.count, self.damages)
The following line magically creates the database and table:
>>> Base.metadata.create_all(conn)
You can then insert data by creating Python objects. The ORM manages these
internally:
>>> first = Zoo('duck', 10, 0.0)
>>> second = Zoo('bear', 2, 1000.0)
>>> third = Zoo('weasel', 1, 2000.0)
>>> first
<Zoo(duck, 10, 0.0)>
Next, we get the ORM to take us to SQL land. We create a session to talk to the database:
>>> from sqlalchemy.orm import sessionmaker
>>> Session = sessionmaker(bind=conn)
>>> session = Session()
Within the session, we write the three objects that we created to the database. The add()
function adds one object, and add_all() adds a list:
>>> session.add(first)
>>> session.add_all([second, third])
Finally, we need to force everything to complete:
>>> session.commit()
Did it work? Well, it created a zoo.db file in the current directory. You can use the
command-line sqlite3 program to check it:
$ sqlite3 zoo.db
SQLite version 3.6.12
Enter ".help" for instructions
Enter SQL statements terminated with a ";"
sqlite> .tables
zoo
sqlite> select * from zoo;
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duck|10|0.0
bear|2|1000.0
weasel|1|2000.0
The purpose of this section was to show what an ORM is and how it works at a high
level. The author of SQLAlchemy has written a full tutorial. After reading this, decide
which of the following levels would best fit your needs:
• Plain DB-API, as in the earlier SQLite section
• The SQLAlchemy engine room
• The SQLAlchemy Expression Language
• The SQLAlchemy ORM
It seems like a natural choice to use an ORM to avoid the complexities of SQL. Should
you use one? Some people think ORMs should be avoided, but others think the criticism
is overdone. Whoever’s right, an ORM is an abstraction, and all abstractions break down
at some point; they’re leaky. When your ORM doesn’t do what you want, you must figure
out both how it works and how to fix it in SQL. To borrow an Internet meme: Some
people, when confronted with a problem, think, “I know, I’ll use an ORM.” Now they have
two problems. Use ORMs sparingly, and mostly for simple applications. If the application
is that simple, maybe you can just use straight SQL (or the SQL Expression Language),
anyhow.
Or, you can try something simpler such as dataset. It’s built on SQLAlchemy and pro‐
vides a simple ORM for SQL, JSON, and CSV storage.
NoSQL Data Stores
Some databases are not relational and don’t support SQL. These were written to handle
very large data sets, allow more flexible data definitions, or support custom data oper‐
ations. They’ve been collectively labeled NoSQL (formerly meaning no SQL; now the
less confrontational not only SQL).
The dbm Family
The dbm formats were around long before NoSQL was coined. They’re key-value stores,
often embedded in applications such as web browsers to maintain various settings. A
dbm database is like a Python dictionary in the following ways:
• You can assign a value to a key, and it’s automatically saved to the database on disk.
• You can get a value from a key.
The following is a quick example. The second argument to the following open() method
is 'r' to read, 'w' to write, and 'c' for both, creating the file if it doesn’t exist:
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>>> import dbm
>>> db = dbm.open('definitions', 'c')
To create key-value pairs, just assign a value to a key just as you would a dictionary:
>>> db['mustard'] = 'yellow'
>>> db['ketchup'] = 'red'
>>> db['pesto'] = 'green'
Let’s pause and check what we have so far:
>>> len(db)
3
>>> db['pesto']
b'green'
Now close, then reopen to see if it actually saved what we gave it:
>>> db.close()
>>> db = dbm.open('definitions', 'r')
>>> db['mustard']
b'yellow'
Keys and values are stored as bytes. You cannot iterate over the database object db, but
you can get the number of keys by using len(). Note that get() and setdefault()
work as they do for dictionaries.
Memcached
memcached is a fast in-memory key-value cache server. It’s often put in front of a database,
or used to store web server session data. You can download versions for Linux and OS
X, and for Windows. If you want to try out this section, you’ll need a memcached server
and Python driver.
There are many Python drivers; one that works with Python 3 is python3-memcached,
which you can install by using this command:
$ pip install python-memcached
To use it, connect to a memcached server, after which you can do the following:
• Set and get values for keys
• Increment or decrement a value
• Delete a key
Data is not persistent, and data that you wrote earlier might disappear. This is inherent
in memcached, being that it’s a cache server. It avoids running out of memory by dis‐
carding old data.
You can connect to multiple memcached servers at the same time. In this next example,
we’re just talking to one on the same computer:
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>>> import memcache
>>> db = memcache.Client(['127.0.0.1:11211'])
>>> db.set('marco', 'polo')
True
>>> db.get('marco')
'polo'
>>> db.set('ducks', 0)
True
>>> db.get('ducks')
0
>>> db.incr('ducks', 2)
2
>>> db.get('ducks')
2
Redis
Redis is a data structure server. Like memcached, all of the data in a Redis server should
fit in memory (although there is now an option to save the data to disk). Unlike memc‐
ached, Redis can do the following:
• Save data to disk for reliability and restarts
• Keep old data
• Provide more data structures than simple strings
The Redis data types are a close match to Python’s, and a Redis server can be a useful
intermediary for one or more Python applications to share data. I’ve found it so useful
that it’s worth a little extra coverage here.
The Python driver redis-py has its source code and tests on GitHub, as well as online
documentation. You can install it by using this command:
$ pip install redis
The Redis server itself has good documentation. If you install and start the Redis server
on your local computer (with the network nickname localhost), you can try the pro‐
grams in the following sections.
Strings
A key with a single value is a Redis string. Simple Python data types are automatically
converted. Connect to a Redis server at some host (default is localhost) and port (de‐
fault is 6379):
>>> import redis
>>> conn = redis.Redis()
redis.Redis('localhost') or redis.Redis('localhost', 6379) would have given
the same result.
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List all keys (none so far):
>>> conn.keys('*')
[]
Set a simple string (key 'secret'), integer (key 'carats'), and float (key 'fever'):
>>> conn.set('secret', 'ni!')
True
>>> conn.set('carats', 24)
True
>>> conn.set('fever', '101.5')
True
Get the values back by key:
>>> conn.get('secret')
b'ni!'
>>> conn.get('carats')
b'24'
>>> conn.get('fever')
b'101.5'
Here, the setnx() method sets a value only if the key does not exist:
>>> conn.setnx('secret', 'icky-icky-icky-ptang-zoop-boing!')
False
It failed because we had already defined 'secret':
>>> conn.get('secret')
b'ni!'
The getset() method returns the old value and sets it to a new one at the same time:
>>> conn.getset('secret', 'icky-icky-icky-ptang-zoop-boing!')
b'ni!'
Let’s not get too far ahead of ourselves. Did it work?
>>> conn.get('secret')
b'icky-icky-icky-ptang-zoop-boing!'
Now, get a substring by using getrange() (as in Python, offset 0=start, -1=end):
>>> conn.getrange('secret', -6, -1)
b'boing!'
Replace a substring by using setrange() (using a zero-based offset):
>>> conn.setrange('secret', 0, 'ICKY')
32
>>> conn.get('secret')
b'ICKY-icky-icky-ptang-zoop-boing!'
Next, set multiple keys at once by using mset():
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>>> conn.mset({'pie': 'cherry', 'cordial': 'sherry'})
True
Get more than one value at once by using mget():
>>> conn.mget(['fever', 'carats'])
[b'101.5', b'24']
Delete a key by using delete():
>>> conn.delete('fever')
True
Increment by using the incr() or incrbyfloat() commands, and decrement with
decr():
>>> conn.incr('carats')
25
>>> conn.incr('carats', 10)
35
>>> conn.decr('carats')
34
>>> conn.decr('carats', 15)
19
>>> conn.set('fever', '101.5')
True
>>> conn.incrbyfloat('fever')
102.5
>>> conn.incrbyfloat('fever', 0.5)
103.0
There’s no decrbyfloat(). Use a negative increment to reduce the fever:
>>> conn.incrbyfloat('fever', -2.0)
101.0
Lists
Redis lists can contain only strings. The list is created when you do your first insertion.
Insert at the beginning by using lpush():
>>> conn.lpush('zoo', 'bear')
1
Insert more than one item at the beginning:
>>> conn.lpush('zoo', 'alligator', 'duck')
3
Insert before or after a value by using linsert():
>>> conn.linsert('zoo', 'before', 'bear', 'beaver')
4
>>> conn.linsert('zoo', 'after', 'bear', 'cassowary')
5
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Insert at an offset by using lset() (the list must exist already):
>>> conn.lset('zoo', 2, 'marmoset')
True
Insert at the end by using rpush():
>>> conn.rpush('zoo', 'yak')
6
Get the value at an offset by using lindex():
>>> conn.lindex('zoo', 3)
b'bear'
Get the values in an offset range by using lrange() (0 to -1 for all):
>>> conn.lrange('zoo', 0, 2)
[b'duck', b'alligator', b'marmoset']
Trim the list with ltrim(), keeping only those in a range of offsets:
>>> conn.ltrim('zoo', 1, 4)
True
Get a range of values (use 0 to -1 for all) by using lrange():
>>> conn.lrange('zoo', 0, -1)
[b'alligator', b'marmoset', b'bear', b'cassowary']
Chapter 10 shows you how you can use Redis lists and publish-subscribe to implement
job queues.
Hashes
Redis hashes are similar to Python dictionaries but can contain only strings. Thus, you
can go only one level deep, not make deep-nested structures. Here are examples that
create and play with a Redis hash called song:
Set the fields do and re in hash song at once by using hmset():
>>> conn.hmset('song', {'do': 'a deer', 're': 'about a deer'})
True
Set a single field value in a hash by using hset():
>>> conn.hset('song', 'mi', 'a note to follow re')
1
Get one field’s value by using hget():
>>> conn.hget('song', 'mi')
b'a note to follow re'
Get multiple field values by using hmget():
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>>> conn.hmget('song', 're', 'do')
[b'about a deer', b'a deer']
Get all field keys for the hash by using hkeys():
>>> conn.hkeys('song')
[b'do', b're', b'mi']
Get all field values for the hash by using hvals():
>>> conn.hvals('song')
[b'a deer', b'about a deer', b'a note to follow re']
Get the number of fields in the hash by using hlen():
>>> conn.hlen('song')
3
Get all field keys and values in the hash by using hgetall():
>>> conn.hgetall('song')
{b'do': b'a deer', b're': b'about a deer', b'mi': b'a note to follow re'}
Set a field if its key doesn’t exist by using hsetnx():
>>> conn.hsetnx('song', 'fa', 'a note that rhymes with la')
1
Sets
Redis sets are similar to Python sets, as you can see in the series of examples that follow.
Add one or more values to a set:
>>> conn.sadd('zoo', 'duck', 'goat', 'turkey')
3
Get the number of values from the set:
>>> conn.scard('zoo')
3
Get all the set’s values:
>>> conn.smembers('zoo')
{b'duck', b'goat', b'turkey'}
Remove a value from the set:
>>> conn.srem('zoo', 'turkey')
True
Let’s make a second set to show some set operations:
>>> conn.sadd('better_zoo', 'tiger', 'wolf', 'duck')
0
Intersect (get the common members of) the zoo and better_zoo sets:
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>>> conn.sinter('zoo', 'better_zoo')
{b'duck'}
Get the intersection of zoo and better_zoo, and store the result in the set fowl_zoo:
>>> conn.sinterstore('fowl_zoo', 'zoo', 'better_zoo')
1
Who’s in there?
>>> conn.smembers('fowl_zoo')
{b'duck'}
Get the union (all members) of zoo and better_zoo:
>>> conn.sunion('zoo', 'better_zoo')
{b'duck', b'goat', b'wolf', b'tiger'}
Store that union result in the set fabulous_zoo:
>>> conn.sunionstore('fabulous_zoo', 'zoo', 'better_zoo')
4
>>> conn.smembers('fabulous_zoo')
{b'duck', b'goat', b'wolf', b'tiger'}
What does zoo have that better_zoo doesn’t? Use sdiff() to get the set difference, and
sdiffstore() to save it in the zoo_sale set:
>>> conn.sdiff('zoo', 'better_zoo')
{b'goat'}
>>> conn.sdiffstore('zoo_sale', 'zoo', 'better_zoo')
1
>>> conn.smembers('zoo_sale')
{b'goat'}
Sorted sets
One of the most versatile Redis data types is the sorted set, or zset. It’s a set of unique
values, but each value has an associated floating point score. You can access each item
by its value or score. Sorted sets have many uses:
• Leader boards
• Secondary indexes
• Timeseries, using timestamps as scores
We’ll show the last use case, tracking user logins via timestamps. We’re using the Unix
epoch value (more on this in Chapter 10) that’s returned by the Python time() function:
>>> import time
>>> now = time.time()
>>> now
1361857057.576483
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Let’s add our first guest, looking nervous:
>>> conn.zadd('logins', 'smeagol', now)
1
Five minutes later, another guest:
>>> conn.zadd('logins', 'sauron', now+(5*60))
1
Two hours later:
>>> conn.zadd('logins', 'bilbo', now+(2*60*60))
1
One day later, not hasty:
>>> conn.zadd('logins', 'treebeard', now+(24*60*60))
1
In what order did bilbo arrive?
>>> conn.zrank('logins', 'bilbo')
2
When was that?
>>> conn.zscore('logins', 'bilbo')
1361864257.576483
Let’s see everyone in login order:
>>> conn.zrange('logins', 0, -1)
[b'smeagol', b'sauron', b'bilbo', b'treebeard']
With their times, please:
>>> conn.zrange('logins', 0, -1, withscores=True)
[(b'smeagol', 1361857057.576483), (b'sauron', 1361857357.576483),
(b'bilbo', 1361864257.576483), (b'treebeard', 1361943457.576483)]
Bits
This is a very space-efficient and fast way to deal with large sets of numbers. Suppose
that you have a website with registered users. You’d like to track how often people log
in, how many users visit on a particular day, how often the same user visits on following
days, and so on. You could use Redis sets, but if you’ve assigned increasing numeric user
IDs, bits are more compact and faster.
Let’s begin by creating a bitset for each day. For this test, we’ll just use three days and a
few user IDs:
>>>
>>>
>>>
>>>
212
days = ['2013-02-25', '2013-02-26', '2013-02-27']
big_spender = 1089
tire_kicker = 40459
late_joiner = 550212
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Each date is a separate key. Set the bit for a particular user ID for that date. For example,
on the first date (2013-02-25), we had visits from big_spender (ID 1089) and
tire_kicker (ID 40459):
>>> conn.setbit(days[0], big_spender, 1)
0
>>> conn.setbit(days[0], tire_kicker, 1)
0
The next day, big_spender came back:
>>> conn.setbit(days[1], big_spender, 1)
0
The next day had yet another visit from our friend, big_spender, and a new person
whom we’re calling late_joiner:
>>> conn.setbit(days[2], big_spender, 1)
0
>>> conn.setbit(days[2], late_joiner, 1)
0
Let’s get the daily visitor count for these three days:
>>> for day in days:
...
conn.bitcount(day)
...
2
1
2
Did a particular user visit on a particular day?
>>> conn.getbit(days[1], tire_kicker)
0
So, tire_kicker did not visit on the second day.
How many users visited every day?
>>> conn.bitop('and', 'everyday', *days)
68777
>>> conn.bitcount('everyday')
1
I’ll give you three guesses who it was:
>>> conn.getbit('everyday', big_spender)
1
Finally, what was the number of total unique users in these three days?
>>> conn.bitop('or', 'alldays', *days)
68777
>>> conn.bitcount('alldays')
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Caches and expiration
All Redis keys have a time-to-live, or expiration date. By default, this is forever. We can
use the expire() function to instruct Redis how long to keep the key. As is demonstrated
here, the value is a number of seconds:
>>> import time
>>> key = 'now you see it'
>>> conn.set(key, 'but not for long')
True
>>> conn.expire(key, 5)
True
>>> conn.ttl(key)
5
>>> conn.get(key)
b'but not for long'
>>> time.sleep(6)
>>> conn.get(key)
>>>
The expireat() command expires a key at a given epoch time. Key expiration is useful
to keep caches fresh and to limit login sessions.
Other NoSQL
The NoSQL servers listed here handle data larger than memory, and many of them use
multiple computers. Table 8-6 presents notable servers and their Python libraries.
Table 8-6. NoSQL databases
Site
Python API
Cassandra
pycassa
CouchDB
couchdb-python
HBase
happybase
Kyoto Cabinet kyotocabinet
MongoDB
mongodb
Riak
riak-python-client
Full-Text Databases
Finally, there’s a special category of databases for full-text search. They index everything,
so you can find that poem that talks about windmills and giant wheels of cheese. You
can see some popular open source examples, and their Python APIs, in Table 8-7.
Table 8-7. Full-text databases
Site
Python API
Lucene
pylucene
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Site
Python API
Solr
SolPython
ElasticSearch pyes
Sphinx
sphinxapi
Xapian
xappy
Whoosh
(written in Python, includes an API)
Things to Do
8.1. Assign the string 'This is a test of the emergency text system' to the vari‐
able test1, and write test1 to a file called test.txt.
8.2. Open the file test.txt and read its contents into the string test2. Are test1 and
test2 the same?
8.3. Save these text lines to a file called books.csv. Notice that if the fields are separated
by commas, you need to surround a field with quotes if it contains a comma.
author,book
J R R Tolkien,The Hobbit
Lynne Truss,"Eats, Shoots & Leaves"
8.4. Use the csv module and its DictReader method to read books.csv to the variable
books. Print the values in books. Did DictReader handle the quotes and commas in the
second book’s title?
8.5. Create a CSV file called books.csv by using these lines:
title,author,year
The Weirdstone of Brisingamen,Alan Garner,1960
Perdido Street Station,China Miéville,2000
Thud!,Terry Pratchett,2005
The Spellman Files,Lisa Lutz,2007
Small Gods,Terry Pratchett,1992
8.6. Use the sqlite3 module to create a SQLite database called books.db, and a table
called books with these fields: title (text), author (text), and year (integer).
8.7. Read books.csv and insert its data into the book table.
8.8. Select and print the title column from the book table in alphabetical order.
8.9. Select and print all columns from the book table in order of publication.
8.10. Use the sqlalchemy module to connect to the sqlite3 database books.db that you
just made in exercise 8.6. As in 8.8, select and print the title column from the book
table in alphabetical order.
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8.11. Install the Redis server and the Python redis library (pip install redis) on
your computer. Create a Redis hash called test with the fields count (1) and name
('Fester Bestertester'). Print all the fields for test.
8.12. Increment the count field of test and print it.
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CHAPTER 9
The Web, Untangled
Straddling the French-Swiss border is CERN—a particle physics research institute that
would seem a good lair for a Bond villain. Luckily, its quest is not world domination
but to understand how the universe works. This has always led CERN to generate pro‐
digious amounts of data, challenging physicists and computer scientists just to keep up.
In 1989, the English scientist Tim Berners-Lee first circulated a proposal to help dis‐
seminate information within CERN and the research community. He called it the World
Wide Web, and soon distilled its design into three simple ideas:
HTTP (Hypertext Transfer Protocol)
A specification for web clients and servers to interchange requests and responses
HTML (Hypertext Markup Language)
A presentation format for results
URL (Uniform Resource Locator)
A way to uniquely represent a server and a resource on that server
In its simplest usage, a web client (I think Berners-Lee was the first to use the term
browser) connected to a web server with HTTP, requested a URL, and received HTML.
He wrote the first web browser and server on a NeXT computer, invented by a shortlived company Steve Jobs founded during his hiatus from Apple Computer. Web
awareness really expanded in 1993, when a group of students at the University of Illinois
released the Mosaic web browser (for Windows, the Macintosh, and Unix) and NCSA
httpd server. When I downloaded these and started building sites, I had no idea that
the Web and the Internet would soon become part of everyday life. At the time, the
Internet was still officially noncommercial; there were about 500 known web servers in
the world. By the end of 1994, the number of web servers had grown to 10,000. The
Internet was opened to commercial use, and the authors of Mosaic founded Netscape
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to write commercial web software. Netscape went public as part of the Internet frenzy
that was occurring at the time, and the Web’s explosive growth has never stopped.
Almost every computer language has been used to write web clients and web servers.
The dynamic languages Perl, PHP, and Ruby have been especially popular. In this chap‐
ter, I’ll show why Python is a particularly good language for web work at every level:
• Clients, to access remote sites
• Servers, to provide data for websites and web APIs
• Web APIs and services, to interchange data in other ways than viewable web pages
And while we’re at it, we’ll build an actual interactive website in the exercises at the end
of this chapter.
Web Clients
The low-level network plumbing of the Internet is called Transmission Control Proto‐
col/Internet Protocol, or more commonly, simply TCP/IP (“TCP/IP” on page 280 goes
into more detail about this). It moves bytes among computers, but doesn’t care about
what those bytes mean. That’s the job of higher-level protocols—syntax definitions for
specific purposes. HTTP is the standard protocol for web data interchange.
The Web is a client-server system. The client makes a request to a server: it opens a TCP/
IP connection, sends the URL and other information via HTTP, and receives a response.
The format of the response is also defined by HTTP. It includes the status of the request,
and (if the request succeeded) the response’s data and format.
The most well-known web client is a web browser. It can make HTTP requests in a
number of ways. You might initiate a request manually by typing a URL into the location
bar or clicking on a link in a web page. Very often, the data returned is used to display
a website—HTML documents, JavaScript files, CSS files, and images—but it can be any
type of data, not just that intended for display.
An important aspect of HTTP is that it’s stateless. Each HTTP connection that you make
is independent of all the others. This simplifies basic web operations but complicates
others. Here are just a few samples of the challenges:
Caching
Remote content that doesn’t change should be saved by the web client and used to
avoid downloading from the server again.
Sessions
A shopping website should remember the contents of your shopping cart.
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Authentication
Sites that require your username and password should remember them while you’re
logged in.
Solutions to statelessness include cookies, in which the server sends the client enough
specific information to be able to identify it uniquely when the client sends the cookie
back.
Test with telnet
HTTP is a text-based protocol, so you can actually type it yourself for web testing. The
ancient telnet program lets you connect to any server and port and type commands.
Let’s ask everyone’s favorite test site, Google, some basic information about its home
page. Type this:
$ telnet www.google.com 80
If there is a web server on port 80 at google.com (I think that’s a safe bet), telnet will
print some reassuring information and then display a final blank line that’s your cue to
type something else:
Trying 74.125.225.177...
Connected to www.google.com.
Escape character is '^]'.
Now, type an actual HTTP command for telnet to send to the Google web server. The
most common HTTP command (the one your browser uses when you type a URL in
its location bar) is GET. This retrieves the contents of the specified resource, such as an
HTML file, and returns it to the client. For our first test, we’ll use the HTTP command
HEAD, which just retrieves some basic information about the resource:
HEAD / HTTP/1.1
That HEAD / sends the HTTP HEAD verb (command) to get information about the home
page (/). Add an extra carriage return to send a blank line so the remote server knows
you’re all done and want a response. You’ll receive a response such as this (we trimmed
some of the long lines using … so they wouldn’t stick out of the book):
HTTP/1.1 200 OK
Date: Sat, 26 Oct 2013 17:05:17 GMT
Expires: -1
Cache-Control: private, max-age=0
Content-Type: text/html; charset=ISO-8859-1
Set-Cookie: PREF=ID=962a70e9eb3db9d9:FF=0:TM=1382807117:LM=1382807117:S=y...
expires=Mon, 26-Oct-2015 17:05:17 GMT;
path=/;
domain=.google.com
Set-Cookie: NID=67=hTvtVC7dZJmZzGktimbwVbNZxPQnaDijCz716B1L56GM9qvsqqeIGb...
expires=Sun, 27-Apr-2014 17:05:17 GMT
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path=/;
domain=.google.com;
HttpOnly
P3P: CP="This is not a P3P policy! See http://www.google.com/support/accounts...
Server: gws
X-XSS-Protection: 1; mode=block
X-Frame-Options: SAMEORIGIN
Alternate-Protocol: 80:quic
Transfer-Encoding: chunked
These are HTTP response headers and their values. Some, like Date and ContentType, are required. Others, such as Set-Cookie, are used to track your activity across
multiple visits (we’ll talk about state management a little later in this chapter). When
you make an HTTP HEAD request, you get back only headers. If you had used the HTTP
GET or POST commands, you would also receive data from the home page (a mixture of
HTML, CSS, JavaScript, and whatever else Google decided to throw into its home page).
I don’t want to leave you stranded in telnet. To close telnet, type the following:
q
Python’s Standard Web Libraries
In Python 2, web client and server modules were a bit scattered. One of the Python 3
goals was to bundle these modules into two packages (remember from Chapter 5 that a
package is just a directory containing module files):
• http manages all the client-server HTTP details:
— client does the client-side stuff
— server helps you write Python web servers
— cookies and cookiejar manage cookies, which save data between site visits
• urllib runs on top of http:
— request handles the client request
— response handles the server response
— parse cracks the parts of a URL
Let’s use the standard library to get something from a website. The URL in the following
example returns a random text quote, similar to a fortune cookie:
>>> import urllib.request as ur
>>> url = 'http://www.iheartquotes.com/api/v1/random'
>>> conn = ur.urlopen(url)
>>> print(conn)
<http.client.HTTPResponse object at 0x1006fad50>
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In the official documentation, we find that conn is an HTTPResponse object with a num‐
ber of methods, and that its read() method will give us data from the web page:
>>> data = conn.read()
>>> print(data)
b'You will be surprised by a loud noise.\r\n\n[codehappy]
http://iheartquotes.com/fortune/show/20447\n'
This little chunk of Python opened a TCP/IP connection to the remote quote server,
made an HTTP request, and received an HTTP response. The response contained more
than just the page data (the fortune). One of the most important parts of the response
is the HTTP status code:
>>> print(conn.status)
200
A 200 means that everything was peachy. There are dozens of HTTP status codes, grou‐
ped into five ranges by their first (hundreds) digit:
1xx (information)
The server received the request but has some extra information for the client.
2xx (success)
It worked; every success code other than 200 conveys extra details.
3xx (redirection)
The resource moved, so the response returns the new URL to the client.
4xx (client error)
Some problem from the client side, such as the famous 404 (not found). 418 (I’m a
teapot) was an April Fool’s joke.
5xx (server error)
500 is the generic whoops; you might see a 502 (bad gateway) if there’s some dis‐
connect between a web server and a backend application server.
Web servers can send data back to you in any format they like. It’s usually HTML (and
usually some CSS and JavaScript), but in our fortune cookie example it’s plain text. The
data format is specified by the HTTP response header value with the name ContentType, which we also saw in our google.com example:
>>> print(conn.getheader('Content-Type'))
text/plain
That text/plain string is a MIME type, and it means plain old text. The MIME type
for HTML, which the google.com example sent, is text/html. I’ll show you more MIME
types in this chapter.
Out of sheer curiosity, what other HTTP headers were sent back to us?
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>>> for key, value in conn.getheaders():
...
print(key, value)
...
Server nginx
Date Sat, 24 Aug 2013 22:48:39 GMT
Content-Type text/plain
Transfer-Encoding chunked
Connection close
Etag "8477e32e6d053fcfdd6750f0c9c306d6"
X-Ua-Compatible IE=Edge,chrome=1
X-Runtime 0.076496
Cache-Control max-age=0, private, must-revalidate
Remember that telnet example a little earlier? Now, our Python library is parsing all
those HTTP response headers and providing them in a dictionary. Date and Server
seem straightforward; some of the others, less so. It’s helpful to know that HTTP has a
set of standard headers such as Content-Type, and many optional ones.
Beyond the Standard Library: Requests
At the beginning of Chapter 1, there’s a program that accesses a YouTube API by using
the standard libraries urllib.request and json. Following that example is a version
that uses the third-party module requests. The requests version is shorter and easier
to understand.
For most purposes, I think web client development with requests is easier. You can
browse the documentation (which is pretty good) for full details. I’ll show the basics of
requests in this section and use it throughout this book for web client tasks.
First, install the requests library into your Python environment. From a terminal win‐
dow (Windows users, type cmd to make one), type the following command to make the
Python package installer pip download the latest version of the requests package and
install it:
$ pip install requests
If you have trouble, read Appendix D for details on how to install and use pip.
Let’s redo our previous call to the quotes service with requests:
>>> import requests
>>> url = 'http://www.iheartquotes.com/api/v1/random'
>>> resp = requests.get(url)
>>> resp
<Response [200]>
>>> print(resp.text)
I know that there are people who do not love their fellow man, and I hate
people like that!
-- Tom Lehrer, Satirist and Professor
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[codehappy] http://iheartquotes.com/fortune/show/21465
It isn’t that different from using urllib.request.urlopen, but I think it feels a little less
wordy.
Web Servers
Web developers have found Python to be an excellent language for writing web servers
and server-side programs. This has led to such a variety of Python-based web frame‐
works that it can be hard to navigate among them and make choices—not to mention
deciding what deserves to go into a book.
A web framework provides features with which you can build websites, so it does more
than a simple web (HTTP) server. You’ll see features such as routing (URL to server
function), templates (HTM with dynamic inclusions), debugging, and more.
I’m not going to cover all of the frameworks here—just those that I’ve found to be
relatively simple to use and suitable for real websites. I’ll also show how to run the
dynamic parts of a website with Python and other parts with a traditional web server.
The Simplest Python Web Server
You can run a simple web server by typing just one line of Python:
$ python -m http.server
This implements a bare-bones Python HTTP server. If there are no problems, this will
print an initial status message:
Serving HTTP on 0.0.0.0 port 8000 ...
That 0.0.0.0 means any TCP address, so web clients can access it no matter what address
the server has. There’s more low-level details on TCP and other network plumbing for
you to read about in Chapter 11.
You can now request files, with paths relative to your current directory, and they will be
returned. If you type http://localhost:8000 in your web browser, you should see a
directory listing there, and the server will print access log lines such as this:
127.0.0.1 - - [20/Feb/2013 22:02:37] "GET / HTTP/1.1" 200 -
localhost and 127.0.0.1 are TCP synonyms for your local computer, so this works
regardless of whether you’re connected to the Internet. You can interpret this line as
follows:
• 127.0.0.1 is the client’s IP address
• The first "-" is the remote username, if found
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• The second "-" is the login username, if required
• [20/Feb/2013 22:02:37] is the access date and time
• "GET / HTTP/1.1" is the command sent to the web server:
— The HTTP method (GET)
— The resource requested (/, the top)
— The HTTP version (HTTP/1.1)
• The final 200 is the HTTP status code returned by the web server
Click any file. If your browser can recognize the format (HTML, PNG, GIF, JPEG, and
so on) it should display it, and the server will log the request. For instance, if you have
the file oreilly.png in your current directory, a request for http://localhost:8000/oreil‐
ly.png should return the image of the unsettling fellow in Figure 7-1, and the log should
show something such as this:
127.0.0.1 - - [20/Feb/2013 22:03:48] "GET /oreilly.png HTTP/1.1" 200 -
If you have other files in the same directory on your computer, they should show up in
a listing on your display, and you can click any one to download it. If your browser is
configured to display that file’s format, you’ll see the results on your screen; otherwise,
your browser will ask you if you want to download and save the file.
The default port number used is 8000, but you can specify another:
$ python -m http.server 9999
You should see this:
Serving HTTP on 0.0.0.0 port 9999 ...
This Python-only server is best suited for quick tests. You can stop it by killing its
process; in most terminals, press Ctrl+C.
You should not use this basic server for a busy production website. Traditional web
servers such as Apache and Nginx are much faster for serving static files. In addition,
this simple server has no way to handle dynamic content, which more extensive servers
can do by accepting parameters.
Web Server Gateway Interface
All too soon, the allure of serving simple files wears off, and we want a web server that
can also run programs dynamically. In the early days of the Web, the Common Gateway
Interface (CGI) was designed for clients to make web servers run external programs and
return the results. CGI also handled getting input arguments from the client through
the server to the external programs. However, the programs were started anew for each
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client access. This could not scale well, because even small programs have appreciable
startup time.
To avoid this startup delay, people began merging the language interpreter into the web
server. Apache ran PHP within its mod_php module, Perl in mod_perl, and Python in
mod_python. Then, code in these dynamic languages could be executed within the longrunning Apache process itself rather than in external programs.
An alternative method was to run the dynamic language within a separate long-running
program and have it communicate with the web server. FastCGI and SCGI
are examples.
Python web development made a leap with the definition of Web Server Gateway In‐
terface (WSGI), a universal API between Python web applications and web servers. All
of the Python web frameworks and web servers in the rest of this chapter use WSGI.
You don’t normally need to know how WSGI works (there really isn’t much to it), but
it helps to know what some of the parts under the hood are called.
Frameworks
Web servers handle the HTTP and WSGI details, but you use web frameworks to actually
write the Python code that powers the site. So, we’ll talk about frameworks for a while
and then get back to alternative ways of actually serving sites that use them.
If you want to write a website in Python, there are many Python web frameworks (some
might say too many). A web framework handles, at a minimum, client requests and
server responses. It might provide some or all of these features:
Routes
Interpret URLs and find the corresponding server files or Python server code
Templates
Merge server-side data into pages of HTML
Authentication and authorization
Handle usernames, passwords, permissions
Sessions
Maintain transient data storage during a user’s visit to the website
In the coming sections, we’ll write example code for two frameworks (bottle and
flask). Then, we’ll talk about alternatives, especially for database-backed websites. You
can find a Python framework to power any site that you can think of.
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Bottle
Bottle consists of a single Python file, so it’s very easy to try out, and it’s easy to deploy
later. Bottle isn’t part of standard Python, so to install it, type the following command:
$ pip install bottle
Here’s code that will run a test web server and return a line of text when your browser
accesses the URL http://localhost:9999/. Save it as bottle1.py:
from bottle import route, run
@route('/')
def home():
return "It isn't fancy, but it's my home page"
run(host='localhost', port=9999)
Bottle uses the route decorator to associate a URL with the following function; in this
case, / (the home page) is handled by the home() function. Make Python run this server
script by typing this:
$ python bottle1.py
You should see this on your browser when you access http://localhost:9999:
It isn't fancy, but it's my home page
The run() function executes bottle’s built-in Python test web server. You don’t need
to use this for bottle programs, but it’s useful for initial development and testing.
Now, instead of creating text for the home page in code, let’s make a separate HTML
file called index.html that contains this line of text:
My <b>new</b> and <i>improved</i> home page!!!
Make bottle return the contents of this file when the home page is requested. Save this
script as bottle2.py:
from bottle import route, run, static_file
@route('/')
def main():
return static_file('index.html', root='.')
run(host='localhost', port=9999)
In the call to static_file(), we want the file index.html in the directory indicated by
root (in this case, '.', the current directory). If your previous server example code was
still running, stop it. Now, run the new server:
$ python bottle2.py
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When you ask your browser to get http:/localhost:9999/, you should see:
My new and improved home page!!!
Let’s add one last example that shows how to pass arguments to a URL and use them.
Of course, this will be bottle3.py:
from bottle import route, run, static_file
@route('/')
def home():
return static_file('index.html', root='.')
@route('/echo/<thing>')
def echo(thing):
return "Say hello to my little friend: %s!" % thing
run(host='localhost', port=9999)
We have a new function called echo() and want to pass it a string argument in a URL.
That’s what the line @route('/echo/<thing>') in the preceding example does. That
<thing> in the route means that whatever was in the URL after /echo/ is assigned to
the string argument thing, which is then passed to the echo function. To see what
happens, stop the old server if it’s still running, and start it with the new code:
$ python bottle3.py
Then, access http://localhost:9999/echo/Mothra in your web browser. You should see the
following:
Say hello to my little friend: Mothra!
Now, leave bottle3.py running for a minute so that we can try something else. You’ve
been verifying that these examples work by typing URLs into your browser and looking
at the displayed pages. You can also use client libraries such as requests to do your work
for you. Save this as bottle_test.py:
import requests
resp = requests.get('http://localhost:9999/echo/Mothra')
if resp.status_code == 200 and \
resp.text == 'Say hello to my little friend: Mothra!':
print('It worked! That almost never happens!')
else:
print('Argh, got this:', resp.text)
Great! Now, run it:
$ python bottle_test.py
You should see this in your terminal:
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It worked! That almost never happens!
This is a little example of a unit test. Chapter 8 provides more details on why tests are
good and how to write them in Python.
There’s more to bottle than I’ve shown here. In particular, you can try adding these
arguments when you call run():
• debug=True creates a debugging page if you get an HTTP error;
• reloader=True reloads the page in the browser if you change any of the Python
code.
It’s well documented at the developer site.
Flask
Bottle is a good initial web framework. If you need a few more cowbells and whistles,
try Flask. It started in 2010 as an April Fools’ joke, but enthusiastic response encouraged
the author, Armin Ronacher, to make it a real framework. He named the result Flask
as a wordplay on bottle.
Flask is about as simple to use as Bottle, but it supports many extensions that are useful
in professional web development, such as Facebook authentication and database inte‐
gration. It’s my personal favorite among Python web frameworks because it balances
ease of use with a rich feature set.
The Flask package includes the werkzeug WSGI library and the jinja2 template library.
You can install it from a terminal:
$ pip install flask
Let’s replicate the final bottle example code in flask. First, though, we need to make
a few changes:
• Flask’s default directory home for static files is static, and URLs for files there also
begin with /static. We change the folder to '.' (current directory) and the URL
prefix to '' (empty) to allow the URL / to map to the file index.html.
• In the run() function, setting debug=True also activates the automatic reloader;
bottle used separate arguments for debugging and reloading.
Save this file to flask1.py:
from flask import Flask
app = Flask(__name__, static_folder='.', static_url_path='')
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@app.route('/')
def home():
return app.send_static_file('index.html')
@app.route('/echo/<thing>')
def echo(thing):
return "Say hello to my little friend: %s" % thing
app.run(port=9999, debug=True)
Then, run the server from a terminal or window:
$ python flask1.py
Test the home page by typing this URL into your browser:
http://localhost:9999/
You should see the following (as you did for bottle):
My new and improved home page!!!
Try the /echo endpoint:
http://localhost:9999/echo/Godzilla
You should see this:
Say hello to my little friend: Godzilla
There’s another benefit to setting debug to True when calling run. If an exception occurs
in the server code, Flask returns a specially formatted page with useful details about
what went wrong, and where. Even better, you can type some commands to see the
values of variables in the server program.
Do not set debug = True in production web servers. It exposes too
much information about your server to potential intruders.
So far, the Flask example just replicates what we did with bottle. What can Flask do
that bottle can’t? Flask includes jinja2, a more extensive templating system. Here’s a
tiny example of how to use jinja2 and flask together.
Create a directory called templates, and a file within it called flask2.html:
<html>
<head>
<title>Flask2 Example</title>
</head>
<body>
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Say hello to my little friend: {{ thing }}
</body>
</html>
Next, we’ll write the server code to grab this template, fill in the value of thing that we
passed it, and render it as HTML (I’m dropping the home() function here to save space).
Save this as flask2.py:
from flask import Flask, render_template
app = Flask(__name__)
@app.route('/echo/<thing>')
def echo(thing):
return render_template('flask2.html', thing=thing)
app.run(port=9999, debug=True)
That thing = thing argument means to pass a variable named thing to the template,
with the value of the string thing.
Ensure that flask1.py isn’t still running, and start flask2.py:
$ python flask2.py
Now, type this URL:
http://localhost:9999/echo/Gamera
You should see the following:
Say hello to my little friend: Gamera
Let’s modify our template and save it in the templates directory as flask3.html:
<html>
<head>
<title>Flask3 Example</title>
</head>
<body>
Say hello to my little friend: {{ thing }}.
Alas, it just destroyed {{ place }}!
</body>
</html>
You can pass this second argument to the echo URL in many ways.
Pass an argument as part of the URL path
Using this method, you simply extend the URL itself (save this as flask3a.py):
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from flask import Flask, render_template
app = Flask(__name__)
@app.route('/echo/<thing>/<place>')
def echo(thing, place):
return render_template('flask3.html', thing=thing, place=place)
app.run(port=9999, debug=True)
As usual, stop the previous test server script if it’s still running and then try this new
one:
$ python flask3a.py
The URL would look like this:
http://localhost:9999/echo/Rodan/McKeesport
And you should see the following:
Say hello to my little friend: Rodan. Alas, it just destroyed McKeesport!
Or, you can provide the arguments as GET parameters (save this as flask3b.py):
from flask import Flask, render_template, request
app = Flask(__name__)
@app.route('/echo/')
def echo():
thing = request.args.get('thing')
place = request.args.get('place')
return render_template('flask3.html', thing=thing, place=place)
app.run(port=9999, debug=True)
Run the new server script:
$ python flask3b.py
This time, use this URL:
http://localhost:9999/echo?thing=Gorgo&place=Wilmerding
You should get back what you see here:
Say hello to my little friend: Gorgo. Alas, it just destroyed Wilmerding!
When a GET command is used for a URL, any arguments are passed in the form
&key1=val1&key2=val2&...
You can also use the dictionary ** operator to pass multiple arguments to a template
from a single dictionary (call this flask3c.py):
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from flask import Flask, render_template, request
app = Flask(__name__)
@app.route('/echo/')
def echo():
kwargs = {}
kwargs['thing'] = request.args.get('thing')
kwargs['place'] = request.args.get('place')
return render_template('flask3.html', **kwargs)
app.run(port=9999, debug=True)
That **kwargs acts like thing=thing, place=place. It saves some typing if there are
a lot of input arguments.
The jinja2 templating language does a lot more than this. If you’ve programmed in
PHP, you’ll see many similarities.
Non-Python Web Servers
So far, the web servers we’ve used have been simple: the standard library’s http.serv
er or the debugging servers in Bottle and Flask. In production, you’ll want to run Python
with a faster web server. The usual choices are the following:
• apache with the mod_wsgi module
• nginx with the uWSGI app server
Both work well; apache is probably the most popular, and nginx has a reputation for
stability and lower memory use.
Apache
The apache web server’s best WSGI module is mod_wsgi. This can run Python code
within the Apache process or in separate processes that communicate with Apache.
You should already have apache if your system is Linux or OS X. For Windows, you’ll
need to install apache.
Finally, install your preferred WSGI-based Python web framework. Let’s try bottle
here. Almost all of the work involves configuring Apache, which can be a dark art.
Create this test file and save it as /var/www/test/home.wsgi:
import bottle
application = bottle.default_app()
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@bottle.route('/')
def home():
return "apache and wsgi, sitting in a tree"
Do not call run() this time, because that starts the built-in Python web server. We need
to assign to the variable application because that’s what mod_wsgi looks for to marry
the web server and the Python code.
If apache and its mod_wsgi module are working correctly, we just need to connect them
to our Python script. We want to add one line to the file that defines the default website
for this apache server, but finding that file is a task in and of itself. It could be /etc/
apache2/httpd.conf, or /etc/apache2/sites-available/default, or the Latin name of some‐
one’s pet salamander.
Let’s assume for now that you understand apache and found that file. Add this line inside
the <VirtualHost> section that governs the default website:
WSGIScriptAlias / /var/www/test/home.wsgi
That section might then look like this:
<VirtualHost *:80>
DocumentRoot /var/www
WSGIScriptAlias / /var/www/test/home.wsgi
<Directory /var/www/test>
Order allow,deny
Allow from all
</Directory>
</VirtualHost>
Start apache, or restart it if it was running to make it use this new configuration. If you
then browse to http://localhost/, you should see:
apache and wsgi, sitting in a tree
This runs mod_wsgi in embedded mode, as part of apache itself.
You can also run it in daemon mode: as one or more processes, separate from apache.
To do this, add two new directive lines to your apache config file:
$ WSGIDaemonProcess domain-name user=user-name group=group-name threads=25
WSGIProcessGroup domain-name
In the preceding example, user-name and group-name are the operating system user
and group names, and the domain-name is the name of your Internet domain. A minimal
apache config might look like this:
<VirtualHost *:80>
DocumentRoot /var/www
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WSGIScriptAlias / /var/www/test/home.wsgi
WSGIDaemonProcess mydomain.com user=myuser group=mygroup threads=25
WSGIProcessGroup mydomain.com
<Directory /var/www/test>
Order allow,deny
Allow from all
</Directory>
</VirtualHost>
The nginx Web Server
The nginx web server does not have an embedded Python module. Instead, it commu‐
nicates by using a separate WSGI server such as uWSGI. Together they make a very fast
and configurable platform for Python web development.
You can install nginx from its website. You also need to install uWSGI. uWSGI is a large
system, with many levers and knobs to adjust. A short documentation page gives you
instructions on how to combine Flask, nginx, and uWSGI.
Other Frameworks
Websites and databases are like peanut butter and jelly—you see them together a lot.
The smaller frameworks such as bottle and flask do not include direct support for
databases, although some of their contributed add-ons do.
If you need to crank out database-backed websites, and the database design doesn’t
change very often, it might be worth the effort to try one of the larger Python web
frameworks. The current main contenders include:
django
This is the most popular, especially for large sites. It’s worth learning for many
reasons, among them the frequent requests for django experience in Python job
ads. It includes ORM code (we talked about ORMs in “The Object-Relational Map‐
per” on page 202) to create automatic web pages for the typical database CRUD
functions (create, replace, update, delete) that I discussed in “SQL” on page 194.
You don’t have to use django’s ORM if you prefer another, such as SQLAlchemy,
or direct SQL queries.
web2py
This covers much the same ground as django, with a different style.
pyramid
This grew from the earlier pylons project, and is similar to django in scope.
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turbogears
This framework supports an ORM, many databases, and multiple template lan‐
guages.
wheezy.web
This is a newer framework optimized for performance. It was faster than the others
in a recent test.
You can compare the frameworks by viewing this online table.
If you want to build a website backed by a relational database, you don’t necessarily need
one of these larger frameworks. You can use bottle, flask, and others directly with
relational database modules, or use SQLAlchemy to help gloss over the differences.
Then, you’re writing generic SQL instead of specific ORM code, and more developers
know SQL than any particular ORM’s syntax.
Also, there’s nothing written in stone demanding that your database must be a relational
one. If your data schema varies significantly—columns that differ markedly across rows
—it might be worthwhile to consider a schemaless database, such as one of the NoSQL
databases discussed in “NoSQL Data Stores” on page 204. I once worked on a website
that initially stored its data in a NoSQL database, switched to a relational one, on to
another relational one, to a different NoSQL one, and then finally back to one of the
relational ones.
Other Python Web Servers
Following are some of the independent Python-based WSGI servers that work like
apache or nginx, using multiple processes and/or threads (see “Concurrency” on page
262) to handle simultaneous requests:
• uwsgi
• cherrypy
• pylons
Here are some event-based servers, which use a single process but avoid blocking on
any single request:
• tornado
• gevent
• gunicorn
I have more to say about events in the discussion about concurrency in Chapter 11.
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Web Services and Automation
We’ve just looked at traditional web client and server applications, consuming and gen‐
erating HTML pages. Yet the Web has turned out to be a powerful way to glue applica‐
tions and data in many more formats than HTML.
The webbrowser Module
Let’s start begin a little surprise. Start a Python session in a terminal window and type
the following:
>>> import antigravity
This secretly calls the standard library’s webbrowser module and directs your browser
to an enlightening Python link.1
You can use this module directly. This program loads the main Python site’s page in
your browser:
>>> import webbrowser
>>> url = 'http://www.python.org/'
>>> webbrowser.open(url)
True
This opens it in a new window:
>>> webbrowser.open_new(url)
True
And this opens it in a new tab, if your browser supports tabs:
>>> webbrowser.open_new_tab('http://www.python.org/')
True
The webbrowser makes your browser do all the work.
Web APIs and Representational State Transfer
Often, data is only available within web pages. If you want to access it, you need to access
the pages through a web browser and read it. If the authors of the website made any
changes since the last time you visited, the location and style of the data might have
changed.
Instead of publishing web pages, you can provide data through a web application pro‐
gramming interface (API). Clients access your service by making requests to URLs and
getting back responses containing status and data. Instead of HTML pages, the data is
1. If you don’t see it for some reason, visit xkcd.
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in formats that are easier for programs to consume, such as JSON or XML (refer to
Chapter 8 for more about these formats).
Representational State Transfer (REST) was defined by Roy Fielding in his doctoral the‐
sis. Many products claim to have a REST interface or a RESTful interface. In practice,
this often only means that they have a web interface—definitions of URLs to access a
web service.
A RESTful service uses the HTTP verbs in specific ways, as is described here:
HEAD
Gets information about the resource, but not its data.
GET
As its name implies, GET retrieves the resource’s data from the server. This is the
standard method used by your browser. Any time you see a URL with a question
mark (?) followed by a bunch of arguments, that’s a GET request. GET should not be
used to create, change, or delete data.
POST
This verb updates data on the server. It’s often used by HTML forms and web APIs.
PUT
This verb creates a new resource.
DELETE
This one speaks for itself: DELETE deletes. Truth in advertising!
A RESTful client can also request one or more content types from the server by using
HTTP request headers. For example, a complex service with a REST interface might
prefer its input and output to be JSON strings.
JSON
Chapter 1 shows two Python code samples to get information on popular YouTube
videos, and Chapter 8 introduces JSON. JSON is especially well suited to web clientserver data interchange. It’s especially popular in web-based APIs, such as OpenStack.
Crawl and Scrape
Sometimes, you might want a little bit of information—a movie rating, stock price, or
product availability—but the information is available only in HTML pages, surrounded
by ads and extraneous content.
You could extract what you’re looking for manually by doing the following:
1. Type the URL into your browser.
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2. Wait for the remote page to load.
3. Look through the displayed page for the information you want.
4. Write it down somewhere.
5. Possibly repeat the process for related URLs.
However, it’s much more satisfying to automate some or all of these steps. An automated
web fetcher is called a crawler or spider (unappealing terms to arachnophobes). After
the contents have been retrieved from the remote web servers, a scraper parses it to find
the needle in the haystack.
If you need an industrial-strength combined crawler and scraper, Scrapy is worth
downloading:
$ pip install scrapy
Scrapy is a framework, not a module such as BeautifulSoup. It does more, but it’s more
complex to set up. To learn more about Scrapy, read the documentation or the online
introduction.
Scrape HTML with BeautifulSoup
If you already have the HTML data from a website and just want to extract data from
it, BeautifulSoup is a good choice. HTML parsing is harder than it sounds. This is
because much of the HTML on public web pages is technically invalid: unclosed tags,
incorrect nesting, and other complications. If you try to write your own HTML parser
by using regular expressions (discussed in Chapter 7) you’ll soon encounter these mes‐
ses.
To install BeautifulSoup, type the following command (don’t forget the final 4, or pip
will try to install an older version and probably fail):
$ pip install beautifulsoup4
Now, let’s use it to get all the links from a web page. The HTML a element represents a
link, and href is its attribute representing the link destination. In the following example,
we’ll define the function get_links() to do the grunt work, and a main program to get
one or more URLs as command-line arguments:
def get_links(url):
import requests
from bs4 import BeautifulSoup as soup
result = requests.get(url)
page = result.text
doc = soup(page)
links = [element.get('href') for element in doc.find_all('a')]
return links
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if __name__ == '__main__':
import sys
for url in sys.argv[1:]:
print('Links in', url)
for num, link in enumerate(get_links(url), start=1):
print(num, link)
print()
I saved this program as links.py and then ran this command:
$ python links.py http://boingboing.net
Here are the first few lines that it printed:
Links in http://boingboing.net/
1 http://boingboing.net/suggest.html
2 http://boingboing.net/category/feature/
3 http://boingboing.net/category/review/
4 http://boingboing.net/category/podcasts
5 http://boingboing.net/category/video/
6 http://bbs.boingboing.net/
7 javascript:void(0)
8 http://shop.boingboing.net/
9 http://boingboing.net/about
10 http://boingboing.net/contact
Things to Do
9.1. If you haven’t installed flask yet, do so now. This will also install werkzeug,
jinja2, and possibly other packages.
9.2. Build a skeleton website, using Flask’s debug/reload development web server. Ensure
that the server starts up for hostname localhost on default port 5000. If your computer
is already using port 5000 for something else, use another port number.
9.3. Add a home() function to handle requests for the home page. Set it up to return the
string It's alive!.
9.4. Create a Jinja2 template file called home.html with the following contents:
<html>
<head>
<title>It's alive!</title>
<body>
I'm of course referring to {{thing}}, which is {{height}} feet tall and {{color}}.
</body>
</html>
9.5. Modify your server’s home() function to use the home.html template. Provide it with
three GET parameters: thing, height, and color.
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CHAPTER 10
Systems
One thing a computer can do that most humans can’t is be sealed up in a cardboard box
and sit in a warehouse.
— Jack Handey
In your everyday use of a computer, you do such things as list the contents of a folder
or directory, create and remove files, and other housekeeping that’s necessary if not
particularly exciting. You can also carry out these tasks, and more, within your own
Python programs. Will this power drive you mad or cure your insomnia? We’ll see.
Python provides many system functions through a module named os (for “operating
system”), which we’ll import for all the programs in this chapter.
Files
Python, like many other languages, patterned its file operations after Unix. Some func‐
tions, such as chown() and chmod(), have the same names, but there are a few new ones.
Create with open()
“File Input/Output” on page 173 introduced you to the open() function and explains
how you can use it to open a file or create one if it doesn’t already exist. Let’s create a
text file called oops.txt:
>>> fout = open('oops.txt', 'wt')
>>> print('Oops, I created a file.', file=fout)
>>> fout.close()
With that done, let’s perform some tests with it.
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Check Existence with exists()
To verify whether the file or directory is really there or you just imagined it, you can
provide exists(), with a relative or absolute pathname, as demonstrated here:
>>> import os
>>> os.path.exists('oops.txt')
True
>>> os.path.exists('./oops.txt')
True
>>> os.path.exists('waffles')
False
>>> os.path.exists('.')
True
>>> os.path.exists('..')
True
Check Type with isfile()
The functions in this section check whether a name refers to a file, directory, or symbolic
link (see the examples that follow for a discussion of links).
The first function we’ll look at, isfile, asks a simple question: is it a plain old lawabiding file?
>>> name = 'oops.txt'
>>> os.path.isfile(name)
True
Here’s how you determine a directory:
>>> os.path.isdir(name)
False
A single dot (.) is shorthand for the current directory, and two dots (..) stands for the
parent directory. These always exist, so a statement such as the following will always
report True:
>>> os.path.isdir('.')
True
The os module contains many functions dealing with pathnames (fully qualified file‐
names, starting with / and including all parents). One such function, isabs(), deter‐
mines whether its argument is an absolute pathname. The argument doesn’t need to be
the name of a real file:
>>> os.path.isabs(name)
False
>>> os.path.isabs('/big/fake/name')
True
>>> os.path.isabs('big/fake/name/without/a/leading/slash')
False
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Copy with copy()
The copy() function comes from another module, shutil. This example copies the file
oops.txt to the file ohno.txt:
>>> import shutil
>>> shutil.copy('oops.txt', 'ohno.txt')
The shutil.move() function copies a file and then removes the original.
Change Name with rename()
This function does exactly what it says. In the example here, it renames ohno.txt to
ohwell.txt:
>>> import os
>>> os.rename('ohno.txt', 'ohwell.txt')
Link with link() or symlink()
In Unix, a file exists in one place, but it can have multiple names, called links. In lowlevel hard links, it’s not easy to find all the names for a given file. A symbolic link is an
alternative method that stores the new name as its own file, making it possible for you
to get both the original and new names at once. The link() call creates a hard link, and
symlink() makes a symbolic link. The islink() function checks whether the file is a
symbolic link.
Here’s how to make a hard link to the existing file oops.txt from the new file yikes.txt:
>>> os.link('oops.txt', 'yikes.txt')
>>> os.path.isfile('yikes.txt')
True
To create a symbolic link to the existing file oops.txt from the new file jeepers.txt, use
the following:
>>> os.path.islink('yikes.txt')
False
>>> os.symlink('oops.txt', 'jeepers.txt')
>>> os.path.islink('jeepers.txt')
True
Change Permissions with chmod()
On a Unix system, chmod() changes file permissions.There are read, write, and execute
permissions for the user (that’s usually you, if you created the file), the main group that
the user is in, and the rest of the world. The command takes an intensely compressed
octal (base 8) value that combines user, group, and other permissions. For instance, to
make oops.txt only readable by its owner, type the following:
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>>> os.chmod('oops.txt', 0o400)
If you don’t want to deal with cryptic octal values and would rather deal with (slightly)
obscure cryptic symbols, you can import some constants from the stat module and
use a statement such as the following:
>>> import stat
>>> os.chmod('oops.txt', stat.S_IRUSR)
Change Ownership with chown()
This function is also Unix/Linux/Mac–specific. You can change the owner and/or group
ownership of a file by specifying the numeric user ID (uid) and group ID (gid):
>>> uid = 5
>>> gid = 22
>>> os.chown('oops', uid, gid)
Get a Pathname with abspath()
This function expands a relative name to an absolute one. If your current directory is /
usr/gaberlunzie and the file oops.txt is there, also, you can type the following:
>>> os.path.abspath('oops.txt')
'/usr/gaberlunzie/oops.txt'
Get a symlink Pathname with realpath()
In one of the earlier sections, we made a symbolic link to oops.txt from the new file
jeepers.txt. In circumstances such as this, you can get the name of oops.txt from jeep‐
ers.txt by using the realpath() function, as shown here:
>>> os.path.realpath('jeepers.txt')
'/usr/gaberlunzie/oops.txt'
Delete a File with remove()
In this snippet, we use the remove() function and say farewell to oops.txt:
>>> os.remove('oops.txt')
>>> os.path.exists('oops.txt')
False
Directories
In most operating systems, files exist in a hierarchy of directories (more often called
folders these days). The container of all of these files and directories is a file system
(sometimes called a volume). The standard os module deals with operating specifics
such as these and provides the following functions with which you can manipulate them.
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Create with mkdir()
This example shows how to create a directory called poems to store that precious verse:
>>> os.mkdir('poems')
>>> os.path.exists('poems')
True
Delete with rmdir()
Upon second thought, you decide you don’t need that directory after all. Here’s how to
delete it:
>>> os.rmdir('poems')
>>> os.path.exists('poems')
False
List Contents with listdir()
Okay, take two; let’s make poems again, with some contents:
>>> os.mkdir('poems')
Now, get a list of its contents (none so far):
>>> os.listdir('poems')
[]
Next, make a subdirectory:
>>> os.mkdir('poems/mcintyre')
>>> os.listdir('poems')
['mcintyre']
Create a file in this subdirectory (don’t type all these lines unless you really feel poetic;
just make sure you begin and end with matching quotes, either single or tripled):
>>>
>>>
...
...
...
...
...
...
...
...
...
...
344
>>>
fout = open('poems/mcintyre/the_good_man', 'wt')
fout.write('''Cheerful and happy was his mood,
He to the poor was kind and good,
And he oft' times did find them food,
Also supplies of coal and wood,
He never spake a word was rude,
And cheer'd those did o'er sorrows brood,
He passed away not understood,
Because no poet in his lays
Had penned a sonnet in his praise,
'Tis sad, but such is world's ways.
''')
fout.close()
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Finally, let’s see what we have. It had better be there:
>>> os.listdir('poems/mcintyre')
['the_good_man']
Change Current Directory with chdir()
With this function, you can go from one directory to another. Let’s leave the current
directory and spend a little time in poems:
>>> import os
>>> os.chdir('poems')
>>> os.listdir('.')
['mcintyre']
List Matching Files with glob()
The glob() function matches file or directory names by using Unix shell rules rather
than the more complete regular expression syntax. Here are those rules:
• * matches everything (re would expect .*)
• ? matches a single character
• [abc] matches character a, b, or c
• [!abc] matches any character except a, b, or c
Try getting all files or directories that begin with m:
>>> import glob
>>> glob.glob('m*')
['mcintyre']
How about any two-letter files or directories?
>>> glob.glob('??')
[]
I’m thinking of an eight-letter word that begins with m and ends with e:
>>> glob.glob('m??????e')
['mcintyre']
What about anything that begins with a k, l, or m, and ends with e?
>>> glob.glob('[klm]*e')
['mcintyre']
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Programs and Processes
When you run an individual program, your operating system creates a single process. It
uses system resources (CPU, memory, disk space) and data structures in the operating
system’s kernel (file and network connections, usage statistics, and so on). A process is
isolated from other processes—it can’t see what other processes are doing or interfere
with them.
The operating system keeps track of all the running processes, giving each a little time
to run and then switching to another, with the twin goals of spreading the work around
fairly and being responsive to the user. You can see the state of your processes with
graphical interfaces such as the Mac’s Activity Monitor (OS X), or Task Manager on
Windows-based computers.
You can also access process data from your own programs. The standard library’s os
module provides a common way of accessing some system information. For instance,
the following functions get the process ID and the current working directory of the run‐
ning Python interpreter:
>>> import os
>>> os.getpid()
76051
>>> os.getcwd()
'/Users/williamlubanovic'
And these get my user ID and group ID:
>>> os.getuid()
501
>>> os.getgid()
20
Create a Process with subprocess
All of the programs that you’ve seen here so far have been individual processes. You can
start and stop other existing programs from Python by using the standard library’s
subprocess module. If you just want to run another program in a shell and grab what‐
ever output it created (both standard output and standard error output), use the
getoutput() function. Here, we’ll get the output of the Unix date program:
>>> import subprocess
>>> ret = subprocess.getoutput('date')
>>> ret
'Sun Mar 30 22:54:37 CDT 2014'
You won’t get anything back until the process ends. If you need to call something that
might take a lot of time, see the discussion on concurrency in “Concurrency” on page
262. Because the argument to getoutput() is a string representing a complete shell com‐
mand, you can include arguments, pipes, < and > I/O redirection, and so on:
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>>> ret = subprocess.getoutput('date -u')
>>> ret
'Mon Mar 31 03:55:01 UTC 2014'
Piping that output string to the wc command counts one line, six “words,” and 29 char‐
acters:
>>> ret = subprocess.getoutput('date -u | wc')
>>> ret
'
1
6
29'
A variant method called check_output() takes a list of the command and arguments.
By default it only returns standard output as type bytes rather than a string and does
not use the shell:
>>> ret = subprocess.check_output(['date', '-u'])
>>> ret
b'Mon Mar 31 04:01:50 UTC 2014\n'
To show the exit status of the other program, getstatusoutput() returns a tuple with
the status code and output:
>>> ret = subprocess.getstatusoutput('date')
>>> ret
(0, 'Sat Jan 18 21:36:23 CST 2014')
If you don’t want to capture the output but might want to know its exit status, use call():
>>> ret = subprocess.call('date')
Sat Jan 18 21:33:11 CST 2014
>>> ret
0
(In Unix-like systems, 0 is usually the exit status for success.)
That date and time was printed to output but not captured within our program. So, we
saved the return code as ret.
You can run programs with arguments in two ways. The first is to specify them in a
single string. Our sample command is date -u, which prints the current date and time
in UTC (you’ll read more about UTC in a few pages):
>>> ret = subprocess.call('date -u', shell=True)
Tue Jan 21 04:40:04 UTC 2014
You need that shell=True to recognize the command line date -u, splitting it into
separate strings and possibly expanding any wildcard characters such as * (we didn’t
use any in this example).
The second method makes a list of the arguments, so it doesn’t need to call the shell:
>>> ret = subprocess.call(['date', '-u'])
Tue Jan 21 04:41:59 UTC 2014
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Create a Process with multiprocessing
You can run a Python function as a separate process or even run multiple independent
processes in a single program with the multiprocessing module. Here’s a short example
that does nothing useful; save it as mp.py and then run it by typing python mp.py:
import multiprocessing
import os
def do_this(what):
whoami(what)
def whoami(what):
print("Process %s says: %s" % (os.getpid(), what))
if __name__ == "__main__":
whoami("I'm the main program")
for n in range(4):
p = multiprocessing.Process(target=do_this,
args=("I'm function %s" % n,))
p.start()
When I run this, my output looks like this:
Process
Process
Process
Process
Process
6224
6225
6226
6227
6228
says:
says:
says:
says:
says:
I'm
I'm
I'm
I'm
I'm
the main
function
function
function
function
program
0
1
2
3
The Process() function spawned a new process and ran the do_this() function in it.
Because we did this in a loop that had four passes, we generated four new processes that
executed do_this() and then exited.
The multiprocessing module has more bells and whistles than a clown on a calliope.
It’s really intended for those times when you need to farm out some task to multiple
processes to save overall time; for example, downloading web pages for scraping, re‐
sizing images, and so on. It includes ways to queue tasks, enable intercommunication
among processes, and wait for all the processes to finish. “Concurrency” on page 262
delves into some of these details.
Kill a Process with terminate()
If you created one or more processes and want to terminate one for some reason (per‐
haps it’s stuck in a loop, or maybe you’re bored, or you want to be an evil overlord), use
terminate(). In the example that follows, our process would count to a million, sleeping
at each step for a second, and printing an irritating message. However, our main program
runs out of patience in five seconds and nukes it from orbit:
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import multiprocessing
import time
import os
def whoami(name):
print("I'm %s, in process %s" % (name, os.getpid()))
def loopy(name):
whoami(name)
start = 1
stop = 1000000
for num in range(start, stop):
print("\tNumber %s of %s. Honk!" % (num, stop))
time.sleep(1)
if __name__ == "__main__":
whoami("main")
p = multiprocessing.Process(target=loopy, args=("loopy",))
p.start()
time.sleep(5)
p.terminate()
When I run this program, I get the following:
I'm main, in process 97080
I'm loopy, in process 97081
Number 1 of 1000000. Honk!
Number 2 of 1000000. Honk!
Number 3 of 1000000. Honk!
Number 4 of 1000000. Honk!
Number 5 of 1000000. Honk!
Calendars and Clocks
Programmers devote a surprising amount of effort to dates and times. Let’s talk about
some of the problems they encounter, and then get to some best practices and tricks to
make the situation a little less messy.
Dates can be represented in many ways—too many ways, actually. Even in English with
the Roman calendar, you’ll see many variants of a simple date:
• July 29 1984
• 29 Jul 1984
• 29/7/1984
• 7/29/1984
Among other problems, date representations can be ambiguous. In the previous ex‐
amples, it’s easy to determine that 7 stands for the month and 29 is the day of the month,
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largely because months don’t go to 29. But how about 1/6/2012? Is that referring to
January 6 or June 1?
The month name varies by language within the Roman calendar. Even the year and
month can have a different definition in other cultures.
Leap years are another wrinkle. You probably know that every four years is a leap year
(and the summer Olympics and the American presidential election). Did you also know
that every 100 years is not a leap year, but that every 400 years is? Here’s code to test
various years for leapiness:
>>> import calendar
>>> calendar.isleap(1900)
False
>>> calendar.isleap(1996)
True
>>> calendar.isleap(1999)
False
>>> calendar.isleap(2000)
True
>>> calendar.isleap(2002)
False
>>> calendar.isleap(2004)
True
Times have their own sources of grief, especially because of time zones and daylight
savings time. If you look at a time zone map, the zones follow political and historic
boundaries rather than every 15 degrees (360 degrees / 24) of longitude. And countries
start and end daylight saving times on different days of the year. In fact, countries in the
southern hemisphere advance their clocks when the northern hemisphere is winding
them back, and vice versa. (If you think about it a bit, you will see why.)
Python’s standard library has many date and time modules: datetime, time, calendar,
dateutil, and others. There’s some overlap, and it’s a bit confusing.
The datetime Module
Let’s begin by investigating the standard datetime module. It defines four main objects,
each with many methods:
• date for years, months, and days
• time for hours, minutes, seconds, and fractions
• datetime for dates and times together
• timedelta for date and/or time intervals
You can make a date object by specifying a year, month, and day. Those values are then
available as attributes:
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>>> from datetime import date
>>> halloween = date(2014, 10, 31)
>>> halloween
datetime.date(2014, 10, 31)
>>> halloween.day
31
>>> halloween.month
10
>>> halloween.year
2014
You can print a date with its isoformat() method:
>>> halloween.isoformat()
'2014-10-31'
The iso refers to ISO 8601, an international standard for representing dates and times.
It goes from most general (year) to most specific (day). It also sorts correctly: by year,
then month, then day. I usually pick this format for date representation in programs,
and for filenames that save data by date. The next section describes the more complex
strptime() and strftime() methods for parsing and formatting dates.
This example uses the today() method to generate today’s date:
>>> from datetime import date
>>> now = date.today()
>>> now
datetime.date(2014, 2, 2)
This one makes use of a timedelta object to add some time interval to a date:
>>> from datetime import timedelta
>>> one_day = timedelta(days=1)
>>> tomorrow = now + one_day
>>> tomorrow
datetime.date(2014, 2, 3)
>>> now + 17*one_day
datetime.date(2014, 2, 19)
>>> yesterday = now - one_day
>>> yesterday
datetime.date(2014, 2, 1)
The range of date is from date.min (year=1, month=1, day=1) to date.max (year=9999,
month=12, day=31). As a result, you can’t use it for historic or astronomical calculations.
The datetime module’s time object is used to represent a time of day:
>>> from datetime import time
>>> noon = time(12, 0, 0)
>>> noon
datetime.time(12, 0)
>>> noon.hour
12
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>>> noon.minute
0
>>> noon.second
0
>>> noon.microsecond
0
The arguments go from the largest time unit (hours) to the smallest (microseconds). If
you don’t provide all the arguments, time assumes all the rest are zero. By the way, just
because you can store and retrieve microseconds doesn’t mean you can retrieve time
from your computer to the exact microsecond. The accuracy of subsecond measure‐
ments depends on many factors in the hardware and operating system.
The datetime object includes both the date and time of day. You can create one directly,
such as the one that follows, which is for January 2, 2014, at 3:04 A.M., plus 5 seconds
and 6 microseconds:
>>> from datetime import datetime
>>> some_day = datetime(2014, 1, 2, 3, 4, 5, 6)
>>> some_day
datetime.datetime(2014, 1, 2, 3, 4, 5, 6)
The datetime object also has an isoformat() method:
>>> some_day.isoformat()
'2014-01-02T03:04:05.000006'
That middle T separates the date and time parts.
datetime has a now() method with which you can get the current date and time:
>>> from datetime import datetime
>>> now = datetime.now()
>>> now
datetime.datetime(2014, 2, 2, 23, 15, 34, 694988)
14
>>> now.month
2
>>> now.day
2
>>> now.hour
23
>>> now.minute
15
>>> now.second
34
>>> now.microsecond
694988
You can merge a date object and a time object into a datetime object by using
combine():
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>>> from datetime import datetime, time, date
>>> noon = time(12)
>>> this_day = date.today()
>>> noon_today = datetime.combine(this_day, noon)
>>> noon_today
datetime.datetime(2014, 2, 2, 12, 0)
You can yank the date and time from a datetime by using the date() and time()
methods:
>>> noon_today.date()
datetime.date(2014, 2, 2)
>>> noon_today.time()
datetime.time(12, 0)
Using the time Module
It is confusing that Python has a datetime module with a time object, and a separate
time module. Furthermore, the time module has a function called—wait for it—time().
One way to represent an absolute time is to count the number of seconds since some
starting point. Unix time uses the number of seconds since midnight on January 1,
1970.1 This value is often called the epoch, and it is often the simplest way to exchange
dates and times among systems.
The time module’s time() function returns the current time as an epoch value:
>>> import time
>>> now = time.time()
>>> now
1391488263.664645
If you do the math, you’ll see that it has been over one billion seconds since New Year’s,
1970. Where did the time go?
You can convert an epoch value to a string by using ctime():
>>> time.ctime(now)
'Mon Feb 3 22:31:03 2014'
In the next section, you’ll see how to produce more attractive formats for dates and
times.
Epoch values are a useful least-common denominator for date and time exchange with
different systems, such as JavaScript. Sometimes, though, you need actual days, hours,
and so forth, which time provides as struct_time objects. localtime() provides the
time in your system’s time zone, and gmtime() provides it in UTC:
1. This starting point is roughly when Unix was born.
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>>> time.localtime(now)
time.struct_time(tm_year=2014, tm_mon=2, tm_mday=3, tm_hour=22, tm_min=31,
tm_sec=3, tm_wday=0, tm_yday=34, tm_isdst=0)
>>> time.gmtime(now)
time.struct_time(tm_year=2014, tm_mon=2, tm_mday=4, tm_hour=4, tm_min=31,
tm_sec=3, tm_wday=1, tm_yday=35, tm_isdst=0)
In my (Central) time zone, 22:31 was 04:31 of the next day in UTC (formerly called
Greenwich time or Zulu time). If you omit the argument to localtime() or gmtime(),
they assume the current time.
The opposite of these is mktime(), which converts a struct_time object to epoch
seconds:
>>> tm = time.localtime(now)
>>> time.mktime(tm)
1391488263.0
This doesn’t exactly match our earlier epoch value of now() because the struct_time
object preserves time only to the second.
Some advice: wherever possible, use UTC instead of time zones. UTC is an absolute
time, independent of time zones. If you have a server, set its time to UTC; do not use
local time.
Here’s some more advice (free of charge, no less): never use daylight savings time if you
can avoid it. If you use daylight savings time, an hour disappears at one time of year
(“spring ahead”) and occurs twice at another time (“fall back”). For some reason, many
organizations use daylight savings in their computer systems, but are mystified every
year by data duplicates and dropouts. It all ends in tears.
Remember, your friends are UTC for times, and UTF-8 for strings
(for more about UTF-8, see Chapter 7).
Read and Write Dates and Times
isoformat() is not the only way to write dates and times. You already saw the ctime()
function in the time module, which you can use to convert epochs to strings:
>>> import time
>>> now = time.time()
>>> time.ctime(now)
'Mon Feb 3 21:14:36 2014'
You can also convert dates and times to strings by using strftime(). This is provided
as a method in the datetime, date, and time objects, and as a function in the time
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module. strftime() uses format strings to specify the output, which you can see in
Table 10-1.
Table 10-1. Outut specifiers for strftime()
Format string Date/time unit Range
%Y
year
1900-…
%m
month
01-12
%B
month name
January, …
%b
month abbrev
Jan, …
%d
day of month
01-31
Sunday, …
%A
weekday name
a
weekday abbrev Sun, …
%H
hour (24 hr)
%I
hour (12 hr)
01-12
%p
AM/PM
AM, PM
%M
minute
00-59
%S
second
00-59
00-23
Numbers are zero-padded on the left.
Here’s the strftime() function provided by the time module. It converts a struct_time
object to a string. We’ll first define the format string fmt and use it again later:
>>> import time
>>> fmt = "It's %A, %B %d, %Y, local time %I:%M:%S%p"
>>> t = time.localtime()
>>> t
time.struct_time(tm_year=2014, tm_mon=2, tm_mday=4, tm_hour=19,
tm_min=28, tm_sec=38, tm_wday=1, tm_yday=35, tm_isdst=0)
>>> time.strftime(fmt, t)
"It's Tuesday, February 04, 2014, local time 07:28:38PM"
If we try this with a date object, only the date parts will work, and the time defaults to
midnight:
>>> from datetime import date
>>> some_day = date(2014, 7, 4)
>>> fmt = "It's %B %d, %Y, local time %I:%M:%S%p"
>>> some_day.strftime(fmt)
"It's Friday, July 04, 2014, local time 12:00:00AM"
For a time object, only the time parts are converted:
>>> from datetime import time
>>> some_time = time(10, 35)
>>> some_time.strftime(fmt)
"It's Monday, January 01, 1900, local time 10:35:00AM"
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Clearly, you won’t want to use the day parts from a time object, because they’re mean‐
ingless.
To go the other way and convert a string to a date or time, use strptime() with the same
format string. There’s no regular expression pattern matching; the nonformat parts of
the string (without %) need to match exactly. Let’s specify a format that matches yearmonth-day, such as 2012-01-29. What happens if the date string you want to parse has
spaces instead of dashes?
>>> import time
>>> fmt = "%Y-%m-%d"
>>> time.strptime("2012 01 29", fmt)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Library/Frameworks/Python.framework/Versions/3.3/lib/
python3.3/_strptime.py", line 494, in _strptime_time
tt = _strptime(data_string, format)[0]
File "/Library/Frameworks/Python.framework/Versions/3.3/lib/
python3.3/_strptime.py", line 337, in _strptime
(data_string, format))
ValueError: time data '2012 01 29' does not match format '%Y-%m-%d'
If we feed strptime() some dashes, is it happy now?
>>> time.strptime("2012-01-29", fmt)
time.struct_time(tm_year=2012, tm_mon=1, tm_mday=29, tm_hour=0, tm_min=0,
tm_sec=0, tm_wday=6, tm_yday=29, tm_isdst=-1)
Yes.
Even if the string seems to match its format, an exception is raised if a value is out of
range:
>>> time.strptime("2012-13-29", fmt)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Library/Frameworks/Python.framework/Versions/3.3/lib/
python3.3/_strptime.py", line 494, in _strptime_time
tt = _strptime(data_string, format)[0]
File "/Library/Frameworks/Python.framework/Versions/3.3/lib/
python3.3/_strptime.py", line 337, in _strptime
(data_string, format))
ValueError: time data '2012-13-29' does not match format '%Y-%m-%d'
Names are specific to your locale—internationalization settings for your operating sys‐
tem. To print different month and day names, change your locale by using
setlocale(); its first argument is locale.LC_TIME for dates and times, and the second
is a string combining the language and country abbreviation. Let’s invite some inter‐
national friends to a Halloween party. We’ll print the month, day, and day of week in
US English, French, German, Spanish, and Icelandic. (What? You think Icelanders don’t
enjoy a good party as much as anyone else? They even have real elves.)
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>>> import locale
>>> from datetime import date
>>> halloween = date(2014, 10, 31)
>>> for lang_country in ['en_us', 'fr_fr', 'de_de', 'es_es', 'is_is',]:
...
locale.setlocale(locale.LC_TIME, lang_country)
...
halloween.strftime('%A, %B %d')
...
'en_us'
'Friday, October 31'
'fr_fr'
'Vendredi, octobre 31'
'de_de'
'Freitag, Oktober 31'
'es_es'
'viernes, octubre 31'
'is_is'
'föstudagur, október 31'
>>>
Where do you find these magic values for lang_country? This is a bit wonky, but you
can try this to get all of them (there are a few hundred):
>>> import locale
>>> names = locale.locale_alias.keys()
From names, let’s get just locale names that seem to work with setlocale(), such as the
ones we used in the preceding example—a two-character language code followed by an
underscore and a two-character country code:
>>> good_names = [name for name in names if \
len(name) == 5 and name[2] == '_']
What do the first five look like?
>>> good_names[:5]
['sr_cs', 'de_at', 'nl_nl', 'es_ni', 'sp_yu']
So, if you wanted all the German language locales, try this:
>>> de = [name for name in good_names if name.startswith('de')]
>>> de
['de_at', 'de_de', 'de_ch', 'de_lu', 'de_be']
Alternative Modules
If you find the standard library modules confusing, or lacking a particular conversion
that you want, there are many third-party alternatives. Here are just a few of them:
arrow
This combines many date and time functions with a simple API.
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dateutil
This module parses almost any date format and handles relative dates and times
well.
iso8601
This fills in gaps in the standard library for the ISO8601 format.
fleming
This module offers many time zone functions.
Things to Do
10.1 Write the current date as a string to the text file today.txt.
10.2 Read the text file today.txt into the string today_string.
10.3 Parse the date from today_string.
10.4 List the files in your current directory.
10.5 List the files in your parent directory.
10.6 Use multiprocessing to create three separate processes. Make each one wait a ran‐
dom number of seconds between one and five, print the current time, and then exit.
10.7 Create a date object of your day of birth.
10.8 What day of the week was your day of birth?
10.9 When will you be (or when were you) 10,000 days old?
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CHAPTER 11
Concurrency and Networks
Time is nature’s way of keeping everything from happening at once. Space is what prevents
everything from happening to me.
— Quotes about Time
So far, most of the programs that you’ve written run in one place (a single machine) and
one line at a time (sequential). But, we can do more than one thing at a time (concur‐
rency) and in more than one place (distributed computing or networking). There are
many good reasons to challenge time and space:
Performance
Your goal is to keep fast components busy, not waiting for slow ones.
Robustness
There’s safety in numbers, so you want to duplicate tasks to work around hardware
and software failures.
Simplicity
It’s best practice to break complex tasks into many little ones that are easier to create,
understand, and fix.
Communication
It’s just plain fun to send your footloose bytes to distant places, and bring friends
back with them.
We’ll start with concurrency, first building on the non-networking techniques that are
described in Chapter 10—processes and threads. Then we’ll look at other approaches,
such as callbacks, green threads, and coroutines. Finally, we’ll arrive at networking,
initially as a concurrency technique, and then spreading outward.
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Some Python packages discussed in this chapter were not yet por‐
ted to Python 3 when this was written. In many cases, I’ll show ex‐
ample code that would need to be run with a Python 2 interpreter,
which we’re calling python2.
Concurrency
The official Python site discusses concurrency in general and in the standard library.
Those pages have many links to various packages and techniques; we’ll show the most
useful ones in this chapter.
In computers, if you’re waiting for something, it’s usually for one of two reasons:
I/O bound
This is by far more common. Computer CPUs are ridiculously fast—hundreds of
times faster than computer memory and many thousands of times faster than disks
or networks.
CPU bound
This happens with number crunching tasks such as scientific or graphic calculations.
Two more terms are related to concurrency:
synchronous
One thing follows the other, like a funeral procession.
asynchronous
Tasks are independent, like party-goers dropping in and tearing off in separate cars.
As you progress from simple systems and tasks to real-life problems, you’ll need at some
point to deal with concurrency. Consider a website, for example. You can usually provide
static and dynamic pages to web clients fairly quickly. A fraction of a second is consid‐
ered interactive, but if the display or interaction takes longer, people become impatient.
Tests by companies such as Google and Amazon showed that traffic drops off quickly
if the page loads even a little slower.
But what if you can’t help it when something takes a long time, such as uploading a file,
resizing an image, or querying a database? You can’t do it within your synchronous web
server code anymore, because someone’s waiting.
On a single machine, if you want to perform multiple tasks as fast as possible, you want
to make them independent. Slow tasks shouldn’t block all the others.
“Programs and Processes” on page 247 demonstrates how multiprocessing can be used
to overlap work on a single machine. If you needed to resize an image, your web server
code could call a separate, dedicated image resizing process to run asynchronously and
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concurrently. It could scale your application horizontally by invoking multiple resizing
processes.
The trick is getting them all to work with one another. Any shared control or state means
that there will be bottlenecks. An even bigger trick is dealing with failures, because
concurrent computing is harder than regular computing. Many more things can go
wrong, and your odds of end-to-end success are lower.
All right. What methods can help you to deal with these complexities? Let’s begin with
a good way to manage multiple tasks: queues.
Queues
A queue is like a list: things are added at one end and taken away from the other. The
most common is referred to as FIFO (first in, first out).
Suppose that you’re washing dishes. If you’re stuck with the entire job, you need to wash
each dish, dry it, and put it away. You can do this in a number of ways. You might wash
the first dish, dry it, and then put it away. You then repeat with the second dish, and so
on. Or, you might batch operations and wash all the dishes, dry them all, and then put
them away; this assumes you have space in your sink and drainer for all the dishes that
accumulate at each step. These are all synchronous approaches—one worker, one thing
at a time.
As an alternative, you could get a helper or two. If you’re the washer, you can hand each
cleaned dish to the dryer, who hands each dried dish to the put-away-er (look it up; it’s
absolutely a real word!). As long as each of you works at the same pace, you should
finish much faster than by yourself.
However, what if you wash faster than the dryer dries? Wet dishes either fall on the floor,
or you pile them up between you and the dryer, or you just whistle off-key until the
dryer is ready. And if the last person is slower than the dryer, dry dishes can end up
falling on the floor, or piling up, or the dryer does the whistling. You have multiple
workers, but the overall task is still synchronous and can proceed only as fast as the
slowest worker.
Many hands make light work, goes the old saying (I always thought it was Amish, because
it makes me think of barn building). Adding workers can build a barn, or do the dishes,
faster. This involves queues.
In general, queues transport messages, which can be any kind of information. In this
case, we’re interested in queues for distributed task management, also known as work
queues, job queues, or task queues. Each dish in the sink is given to an available washer,
who washes and hands it off to the first available dryer, who dries and hands it to a putaway-er. This can be synchronous (workers wait for a dish to handle and another worker
to whom to give it), or asynchronous (dishes are stacked between workers with different
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paces). As long as you have enough workers, and they keep up with the dishes, things
move a lot faster.
Processes
You can implement queues in many ways. For a single machine, the standard library’s
multiprocessing module (which you can see in “Programs and Processes” on page
247) contains a Queue function. Let’s simulate just a single washer and multiple dryer
processes (someone can put the dishes away later) and an intermediate dish_queue.
Call this program dishes.py:
import multiprocessing as mp
def washer(dishes, output):
for dish in dishes:
print('Washing', dish, 'dish')
output.put(dish)
def dryer(input):
while True:
dish = input.get()
print('Drying', dish, 'dish')
input.task_done()
dish_queue = mp.JoinableQueue()
dryer_proc = mp.Process(target=dryer, args=(dish_queue,))
dryer_proc.daemon = True
dryer_proc.start()
dishes = ['salad', 'bread', 'entree', 'dessert']
washer(dishes, dish_queue)
dish_queue.join()
Run your new program thusly:
$ python dishes.py
Washing salad dish
Washing bread dish
Washing entree dish
Washing dessert dish
Drying salad dish
Drying bread dish
Drying entree dish
Drying dessert dish
This queue looked a lot like a simple Python iterator, producing a series of dishes. It
actually started up separate processes along with the communication between the wash‐
er and dryer. I used a JoinableQueue and the final join() method to let the washer
know that all the dishes have been dried. There are other queue types in the
multiprocessing module, and you can read the documentation for more examples.
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Threads
A thread runs within a process with access to everything in the process, similar to a
multiple personality. The multiprocessing module has a cousin called threading that
uses threads instead of processes (actually, multiprocessing was designed later as its
process-based counterpart). Let’s redo our process example with threads:
import threading
def do_this(what):
whoami(what)
def whoami(what):
print("Thread %s says: %s" % (threading.current_thread(), what))
if __name__ == "__main__":
whoami("I'm the main program")
for n in range(4):
p = threading.Thread(target=do_this,
args=("I'm function %s" % n,))
p.start()
Here’s what prints for me:
Thread <_MainThread(MainThread, started 140735207346960)> says: I'm the main
program
Thread <Thread(Thread-1, started 4326629376)> says: I'm function 0
Thread <Thread(Thread-2, started 4342157312)> says: I'm function 1
Thread <Thread(Thread-3, started 4347412480)> says: I'm function 2
Thread <Thread(Thread-4, started 4342157312)> says: I'm function 3
We can reproduce our process-based dish example by using threads:
import threading, queue
import time
def washer(dishes, dish_queue):
for dish in dishes:
print ("Washing", dish)
time.sleep(5)
dish_queue.put(dish)
def dryer(dish_queue):
while True:
dish = dish_queue.get()
print ("Drying", dish)
time.sleep(10)
dish_queue.task_done()
dish_queue = queue.Queue()
for n in range(2):
dryer_thread = threading.Thread(target=dryer, args=(dish_queue,))
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dryer_thread.start()
dishes = ['salad', 'bread', 'entree', 'desert']
washer(dishes, dish_queue)
dish_queue.join()
One difference between multiprocessing and threading is that threading does not
have a terminate() function. There’s no easy way to terminate a running thread, be‐
cause it can cause all sorts of problems in your code, and possibly in the space-time
continuum itself.
Threads can be dangerous. Like manual memory management in languages such as C
and C++, they can cause bugs that are extremely hard to find, let alone fix. To use threads,
all the code in the program—and in external libraries that it uses—must be threadsafe. In the preceding example code, the threads didn’t share any global variables, so
they could run independently without breaking anything.
Imagine that you’re a paranormal investigator in a haunted house. Ghosts roam the
halls, but none are aware of the others, and at any time, any of them can view, add,
remove, or move any of the house’s contents.
You’re walking apprehensively through the house, taking readings with your impressive
instruments. Suddenly you notice that the candlestick you passed seconds ago is now
missing.
The contents of the house are like the variables in a program. The ghosts are threads in
a process (the house). If the ghosts only looked at the house’s contents, there would be
no problem. It’s like a thread reading the value of a constant or variable without trying
to change it.
Yet, some unseen entity could grab your flashlight, blow cold air down your neck, put
marbles on the stairs, or make the fireplace come ablaze. The really subtle ghosts would
change things in other rooms that you might never notice.
Despite your fancy instruments, you’d have a very hard time figuring out who did it,
and how, and when.
If you used multiple processes instead of threads, it would be like having a number of
houses but with only one (living) person in each. If you put your brandy in front of the
fireplace, it would still be there an hour later. Some lost to evaporation, perhaps, but in
the same place.
Threads can be useful and safe when global data is not involved. In particular, threads
are useful for saving time while waiting for some I/O operation to complete. In these
cases, they don’t have to fight over data, because each has completely separate variables.
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But threads do sometimes have good reasons to change global data. In fact, one common
reason to launch multiple threads is to let them divide up the work on some data, so a
certain degree of change to the data is expected.
The usual way to share data safely is to apply a software lock before modifying a variable
in a thread. This keeps the other threads out while the change is made. It’s like having
a Ghostbuster guard the room you want to remain unhaunted. The trick, though, is that
you need to remember to unlock it. Plus, locks can be nested—what if another Ghost‐
buster is also watching the same room, or the house itself? The use of locks is traditional
but notoriously hard to get right.
In Python, threads do not speed up CPU-bound tasks because of an
implementation detail in the standard Python system called the Glob‐
al Interpreter Lock (GIL). This exists to avoid threading problems in
the Python interpreter, and can actually make a multithreaded pro‐
gram slower than its single-threaded counterpart, or even a multiprocess version.
So for Python, the recommendations are as follows:
• Use threads for I/O bound problems
• Use processes, networking, or events (discussed in the next section) for CPU-bound
problems
Green Threads and gevent
As you’ve seen, developers traditionally avoid slow spots in programs by running them
in separate threads or processes. The Apache web server is an example of this design.
One alternative is event-based programming. An event-based program runs a central
event loop, doles out any tasks, and repeats the loop. The nginx web server follows this
design, and is generally faster than Apache.
The gevent library is event-based and accomplishes a cool trick: you write normal
imperative code, and it magically converts pieces to coroutines. These are like generators
that can communicate with one another and keep track of where they are. gevent
modifies many of Python’s standard objects such as socket to use its mechanism instead
of blocking. This does not work with Python add-in code that was written in C, as some
database drivers are.
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As of this writing, gevent was not completely ported to Python 3, so
these code examples use the Python 2 tools pip2 and python2.
You install gevent by using the Python 2 version of pip:
$ pip2 install gevent
Here’s a variation of sample code at the gevent website. You’ll see the socket module’s
gethostbyname() function in the upcoming DNS section. This function is synchronous,
so you wait (possibly many seconds) while it chases name servers around the world to
look up that address. But you could use the gevent version to look up multiple sites
independently. Save this as gevent_test.py:
import gevent
from gevent import socket
hosts = ['www.crappytaxidermy.com', 'www.walterpottertaxidermy.com',
'www.antique-taxidermy.com']
jobs = [gevent.spawn(gevent.socket.gethostbyname, host) for host in hosts]
gevent.joinall(jobs, timeout=5)
for job in jobs:
print(job.value)
There’s a one-line for-loop in the preceding example. Each hostname is submitted in
turn to a gethostbyname() call, but they can run asynchronously because it’s the gevent
version of gethostbyname().
Run gevent_test.py with Python 2 by typing the following (in bold):
$ python2 gevent_test.py
66.6.44.4
74.125.142.121
78.136.12.50
gevent.spawn() creates a greenlet (also known sometimes as a green thread or a micro‐
thread) to execute each gevent.socket.gethostbyname(url).
The difference from a normal thread is that it doesn’t block. If something occurred that
would have blocked a normal thread, gevent switches control to one of the other green‐
lets.
The gevent.joinall() method waits for all the spawned jobs to finish. Finally, we dump
the IP addresses that we got for these hostnames.
Instead of the gevent version of socket, you can use its evocatively named monkeypatching functions. These modify standard modules such as socket to use greenlets
rather than calling the gevent version of the module. This is useful when you want
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gevent to be applied all the way down, even into code that you might not be able to
access.
At the top of your program, add the following call:
from gevent import monkey
monkey.patch_socket()
This inserts the gevent socket everywhere the normal socket is called, anywhere in
your program, even in the standard library. Again, this works only for Python code, not
libraries written in C.
Another function monkey-patches even more standard library modules:
from gevent import monkey
monkey.patch_all()
Use this at the top of your program to get as many gevent speedups as possible.
Save this program as gevent_monkey.py:
import gevent
from gevent import monkey; monkey.patch_all()
import socket
hosts = ['www.crappytaxidermy.com', 'www.walterpottertaxidermy.com',
'www.antique-taxidermy.com']
jobs = [gevent.spawn(socket.gethostbyname, host) for host in hosts]
gevent.joinall(jobs, timeout=5)
for job in jobs:
print(job.value)
Again, using Python 2, run the program:
$ python2 gevent_monkey.py
66.6.44.4
74.125.192.121
78.136.12.50
There are potential dangers when using gevent. As with any event-based system, each
chunk of code that you execute should be relatively quick. Although it’s nonblocking,
code that does a lot of work is still slow.
The very idea of monkey-patching makes some people nervous. Yet, many large sites
such as Pinterest use gevent to speed up their sites significantly. Like the fine print on
a bottle of pills, use gevent as directed.
Two other popular event-driven frameworks are tornado and guni
corn. They provide both the low-level event handling and a fast web
server. They’re worth a look if you’d like to build a fast website without
messing with a traditional web server such as Apache.
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twisted
twisted is an asynchronous, event-driven networking framework. You connect func‐
tions to events such as data received or connection closed, and those functions are called
when those events occur. This is a callback design, and if you’ve written anything in
JavaScript, it might seem familiar. If it’s new to you, it can seem backwards. For some
developers, callback-based code becomes harder to manage as the application grows.
Like gevent, twisted has not yet been ported to Python 3. We’ll use the Python 2 installer
and interpreter for this section. Type the following to install it:
$ pip2 install twisted
twisted is a large package, with support for many Internet protocols on top of TCP and
UDP. To be short and simple, we’ll show a little knock-knock server and client, adapted
from twisted examples. First, let’s look at the server, knock_server.py (notice the Python
2 syntax for print()):
from twisted.internet import protocol, reactor
class Knock(protocol.Protocol):
def dataReceived(self, data):
print 'Client:', data
if data.startswith("Knock knock"):
response = "Who's there?"
else:
response = data + " who?"
print 'Server:', response
self.transport.write(response)
class KnockFactory(protocol.Factory):
def buildProtocol(self, addr):
return Knock()
reactor.listenTCP(8000, KnockFactory())
reactor.run()
Now, let’s take a glance at its trusty companion, knock_client.py:
from twisted.internet import reactor, protocol
class KnockClient(protocol.Protocol):
def connectionMade(self):
self.transport.write("Knock knock")
def dataReceived(self, data):
if data.startswith("Who's there?"):
response = "Disappearing client"
self.transport.write(response)
else:
self.transport.loseConnection()
reactor.stop()
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class KnockFactory(protocol.ClientFactory):
protocol = KnockClient
def main():
f = KnockFactory()
reactor.connectTCP("localhost", 8000, f)
reactor.run()
if __name__ == '__main__':
main()
Start the server first:
$ python2 knock_server.py
Then start the client:
$ python2 knock_client.py
The server and client exchange messages, and the server prints the conversation:
Client:
Server:
Client:
Server:
Knock knock
Who's there?
Disappearing client
Disappearing client who?
Our trickster client then ends, keeping the server waiting for the punch line.
If you’d like to enter the twisted passages, try some of the other examples from its
documentation.
asyncio
Recently, Guido van Rossum (remember him?) became involved with the Python con‐
currency issue. Many packages had their own event loop, and each event loop kind of
likes to be the only one. How could he reconcile mechanisms such as callbacks, greenlets,
and others? After many discussions and visits, he proposed Asynchronous IO Support
Rebooted: the “asyncio” Module, code-named Tulip. This first appeared in Python 3.4
as the asyncio module. For now, it offers a common event loop that could be compatible
with twisted, gevent, and other asynchronous methods. The goal is to provide a stan‐
dard, clean, well-performing asynchronous API. Watch it expand in future releases of
Python.
Redis
Our earlier dishwashing code examples, using processes or threads, were run on a single
machine. Let’s take another approach to queues that can run on a single machine or
across a network. Even with multiple singing processes and dancing threads, sometimes
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one machine isn’t enough, You can treat this section as a bridge between single-box (one
machine) and multiple-box concurrency.
To try the examples in this section, you’ll need a Redis server and its Python module.
You can see where to get them in “Redis” on page 206. In that chapter, Redis’s role is
that of a database. Here, we’re featuring its concurrency personality.
A quick way to make a queue is with a Redis list. A Redis server runs on one machine;
this can be the same one as its clients, or another that the clients can access through a
network. In either case, clients talk to the server via TCP, so they’re networking. One or
more provider clients pushes messages onto one end of the list. One or more client
workers watches this list with a blocking pop operation. If the list is empty, they all just
sit around playing cards. As soon as a message arrives, the first eager worker gets it.
Like our earlier process- and thread-based examples, redis_washer.py generates a se‐
quence of dishes:
import redis
conn = redis.Redis()
print('Washer is starting')
dishes = ['salad', 'bread', 'entree', 'dessert']
for dish in dishes:
msg = dish.encode('utf-8')
conn.rpush('dishes', msg)
print('Washed', num)
conn.rpush('dishes', 'quit')
print('Washer is done')
The loop generates four messages containing a dish name, followed by a final message
that says “quit.” It appends each message to a list called dishes in the Redis server, similar
to appending to a Python list.
And as soon as the first dish is ready, redis_dryer.py does its work:
import redis
conn = redis.Redis()
print('Dryer is starting')
while True:
msg = conn.blpop('dishes')
if not msg:
break
val = msg[1].decode('utf-8')
if val == 'quit':
break
print('Dried', val)
print('Dishes are dried')
This code waits for messages whose first token is “dishes” and prints that each one is
dried. It obeys the quit message by ending the loop.
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Start the dryer, and then the washer. Using the & at the end puts the first program in the
background; it keeps running, but doesn’t listen to the keyboard anymore. This works
on Linux, OS X, and Windows, although you might see different output on the next
line. In this case (OS X), it’s some information about the background dryer process.
Then, we start the washer process normally (in the foreground). You’ll see the mingled
output of the two processes:
$ python redis_dryer.py &
[2] 81691
Dryer is starting
$ python redis_washer.py
Washer is starting
Washed salad
Dried salad
Washed bread
Dried bread
Washed entree
Dried entree
Washed dessert
Washer is done
Dried dessert
Dishes are dried
[2]+ Done
python redis_dryer.py
As soon as dish IDs started arriving at Redis from the washer process, our hard-working
dryer process started pulling them back out. Each dish ID was a number, except the
final sentinel value, the string 'quit'. When the dryer process read that quit dish ID,
it quit, and some more background process information printed to the terminal (also
system-dependent). You can use a sentinel (an otherwise invalid value) to indicate
something special from the data stream itself—in this case, that we’re done. Otherwise,
we’d need to add a lot more program logic, such as the following:
• Agreeing ahead of time on some maximum dish number, which would kind of be
a sentinel anyway.
• Doing some special out-of-band (not in the data stream) interprocess
communication.
• Timing out after some interval with no new data.
Let’s make a few last changes:
• Create multiple dryer processes.
• Add a timeout to each dryer rather than looking for a sentinel.
The new redis_dryer2.py:
def dryer():
import redis
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import os
import time
conn = redis.Redis()
pid = os.getpid()
timeout = 20
print('Dryer process %s is starting' % pid)
while True:
msg = conn.blpop('dishes', timeout)
if not msg:
break
val = msg[1].decode('utf-8')
if val == 'quit':
break
print('%s: dried %s' % (pid, val))
time.sleep(0.1)
print('Dryer process %s is done' % pid)
import multiprocessing
DRYERS=3
for num in range(DRYERS):
p = multiprocessing.Process(target=dryer)
p.start()
Start the dryer processes in the background, and then the washer process in the fore‐
ground:
$ python redis_dryer2.py &
Dryer process 44447 is starting
Dryer process 44448 is starting
Dryer process 44446 is starting
$ python redis_washer.py
Washer is starting
Washed salad
44447: dried salad
Washed bread
44448: dried bread
Washed entree
44446: dried entree
Washed dessert
Washer is done
44447: dried dessert
One dryer process reads the quit ID and quits:
Dryer process 44448 is done
After 20 seconds, the other dryer processes get a return value of None from their blpop
calls, indicating that they’ve timed out. They say their last words and exit:
Dryer process 44447 is done
Dryer process 44446 is done
After the last dryer subprocess quits, the main dryer program ends:
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[1]+
Done
python redis_dryer2.py
Beyond Queues
With more moving parts, there are more possibilities for our lovely assembly lines to
be disrupted. If we need to wash the dishes from a banquet, do we have enough workers?
What if the dryers get drunk? What if the sink clogs? Worries, worries!
How will you cope with it all? Fortunately, there are some techniques available that you
can apply. They include the following:
Fire and forget
Just pass things on and don’t worry about the consequences, even if no one is there.
That’s the dishes-on-the-floor approach.
Request-reply
The washer receives an acknowledgement from the dryer, and the dryer from the
put-away-er, for each dish in the pipeline.
Back pressure or throttling
This technique directs a fast worker to take it easy if someone downstream can’t
keep up.
In real systems, you need to be careful that workers are keeping up with the demand;
otherwise, you hear the dishes hitting the floor. You might add new tasks to a pending
list, while some worker process pops the latest message and adds it to a working list.
When the message is done, it’s removed from the working list and added to a comple‐
ted list. This lets you know what tasks have failed or are taking too long. You can do this
with Redis yourself, or use a system that someone else has already written and tested.
Some Python-based queue packages that add this extra level of management—some of
which use Redis—include:
celery
This particular package is well worth a look. It can execute distributed tasks syn‐
chronously or asynchronously, using the methods we’ve discussed: multiprocess
ing, gevent, and others.
thoonk
This package builds on Redis to provide job queues and pub-sub (coming in the
next section).
rq
This is a Python library for job queues, also based on Redis.
Queues
This site offers a discussion of queuing software, Python-based and otherwise.
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Networks
In our discussion of concurrency, we talked mostly about time: single-machine solutions
(processes, threads, green threads). We also briefly touched upon some solutions that
can span networks (Redis, ZeroMQ). Now, we’ll look at networking in its own right,
distributing computing across space.
Patterns
You can build networking applications from some basic patterns.
The most common pattern is request-reply, also known as client-server. This pattern is
synchronous: the client waits until the server responds. You’ve seen many examples of
request-reply in this book. Your web browser is also a client, making an HTTP request
to a web server, which returns a reply.
Another common pattern is push, or fanout: you send data to any available worker in a
pool of processes. An example is a web server behind a load balancer.
The opposite of push is pull, or fanin: you accept data from one or more sources. An
example would be a logger that takes text messages from multiple processes and writes
them to a single log file.
One pattern is similar to radio or television broadcasting: publish-subscribe, or pub-sub.
With this pattern, a publisher sends out data. In a simple pub-sub system, all subscribers
would receive a copy. More often, subscribers can indicate that they’re interested only
in certain types of data (often called a topic), and the publisher will send just those. So,
unlike the push pattern, more than one subscriber might receive a given piece of data.
If there’s no subscriber for a topic, the data is ignored.
The Publish-Subscribe Model
Publish-subscribe is not a queue but a broadcast. One or more processes publish mes‐
sages. Each subscriber process indicates what type of messages it would like to receive.
A copy of each message is sent to each subscriber that matched its type. Thus, a given
message might be processed once, more than once, or not at all. Each publisher is just
broadcasting and doesn’t know who—if anyone—is listening.
Redis
You can build a quick pub-sub system by using Redis. The publisher emits messages
with a topic and a value, and subscribers say which topics they want to receive.
Here’s the publisher, redis_pub.py:
import redis
import random
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conn = redis.Redis()
cats = ['siamese', 'persian', 'maine coon', 'norwegian forest']
hats = ['stovepipe', 'bowler', 'tam-o-shanter', 'fedora']
for msg in range(10):
cat = random.choice(cats)
hat = random.choice(hats)
print('Publish: %s wears a %s' % (cat, hat))
conn.publish(cat, hat)
Each topic is a breed of cat, and the accompanying message is a type of hat.
Here’s a single subscriber, redis_sub.py:
import redis
conn = redis.Redis()
topics = ['maine coon', 'persian']
sub = conn.pubsub()
sub.subscribe(topics)
for msg in sub.listen():
if msg['type'] == 'message':
cat = msg['channel']
hat = msg['data']
print('Subscribe: %s wears a %s' % (cat, hat))
The subscriber just shown wants all messages for cat types 'maine coon' and
'persian', and no others. The listen() method returns a dictionary. If its type is
'message', it was sent by the publisher and matches our criteria. The 'channel' key is
the topic (cat), and the 'data' key contains the message (hat).
If you start the publisher first and no one is listening, it’s like a mime falling in the forest
(does he make a sound?), so start the subscriber first:
$ python redis_sub.py
Next, start the publisher. It will send 10 messages, and then quit:
$ python
Publish:
Publish:
Publish:
Publish:
Publish:
Publish:
Publish:
Publish:
Publish:
Publish:
redis_pub.py
maine coon wears a stovepipe
norwegian forest wears a stovepipe
norwegian forest wears a tam-o-shanter
maine coon wears a bowler
siamese wears a stovepipe
norwegian forest wears a tam-o-shanter
maine coon wears a bowler
persian wears a bowler
norwegian forest wears a bowler
maine coon wears a stovepipe
The subscriber cares about only two types of cat:
$ python redis_sub.py
Subscribe: maine coon wears a stovepipe
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Subscribe:
Subscribe:
Subscribe:
Subscribe:
maine coon wears a bowler
maine coon wears a bowler
persian wears a bowler
maine coon wears a stovepipe
We didn’t tell the subscriber to quit, so it’s still waiting for messages. If you restart the
publisher, the subscriber will grab a few more messages and print them.
You can have as many subscribers (and publishers) as you want. If there’s no subscriber
for a message, it disappears from the Redis server. However, if there are subscribers, the
messages stay in the server until all subscribers have retrieved them.
ZeroMQ
Remember those ZeroMQ PUB and SUB sockets from a few pages ago? This is what they’re
for. ZeroMQ has no central server, so each publisher writes to all subscribers. Let’s
rewrite the cat-hat pub-sub for ZeroMQ. The publisher, zmq_pub.py, looks like this:
import zmq
import random
import time
host = '*'
port = 6789
ctx = zmq.Context()
pub = ctx.socket(zmq.PUB)
pub.bind('tcp://%s:%s' % (host, port))
cats = ['siamese', 'persian', 'maine coon', 'norwegian forest']
hats = ['stovepipe', 'bowler', 'tam-o-shanter', 'fedora']
time.sleep(1)
for msg in range(10):
cat = random.choice(cats)
cat_bytes = cat.encode('utf-8')
hat = random.choice(hats)
hat_bytes = hat.encode('utf-8')
print('Publish: %s wears a %s' % (cat, hat))
pub.send_multipart([cat_bytes, hat_bytes])
Notice how this code uses UTF-8 encoding for the topic and value strings.
The file for the subscriber is zmq_sub.py:
import zmq
host = '127.0.0.1'
port = 6789
ctx = zmq.Context()
sub = ctx.socket(zmq.SUB)
sub.connect('tcp://%s:%s' % (host, port))
topics = ['maine coon', 'persian']
for topic in topics:
sub.setsockopt(zmq.SUBSCRIBE, topic.encode('utf-8'))
while True:
cat_bytes, hat_bytes = sub.recv_multipart()
cat = cat_bytes.decode('utf-8')
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hat = hat_bytes.decode('utf-8')
print('Subscribe: %s wears a %s' % (cat, hat))
In this code, we subscribe to two different byte values: the two strings in topics, encoded
as UTF-8.
It seems a little backward, but if you want all topics, you need to
subscribe to the empty bytestring b''; if you don’t, you’ll get nothing.
Notice that we call send_multipart() in the publisher and recv_multipart() in the
subscriber. This makes it possible for us to send multipart messages, and use the first
part as the topic. We could also send the topic and message as a single string or bytestring,
but it seems cleaner to keep cats and hats separate.
Start the subscriber:
$ python zmq_sub.py
Start the publisher. It immediately sends 10 messages, and then quits:
$ python
Publish:
Publish:
Publish:
Publish:
Publish:
Publish:
Publish:
Publish:
Publish:
Publish:
zmq_pub.py
norwegian forest wears a stovepipe
siamese wears a bowler
persian wears a stovepipe
norwegian forest wears a fedora
maine coon wears a tam-o-shanter
maine coon wears a stovepipe
persian wears a stovepipe
norwegian forest wears a fedora
norwegian forest wears a bowler
maine coon wears a bowler
The subscriber prints what it requested and received:
Subscribe:
Subscribe:
Subscribe:
Subscribe:
Subscribe:
persian wears a stovepipe
maine coon wears a tam-o-shanter
maine coon wears a stovepipe
persian wears a stovepipe
maine coon wears a bowler
Other Pub-sub Tools
You might like to explore some of these other Python pub-sub links:
RabbitMQ
This is a well-known messaging broker, and pika is a Python API for it. See the
pika documentation and a pub-sub tutorial.
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pypi.python.org
Go to the upper-right corner of the search window and type pubsub to find Python
packages like pypubsub.
pubsubhubbub
This mellifluous protocol enables subscribers to register callbacks with publishers.
TCP/IP
We’ve been walking through the networking house, taking for granted that whatever’s
in the basement works correctly. Now, let’s actually visit the basement and look at the
wires and pipes that keep everything running above ground.
The Internet is based on rules about how to make connections, exchange data, terminate
connections, handle timeouts, and so on. These are called protocols, and they are ar‐
ranged in layers. The purpose of layers is to allow innovation and alternative ways of
doing things; you can do anything you want on one layer as long as you follow the
conventions in dealing with the layers above and below you.
The very lowest layer governs aspects such as electrical signals; each higher layer builds
on those below. In the middle, more or less, is the IP (Internet Protocol) layer, which
specifies how network locations are addressed and how packets (chunks) of data flow.
In the layer above that, two protocols describe how to move bytes between locations:
UDP (User Datagram Protocol)
This is used for short exchanges. A datagram is a tiny message sent in a single burst,
like a note on a postcard.
TCP (Transmission Control Protocol)
This protocol is used for longer-lived connections. It sends streams of bytes and
ensures that they arrive in order without duplication.
UDP messages are not acknowledged, so you’re never sure if they arrive at their desti‐
nation. If you wanted to tell a joke over UDP:
Here's a UDP joke. Get it?
TCP sets up a secret handshake between sender and receiver to ensure a good connec‐
tion. A TCP joke would start like this:
Do you want to hear a TCP joke?
Yes, I want to hear a TCP joke.
Okay, I'll tell you a TCP joke.
Okay, I'll hear a TCP joke.
Okay, I'll send you a TCP joke now.
Okay, I'll receive the TCP joke now.
... (and so on)
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Your local machine always has the IP address 127.0.0.1 and the name localhost. You
might see this called the loopback interface. If it’s connected to the Internet, your machine
will also have a public IP. If you’re just using a home computer, it’s behind equipment
such as a cable modem or router. You can run Internet protocols even between processes
on the same machine.
Most of the Internet with which we interact—the Web, database servers, and so on—is
based on the TCP protocol running atop the IP protocol; for brevity, TCP/IP. Let’s first
look at some basic Internet services. After that, we’ll explore general networking pat‐
terns.
Sockets
We’ve saved this topic until now because you don’t need to know all the low-level details
to use the higher levels of the Internet. But if you like to know how things work, this is
for you.
The lowest level of network programming uses a socket, borrowed from the C language
and the Unix operating system. Socket-level coding is tedious. You’ll have more fun
using something like ZeroMQ, but it’s useful to see what lies beneath. For instance,
messages about sockets often turn up when networking errors take place.
Let’s write a very simple client-server exchange. The client sends a string in a UDP
datagram to a server, and the server returns a packet of data containing a string. The
server needs to listen at a particular address and port—like a post office and a post office
box. The client needs to know these two values to deliver its message, and receive any
reply.
In the following client and server code, address is a tuple of (address, port). The address
is a string, which can be a name or an IP address. When your programs are just talking
to one another on the same machine, you can use the name 'localhost' or the equiv‐
alent address '127.0.0.1'.
First, let’s send a little data from one process to another and return a little data back to
the originator. The first program is the client and the second is the server. In each
program, we’ll print the time and open a socket. The server will listen for connections
to its socket, and the client will write to its socket, which transmits a message to the
server.
Here’s the first program, udp_server.py:
from datetime import datetime
import socket
server_address = ('localhost', 6789)
max_size = 4096
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print('Starting the server at', datetime.now())
print('Waiting for a client to call.')
server = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
server.bind(server_address)
data, client = server.recvfrom(max_size)
print('At', datetime.now(), client, 'said', data)
server.sendto(b'Are you talking to me?', client)
server.close()
The server has to set up networking through two methods imported from the socket
package. The first method, socket.socket, creates a socket, and the second, bind, binds
to it (listens to any data arriving at that IP address and port). AF_INET means we’ll create
an Internet (IP) socket. (There’s another type for Unix domain sockets, but those work
only on the local machine.) SOCK_DGRAM means we’ll send and receive datagrams—in
other words, we’ll use UDP.
At this point, the server sits and waits for a datagram to come in (recvfrom). When one
arrives, the server wakes up and gets both the data and information about the client.
The client variable contains the address and port combination needed to reach the
client. The server ends by sending a reply and closing its connection.
Let’s take a look at udp_client.py:
import socket
from datetime import datetime
server_address = ('localhost', 6789)
max_size = 4096
print('Starting the client at', datetime.now())
client = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
client.sendto(b'Hey!', server_address)
data, server = client.recvfrom(max_size)
print('At', datetime.now(), server, 'said', data)
client.close()
The client has most of the same methods as the server (with the exception of bind()).
The client sends and then receives, whereas the server receives first.
Start the server first, in its own window. It will print its greeting and then wait with an
eerie calm until a client sends it some data:
$ python udp_server.py
Starting the server at 2014-02-05 21:17:41.945649
Waiting for a client to call.
Next, start the client in another window. It will print its greeting, send data to the server,
print the reply, and then exit:
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$ python udp_client.py
Starting the client at 2014-02-05 21:24:56.509682
At 2014-02-05 21:24:56.518670 ('127.0.0.1', 6789) said b'Are you talking to me?'
Finally, the server will print something like this, and then exit:
At 2014-02-05 21:24:56.518473 ('127.0.0.1', 56267) said b'Hey!'
The client needed to know the server’s address and port number but didn’t need to
specify a port number for itself. That was automatically assigned by the system—in this
case, it was 56267.
UDP sends data in single chunks. It does not guarantee delivery. If
you send multiple messages via UDP, they can arrive out of order, or
not at all. It’s fast, light, connectionless, and unreliable.
Which brings us to TCP (Transmission Control Protocol). TCP is used for longer-lived
connections, such as the Web. TCP delivers data in the order in which you send it. If
there were any problems, it tries to send it again. Let’s shoot a few packets from client
to server and back with TCP.
tcp_client.py acts like the previous UDP client, sending only one string to the server, but
there are small differences in the socket calls, illustrated here:
import socket
from datetime import datetime
address = ('localhost', 6789)
max_size = 1000
print('Starting the client at', datetime.now())
client = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
client.connect(address)
client.sendall(b'Hey!')
data = client.recv(max_size)
print('At', datetime.now(), 'someone replied', data)
client.close()
We’ve replaced SOCK_DGRAM with SOCK_STREAM to get the streaming protocol, TCP. We
also added a connect() call to set up the stream. We didn’t need that for UDP because
each datagram was on its own in the wild, wooly Internet.
tcp_server.py also differs from its UDP cousin:
from datetime import datetime
import socket
address = ('localhost', 6789)
max_size = 1000
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print('Starting the server at', datetime.now())
print('Waiting for a client to call.')
server = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
server.bind(address)
server.listen(5)
client, addr = server.accept()
data = client.recv(max_size)
print('At', datetime.now(), client, 'said', data)
client.sendall(b'Are you talking to me?')
client.close()
server.close()
server.listen(5) is configured to queue up to five client connections before refusing
new ones. server.accept() gets the first available message as it arrives. The cli
ent.recv(1000) sets a maximum acceptable message length of 1,000 bytes.
As you did earlier, start the server and then the client, and watch the fun. First, the
server:
$ python tcp_server.py
Starting the server at 2014-02-06 22:45:13.306971
Waiting for a client to call.
At 2014-02-06 22:45:16.048865 <socket.socket object, fd=6, family=2, type=1,
proto=0> said b'Hey!'
Now, start the client. It will send its message to the server, receive a response, and then
exit:
$ python tcp_client.py
Starting the client at 2014-02-06 22:45:16.038642
At 2014-02-06 22:45:16.049078 someone replied b'Are you talking to me?'
The server collects the message, prints it, responds, and then quits:
At 2014-02-06 22:45:16.048865 <socket.socket object, fd=6, family=2, type=1,
proto=0> said b'Hey!'
Notice that the TCP server called client.sendall() to respond, and the earlier UDP
server called client.sendto(). TCP maintains the client-server connection across
multiple socket calls and remembers the client’s IP address.
This didn’t look so bad, but if you try to write anything more complex, you’ll see how
low-level sockets really are. Here are some of the complications with which you need
to cope:
• UDP sends messages, but their size is limited, and they’re not guaranteed to reach
their destination.
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• TCP sends streams of bytes, not messages. You don’t know how many bytes the
system will send or receive with each call.
• To exchange entire messages with TCP, you need some extra information to reas‐
semble the full message from its segments: a fixed message size (bytes), or the size
of the full message, or some delimiting character.
• Because messages are bytes, not Unicode text strings, you need to use the Python
bytes type. For more information on that, see Chapter 7.
After all of this, if you find yourself fascinated by socket programming, check out the
Python socket programming HOWTO for more details.
ZeroMQ
We’ve already seen ZeroMQ sockets used for pub-sub. ZeroMQ is a library. Sometimes
described as sockets on steroids, ZeroMQ sockets do the things that you sort of expected
plain sockets to do:
• Exchange entire messages
• Retry connections
• Buffer data to preserve it when the timing between senders and receivers doesn’t
line up
The online guide is well written and witty, and it presents the best description of net‐
working patterns that I’ve seen. The printed version (ZeroMQ: Messaging for Many Ap‐
plications, by Pieter Hintjens, from that animal house, O’Reilly) has that good code smell
and a big fish on the cover, rather than the other way around. All the examples in the
printed guide are in the C language, but the online version lets you pick from multiple
languages for each code example. The Python examples are also viewable. In this chapter,
I’ll show you some basic uses for ZeroMQ in Python.
ZeroMQ is like a Lego set, and we all know that you can build an amazing variety of
things from a few Lego shapes. In this case, you construct networks from a few socket
types and patterns. The basic “Lego pieces” presented in the following list are the
ZeroMQ socket types, which by some twist of fate look like the network patterns we’ve
already discussed:
• REQ (synchronous request)
• REP (synchronous reply)
• DEALER (asynchronous request)
• ROUTER (asynchronous reply)
• PUB (publish)
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• SUB (subscribe)
• PUSH (fanout)
• PULL (fanin)
To try these yourself, you’ll need to install the Python ZeroMQ library by typing this
command:
$ pip install pyzmq
The simplest pattern is a single request-reply pair. This is synchronous: one socket
makes a request and then the other replies. First, the code for the reply (server),
zmq_server.py:
import zmq
host = '127.0.0.1'
port = 6789
context = zmq.Context()
server = context.socket(zmq.REP)
server.bind("tcp://%s:%s" % (host, port))
while True:
# Wait for next request from client
request_bytes = server.recv()
request_str = request_bytes.decode('utf-8')
print("That voice in my head says: %s" % request_str)
reply_str = "Stop saying: %s" % request_str
reply_bytes = bytes(reply_str, 'utf-8')
server.send(reply_bytes)
We create a Context object: this is a ZeroMQ object that maintains state. Then, we make
a ZeroMQ socket of type REP (for REPly). We call bind() to make it listen on a particular
IP address and port. Notice that they’re specified in a string such as 'tcp://localhost:
6789' rather than a tuple, as in the plain socket examples.
This example keeps receiving requests from a sender and sending a response. The mes‐
sages can be very long—ZeroMQ takes care of the details.
Following is the code for the corresponding request (client), zmq_client.py. Its type is
REQ (for REQuest), and it calls connect() rather than bind().
import zmq
host = '127.0.0.1'
port = 6789
context = zmq.Context()
client = context.socket(zmq.REQ)
client.connect("tcp://%s:%s" % (host, port))
for num in range(1, 6):
request_str = "message #%s" % num
request_bytes = request_str.encode('utf-8')
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client.send(request_bytes)
reply_bytes = client.recv()
reply_str = reply_bytes.decode('utf-8')
print("Sent %s, received %s" % (request_str, reply_str))
Now it’s time to start them. One interesting difference from the plain socket examples
is that you can start the server and client in either order. Go ahead and start the server
in one window in the background:
$ python zmq_server.py &
Start the client in the same window:
$ python zmq_client.py
You’ll see these alternating output lines from the client and server:
That
Sent
That
Sent
That
Sent
That
Sent
That
Sent
voice in
'message
voice in
'message
voice in
'message
voice in
'message
voice in
'message
my head says 'message #1'
#1', received 'Stop saying
my head says 'message #2'
#2', received 'Stop saying
my head says 'message #3'
#3', received 'Stop saying
my head says 'message #4'
#4', received 'Stop saying
my head says 'message #5'
#5', received 'Stop saying
message #1'
message #2'
message #3'
message #4'
message #5'
Our client ends after sending its fifth message, but we didn’t tell the server to quit, so it
sits by the phone, waiting for another message. If you run the client again, it will print
the same five lines, and the server will print its five also. If you don’t kill the zmq_serv‐
er.py process and try to run another one, Python will complain that the address is already
is use:
$ python zmq_server.py &
[2] 356
Traceback (most recent call last):
File "zmq_server.py", line 7, in <module>
server.bind("tcp://%s:%s" % (host, port))
File "socket.pyx", line 444, in zmq.backend.cython.socket.Socket.bind
(zmq/backend/cython/socket.c:4076)
File "checkrc.pxd", line 21, in zmq.backend.cython.checkrc._check_rc
(zmq/backend/cython/socket.c:6032)
zmq.error.ZMQError: Address already in use
The messages need to be sent as byte strings, so we encoded our example’s text strings
in UTF-8 format. You can send any kind of message you like, as long as you convert it
to bytes. We used simple text strings as the source of our messages, so encode() and
decode() were enough to convert to and from byte strings. If your messages have other
data types, you can use a library such as MessagePack.
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Even this basic REQ-REP pattern allows for some fancy communication patterns, be‐
cause any number of REQ clients can connect() to a single REP server. The server
handles requests one at a time, synchronously, but doesn’t drop other requests that are
arriving in the meantime. ZeroMQ buffers messages, up to some specified limit, until
they can get through; that’s where it earns the Q in its name. The Q stands for Queue,
the M stands for Message, and the Zero means there doesn’t need to be any broker.
Although ZeroMQ doesn’t impose any central brokers (intermediaries), you can build
them where needed. For example, use DEALER and ROUTER sockets to connect mul‐
tiple sources and/or destinations asynchronously.
Multiple REQ sockets connect to a single ROUTER, which passes each request to a
DEALER, which then contacts any REP sockets that have connected to it (Figure 11-1).
This is similar to a bunch of browsers contacting a proxy server in front of a web server
farm. It lets you add multiple clients and servers as needed.
Figure 11-1. Using a broker to connect multiple clients and services
The REQ sockets connect only to the ROUTER socket; the DEALER connects to the
multiple REP sockets behind it. ZeroMQ takes care of the nasty details, ensuring that
the requests are load balanced and that the replies go back to the right place.
Another networking pattern called the ventilator uses PUSH sockets to farm out asyn‐
chronous tasks, and PULL sockets to gather the results.
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The last notable feature of ZeroMQ is that it scales up and down, just by changing the
connection type of the socket when it’s created:
• tcp between processes, on one or more machines
• ipc between processes on one machine
• inproc between threads in a single process
That last one, inproc, is a way to pass data between threads without locks, and an
alternative to the threading example in “Threads” on page 265.
After using ZeroMQ, you might never want to write raw socket code again.
ZeroMQ is certainly not the only message-passing library that Python
supports. Message passing is one of the most popular ideas in net‐
working, and Python keeps up with other languages. The Apache
project, whose web server we saw in “Apache” on page 232, also
maintains the ActiveMQ project, including several Python inter‐
faces using the simple-text STOMP protocol. RabbitMQ is also pop‐
ular, and has useful online Python tutorials.
Scapy
Sometimes you need to dip into the networking stream and see the bytes swimming
by. You might want to debug a web API, or track down some security issue. The scapy
library is an excellent Python tool for packet investigation, and much easier than writing
and debugging C programs. It’s actually a little language for constructing and analyzing
packets.
I planned to include some example code here but changed my mind for two reasons:
• scapy hasn’t been ported to Python 3 yet. That hasn’t stopped us before, when we’ve
used pip2 and python2, but …
• The installation instructions for scapy are, I think, too intimidating for an intro‐
ductory book.
If you’re so inclined, take a look at the examples in the main documentation site. They
might encourage you to brave an installation on your machine.
Finally, don’t confuse scapy with scrapy, which is covered in “Crawl and Scrape” on
page 237.
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Internet Services
Python has an extensive networking toolset. In the following sections, we’ll look at ways
to automate some of the most popular Internet services. The official, comprehensive
documentation is available online.
Domain Name System
Computers have numeric IP addresses such as 85.2.101.94, but we remember names
better than numbers. The Domain Name System (DNS) is a critical Internet service that
converts IP addresses to and from names via a distributed database. Whenever you’re
using a web browser and suddenly see a message like “looking up host,” you’ve probably
lost your Internet connection, and your first clue is a DNS failure.
Some DNS functions are found in the low-level socket module. gethostbyname()
returns the IP address for a domain name, and the extended edition gethostby
name_ex() returns the name, a list of alternative names, and a list of addresses:
>>> import socket
>>> socket.gethostbyname('www.crappytaxidermy.com')
'66.6.44.4'
>>> socket.gethostbyname_ex('www.crappytaxidermy.com')
('crappytaxidermy.com', ['www.crappytaxidermy.com'], ['66.6.44.4'])
The getaddrinfo() method looks up the IP address, but it also returns enough infor‐
mation to create a socket to connect to it:
>>> socket.getaddrinfo('www.crappytaxidermy.com', 80)
[(2, 2, 17, '', ('66.6.44.4', 80)), (2, 1, 6, '', ('66.6.44.4', 80))]
The preceding call returned two tuples, the first for UDP, and the second for TCP (the
6 in the 2, 1, 6 is the value for TCP).
You can ask for TCP or UDP information only:
>>> socket.getaddrinfo('www.crappytaxidermy.com', 80, socket.AF_INET,
socket.SOCK_STREAM)
[(2, 1, 6, '', ('66.6.44.4', 80))]
Some TCP and UDP port numbers are reserved for certain services by IANA, and are
associated with service names. For example, HTTP is named http and is assigned TCP
port 80.
These functions convert between service names and port numbers:
>>> import socket
>>> socket.getservbyname('http')
80
>>> socket.getservbyport(80)
'http'
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Python Email Modules
The standard library contains these email modules:
• smtplib for sending email messages via Simple Mail Transfer Protocol (SMTP)
• email for creating and parsing email messages
• poplib for reading email via Post Office Protocol 3 (POP3)
• imaplib for reading email via Internet Message Access Protocol (IMAP)
The official documentation contains sample code for all of these libraries.
If you want to write your own Python SMTP server, try smtpd.
A pure-python SMTP server called Lamson allows you to store messages in databases,
and you can even block spam.
Other protocols
Using the standard ftplib module, you can push bytes around by using the File Transfer
Protocol (FTP). Although it’s an old protocol, FTP still performs very well.
You’ve seen many of these modules in various places in this book, but also try the doc‐
umentation for standard library support of Internet protocols.
Web Services and APIs
Information providers always have a website, but those are targeted for human eyes,
not automation. If data is published only on a website, anyone who wants to access and
structure the data needs to write scrapers (as shown in “Crawl and Scrape” on page
237), and rewrite them each time a page format changes. This is usually tedious. In
contrast, if a website offers an API to its data, the data becomes directly available to
client programs. APIs change less often than web page layouts, so client rewrites are less
common. A fast, clean data pipeline also makes it easier to build mashups—combina‐
tions that might not have been foreseen but can be useful and even profitable.
In many ways, the easiest API is a web interface, but one that provides data in a structured
format such as JSON or XML rather than plain text or HTML. The API might be minimal
or a full-fledged RESTful API (defined in “Web APIs and Representational State Trans‐
fer” on page 236), but it provides another outlet for those restless bytes.
At the very beginning of this book, you can see a web API: it picks up the most popular
videos from YouTube. This next example might make more sense now that you’ve read
about web requests, JSON, dictionaries, lists, and slices:
import requests
url = "https://gdata.youtube.com/feeds/api/standardfeeds/top_rated?alt=json"
response = requests.get(url)
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data = response.json()
for video in data['feed']['entry'][0:6]:
print(video['title']['$t'])
APIs are especially useful for mining well-known social media sites such as Twitter,
Facebook, and LinkedIn. All these sites provide APIs that are free to use, but they require
you to register and get a key (a long-generated text string, sometimes also known as a
token) to use when connecting. The key lets a site determine who’s accessing its data. It
can also serve as a way to limit request traffic to servers. The YouTube example you just
looked at did not require an API key for searching, but it would if you made calls that
updated data at YouTube.
Here are some interesting service APIs:
• New York Times
• YouTube
• Twitter
• Facebook
• Weather Underground
• Marvel Comics
You can see examples of APIs for maps in Appendix B, and others in Appendix C.
Remote Processing
Most of the examples in this book have demonstrated how to call Python code on the
same machine, and usually in the same process. Thanks to Python’s expressiveness, you
can also call code on other machines as though they were local. In advanced settings, if
you run out of space on your single machine, you can expand beyond it. A network of
machines gives you access to more processes and/or threads.
Remote Procedure Calls
Remote Procedure Calls (RPCs) look like normal functions but execute on remote ma‐
chines across a network. Instead of calling a RESTful API with arguments encoded in
the URL or request body, you call an RPC function on your own machine. Here’s what
happens under the hood of the RPC client:
1. It converts your function arguments into bytes (sometimes this is called marshal‐
ling, or serializing, or just encoding).
2. It sends the encoded bytes to the remote machine.
And here’s what happens on the remote machine:
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1. It receives the encoded request bytes.
2. After receiving the bytes, the RPC client decodes the bytes back to the original data
structures (or equivalent ones, if the hardware and software differ between the two
machines).
3. The client then finds and calls the local function with the decoded data.
4. Next, it encodes the function results.
5. Last, the client sends the encoded bytes back to the caller.
And finally, the machine that started it all decodes the bytes to return values.
RPC is a popular technique, and people have implemented it in many ways. On the
server side, you start a server program, connect it with some byte transport and en‐
coding/decoding method, define some service functions, and light up your RPC is open
for business sign. The client connects to the server and calls one of its functions via RPC.
The standard library includes one RPC implementation that uses XML as the exchange
format: xmlrpc. You define and register functions on the server, and the client calls them
as though they were imported. First, let’s explore the file xmlrpc_server.py:
from xmlrpc.server import SimpleXMLRPCServer
def double(num):
return num * 2
server = SimpleXMLRPCServer(("localhost", 6789))
server.register_function(double, "double")
server.serve_forever()
The function we’re providing on the server is called double(). It expects a number as
an argument and returns the value of that number times two. The server starts up on
an address and port. We need to register the function to make it available to clients via
RPC. Finally, start serving and carry on.
Now, you guessed it, xmlrpc_client.py:
import xmlrpc.client
proxy = xmlrpc.client.ServerProxy("http://localhost:6789/")
num = 7
result = proxy.double(num)
print("Double %s is %s" % (num, result))
The client connects to the server by using ServerProxy(). Then, it calls the function
proxy.double(). Where did that come from? It was created dynamically by the server.
The RPC machinery magically hooks this function name into a call to the remote server.
Give it a try—start the server and then run the client:
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$ python xmlrpc_server.py
Next, run the client:
$ python xmlrpc_client.py
Double 7 is 14
The server then prints the following:
127.0.0.1 - - [13/Feb/2014 20:16:23] "POST / HTTP/1.1" 200 -
Popular transport methods are HTTP and ZeroMQ. Common encodings besides XML
include JSON, Protocol Buffers, and MessagePack. There are many Python packages
for JSON-based RPC, but many of them either don’t support Python 3 or seem a bit
tangled. Let’s look at something different: MessagePack’s own Python RPC implemen‐
tation. Here’s how to install it:
$ pip install msgpack-rpc-python
This will also install tornado, a Python event-based web server that this library uses as
a transport. As usual, the server comes first (msgpack_server.py):
from msgpackrpc import Server, Address
class Services():
def double(self, num):
return num * 2
server = Server(Services())
server.listen(Address("localhost", 6789))
server.start()
The Services class exposes its methods as RPC services. Go ahead and start the client,
msgpack_client.py:
from msgpackrpc import Client, Address
client = Client(Address("localhost", 6789))
num = 8
result = client.call('double', num)
print("Double %s is %s" % (num, result))
To run these, follow the usual drill: start the server, start the client, see the results:
$ python msgpack_server.py
$ python msgpack_client.py
Double 8 is 16
fabric
The fabric package lets you run remote or local commands, upload or download files,
and run as a privileged user with sudo. The package uses Secure Shell (SSH; the en‐
crypted text protocol that has largely replaced telnet) to run programs on remote
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machines. You write functions (in Python) in a so-called fabric file and indicate if they
should be run locally or remotely. When you run these with the fabric program (called
fab, but not as a tribute to the Beatles or detergent) you indicate which remote machines
to use and which functions to call. It’s simpler than the RPC examples we’ve seen.
As this was written, the author of fabric was merging some fixes to
work with Python 3. If those go through, the examples below will
work. Until then, you’ll need to run them using Python 2.
First, install fabric by typing the following:
$ pip2 install fabric
You can run Python code locally from a fabric file directly without SSH. Save this first
file as fab1.py:
def iso():
from datetime import date
print(date.today().isoformat())
Now, type the following to run it:
$ fab -f fab1.py -H localhost iso
[localhost] Executing task 'iso'
2014-02-22
Done.
The -f fab1.py option specifies to use fabric file fab1.py instead of the default fab‐
file.py. The -H localhost option indicates to run the command on your local machine.
Finally, iso is the name of the function in the fab file to run. It works like the RPCs that
you saw earlier. You can find options on the site’s documentation.
To run external programs on your local or remote machines, they need to have an SSH
server running. On Unix-like systems, this server is sshd; service sshd status will
report if it’s up, and service sshd start will start it, if needed. On a Mac, open System
Preferences, click the Sharing tab, and then click the Remote Login checkbox. Windows
doesn’t have built-in SSH support; your best bet is to install putty.
We’ll reuse the function name iso, but this time have it run a command by using
local(). Here’s the command and its output:
from fabric.api import local
def iso():
local('date -u')
$ fab -f fab2.py -H localhost iso
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[localhost] Executing task 'iso'
[localhost] local: date -u
Sun Feb 23 05:22:33 UTC 2014
Done.
Disconnecting from localhost... done.
The remote counterpart of local() is run(). Here’s fab3.py:
from fabric.api import run
def iso():
run('date -u')
Using run() instructs fabric to use SSH to connect to whatever hosts were specified
on the command line with -H. If you have a local network and can connect via SSH to
a host, use that hostname in the command after the -H (shown in the example that
follows). If not, use localhost, and it will act as though it were talking to another
machine; this can be handy for testing. For this example, let’s use localhost again:
$ fab -f fab3.py -H localhost iso
[localhost]
[localhost]
[localhost]
[localhost]
[localhost]
Executing task 'iso'
run: date -u
Login password for 'yourname':
out: Sun Feb 23 05:26:05 UTC 2014
out:
Done.
Disconnecting from localhost... done.
Notice that it prompted for my login password. To avoid this, you can embed your
password in the fabric file as follows:
from fabric.api import run
from fabric.context_managers import env
env.password = "your password goes here"
def iso():
run('date -u')
Go ahead and run it:
$ fab -f fab4.py -H localhost iso
[localhost]
[localhost]
[localhost]
[localhost]
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Executing task 'iso'
run: date -u
out: Sun Feb 23 05:31:00 UTC 2014
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Done.
Disconnecting from localhost... done.
Putting your password in your code is both brittle and insecure. A
better way to specify the necessary password is to configure SSH with
public and private keys, by using ssh-keygen.
Salt
Salt started as a way to implement remote execution, but it grew to a full-fledged systems
management platform. Based on ZeroMQ rather than SSH, it can scale to thousands of
servers.
Salt has not yet been ported to Python 3. In this case, I won’t show Python 2 examples.
If you’re interested in this area, read the documents, and watch for announcements
when they do complete the port.
Alternative products include puppet and chef, which are closely tied
to Ruby. The ansible package, which like Salt is written in Python,
is also comparable. It’s free to download and use, but support and
some add-on packages require a commercial license. It uses SSH by
default and does not require any special software to be installed on
the machines that it will manage.
salt and ansible are both functional supersets of fabric, handling
initial configuration, deployment, and remote execution.
Big Fat Data and MapReduce
As Google and other Internet companies grew, they found that traditional computing
solutions didn’t scale. Software that worked for single machines, or even a few dozen,
could not keep up with thousands.
Disk storage for databases and files involved too much seeking, which requires me‐
chanical movement of disk heads. (Think of a vinyl record, and the time it takes to move
the needle from one track to another manually. And think of the screeching sound it
makes when you drop it too hard, not to mention the sounds made by the record’s
owner.) But you could stream consecutive segments of the disk more quickly.
Developers found that it was faster to distribute and analyze data on many networked
machines than on individual ones. They could use algorithms that sounded simplistic,
but actually worked better overall with massively distributed data. One of these is Map‐
Reduce, which spreads a calculation across many machines and then gathers the results.
It’s similar to working with queues.
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After Google published its results in a paper, Yahoo followed with an open source Javabased package named Hadoop (named after the toy stuffed elephant of the lead pro‐
grammer’s son).
The phrase big data applies here. Often it just means “data too big to fit on my machine”:
data that exceeds the disk, memory, CPU time, or all of the above. To some organizations,
if big data is mentioned somewhere in a question, the answer is always Hadoop. Hadoop
copies data among machines, running them through map and reduce programs, and
saving the results on disk at each step.
This batch process can be slow. A quicker method called Hadoop streaming works like
Unix pipes, streaming the data through programs without requiring disk writes at each
step. You can write Hadoop streaming programs in any language, including Python.
Many Python modules have been written for Hadoop, and some are discussed in the
blog post “A Guide to Python Frameworks for Hadoop”. The Spotify company, known
for streaming music, open sourced its Python component for Hadoop streaming, Lui‐
gi. The Python 3 port is still incomplete.
A rival named Spark was designed to run ten to a hundred times faster than Hadoop.
It can read and process any Hadoop data source and format. Spark includes APIs for
Python and other languages. You can find the installation documents online.
Another alternative to Hadoop is Disco, which uses Python for MapReduce processing
and Erlang for communication. Alas, you can’t install it with pip; see the documentation.
See Appendix C for related examples of parallel programming, in which a large structured
calculation is distributed among many machines.
Working in the Clouds
Not so long ago, you would buy your own servers, bolt them into racks in data centers,
and install layers of software on them: operating systems, device drivers, file systems,
databases, web servers, email servers, name servers, load balancers, monitors, and more.
Any initial novelty wore off as you tried to keep multiple systems alive and responsive.
And you worried constantly about security.
Many hosting services offered to take care of your servers for a fee, but you still leased
the physical devices and had to pay for your peak load configuration at all times.
With more individual machines, failures are no longer infrequent: they’re very common.
You need to scale services horizontally and store data redundantly. You can’t assume
that the network operates like a single machine. The eight fallacies of distributed com‐
puting, according to Peter Deutsch, are as follows:
• The network is reliable.
• Latency is zero.
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• Bandwidth is infinite.
• The network is secure.
• Topology doesn’t change.
• There is one administrator.
• Transport cost is zero.
• The network is homogeneous.
You can try to build these complex distributed systems, but it’s a lot of work, and a
different toolset is needed. To borrow an analogy, when you have a handful of servers,
you treat them like pets—you give them names, know their personalities, and nurse
them back to health when needed. But at scale, you treat servers more like livestock:
they look alike, have numbers, and are just replaced if they have any problems.
Instead of building, you can rent servers in the cloud. By adopting this model, mainte‐
nance is someone else’s problem, and you can concentrate on your service, or blog, or
whatever you want to show the world. Using web dashboards and APIs, you can spin
up servers with whatever configuration you need, quickly and easily—they’re elastic.
You can monitor their status, and be alerted if some metric exceeds a given threshold.
Clouds are currently a pretty hot topic, and corporate spending on cloud components
has spiked.
Let’s see how Python interacts with some popular clouds.
Google
Google uses Python a lot internally, and it employs some prominent Python developers
(even Guido van Rossum himself, for some time).
Go to the App Engine site and then, under “Choose a Language,” click in the Python
box. You can type Python code into the Cloud Playground and see results just below.
Just after that are links and directions to download the Python SDK to your machine.
This allows you to develop against Google’s cloud APIs on your own hardware. Fol‐
lowing this are details on how to deploy your application to AppEngine itself.
From Google’s main cloud page, you can find details on its services, including these:
App Engine
A high-level platform, including Python tools such as flask and django.
Compute Engine
Create clusters of virtual machines for large distributed computing tasks.
Cloud Storage
Object storage (objects are files, but there are no directory hierarchies).
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Cloud Datastore
A large NoSQL database.
Cloud SQL
A large SQL database.
Cloud Endpoints
Restful access to applications.
BigQuery
Hadoop-like big data.
Google services compete with Amazon and OpenStack, a segue if there ever was one.
Amazon
As Amazon was growing from hundreds to thousands to millions of servers, developers
ran into all the nasty problems of distributed systems. One day in 2002 or thereabouts,
CEO Jeff Bezos declared to Amazon employees that, henceforth, all data and function‐
ality needed to be exposed only via network service interfaces—not files, or databases,
or local function calls. They had to design these interfaces as though they were being
offered to the public. The memo ended with a motivational nugget: “Anyone who doesn’t
do this will be fired.”
Not surprisingly, developers got to work, and over time built a very large serviceoriented architecture. They borrowed or innovated many solutions, evolving into Am‐
azon Web Services (AWS), which now dominates the market. It now contains dozens
of services, but the most relevant are the following:
Elastic Beanstalk
High-level application platform
EC2 (Elastic Compute)
Distributed computing
S3 (Simple Storage Service)
Object storage
RDS
Relational databases (MySQL, PostgreSQL, Oracle, MSSQL)
DynamoDB
NoSQL database
Redshift
Data warehouse
EMR
Hadoop
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For details on these and other AWS services, download the Amazon Python SDK and
read the help section.
The official Python AWS library, boto, is another footdragger, not yet fully ported to
Python 3. You’ll need to use Python 2, or try an alternative, which you can do by search‐
ing the Python Package Index for “aws” or “amazon.”
OpenStack
The second most popular cloud service provider has been Rackspace. In 2010, it formed
an unusual partnership with NASA to merge some of their cloud infrastructure into
OpenStack. This is a freely available open source platform to build public, private, and
hybrid clouds. A new release is made every six months, the most recent containing over
1.25 million lines of Python from many contributors. OpenStack is used in production
by a growing number of organizations, including CERN and PayPal.
OpenStack’s main APIs are RESTful, with Python modules providing programmatic
interfaces, and command-line Python programs for shell automation. Here are some
of the standard services in the current release:
Keystone
Identity service, providing authentication (for example, user/password), authori‐
zation (capabilities), and service discovery.
Nova
Compute service, distributing work across networked servers.
Swift
Object storage, such as Amazon’s S3. It’s used by Rackspace’s Cloud Files service.
Glance
Mid-level image storage service.
Cinder
Low-level block storage service.
Horizon
Web-based dashboard for all the services.
Neutron
Network management service.
Heat
Orchestration (multicloud) service.
Ceilometer
Telemetry (metrics, monitoring, and metering) service.
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Other services are proposed from time to time, which then go through an incubation
process and might become part of the standard OpenStack platform.
OpenStack runs on Linux or within a Linux virtual machine (VM). The installation of
its core services is still somewhat involved. The fastest way to install OpenStack on Linux
is to use Devstack and watch all the explanatory text flying by as it runs. You’ll end up
with a web dashboard that can view and control the other services.
If you want to install some or all of OpenStack manually, use your Linux distribution’s
package manager. All of the major Linux vendors support OpenStack and are providing
official packages on their download servers. Browse the main OpenStack site for in‐
stallation documents, news, and related information.
OpenStack development and corporate support are accelerating. It’s been compared to
Linux when it was disrupting the proprietary Unix versions.
Things to Do
11.1 Use a plain socket to implement a current-time-service. When a client sends the
string time to the server, return the current date and time as an ISO string.
11.2 Use ZeroMQ REQ and REP sockets to do the same thing.
11.3 Try the same with XMLRPC.
11.4 You may have seen the old I Love Lucy television episode in which Lucy and Ethel
worked in a chocolate factory (it’s a classic). The duo fell behind as the conveyor belt
that supplied the confections for them to process began operating at an ever-faster rate.
Write a simulation that pushes different types of chocolates to a Redis list, and Lucy is
a client doing blocking pops of this list. She needs 0.5 seconds to handle a piece of
chocolate. Print the time and type of each chocolate as Lucy gets it, and how many
remain to be handled.
11.5 Use ZeroMQ to publish the poem from exercise 7.7 (from “Things to Do” on page
170), one word at a time. Write a ZeroMQ consumer that prints every word that starts
with a vowel, and another that prints every word that contains five letters. Ignore punc‐
tuation characters.
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CHAPTER 12
Be a Pythonista
Always wanted to travel back in time to try fighting a younger version of yourself? Soft‐
ware development is the career for you!
— Elliot Loh
This chapter is devoted to the art and science of Python development, with “best prac‐
tice” recommendations. Absorb them, and you too can be a card-carrying Pythonista.
About Programming
First, a few notes about programming, based on personal experience.
My original career path was science, and I taught myself programming to analyze and
display experimental data. I expected computer programming to be like my impression
of accounting—precise but dull. I was surprised to find that I enjoyed it. Part of the fun
was its logical aspects—like solving puzzles—but part was creative. You had to write
your program correctly to get the right results, but you had the freedom to write it any
way you wanted. It was an unusual balance of right-brain and left-brain thinking.
After I wandered off into a career in programming, I also learned that the field had many
niches, with very different tasks and types of people. You could delve into computer
graphics, operating systems, business applications—even science.
If you’re a programmer, you might have had a similar experience yourself. If you’re not,
you might try programming a bit to see if it fits your personality, or at least helps you
to get something done. As I may have mentioned much earlier in this book, math skills
are not so important. It seems that the ability to think logically is most important, and
that an aptitude for languages seems to help. Finally, patience helps, especially when
you’re tracking down an elusive bug in your code.
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Find Python Code
When you need to develop some code, the fastest solution is to steal it. Well…that is,
from a source from which you’re allowed to steal code.
The Python standard library is wide, deep, and mostly clear. Dive in and look for those
pearls.
Like the halls of fame for various sports, it takes time for a module to get into the standard
library. New packages are appearing outside constantly, and throughout this book I’ve
highlighted some that either do something new or do something old better. Python is
advertised as batteries included, but you might need a new kind of battery.
So where, outside the standard library, should you look for good Python code?
The first place to look is the Python Package Index (PyPI). Formerly named the Cheese
Shop after a Monty Python skit, this site is constantly updated with Python packages—
over 39,000 as I write this. When you use pip (see the next section), it searches PyPI.
The main PyPI page shows the most recently added packages. You can also conduct a
direct search. For instance, Table 12-1 lists the results of a search for genealogy.
Table 12-1. Packages on genealogy that you can find on PyPi
Package
Weight* Description
Gramps 3.4.2
5
Research, organize, and share your family genealogy
python-fs-stack 0.2
2
Python wrapper for all FamilySearch APIs
human-names 0.1.1 1
Human names
nameparser 0.2.8
A simple Python module for parsing human names into their individual components
1
The best matches have higher weight values, so Gramps looks like your best bet here. Go
to the Python website to see the documentation and download links.
Another popular repository is GitHub. See what Python packages are currently popular.
Popular Python recipes has over four thousand short Python programs on every subject.
Install Packages
There are three ways to install Python packages:
• Use pip if you can. You can install most of the Python packages you’re likely to
encounter with pip.
• Sometimes, you can use a package manager for your operating system.
• Install from source.
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If you’re interested in several packages in the same area, you might find a Python dis‐
tribution that already includes them. For instance, in Appendix C, you can try out a
number of numeric and scientific programs that would be tedious to install individually
but are included with distributions such as Anaconda.
Use pip
Python packaging has had some limitations. An earlier installation tool called easy_in
stall has been replaced by one called pip, but neither had been in the standard Python
installation. If you’re supposed to install things by using pip, from where did you get
pip? Starting with Python 3.4, pip will finally be included with the rest of Python to
avoid such existential crises. If you’re using an earlier version of Python 3 and don’t have
pip, you can get it from http://www.pip-installer.org.
The simplest use of pip is to install the latest version of a single package by using the
following command:
$ pip install flask
You will see details on what it’s doing, just so you don’t think it’s goofing off: down‐
loading, running setup.py, installing files on your disk, and other details.
You can also ask pip to install a specific version:
$ pip install flask==0.9.0
Or, a minimum version (this is useful when some feature that you can’t live without
turns up in a particular version):
$ pip install 'flask>=0.9.0'
In the preceding example, those single quotes prevent the > from being interpreted by
the shell to redirect output to a file called =0.9.0.
If you want to install more than one Python package, you can use a requirements file.
Although it has many options, the simplest use is a list of packages, one per line, op‐
tionally with a specific or relative version:
$ pip -r requirements.txt
Your sample requirements.txt file might contain this:
flask==0.9.0
django
psycopg2
Use a Package Manager
Apple’s OS X includes the third-party packagers homebrew (brew) and ports. They
work a little like pip, but aren’t restricted to Python packages.
Install Packages
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Linux has a different manager for each distribution. The most popular are apt-get, yum,
dpkg, and zypper.
Windows has the Windows Installer and package files with a .msi suffix. If you installed
Python for Windows, it was probably in the MSI format.
Install from Source
Occasionally, a Python package is new, or the author hasn’t managed to make it available
with pip. To build the package, you generally do the following:
1. Download the code.
2. Extract the files by using zip, tar, or another appropriate tool if they’re archived or
compressed.
3. Run python install setup.py in the directory containing a setup.py file.
As always, be careful what you download and install. It’s a little hard‐
er to hide malware in Python programs, which are readable text, but
it has happened.
Integrated Development Environments
I’ve used a plain-text interface for programs in this book, but that doesn’t mean that you
need to run everything in a console or text window. There are many free and commercial
integrated development environments (IDEs), which are GUIs with support for such
tools as text editors, debuggers, library searching, and so on.
IDLE
IDLE is the only Python IDE that’s included with the standard distribution. It’s based
on tkinter, and its GUI is plain.
PyCharm
PyCharm is a recent graphic IDE with many features. The community edition is free,
and you can get a free license for the professional edition to use in a classroom or an
open source project. Figure 12-1 shows its initial display.
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Figure 12-1. Startup screen for PyCharm
IPython
iPython, which you can see in Appendix C, is a publishing platform as well as an ex‐
tensive IDE.
Name and Document
You won’t remember what you wrote. There are times when I look at code I wrote even
recently and wonder where on earth it came from. That’s why it helps to document your
code. Documentation can include comments and docstrings, but it can also incorporate
informative naming of variables, functions, modules, and classes. Don’t be obsessive, as
in this example:
>>>
...
>>>
...
10
>>>
# I'm going to assign 10 to the variable "num" here:
num = 10
# I hope that worked
print(num)
# Whew.
Instead, say why you assigned the value 10. Point out why you called the variable num.
If you were writing the venerable Fahrenheit to Celsius converter, you might name
variables to explain what they do, rather than a lump of magic code. And a little test
code wouldn’t hurt:
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def ftoc(f_temp):
"Convert Fahrenheit temperature <f_temp> to Celsius and return it."
f_boil_temp = 212.0
f_freeze_temp = 32.0
c_boil_temp = 100.0
c_freeze_temp = 0.0
f_range = f_boil_temp - f_freeze_temp
c_range = c_boil_temp - c_freeze_temp
f_c_ratio = c_range / f_range
c_temp = (f_temp - f_freeze_temp) * f_c_ratio + c_freeze_temp
return c_temp
if __name__ == '__main__':
for f_temp in [-40.0, 0.0, 32.0, 100.0, 212.0]:
c_temp = ftoc(f_temp)
print('%f F => %f C' % (f_temp, c_temp))
Let’s run the tests:
$ python ftoc1.py
-40.000000 F => -40.000000 C
0.000000 F => -17.777778 C
32.000000 F => 0.000000 C
100.000000 F => 37.777778 C
212.000000 F => 100.000000 C
We can make (at least) two improvements:
• Python doesn’t have constants, but the PEP8 stylesheet recommends using capital
letters and underscores (e.g., ALL_CAPS) when naming variables that should be
considered constants. Let’s rename those constant-y variables in our example.
• Because we precompute values based on constant values, let’s move them to the top
level of the module. Then, they’ll only be calculated once rather than in every call
to the ftoc() function.
Here’s the result of our rework:
F_BOIL_TEMP = 212.0
F_FREEZE_TEMP = 32.0
C_BOIL_TEMP = 100.0
C_FREEZE_TEMP = 0.0
F_RANGE = F_BOIL_TEMP - F_FREEZE_TEMP
C_RANGE = C_BOIL_TEMP - C_FREEZE_TEMP
F_C_RATIO = C_RANGE / F_RANGE
def ftoc(f_temp):
"Convert Fahrenheit temperature <f_temp> to Celsius and return it."
c_temp = (f_temp - F_FREEZE_TEMP) * F_C_RATIO + C_FREEZE_TEMP
return c_temp
if __name__ == '__main__':
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for f_temp in [-40.0, 0.0, 32.0, 100.0, 212.0]:
c_temp = ftoc(f_temp)
print('%f F => %f C' % (f_temp, c_temp))
Testing Your Code
Once in a while, I’ll make some trivial code change and say to myself, “Looks good, ship
it.” And then it breaks. Oops. Every time I do this (thankfully, less and less over time) I
feel like a doofus, and I swear to write even more tests next time.
The very simplest way to test Python programs is to add print() statements. The Python
interactive interpreter’s Read-Evaluate-Print Loop (REPL) lets you edit and test changes
quickly. However, you probably don’t want print() statements in production code, so
you need to remember to take them all out. Furthermore, cut-and-paste errors are really
easy to make.
Check with pylint, pyflakes, and pep8
The next step, before creating actual test programs, is to run a Python code checker. The
most popular are pylint and pyflakes. You can install either or both by using pip:
$ pip install pylint
$ pip install pyflakes
These check for actual code errors (such as referring to a variable before assigning it a
value) and style faux pas (the code equivalent of wearing plaids and stripes). Here’s a
fairly meaningless program with a bug and style issue:
a = 1
b = 2
print(a)
print(b)
print(c)
Here’s the initial output of pylint:
$ pylint style1.py
No config file found, using default configuration
************* Module style1
C: 1,0: Missing docstring
C: 1,0: Invalid name "a" for type constant
(should match (([A-Z_][A-Z0-9_]*)|(__.*__))$)
C: 2,0: Invalid name "b" for type constant
(should match (([A-Z_][A-Z0-9_]*)|(__.*__))$)
E: 5,6: Undefined variable 'c'
Much further down, under Global evaluation, is our score (10.0 is perfect):
Your code has been rated at -3.33/10
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Ouch. Let’s fix the bug first. That pylint output line starting with an E indicates an
Error, which occurred because we didn’t assign a value to c before we printed it. Let’s
fix that:
a = 1
b = 2
c = 3
print(a)
print(b)
print(c)
$ pylint style2.py
No config file found, using default configuration
************* Module style2
C: 1,0: Missing docstring
C: 1,0: Invalid name "a" for type constant
(should match (([A-Z_][A-Z0-9_]*)|(__.*__))$)
C: 2,0: Invalid name "b" for type constant
(should match (([A-Z_][A-Z0-9_]*)|(__.*__))$)
C: 3,0: Invalid name "c" for type constant
(should match (([A-Z_][A-Z0-9_]*)|(__.*__))$)
Good, no more E lines. And our score jumped from -3.33 to 4.29:
Your code has been rated at 4.29/10
pylint wants a docstring (a short text at the top of a module or function, describing the
code), and it thinks short variable names such as a, b, and c are tacky. Let’s make pylint
happier and improve style2.py to style3.py:
"Module docstring goes here"
def func():
"Function docstring goes here. Hi, Mom!"
first = 1
second = 2
third = 3
print(first)
print(second)
print(third)
func()
$ pylint style3.py
No config file found, using default configuration
Hey, no complaints. And our score?
Your code has been rated at 10.00/10
Not too shabby at all, right?
Another style checker is pep8, which you can install in the usual way:
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$ pip install pep8
What does it say about our style makeover?
$ pep8 style3.py
style3.py:3:1: E302 expected 2 blank lines, found 1
To be really stylish, it’s recommending that I add a blank line after the initial module
docstring.
Test with unittest
We’ve verified that we’re no longer insulting the style senses of the code gods, so let’s
move on to actual tests of the logic in your program.
It’s a good practice to write independent test programs first, to ensure that they all pass
before you commit your code to any source control system. Writing tests can seem
tedious at first, but they really do help you find problems faster—especially regressions
(breaking something that used to work). Painful experience teaches all developers that
even the teeniest change, which they swear could not possibly affect anything else, ac‐
tually does. If you look at well-written Python packages, they always include a test suite.
The standard library contains not one, but two test packages. Let’s start with uni
ttest. We’ll write a module that capitalizes words. Our first version just uses the stan‐
dard string function capitalize(), with some unexpected results as we’ll see. Save this
as cap.py:
def just_do_it(text):
return text.capitalize()
The basis of testing is to decide what outcome you want from a certain input (here, you
want the capitalized version of whatever text you input), submit the input to the function
you’re testing, and then check whether it returned the expected results. The expected
result is called an assertion, so in unittest you check your results by using methods
with names that begin with assert, like the assertEqual method shown in the following
example.
Save this test script as test_cap.py:
import unittest
import cap
class TestCap(unittest.TestCase):
def setUp(self):
pass
def tearDown(self):
pass
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def test_one_word(self):
text = 'duck'
result = cap.just_do_it(text)
self.assertEqual(result, 'Duck')
def test_multiple_words(self):
text = 'a veritable flock of ducks'
result = cap.just_do_it(text)
self.assertEqual(result, 'A Veritable Flock Of Ducks')
if __name__ == '__main__':
unittest.main()
The setUp() method is called before each test method, and the tearDown() method is
called after each. Their purpose is to allocate and free external resources needed by the
tests, such as a database connection or some test data. In this case, our tests are selfcontained, and we wouldn’t even need to define setUp() and tearDown(), but it doesn’t
hurt to have empty versions there. The heart of our test is the two functions named
test_one_word() and test_multiple_words(). Each runs the just_do_it() function
we defined with different input and checks whether we got back what we expect.
Okay, let’s run it. This will call our two test methods:
$ python test_cap.py
F.
======================================================================
FAIL: test_multiple_words (__main__.TestCap)
---------------------------------------------------------------------Traceback (most recent call last):
File "test_cap.py", line 20, in test_multiple_words
self.assertEqual(result, 'A Veritable Flock Of Ducks')
AssertionError: 'A veritable flock of ducks' != 'A Veritable Flock Of Ducks'
- A veritable flock of ducks
?
^
^
^ ^
+ A Veritable Flock Of Ducks
?
^
^
^ ^
---------------------------------------------------------------------Ran 2 tests in 0.001s
FAILED (failures=1)
It liked the first test (test_one_word) but not the second (test_multiple_words). The
up arrows (^) shows where the strings actually differed.
What’s special about multiple words? Reading the documentation for the string capi
talize function yields an important clue: it capitalizes only the first letter of the first
word. Maybe we should have read that first.
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Consequently, we need another function. Gazing down that page a bit, we find ti
tle(). So, let’s change cap.py to use title() instead of capitalize():
def just_do_it(text):
return text.title()
Rerun the tests, and let’s see what happens:
$ python test_cap.py
..
---------------------------------------------------------------------Ran 2 tests in 0.000s
OK
Everything is great. Well, actually, they’re not. We need to add at least one more method
to test_cap.py:
def test_words_with_apostrophes(self):
text = "I'm fresh out of ideas"
result = cap.just_do_it(text)
self.assertEqual(result, "I'm Fresh Out Of Ideas")
Go ahead and try it again:
$ python test_cap.py
..F
======================================================================
FAIL: test_words_with_apostrophes (__main__.TestCap)
---------------------------------------------------------------------Traceback (most recent call last):
File "test_cap.py", line 25, in test_words_with_apostrophes
self.assertEqual(result, "I'm Fresh Out Of Ideas")
AssertionError: "I'M Fresh Out Of Ideas" != "I'm Fresh Out Of Ideas"
- I'M Fresh Out Of Ideas
?
^
+ I'm Fresh Out Of Ideas
?
^
---------------------------------------------------------------------Ran 3 tests in 0.001s
FAILED (failures=1)
Our function capitalized the m in I'm. A quick run back to the documentation for
title() shows that it doesn’t handle apostrophes well. We really should have read the
entire text first.
At the bottom of the standard library’s string documentation is another candidate: a
helper function called capwords(). Let’s use it in cap.py:
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def just_do_it(text):
from string import capwords
return capwords(text)
$ python test_cap.py
...
---------------------------------------------------------------------Ran 3 tests in 0.004s
OK
At last, we’re finally done! Uh, no. One more test to add to test_cap.py:
def test_words_with_quotes(self):
text = "\"You're despicable,\" said Daffy Duck"
result = cap.just_do_it(text)
self.assertEqual(result, "\"You're Despicable,\" Said Daffy Duck")
Did it work?
$ python test_cap.py
...F
======================================================================
FAIL: test_words_with_quotes (__main__.TestCap)
---------------------------------------------------------------------Traceback (most recent call last):
File "test_cap.py", line 30, in test_words_with_quotes
self.assertEqual(result, "\"You're
Despicable,\" Said Daffy Duck")
AssertionError: '"you\'re Despicable," Said Daffy Duck'
!= '"You\'re Despicable," Said Daffy Duck'
- "you're Despicable," Said Daffy Duck
? ^
+ "You're Despicable," Said Daffy Duck
? ^
---------------------------------------------------------------------Ran 4 tests in 0.004s
FAILED (failures=1)
It looks like that first double quote confused even capwords, our favorite capitalizer thus
far. It tried to capitalize the ", and lowercased the rest (You're). We should have also
tested that our capitalizer left the rest of the string untouched.
People who do testing for a living have a knack for spotting these edge cases, but de‐
velopers often have blind spots when it comes to their own code.
unittest provides a small but powerful set of assertions, letting you check values, con‐
firm whether you have the class you want, determine whether an error was raised, and
so on.
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Test with doctest
The second test package in the standard library is doctest. With this package, you can
write tests within the docstring itself, also serving as documentation. It looks like the
interactive interpreter: the characters >>>, followed by the call, and then the results on
the following line. You can run some tests in the interactive interpreter and just paste
the results into your test file. We’ll modify cap.py (without that troublesome last test
with quotes):
def just_do_it(text):
"""
>>> just_do_it('duck')
'Duck'
>>> just_do_it('a veritable flock of ducks')
'A Veritable Flock Of Ducks'
>>> just_do_it("I'm fresh out of ideas")
"I'm Fresh Out Of Ideas"
"""
from string import capwords
return capwords(text)
if __name__ == '__main__':
import doctest
doctest.testmod()
When you run it, it doesn’t print anything if all tests passed:
$ python cap.py
Give it the verbose (-v) option to see what actually happened:
$ python cap.py -v
Trying:
just_do_it('duck')
Expecting:
'Duck'
ok
Trying:
just_do_it('a veritable flock of ducks')
Expecting:
'A Veritable Flock Of Ducks'
ok
Trying:
just_do_it("I'm fresh out of ideas")
Expecting:
"I'm Fresh Out Of Ideas"
ok
1 items had no tests:
__main__
1 items passed all tests:
3 tests in __main__.just_do_it
3 tests in 2 items.
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3 passed and 0 failed.
Test passed.
Test with nose
The third-party package called nose is another alternative to unittest. Here’s the com‐
mand to install it:
$ pip install nose
You don’t need to create a class that includes test methods, as we did with unittest.
Any function with a name matching test somewhere in its name will be run. Let’s
modify our last version of our unittest tester and save it as test_cap_nose.py:
import cap
from nose.tools import eq_
def test_one_word():
text = 'duck'
result = cap.just_do_it(text)
eq_(result, 'Duck')
def test_multiple_words():
text = 'a veritable flock of ducks'
result = cap.just_do_it(text)
eq_(result, 'A Veritable Flock Of Ducks')
def test_words_with_apostrophes():
text = "I'm fresh out of ideas"
result = cap.just_do_it(text)
eq_(result, "I'm Fresh Out Of Ideas")
def test_words_with_quotes():
text = "\"You're despicable,\" said Daffy Duck"
result = cap.just_do_it(text)
eq_(result, "\"You're Despicable,\" Said Daffy Duck")
Run the tests:
$ nosetests test_cap_nose.py
...F
======================================================================
FAIL: test_cap_nose.test_words_with_quotes
---------------------------------------------------------------------Traceback (most recent call last):
File "/Users/.../site-packages/nose/case.py", line 198, in runTest
self.test(*self.arg)
File "/Users/.../book/test_cap_nose.py", line 23, in test_words_with_quotes
eq_(result, "\"You're Despicable,\" Said Daffy Duck")
AssertionError: '"you\'re Despicable," Said Daffy Duck'
!= '"You\'re Despicable," Said Daffy Duck'
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---------------------------------------------------------------------Ran 4 tests in 0.005s
FAILED (failures=1)
This is the same bug we found when we used unittest for testing; fortunately, there’s
an exercise to fix it at the end of this chapter.
Other Test Frameworks
For some reason, people like to write Python test frameworks. If you’re curious, you can
check out some other popular ones, including tox and py.test.
Continuous Integration
When your group is cranking out a lot of code daily, it helps to automate tests as soon
as changes arrive. You can automate source control systems to run tests on all code as
it’s checked in. This way, everyone knows if someone broke the build and just disappeared
for an early lunch.
These are big systems, and I’m not going into installation and usage details here. In case
you need them someday, you’ll know where to find them:
buildbot
Written in Python, this source control system automates building, testing, and re‐
leasing.
jenkins
This is written in Java and seems to be the preferred CI tool of the moment.
travis-ci
This automates projects hosted at GitHub, and it’s free for open source projects.
Debugging Python Code
Debugging is twice as hard as writing the code in the first place. Therefore, if you write
the code as cleverly as possible, you are, by definition, not smart enough to debug it.
— Brian Kernighan
Test first. The better your tests are, the less you’ll have to fix later. Yet, bugs happen and
need to be fixed when they’re found later. Again, the simplest way to debug in Python
is to print out strings. Some useful things to print include vars(), which extracts the
values of your local variables, including function arguments:
>>> def func(*args, **kwargs):
...
print(vars())
...
>>> func(1, 2, 3)
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{'args': (1, 2, 3), 'kwargs': {}}
>>> func(['a', 'b', 'argh'])
{'args': (['a', 'b', 'argh'],), 'kwargs': {}}
As you read in “Decorators” on page 99, a decorator can call code before or after a
function without modifying the code within the function itself. This means that you
can use a decorator to do something before or after any Python function, not just ones
that you wrote. Let’s define the decorator dump to print the input arguments and output
values of any function as it’s called (designers know that a dump often needs decorating):
def dump(func):
"Print input arguments and output value(s)"
def wrapped(*args, **kwargs):
print("Function name: %s" % func.__name__)
print("Input arguments: %s" % ' '.join(map(str, args)))
print("Input keyword arguments: %s" % kwargs.items())
output = func(*args, **kwargs)
print("Output:", output)
return output
return wrapped
Now the decoratee. This is a function called double() that expects numeric arguments,
either named or unnamed, and returns them in a list with their values doubled:
from dump1 import dump
@dump
def double(*args, **kwargs):
"Double every argument"
output_list = [ 2 * arg for arg in args ]
output_dict = { k:2*v for k,v in kwargs.items() }
return output_list, output_dict
if __name__ == '__main__':
output = double(3, 5, first=100, next=98.6, last=-40)
Take a moment to run it:
$ python test_dump.py
Function name: double
Input arguments: 3 5
Input keyword arguments: dict_items([('last', -40), ('first', 100),
('next', 98.6)])
Output: ([6, 10], {'last': -80, 'first': 200, 'next': 197.2})
Debug with pdb
These techniques help, but sometimes there’s no substitute for a real debugger. Most
IDEs include a debugger, with varying features and user interfaces. Here, I’ll describe
use of the standard Python debugger, pdb.
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If you run your program with the -i flag, Python will drop you into
its interactive interpreter if the program fails.
Here’s a program with a bug that depends on data—the kind of bug that can be partic‐
ularly hard to find. This is a real bug from the early days of computing, and it baffled
programmers for quite a while.
We’re going to read a file of countries and their capital cities, separated by a comma,
and write them out as capital, country. They might be capitalized incorrectly, so we
should fix that also when we print. Oh, and there might be extra spaces here and there,
and you’ll want to get rid of those, too. Finally, although it would make sense for the
program to just read to the end of the file, for some reason our manager told us to stop
when we encounter the word quit (in any mixture of uppercase and lowercase char‐
acters). Here’s a sample data file:
France, Paris
venuzuela,caracas
LithuniA,vilnius
quit
Let’s design our algorithm (method for solving the problem). This is pseudocode—it
looks like a program, but is just a way to explain the logic in normal language before
converting it to an actual program. One reason programmers like Python is because it
looks a lot like pseudocode, so there’s less work involved to convert it to a working pro‐
gram:
for each line in the text file:
read the line
strip leading and trailing spaces
if `quit` occurs in the lower-case copy of the line:
stop
else:
split the country and capital by the comma character
trim any leading and trailing spaces
convert the country and capital to titlecase
print the capital, a comma, and the country
We need to strip initial and trailing spaces from the names because that was a require‐
ment. Likewise for the lowercase comparison with quit and converting the city and
country names to title case. That being the case, let’s whip out capitals.py, which is sure
to work perfectly:
def process_cities(filename):
with open(filename, 'rt') as file:
for line in file:
line = line.strip()
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if 'quit' in line.lower():
return
country, city = line.split(',')
city = city.strip()
country = country.strip()
print(city.title(), country.title(), sep=',')
if __name__ == '__main__':
import sys
process_cities(sys.argv[1])
Let’s try it with that sample data file we made earlier. Ready, fire, aim:
$ python capitals.py
Paris,France
Caracas,Venuzuela
Vilnius,Lithunia
cities1.csv
Looks great! It passed one test, so let’s put it in production, processing capitals and
countries from around the world—until it fails, but only for this data file:
argentina,buenos aires
bolivia,la paz
brazil,brasilia
chile,santiago
colombia,Bogotá
ecuador,quito
falkland islands,stanley
french guiana,cayenne
guyana,georgetown
paraguay,Asunción
peru,lima
suriname,paramaribo
uruguay,montevideo
venezuela,caracas
quit
The program ends after printing only 5 lines of the 15 in the data file, as demonstrated
here:
$ python capitals.py cities2.csv
Buenos Aires,Argentina
La Paz,Bolivia
Brazilia,Brazil
Santiago,Chile
Bogotá,Colombia
What happened? We can keep editing capitals.py, putting print() statements in likely
places, but let’s see if the debugger can help us.
To use the debugger, import the pdb module from the command line by typing -m
pdb, like so:
$ python -m pdb capitals.py cities2.csv
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> /Users/williamlubanovic/book/capitals.py(1)<module>()
-> def process_cities(filename):
(Pdb)
This starts the program and places you at the first line. If you type c (continue), the
program will run until it ends, either normally or with an error:
(Pdb) c
Buenos Aires,Argentina
La Paz,Bolivia
Brazilia,Brazil
Santiago,Chile
Bogotá,Colombia
The program finished and will be restarted
> /Users/williamlubanovic/book/capitals.py(1)<module>()
-> def process_cities(filename):
It completed normally, just as it did when we ran it earlier outside of the debugger. Let’s
try again, using some commands to narrow down where the problem lies. It seems to
be a logic error rather than a syntax problem or exception (which would have printed
error messages).
Type s (step) to single-step through Python lines. This steps through all Python code
lines: yours, the standard library’s, and any other modules you might be using. When
you use s, you also go into functions and single-step within them. Type n (next) to singlestep but not to go inside functions; when you get to a function, a single n causes the
entire function to execute and take you to the next line of your program. Thus, use s
when you’re not sure where the problem is; use n when you’re sure that a particular
function isn’t the cause, especially if it’s a long function. Often you’ll single-step through
your own code and step over library code, which is presumably well tested. We’ll use s
to step from the beginning of the program, into the function process_cities():
(Pdb) s
> /Users/williamlubanovic/book/capitals.py(12)<module>()
-> if __name__ == '__main__':
(Pdb) s
> /Users/williamlubanovic/book/capitals.py(13)<module>()
-> import sys
(Pdb) s
> /Users/williamlubanovic/book/capitals.py(14)<module>()
-> process_cities(sys.argv[1])
(Pdb) s
--Call-> /Users/williamlubanovic/book/capitals.py(1)process_cities()
-> def process_cities(filename):
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(Pdb) s
> /Users/williamlubanovic/book/capitals.py(2)process_cities()
-> with open(filename, 'rt') as file:
Type l (list) to see the next few lines of your program:
(Pdb) l
1
def process_cities(filename):
2 ->
with open(filename, 'rt') as file:
3
for line in file:
4
line = line.strip()
5
if 'quit' in line.lower():
6
return
7
country, city = line.split(',')
8
city = city.strip()
9
country = country.strip()
10
print(city.title(), country.title(), sep=',')
11
(Pdb)
The arrow (->) denotes the current line.
We could continue using s or n, hoping to spot something, but let’s use one of the main
features of a debugger: breakpoints. A breakpoint stops execution at the line you indicate.
In our case, we want to know why process_cities() bails out before it’s read all of the
input lines. Line 3 (for line in file:) will read every line in the input file, so that
seems innocent. The only other place where we could return from the function before
reading all of the data is at line 6 (return). Let’s set a breakpoint on line 6:
(Pdb) b 6
Breakpoint 1 at /Users/williamlubanovic/book/capitals.py:6
Next, let’s continue the program until it either hits the breakpoint or reads all of the
input lines and finishes normally:
(Pdb) c
Buenos Aires,Argentina
La Paz,Bolivia
Brasilia,Brazil
Santiago,Chile
Bogotá,Colombia
> /Users/williamlubanovic/book/capitals.py(6)process_cities()
-> return
Aha, it stopped at our line 6 breakpoint. This indicates that the program wants to return
early after reading the country after Colombia. Let’s print the value of line to see what
we just read:
(Pdb) p line
'ecuador,quito'
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What’s so special about—oh, never mind.
Really? *quit*o? Our manager never expected the string quit to turn up inside normal
data, so using it as a sentinel (end indicator) value like this was a boneheaded idea. You
march right in there and tell him that, while I wait here.
If at this point you still have a job, you can see all your breakpoints by using a plain b
command:
(Pdb) b
Num Type
Disp Enb
Where
1
breakpoint
keep yes
at /Users/williamlubanovic/book/capitals.py:6
breakpoint already hit 1 time
An l will show your code lines, the current line (->), and any breakpoints (B). A plain
l will start listing from the end of your previous call to l, so include the optional starting
line (here, let’s start from line 1):
(Pdb) l 1
1
def process_cities(filename):
2
with open(filename, 'rt') as file:
3
for line in file:
4
line = line.strip()
5
if 'quit' in line.lower():
6 B->
return
7
country, city = line.split(',')
8
city = city.strip()
9
country = country.strip()
10
print(city.title(), country.title(), sep=',')
11
Okay, let’s fix that quit test to only match the full line, not within other characters:
def process_cities(filename):
with open(filename, 'rt') as file:
for line in file:
line = line.strip()
if 'quit' == line.lower():
return
country, city = line.split(',')
city = city.strip()
country = country.strip()
print(city.title(), country.title(), sep=',')
if __name__ == '__main__':
import sys
process_cities(sys.argv[1])
Once more, with feeling:
$ python capitals2.py cities2.csv
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Buenos Aires,Argentina
La Paz,Bolivia
Brasilia,Brazil
Santiago,Chile
Bogotá,Colombia
Quito,Ecuador
Stanley,Falkland Islands
Cayenne,French Guiana
Georgetown,Guyana
Asunción,Paraguay
Lima,Peru
Paramaribo,Suriname
Montevideo,Uruguay
Caracas,Venezuela
That was a skimpy overview of the debugger—just enough to show you what it can do
and what commands you’d use most of the time.
Remember: more tests, less debugging.
Logging Error Messages
At some point you might need to graduate from using print() statements to logging
messages. A log is usually a system file that accumulates messages, often inserting useful
information such as a timestamp or the name of the user who’s running the program.
Often logs are rotated (renamed) daily and compressed; by doing so, they don’t fill up
your disk and cause problems themselves. When something goes wrong with your pro‐
gram, you can look at the appropriate log file to see what happened. The contents of
exceptions are especially useful in logs because they show you the actual line at which
your program croaked, and why.
The standard Python library module is logging. I’ve found most descriptions of it
somewhat confusing. After a while it makes more sense, but it does seem overly com‐
plicated at first. The logging module includes these concepts:
• The message that you want to save to the log
• Ranked priority levels and matching functions: debug(), info(), warn(),
error(), and critical()
• One or more logger objects as the main connection with the module
• Handlers that direct the message to your terminal, a file, a database, or somewhere
else
• Formatters that create the output
• Filters that make decisions based on the input
For the simplest logging example, just import the module and use some of its functions:
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>>> import logging
>>> logging.debug("Looks like rain")
>>> logging.info("And hail")
>>> logging.warn("Did I hear thunder?")
WARNING:root:Did I hear thunder?
>>> logging.error("Was that lightning?")
ERROR:root:Was that lightning?
>>> logging.critical("Stop fencing and get inside!")
CRITICAL:root:Stop fencing and get inside!
Did you notice that debug() and info() didn’t do anything, and the other two printed
LEVEL:root: before each message? So far, it’s like a print() statement with multiple
personalities, some of them hostile.
But it is useful. You can scan for a particular value of LEVEL in a log file to find particular
messages, compare timestamps to see what happened before your server crashed, and
so on.
A lot of digging through the documentation answers the first mystery (we’ll get to the
second one in a page or two): the default priority level is WARNING, and that got locked
in as soon as we called the first function (logging.debug()). We can set the default level
by using basicConfig(). DEBUG is the lowest level, so this enables it and all the higher
levels to flow through:
>>> import logging
>>> logging.basicConfig(level=logging.DEBUG)
>>> logging.debug("It's raining again")
DEBUG:root:It's raining again
>>> logging.info("With hail the size of hailstones")
INFO:root:With hail the size of hailstones
We did all that with the default logging functions, without actually creating a logger
object. Each logger has a name. Let’s make one called bunyan:
>>> import logging
>>> logging.basicConfig(level='DEBUG')
>>> logger = logging.getLogger('bunyan')
>>> logger.debug('Timber!')
DEBUG:bunyan:Timber!
If the logger name contains any dot characters, they separate levels of a hierarchy of
loggers, each with potentially different properties. This means that a logger named quark
is higher than one named quark.charmed. The special root logger is at the top, and is
called ''.
So far, we’ve just printed messages, which is not a great improvement over print(). We
use handlers to direct the messages to different places. The most common is a log file,
and here’s how you do it:
>>> import logging
>>> logging.basicConfig(level='DEBUG', filename='blue_ox.log')
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>>> logger = logging.getLogger('bunyan')
>>> logger.debug("Where's my axe?")
>>> logger.warn("I need my axe")
>>>
Aha, the lines aren’t on the screen anymore; instead, they’re in the file named blue_ox.log:
DEBUG:bunyan:Where's my axe?
WARNING:bunyan:I need my axe
Calling basicConfig() with a filename argument created a FileHandler for you and
made it available to your logger. The logging module includes at least 15 handlers to
send messages to places such as email and web servers as well as the screen and files.
Finally, you can control the format of your logged messages. In our first example, our
default gave us something similar to this:
WARNING:root:Message...
If you provide a format string to basicConfig(), you can change to the format of your
preference:
>>> import logging
>>> fmt = '%(asctime)s %(levelname)s %(lineno)s %(message)s'
>>> logging.basicConfig(level='DEBUG', format=fmt)
>>> logger = logging.getLogger('bunyan')
>>> logger.error("Where's my other plaid shirt?")
2014-04-08 23:13:59,899 ERROR 1 Where's my other plaid shirt?
We let the logger send output to the screen again, but changed the format. The logging
module recognizes a number of variable names in the fmt format string. We used
asctime (date and time as an ISO 8601 string), levelname, lineno (line number), and
the message itself. There are other built-ins, and you can provide your own variables,
as well.
There’s much more to logging than this little overview can provide. You can log to more
than one place at the same time, with different priorities and formats. The package has
a lot of flexibility, but sometimes at the cost of simplicity.
Optimize Your Code
Python is usually fast enough—until it isn’t. In many cases, you can gain speed by using
a better algorithm or data structure. The trick is knowing where to do this. Even expe‐
rienced programmers guess wrong surprisingly often. You need to be like the careful
quiltmaker, and measure before you cut. And this leads us to timers.
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Measure Timing
You’ve seen that the time function in the time module returns the current epoch time
as a floating-point number of seconds. A quick way of timing something is to get the
current time, do something, get the new time, and then subtract the original time from
the new time. Let’s write this up and call it time1.py:
from time import time
t1 = time()
num = 5
num *= 2
print(time() - t1)
In this example, we’re measuring the the time it takes to assign the value 5 to the name
num and multiply it by 2. This is not a realistic benchmark, just an example of how to
measure some arbitrary Python code. Try running it a few times, just to see how much
it can vary:
$ python time1.py
2.1457672119140625e-06
$ python time1.py
2.1457672119140625e-06
$ python time1.py
2.1457672119140625e-06
$ python time1.py
1.9073486328125e-06
$ python time1.py
3.0994415283203125e-06
That was about two or three millionths of a second. Let’s try something slower, such as
sleep. If we sleep for a second, our timer should take a tiny bit more than a second.
Save this as time2.py:
from time import time, sleep
t1 = time()
sleep(1.0)
print(time() - t1)
Let’s be certain of our results, so run it a few times:
$ python time2.py
1.000797986984253
$ python time2.py
1.0010130405426025
$ python time2.py
1.0010390281677246
As expected, it takes about a second to run. If it didn’t, either our timer or sleep()
should be embarrassed.
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There’s a handier way to measure code snippets like this: the standard module timeit.
It has a function called (you guessed it) timeit(), which will run your test code count
times and print some results. The syntax is: timeit.timeit( code, number, count ).
In the examples in this section, the code needs to be within quotes so that it is not
executed after you press the Return key but is executed inside timeit(). (In the next
section, you’ll see how to time a function by passing its name to timeit().) Let’s run
our previous example just once and time it. Call this file timeit1.py:
from timeit import timeit
print(timeit('num = 5; num *= 2', number=1))
Run it a few times:
$ python timeit1.py
2.5600020308047533e-06
$ python timeit1.py
1.9020008039660752e-06
$ python timeit1.py
1.7380007193423808e-06
Again, these two code lines ran in about two millionths of a second. We can use the
repeat argument of the timeit module’s repeat() function to run more sets. Save this
as timeit2.py:
from timeit import repeat
print(repeat('num = 5; num *= 2', number=1, repeat=3))
Try running it to see what transpires:
$ python timeit2.py
[1.691998477326706e-06, 4.070025170221925e-07, 2.4700057110749185e-07]
The first run took two millionths of a second, and the second and third runs were faster.
Why? There could be many reasons. For one thing, we’re testing a very small piece of
code, and its speed could depend on what else the computer was doing in those instants,
how the Python system optimizes calculations, and many other things.
Or, it could be just chance. Let’s try something more realistic than variable assignment
and sleep. We’ll measure some code to help compare the efficiency of a few algorithms
(program logic) and data structures (storage mechanisms).
Algorithms and Data Structures
The Zen of Python declares that There should be one—and preferably only one—obvious
way to do it. Unfortunately, sometimes it isn’t obvious, and you need to compare alter‐
natives. For example, is it better to use a for loop or a list comprehension to build a list?
And what do we mean by better? Is it faster, easier to understand, using less memory,
or more “Pythonic”?
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In this next exercise, we’ll build a list in different ways, comparing speed, readability,
and Python style. Here’s time_lists.py:
from timeit import timeit
def make_list_1():
result = []
for value in range(1000):
result.append(value)
return result
def make_list_2():
result = [value for value in range(1000)]
return result
print('make_list_1 takes', timeit(make_list_1, number=1000), 'seconds')
print('make_list_2 takes', timeit(make_list_2, number=1000), 'seconds')
In each function, we add 1,000 items to a list, and we call each function 1,000 times.
Notice that in this test we called timeit() with the function name as the first argument
rather than code as a string. Let’s run it:
$ python time_lists.py
make_list_1 takes 0.14117428699682932 seconds
make_list_2 takes 0.06174145900149597 seconds
The list comprehension is at least twice as fast as adding items to the list by using
append(). In general, comprehensions are faster than manual construction.
Use these ideas to make your own code faster.
Cython, NumPy, and C Extensions
If you’re pushing Python as hard as you can and still can’t get the performance you want,
you have yet more options.
Cython is a hybrid of Python and C, designed to translate Python with some perfor‐
mance annotations to compiled C code. These annotations are fairly small, like declaring
the types of some variables, function arguments, or function returns. For scientific-style
loops of numeric calculations, adding these hints will make them much faster—as much
as a thousand times faster. See the Cython wiki for documentation and examples.
You can read much more about NumPy in Appendix C. It’s a Python math library,
written in C for speed.
Many parts of Python and its standard library are written in C for speed and wrapped
in Python for convenience. These hooks are available to you for your applications. If
you know C and Python and really want to make your code fly, writing a C extension
is harder but the improvements can be worth the trouble.
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PyPy
When Java first appeared about 20 years ago, it was as slow as an arthritic schnauzer.
When it started to mean real money to Sun and other companies, though, they put
millions into optimizing the Java interpreter and the underlying Java virtual machine
(JVM), borrowing techniques from earlier languages like Smalltalk and LISP. Microsoft
likewise put great effort into optimizing its rival C# language and .NET VM.
No one owns Python, so no one has pushed that hard to make it faster. You’re probably
using the standard Python implementation. It’s written in C, and often called CPython
(not the same as Cython).
Like PHP, Perl, and even Java, Python is not compiled to machine language, but trans‐
lated to an intermediate language (with names such as bytecode or p-code) which is then
interpreted in a virtual machine.
PyPy is a new Python interpreter that applies some of the tricks that sped up Java. Its
benchmarks show that PyPy is faster than CPython in every test—over 6 times faster
on average, and up to 20 times faster in some cases. It works with Python 2 and 3. You
can download it and use it instead of CPython. PyPy is constantly being improved, and
it might even replace CPython some day. Read the latest release notes on the site to see
if it could work for your purposes.
Source Control
When you’re working on a small group of programs, you can usually keep track of your
changes—until you make a boneheaded mistake and clobber a few days of work. Source
control systems help protect your code from dangerous forces, like you. If you work
with a group of developers, source control becomes a necessity. There are many com‐
mercial and open source packages in this area. The most popular in the open source
world where Python lives are Mercurial and Git. Both are examples of distributed version
control systems, which produce multiple copies of code repositories. Earlier systems
such as Subversion run on a single server.
Mercurial
Mercurial is written in Python. It’s fairly easy to learn, with a handful of subcommands
to download code from a Mercurial repository, add files, check in changes, and merge
changes from different sources. bitbucket and other sites offer free or commercial
hosting.
Git
Git was originally written for Linux kernel development, but now dominates open
source in general. It’s similar to Mercurial, although some find it slightly trickier to
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master. GitHub is the largest git host, with over a million repositories, but there are
many other hosts.
The standalone program examples in this book are available in a public git repository
at GitHub. If you have the git program on your computer, you can download these
programs by using this command:
$ git clone https://github.com/madscheme/introducing-python
You can also download the code by pressing the following buttons on the GitHub page:
• Click “Clone in Desktop” to open your computer’s version of git, if it’s been in‐
stalled.
• Click “Download ZIP” to get a zipped archive of the programs.
If you don’t have git but would like to try it, read the installation guide. I’ll talk about
the command-line version here, but you might be interested in sites such as GitHub
that have extra services and might be easier to use in some cases; git has many features,
but is not always intuitive.
Let’s take git for a test drive. We won’t go far, but the ride will show a few commands
and their output.
Make a new directory and change to it:
$ mkdir newdir
$ cd newdir
Create a local git repository in your current directory newdir:
$ git init
Initialized empty Git repository in /Users/williamlubanovic/newdir/.git/
Create a Python file called test.py with these contents in newdir:
print('Oops')
Add the file to the git repository:
$ git add test.py
What do you think of that, Mr. Git?
$ git status
On branch master
Initial commit
Changes to be committed:
(use "git rm --cached <file>..." to unstage)
new file:
test.py
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This means that test.py is part of the local repository but its changes have not yet been
committed. Let’s commit it:
$ git commit -m "simple print program"
[master (root-commit) 52d60d7] my first commit
1 file changed, 1 insertion(+)
create mode 100644 test.py
That -m "my first commit" was your commit message. If you omitted that, git would
pop you into an editor and coax you to enter the message that way. This becomes a part
of the git change history for that file.
Let’s see what our current status is:
$ git status
On branch master
nothing to commit, working directory clean
Okay, all current changes have been committed. This means that we can change things
and not worry about losing the original version. Make an adjustment now to test.py—
change Oops to Ops! and save the file:
print('Ops!')
Let’s check to see what git thinks now:
$ git status
On branch master
Changes not staged for commit:
(use "git add <file>..." to update what will be committed)
(use "git checkout -- <file>..." to discard changes in working directory)
modified:
test.py
no changes added to commit (use "git add" and/or "git commit -a")
Use git diff to see what lines have changed since the last commit:
$ git diff
diff --git a/test.py b/test.py
index 76b8c39..62782b2 100644
--- a/test.py
+++ b/test.py
@@ -1 +1 @@
-print('Oops')
+print('Ops!')
If you try to commit this change now, git complains:
$ git commit -m "change the print string"
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On branch master
Changes not staged for commit:
modified:
test.py
no changes added to commit
That staged for commit phrase means you need to add the file, which roughly trans‐
lated means hey git, look over here:
$ git add test.py
You could have also typed git add . to add all changed files in the current directory;
that’s handy when you actually have edited multiple files and want to ensure that you
check in all their changes. Now we can commit the change:
$ git commit -m "my first change"
[master e1e11ec] my first change
1 file changed, 1 insertion(+), 1 deletion(-)
If you’d like to see all the terrible things that you’ve done to test.py, most recent first, use
git log:
$ git log test.py
commit e1e11ecf802ae1a78debe6193c552dcd15ca160a
Author: William Lubanovic <[email protected]>
Date:
Tue May 13 23:34:59 2014 -0500
change the print string
commit 52d60d76594a62299f6fd561b2446c8b1227cfe1
Author: William Lubanovic <[email protected]>
Date:
Tue May 13 23:26:14 2014 -0500
simple print program
Clone This Book
You can get a copy of all the programs in this book. Visit the git repository and follow
the directions to copy it to your local machine. If you have git, run the command git
clone https://github.com/madscheme/introducing-python to make a git reposito‐
ry on your computer. You can also download the files in zip format.
How You Can Learn More
This is an introduction. It almost certainly says too much about some things that you
don’t care about and not enough about some things that you do. Let me recommend
some Python resources that I’ve found helpful.
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Books
I’ve found the books in the list that follows to be especially useful. These range from
introductory to advanced, with mixtures of Python 2 and 3.
• Barry, Paul. Head First Python. O’Reilly, 2010.
• Beazley, David M. Python Essential Reference (4th Edition). Addison-Wesley, 2009.
• Beazley, David M. and Brian K. Jones. Python Cookbook (3rd Edition). O’Reilly,
2013.
• Chun, Wesley. Core Python Applications Programming (3rd Edition). Prentice Hall,
2012.
• McKinney, Wes. Python for Data Analysis: Data Wrangling with Pandas, NumPy,
and IPython. O’Reilly, 2012.
• Summerfield, Mark. Python in Practice: Create Better Programs Using Concurrency,
Libraries, and Patterns. Addison-Wesley, 2013.
Of course, there are many more.
Websites
Here are some websites where you can find helpful tutorials:
• Learn Python the Hard Way by Zed Shaw.
• Dive Into Python 3 by Mark Pilgrim.
• Mouse Vs. Python by Michael Driscoll.
If you’re interested in keeping up with what’s going on in the Pythonic world, check out
these news websites:
• comp.lang.python
• comp.lang.python.announce
• python subreddit
• Planet Python
Finally, here are some good websites for downloading code:
• The Python Package Index
• stackoverflow Python questions
• ActiveState Python recipes
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• Python packages trending on GitHub
Groups
Computing communities have varied personalities: enthusiastic, argumentative, dull,
hipster, button-down, and many others across a broad range. The Python community
is friendly and civil. You can find Python groups based on location—meetups and local
user groups around the world. Other groups are distributed and based on common
interests. For instance, PyLadies is a support network for women who are interested in
Python and open source.
Conferences
Of the many conferences and workshops around the world, the largest are held annually
in North America and Europe.
Coming Attractions
But wait, there’s more! Appendixes A, B, and C offer tours of Python in the arts, business,
and science. You’ll find at least one package that you’ll want to explore.
Bright and shiny objects abound on the net. Only you can tell which are costume jewelry
and which are silver bullets. And even if you’re not currently pestered by werewolves,
you might want some of those silver bullets in your pocket. Just in case.
Finally, we have answers to those annoying end-of-chapter exercises, details on instal‐
lation of Python and friends, and a few cheat sheets for things that I always need to look
up. Your brain is almost certainly better tuned, but they’re there if you need them.
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APPENDIX A
Py Art
Well, art is art, isn’t it? Still, on the other hand, water is water! And east is east and west
is west, and if you take cranberries and stew them like applesauce, they taste much more
like prunes than rhubarb does.
— Groucho Marx
Maybe you’re an artist, or a musician. Or maybe you just want to try something creative
and different.
These first three appendices are explorations of some common human endeavors using
Python. If your interest lies in any of these areas, you might get some ideas from the
chapters, or the urge to try something new.
2-D Graphics
All computer languages have been applied to computer graphics to some degree. Many
of the heavy-duty platforms in this chapter were written in C or C++ for speed, but
added Python libraries for productivity. Let’s begin by looking at some 2-D imaging
libraries.
Standard Library
Only a few graphics-related modules are in the standard library. Here are two of them:
imghdr
This module detects the file type of some image files.
colorsys
This module converts colors between various systems: RGB, YIQ, HSV, and HLS.
If you downloaded the O’Reilly logo to a local file called oreilly.png, you could run this:
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>>> import imghdr
>>> imghdr.what('oreilly.png')
'png'
To do anything serious with graphics in Python, we need to get some third-party pack‐
ages. Let’s see what’s out there.
PIL and Pillow
For many years, the Python Image Library (PIL), although not in the standard library,
has been Python’s best-known 2-D image processing library. It predated installers such
as pip, so a “friendly fork” called Pillow was created. Pillow’s imaging code is backwardcompatible with PIL, and its documentation is good, so we’ll use it here.
Installation is simple; just type the following command:
$ pip install Pillow
If you’ve already installed operating system packages such as libjpeg, libfreetype,
and zlib, they’ll be detected and used by Pillow. See the installation page for details on
this.
Open an image file:
>>> from PIL import Image
>>> img = Image.open('oreilly.png')
>>> img.format
'PNG'
>>> img.size
(154, 141)
>>> img.mode
'RGB'
Although the package is called Pillow, you import it as PIL to make it compatible with
the older PIL.
To display the image on your screen using the Image object’s show() method, you’ll first
need to install the ImageMagick package described in the next section, and then try this:
>>> img.show()
The image displayed in Figure A-1 opens in another window. (This screenshot was
captured on a Mac, where the show() function used the Preview application. Your win‐
dow’s appearance might vary.)
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Figure A-1. Image opened via the Python library
Let’s crop the image in memory, save the result as a new object called img2, and display
it. Images are always measured by horizontal (x) values and vertical (y) values, with one
corner of the image known as the origin and arbitrarily assigned an x and y of 0. In this
library, the origin (0, 0) is at the upper left of the image, x increases to the right, and y
increases as you move down. We want to give the values of left x (55), top y (70), right
x (85), and bottom y (100) to the crop() method, so we’ll pass it a tuple with those values
in that order.
>>> crop = (55, 70, 85, 100)
>>> img2 = img.crop(crop)
>>> img2.show()
The results are shown in Figure A-2.
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Figure A-2. The cropped image
Save an image file with the save method. It takes a filename and an optional type. If the
filename has a suffix, the library uses that to determine the type. But, you can also specify
the type explicitly. To save our cropped image as a GIF file, do the following:
>>> img2.save('cropped.gif', 'GIF')
>>> img3 = Image.open('cropped.gif')
>>> img3.format
'GIF'
>>> img3.size
(30, 30)
Let’s “improve” our little mascot. First download an image of moustaches to moustach‐
es.png. We’ll load it, crop it suitably, and then overlay it on our spokescritter:
>>> mustache = Image.open('moustaches.png')
>>> handlebar = mustache.crop( (316, 282, 394, 310) )
>>> handlebar.size
(78, 28)
>>> img.paste(handlebar, (45, 90) )
>>> img.show()
Figure A-3 presents the highly satisfactory results.
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Figure A-3. Our new, dapper mascot
It would be nice to make that moustache background transparent. Hey, there’s an ex‐
ercise for you! If you’d like to play with this, look up transparency and alpha channel in
the Pillow tutorial.
ImageMagick
ImageMagick is a suite of programs to convert, modify, and display 2-D bitmap im‐
ages. It’s been around for more than 20 years. Various Python libraries have connected
to the ImageMagick C library. A recent one that supports Python 3 is wand. To install
it, type the following command:
$ pip install Wand
You can do many of the same things with wand as you can with Pillow:
>>> from wand.image import Image
>>> from wand.display import display
>>>
>>> img = Image(filename='oreilly.png')
>>> img.size
(154, 141)
>>> img.format
'PNG'
As with Pillow, this displays the image on the screen:
>>> display(img)
wand includes rotation, resizing, text and line drawing, format conversion, and other
features that you can also find in Pillow. Both have good APIs and documentation.
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Graphical User Interfaces (GUIs)
The name includes the word graphic, but GUIs concentrate more on the user interface:
widgets to present data, input methods, menus, buttons, and windows to frame every‐
thing.
The GUI programming wiki page and FAQ list many Python-powered GUIs. Let’s begin
with the only one that’s built into the standard library: Tkinter. It’s plain, but it works
on all platforms to produce native-looking windows and widgets.
Here’s a teeny, tiny Tkinter program to display our favorite googly-eyed mascot in a
window:
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
import tkinter
from PIL import Image, ImageTk
main = tkinter.Tk()
img = Image.open('oreilly.png')
tkimg = ImageTk.PhotoImage(img)
tkinter.Label(main, image=tkimg).pack()
main.mainloop()
Notice that it used some modules from PIL/Pillow. You should see the O’Reilly logo
again, as shown in Figure A-4.
Figure A-4. Image shown through Tkinter library
To make the window go away, click its close button, or leave your Python interpreter.
Read more about Tkinter at the tkinter wiki and the Python wiki. Now for the GUIs that
are not in the standard library.
Qt
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This is a professional GUI and application toolkit, originated about 20 years ago by
Trolltech in Norway. It’s been used to help build applications such as Google Earth,
Maya, and Skype. It was also used as the base for KDE, a Linux desktop. There are
two main Python libraries for Qt: PySide is free (LGPL license), and PyQt is licensed
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either with the GPL or commercially. The Qt folks see these differences. Download
PySide from PyPI or Qt and read the tutorial. You can download Qt for free online.
GTK+
GTK+ is a competitor of Qt, and it, too, has been used to create many applica‐
tions, including GIMP and the Gnome desktop for Linux. The Python binding is
PyGTK. To download the code, go to the PyGTK site, where you can also read the
documents.
WxPython
This is the Python binding for WxWidgets. It’s another hefty package, free to
download online.
Kivy
Kivy is a free modern library for building multimedia user interfaces portably across
platforms—desktop (Windows, OS X, Linux), and mobile (Android, iOS). It in‐
cludes multitouch support. You can download for all the platforms on the Kivy
website. Kivy includes application development tutorials.
The Web
Frameworks such as Qt use native components, but some others use the Web. After
all, the Web is a universal GUI, and it has graphics (SVG), text (HTML), and even
multimedia now (in HTML5). Some web-based GUI tools written in Python in‐
clude RCTK (Remote Control Toolkit) and Muntjac. You can build web applications
with any combination of frontend (browser-based) and backend (web server) tools.
A thin client lets the backend do most of the work. If the frontend dominates, it’s a
thick, or fat, or rich client; the last adjective sounds more flattering. It’s common for
the sides to communicate with RESTful APIs, AJAX, and JSON.
3-D Graphics and Animation
Watch the long end-credits for almost any contemporary movie, and you’ll see mass
quantities of people doing special effects and animation. Most of the big studios—Walt
Disney Animation, ILM, Weta, Dreamworks, Pixar—hire people with Python experi‐
ence. Do a web search for “python animation jobs” or visit vfxjobs and search for
“python” to see what’s available now.
If you’d like to experiment with Python and 3-D, animation, multimedia, and games,
try Panda3D. It’s open source and free to use, even for commercial applications. You
can download a version for your computer from the Panda3D website. To try some
sample programs, change to the directory /Developer/Examples/Panda3D. Each sub‐
directory contains one or more .py files. Run one of them by using the ppython com‐
mand that came with Panda3D. For example:
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$ cd /Developer/Examples/Panda3D
$ cd Ball-in-Maze/
$ ppython Tut-Ball-in-Maze.py
DirectStart: Starting the game.
Known pipe types:
osxGraphicsPipe
(all display modules loaded.)
A window similar to Figure A-5 opens.
Figure A-5. An image shown through the Panda3D library
Use your mouse to tilt the box and move the ball in the maze.
If that all worked, and the base Panda3D installation looks good, then you can start
playing with the Python library.
Here’s a simple example application from the Panda3D documentation; save it as pan‐
da1.py:
from direct.showbase.ShowBase import ShowBase
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class MyApp(ShowBase):
def __init__(self):
ShowBase.__init__(self)
# Load the environment model.
self.environ = self.loader.loadModel("models/environment")
# Reparent the model to render.
self.environ.reparentTo(self.render)
# Apply scale and position transforms on the model.
self.environ.setScale(0.25, 0.25, 0.25)
self.environ.setPos(-8, 42, 0)
app = MyApp()
app.run()
Run it by using this command:
$ ppython panda1.py
Known pipe types:
osxGraphicsPipe
(all display modules loaded.)
A window opens displaying the scene in Figure A-6.
The rock and tree are floating above the ground. Click the Next buttons to continue the
guided tour and fix these problems.
Following are some other 3-D packages with Python:
Blender
Blender is a free 3-D animation and game creator. When you download and install
it from www.blender.org/download, it comes bundled with its own copy of Python
3.
Maya
This is a commercial 3-D animation and graphic system. It also comes bundled with
a version of Python, currently 2.6. Chad Vernon has written a free downloadable
book, Python Scripting for Maya Artists. If you search for Python and Maya on the
Web, you’ll find many other resources, both free and commercial, including videos.
Houdini
Houdini is commercial, although you can download a free version called Appren‐
tice. Like the other animation packages, it comes with a Python binding.
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Figure A-6. Scaled image shown through Panda3D library
Plots, Graphs, and Visualization
Python has become a leading solution for plots, graphs, and data visualization. It’s es‐
pecially popular in science, which is covered in Appendix C. The official Python site
has an overview. Allow me to take a few moments to say a little more about some of
these.
matplotlib
The free matplotlib 2-D plotting library can be installed by using the following com‐
mand:
$ pip install matplotlib
The examples in the gallery show the breadth of matplotlib. Let’s try the same image
display application (with results shown in Figure A-7), just to see how the code and
presentation look:
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import matplotlib.pyplot as plot
import matplotlib.image as image
img = image.imread('oreilly.png')
plot.imshow(img)
plot.show()
Figure A-7. Image shown through the matplotlib library
You can see more of matplotlib in Appendix C; it has strong ties to NumPy and other
scientific applications.
bokeh
In the old web days, developers would generate graphics on the server and give the web
browser some URL to access them. More recently, JavaScript has gained performance
and client-side graphics generation tools like D3. A page or two ago, I mentioned the
possibility of using Python as part of a frontend-backend architecture for graphics and
GUIs. A new tool called bokeh combines the strengths of Python (large data sets, ease
of use) and JavaScript (interactivity, less graphics latency). Its emphasis is quick visual‐
ization of large data sets.
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If you’ve already installed its prerequisites (NumPy, Pandas, and Redis) you can install
bokeh by typing this command:
$ pip install bokeh
(You can see NumPy and Pandas in action in Appendix C.)
Or, install everything at once from the Bokeh website. Although matplotlib runs on
the server, bokeh runs mainly in the browser and can take advantage of recent advances
on the client side. Click any image in the gallery for an interactive view of the display
and its Python code.
Games
Python is good at data wrangling, and you’ve seen in this appendix that it’s also good at
multimedia. How about games?
It happens that Python is such a good game development platform that people have
written books about it. Here are a couple of them:
• Invent Your Own Computer Games with Python by Al Sweigart
• The Python Game Book, by Horst Jens (a docuwiki book)
There’s a general discussion at the Python wiki with even more links.
The best known Python game platform is probably pygame. You can download an ex‐
ecutable installer for your platform from the Pygame website, and read a line-by-line
example of a “pummel the chimp” game.
Audio and Music
What about sound, and music, and cats singing Jingle Bells?
Well, maybe just the first two.
The standard library has some rudimentary audio modules under multimedia serv‐
ices. There’s also a discussion of third-party modules.
The following libraries can help you generate music:
• pyknon is used by the book Music for Geeks and Nerds by Pedro Kroger (Create‐
Space).
• mingus is a music sequencer that can read and produce MIDI files.
• remix, as the name implies, is an API to remix music. One example of its use is
morecowbell.dj, which adds more cowbell to uploaded songs.
• sebastian is a library for music theory and analysis.
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• Piano lets you play piano on your computer keyboard with the keys C, D, E, F, A, B,
and C.
Finally, the libraries in the following list can help you organize your collection or give
you access to music data:
• Beets manages your music collection.
• Echonest APIs access music metadata.
• Monstermash mashes song fragments together; it’s built on Echonest, Flask,
ZeroMQ, and Amazon EC2.
• Shiva is a RESTful API and server to query your collection.
• Get album art to match your music.
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APPENDIX B
Py at Work
“Business!” cried the Ghost, wringing its hands again. “Mankind was my business…”
— Charles Dickens
A Christmas Carol
The businessman’s uniform is a suit and tie. But for some reason, when he decides to
get down to business, he tosses his jacket over a chair, loosens his tie, rolls up his sleeves,
and pours some coffee. Meanwhile the business woman, with little fanfare, is actually
getting work done. Maybe with a latte.
In business, we use all of the technologies from the earlier chapters—databases, the Web,
systems, and networks. Python’s productivity is making it more popular in the enter‐
prise and with startups.
Businesses have long sought silver bullets to slay their legacy werewolves—incompatible
file formats, arcane network protocols, language lock-in, and the universal lack of
accurate documentation. However, today we see some technologies and techniques that
can actually interoperate and scale. Businesses can create faster, cheaper, stretchier ap‐
plications by employing the following:
• Dynamic languages like Python
• The Web as a universal graphical user interface
• RESTful APIs as language-independent service interfaces
• Relational and NoSQL databases
• “Big data” and analytics
• Clouds for deployment and capital savings
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The Microsoft Office Suite
Business is heavily dependent on Microsoft Office applications and file formats. Al‐
though they are not well known, and in some cases poorly documented, there are some
Python libraries that can help. Here are some that process Microsoft Office documents:
docx
This library creates, reads, and writes Microsoft Office Word 2007 .docx files.
python-excel
This one discusses the xlrd, xlwt, and xlutils modules via a PDF tutorial. Excel
can also read and write Comma-Separated Value (CSV) files, which you know how
to process by using the standard csv module.
oletools
This library extracts data from Office formats.
These modules automate Windows applications:
pywin32
This module automates many Windows applications. However, it’s limited to
Python 2, and has sparse documentation; see this blog post and this one.
pywinauto
This one also automates Windows applications and is limited to Python 2; see this
blog post.
swapy
swapy generates Python code for pywinauto from native controls.
OpenOffice is an open source alternative to Office. It runs on Linux, Unix, Windows,
and OS X, and reads and writes Office file formats, It also installs a version of Python 3
for its own use. You can program OpenOffice in Python with the PyUNO library.
OpenOffice was owned by Sun Microsystems, and when Oracle acquired Sun, some
people feared for its future availability. LibreOffice was spun off as a result. Documen‐
tHacker describes using the Python UNO library with LibreOffice.
OpenOffice and LibreOffice had to reverse engineer the Microsoft file formats, which
is not easy. The Universal Office Converter module depends on the UNO library in
OpenOffice or LibreOffice. It can convert many file formats: documents, spreadsheets,
graphics, and presentations.
If you have a mystery file, python-magic can guess its format by analyzing specific byte
sequences.
The python open document library lets you provide Python code within templates to
create dynamic documents.
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Although not a Microsoft format, Adobe’s PDF is very common in business. Repor‐
tLab has open source and commercial versions of its Python-based PDF generator. If
you need to edit a PDF, you might find some help at StackOverflow.
Carrying Out Business Tasks
You can find a Python module for almost anything. Visit PyPI and type something into
the search box. Many modules are interfaces to the public APIs of various services. You
might be interested in some examples related to business tasks:
• Ship via Fedex or UPS.
• Mail with the stamps.com API.
• Read a discussion of Python for business intelligence.
• If Aeropresses are flying off the shelves in Anoka, was it customer activity or pol‐
tergeists? Cubes is an Online Analytical Processing (OLAP) web server and data
browser.
• OpenERP is a large commercial Enterprise Resource Planning (ERP) system writ‐
ten in Python and JavaScript, with thousands of add-on modules.
Processing Business Data
Businesses have a particular fondness for data. Sadly, many of them conjure up perverse
ways of making data harder to use.
Spreadsheets were a good invention, and over time businesses became addicted to them.
Many non-programmers were tricked into programming because they were called
macros instead of programs. But the universe is expanding and data is trying to keep
up. Older versions of Excel were limited to 65,536 rows, and even newer versions choke
at a million or so. When an organization’s data outgrow the limits of a single computer,
it’s like headcount growing past a hundred people or so—suddenly you need new layers,
intermediaries, and communication.
Excessive data programs aren’t caused by the size of data on single desktops; rather,
they’re the result of the aggregate of data pouring into the business. Relational databases
handle millions of rows without exploding, but only so many writes or updates at a time.
A plain old text or binary file can grow gigabytes in size, but if you need to process it
all at once, you need enough memory. Traditional desktop software isn’t designed for
all this. Companies such as Google and Amazon had to invent solutions to handle so
much data at scale. Netflix is an example built on Amazon’s AWS cloud, using Python
to glue together RESTful APIs, security, deployment, and databases.
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Extracting, Transforming, and Loading
The underwater portions of the data icebergs include all the work to get the data in the
first place. If you speak enterprise, the common term is extract, transform, load, or ETL.
Synonyms such as data munging or data wrangling give the impression of taming an
unruly beast, which might be apt metaphors. This would seem to be a solved engineering
matter by now, but it remains largely an art. We’ll address data science more broadly in
Appendix C, because this is where most developers spend a large part of their time.
If you’ve seen The Wizard of Oz, you probably remember (besides the flying monkeys)
the part at the end—when the good witch told Dorothy that she could always go home
to Kansas just by clicking her ruby slippers. Even when I was young I thought, “Now
she tells her!” Although, in retrospect, I realize the movie would have been much shorter
if she’d shared that tip earlier.
But this isn’t a movie; we’re talking about the world of business here, where making tasks
shorter is a good thing. So, let me share some tips with you now. Most of the tools that
you need for day-to-day data work in business are those that you’ve already read about
here. Those include high-level data structures such as dictionaries and objects, thou‐
sands of standard and third-party libraries, and an expert community that’s just a google
away.
If you’re a computer programmer working for some business, your workflow almost
always includes:
1. Extracting data from weird file formats or databases
2. “Cleaning up” the data, which covers a lot of ground, all strewn with pointy objects
3. Converting things like dates, times, and character sets
4. Actually doing something with the data
5. Storing resulting data in a file or database
6. Rolling back to step 1 again; lather, rinse, repeat
Here’s an example: you want to move data from a spreadsheet to a database. You can
save the spreadsheet in CSV format and use the Python libraries from Chapter 8. Or,
you can look for a module that reads the binary spreadsheet format directly. Your fingers
know how to type python excel into Google, and find sites such as Working with Excel
files in Python. You can install one of the packages by using pip, and locate a Python
database driver for the last part of the task. I mentioned SQLAlchemy and the direct
low-level database drivers in that same chapter. Now you need some code in the middle,
and that’s where Python’s data structures and libraries can save you time.
Let’s try an example here, and then we’ll try again with a library that saves a few steps.
We’ll read a CSV file, aggregate the counts in one column by unique values in another,
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and print the results. If we did this in SQL, we would use SELECT, JOIN, and GROUP
BY.
First, the file, zoo.csv, which has columns for the type of animal, how many times it has
bitten a visitor, the number of stitches required, and how much we’ve paid the visitor
not to tell local television stations:
animal,bites,stitches,hush
bear,1,35,300
marmoset,1,2,250
bear,2,42,500
elk,1,30,100
weasel,4,7,50
duck,2,0,10
We want to see which animal is costing us the most, so we’ll aggregate the total hush
money by the type of animal. (We’ll leave bites and stitches to an intern.) We’ll use the
csv module from “CSV” on page 181 and Counter from “Count Items with
Counter()” on page 116. Save this code as zoo_counts.py:
import csv
from collections import Counter
counts = Counter()
with open('zoo.csv', 'rt') as fin:
cin = csv.reader(fin)
for num, row in enumerate(cin):
if num > 0:
counts[row[0]] += int(row[-1])
for animal, hush in counts.items():
print("%10s %10s" % (animal, hush))
We skipped the first row because it contained only the column names. counts is a
Counter object, and takes care of initializing the sum for each animal to zero. We also
applied a little formatting to right-align the output. Let’s try it:
$ python zoo_counts.py
duck
10
elk
100
bear
800
weasel
50
marmoset
250
Ha! It was the bear. He was our prime suspect all along, but now we have the numbers.
Next, let’s replicate this with a data processing toolkit called Bubbles. You can install it
by typing this command:
$ pip install bubbles
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It requires SQLAlchemy; if you don’t have that, pip install sqlalchemy will do the
trick. Here’s the test program (call it bubbles1.py), adapted from the documentation:
import bubbles
p = bubbles.Pipeline()
p.source(bubbles.data_object('csv_source', 'zoo.csv', infer_fields=True))
p.aggregate('animal', 'hush')
p.pretty_print()
And now, the moment of truth:
$ python bubbles1.py
2014-03-11 19:46:36,806 DEBUG calling aggregate(rows)
2014-03-11 19:46:36,807 INFO called aggregate(rows)
2014-03-11 19:46:36,807 DEBUG calling pretty_print(records)
+--------+--------+------------+
|animal |hush_sum|record_count|
+--------+--------+------------+
|duck
|
10|
1|
|weasel |
50|
1|
|bear
|
800|
2|
|elk
|
100|
1|
|marmoset|
250|
1|
+--------+--------+------------+
2014-03-11 19:46:36,807 INFO called pretty_print(records)
If you read the documentation, you can avoid those debug print lines, and maybe change
the format of the table.
Looking at the two examples, we see that the bubbles example used a single function
call (aggregate) to replace our manual reading and counting of the CSV format. De‐
pending on your needs, data toolkits can save a lot of work.
In a more realistic example, our zoo file might have thousands of rows (it’s a dangerous
place), with misspellings such as bare, commas in numbers, and so on. For good ex‐
amples of practical data problems with Python and Java code, I’d also recommend Greg
Wilson’s book Data Crunching: Solve Everyday Problems Using Java, Python, and More
(Pragmatic Bookshelf).
Data cleanup tools can save a lot of time, and Python has many of them. For another
example, PETL does row and column extraction and renaming. Its related work page
lists many useful modules and products. Appendix C has detailed discussions of some
especially useful data tools: Pandas, NumPy, and IPython. Although they’re currently
best known among scientists, they’re becoming popular among financial and data de‐
velopers. At the 2012 Pydata conference, AppData discussed how these three and other
Python tools help process 15 terabytes of data daily. Make no mistake: Python can handle
very large real-world data loads.
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Additional Sources of Information
Sometimes, you need data that originates somewhere else. Some business and govern‐
ment data sources include:
data.gov
A gateway to thousands of data sets and tools. Its APIs are built on CKAN, a Python
data management system.
Opening government with Python
See the video and slides.
python-sunlight
Libraries to access the Sunlight APIs.
froide
A Django-based platform for managing freedom of information requests.
30 places to find open data on the Web
Some handy links.
Python in Finance
Recently, the financial industry has developed a great interest in Python. Adapting soft‐
ware from Appendix C as well as some of their own, quants are building a new generation
of financial tools:
Quantitative economics
This is a tool for economic modeling, with lots of math and Python code.
Python for finance
This features the book Derivatives Analytics with Python: Data Analytics, Models,
Simulation, Calibration, and Hedging by Yves Hilpisch (Wiley).
Quantopian
Quantopian is an interactive website on which you can write your own Python code
and run it against historic stock data to see how it would have done.
PyAlgoTrade
This is another that you can use for stock backtesting, but on your own computer.
Quandl
Use this to search millions of financial datasets.
Ultra-finance
A real-time stock collection library.
Python for Finance (O’Reilly)
A book by Yves Hilpisch with Python examples for financial modeling.
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Business Data Security
Security is a special concern for business. Entire books are devoted to this topic, so we’ll
just mention a few Python-related tips here.
• “Scapy” on page 289 discusses scapy, a Python-powered language for packet for‐
ensics. It has been used to explain some major network attacks.
• The Python Security site has discussions of security topics, details on some Python
modules, and cheat sheets.
• The book Violent Python (subtitled A Cookbook for Hackers, Forensic Analysts, Pen‐
etration Testers and Security Engineers) by TJ O’Connor (Syngress) is an extensive
review of Python and computer security.
Maps
Maps have become valuable to many businesses. Python is very good at making maps,
so we’re going to spend a little more time in this area. Managers love graphics, and if
you can quickly whip up a nice map for your organization’s website it wouldn’t hurt.
In the early days of the Web, I used to visit an experimental mapmaking website at Xerox.
When big sites such as Google Maps came along, they were a revelation (along the lines
of “why didn’t I think of that and make millions?”). Now mapping and location-based
services are everywhere, and are particularly useful in mobile devices.
Many terms overlap here: mapping, cartography, GIS (geographic information system),
GPS (Global Positioning System), geospatial analysis, and many more. The blog at
Geospatial Python has an image of the “800-pound gorilla” systems—GDAL/OGR,
GEOS, and PROJ.4 (projections)--and surrounding systems, represented as monkeys.
Many of these have Python interfaces. Let’s talk about some of these, beginning with the
simplest formats.
Formats
The mapping world has lots of formats: vector (lines), raster (images), metadata (words),
and various combinations.
Esri, a pioneer of geographic systems, invented the shapefile format over 20 years ago.
A shapefile actually consists of multiple files, including at the very least the following:
.shp
The “shape” (vector) information
.shx
The shape index
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.dbf
An attribute database
Some useful Python shapefile modules include the following:
• pyshp is a pure-Python shapefile library.
• shapely addresses geometric questions such as, “What buildings in this town are
within the 50-year flood contour?”
• fiona wraps the OGR library, which handles shapefiles and other vector formats.
• kartograph renders shapefiles into SVG maps on the server or client.
• basemap plots 2-D data on maps, and uses matplotlib.
• cartopy uses matplotlib and shapely to draw maps.
Let’s grab a shapefile for our next example- visit the Natural Earth 1:110m Cultural
Vectors page. Under “Admin 1 - States and Provinces,” click the green download states
and provinces box to download a zip file. After it downloads to your computer, unzip
it; you should see these resulting files:
ne_110m_admin_1_states_provinces_shp.README.html
ne_110m_admin_1_states_provinces_shp.sbn
ne_110m_admin_1_states_provinces_shp.VERSION.txt
ne_110m_admin_1_states_provinces_shp.sbx
ne_110m_admin_1_states_provinces_shp.dbf
ne_110m_admin_1_states_provinces_shp.shp
ne_110m_admin_1_states_provinces_shp.prj
ne_110m_admin_1_states_provinces_shp.shx
We’ll use these for our examples.
Draw a Map
You’ll need this library to read a shapefile:
$ pip install pyshp
Now for the program, map1.py, which I’ve modified from a Geospatial Python blog post:
def display_shapefile(name, iwidth=500, iheight=500):
import shapefile
from PIL import Image, ImageDraw
r = shapefile.Reader(name)
mleft, mbottom, mright, mtop = r.bbox
# map units
mwidth = mright - mleft
mheight = mtop - mbottom
# scale map units to image units
hscale = iwidth/mwidth
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vscale = iheight/mheight
img = Image.new("RGB", (iwidth, iheight), "white")
draw = ImageDraw.Draw(img)
for shape in r.shapes():
pixels = [
(int(iwidth - ((mright - x) * hscale)), int((mtop - y) * vscale))
for x, y in shape.points]
if shape.shapeType == shapefile.POLYGON:
draw.polygon(pixels, outline='black')
elif shape.shapeType == shapefile.POLYLINE:
draw.line(pixels, fill='black')
img.show()
if __name__ == '__main__':
import sys
display_shapefile(sys.argv[1], 700, 700)
This reads the shapefile and iterates through its individual shapes. I’m checking for only
two shape types: a polygon, which connects the last point to the first, and a polyline,
which doesn’t. I’ve based my logic on the original post and a quick look at the docu‐
mentation for pyshp, so I’m not really sure how it will work. Sometimes, we just need
to make a start and deal with any problems as we find them.
So, let’s run it. The argument is the base name of the shapefile files, without any exten‐
sion:
$ python map1.py ne_110m_admin_1_states_provinces_shp
You should see something like Figure B-1.
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Figure B-1. Preliminary map
Well, it drew a map that resembles the United States, but:
• It looks like a cat dragged yarn across Alaska and Hawaii; this is a bug.
• The country is squished; I need a projection.
• The picture isn’t pretty; I need better style control.
To address the first point: I have a problem somewhere in my logic, but what should I
do? Chapter 12 discusses development tips, including debugging, but we can consider
other options here. I could write some tests and bear down until I fix this, or I could
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just try some other mapping library. Maybe something at a higher level would solve all
three of my problems (the stray lines, squished appearance, and primitive style).
Here are some links to other Python mapping software:
basemap
Based on matplotlib, draws maps and data overlays
mapnik
A C++ library with Python bindings, for vector (line) and raster (image) maps
tilemill
A map design studio based on mapnik
Vincent
Translates to Vega, a JavaScript visualization tool; see the tutorial Mapping data in
Python with pandas and vincent
Python for ArcGIS
Links to Python resources for Esri’s commercial ArcGIS product
Spatial analysis with python
Links to tutorials, packages, and videos
Using geospatial data with python
Video presentations
So you’d like to make a map using Python
Uses pandas, matplotlib, shapely, and other Python modules to create maps of
historic plaque locations
Python Geospatial Development (Packt)
A book by Eric Westra with examples using mapnik and other tools
Learning Geospatial Analysis with Python (Packt)
Another book by Joel Lawhead reviewing formats and libraries, with geospatial
algorithms
These modules all make beautiful maps, but are harder to install and learn. Some depend
on other software you haven’t seen yet, such as numpy or pandas. Will the benefits out‐
weigh the costs? As developers, we often need to make these trade-offs based on in‐
complete information. If you’re interested in maps, try downloading and installing one
of these packages and see what you can do. Or, you can avoid installing software and
try connecting to a remote web service API yourself; Chapter 9 shows you how to
connect to web servers and decode JSON responses.
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Applications and Data
We’ve been talking about drawing maps, but you can do a lot more with map data.
Geocoding converts between addresses and geographic coordinates. There are many
geocoding APIs (see ProgrammableWeb’s comparison) and Python libraries: geopy,
pygeocoder, and googlemaps. If you sign up with Google or another source to get an
API key, you can access other services such as step-by-step travel directions or local
search.
Here are a few sources of mapping data:
http://www.census.gov/geo/maps-data/
Overview of the U.S. Census Bureau’s map files
http://www.census.gov/geo/maps-data/data/tiger.html
Heaps of geographic and demographic map data
http://wiki.openstreetmap.org/wiki/Potential_Datasources
Worldwide sources
http://www.naturalearthdata.com/
Vector and raster map data at three scales
We should mention the Data Science Toolkit here. It includes free bidirectional geo‐
coding, coordinates to political boundaries and statistics, and more. You can also down‐
load all the data and software as a virtual machine (VM) and run it self-contained on
your own computer.
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APPENDIX C
Py Sci
In her reign the power of steam
On land and sea became supreme,
And all now have strong reliance
In fresh victories of science.
— James McIntyre
Queen’s Jubilee Ode 1887
In the past few years, largely because of the software you’ll see in this appendix, Python
has become extremely popular with scientists. If you’re a scientist or student yourself,
you might have used tools like MATLAB and R, or traditional languages such as Java,
C, or C++. In this appendix, you’ll see how Python makes an excellent platform for
scientific analysis and publishing.
Math and Statistics in the Standard Library
First, let’s take a little trip back to the standard library and visit some features and
modules that we’ve ignored.
Math Functions
Python has a menagerie of math functions in the standard math library. Just type import
math to access them from your programs.
It has a few constants such as pi and e:
>>> import math
>>> math.pi
>>> 3.141592653589793
>>> math.e
2.718281828459045
Most of it consists of functions, so let’s look at the most useful ones.
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fabs() returns the absolute value of its argument:
>>> math.fabs(98.6)
98.6
>>> math.fabs(-271.1)
271.1
Get the integer below (floor()) and above (ceil()) some number:
>>> math.floor(98.6)
98
>>> math.floor(-271.1)
-272
>>> math.ceil(98.6)
99
>>> math.ceil(-271.1)
-271
Calculate the factorial (in math, n !) by using factorial():
>>> math.factorial(0)
1
>>> math.factorial(1)
1
>>> math.factorial(2)
2
>>> math.factorial(3)
6
>>> math.factorial(10)
3628800
Get the logarithm of the argument in base e with log():
>>> math.log(1.0)
0.0
>>> math.log(math.e)
1.0
If you want a different base for the log, provide it as a second argument:
>>> math.log(8, 2)
3.0
The function pow() does the opposite, raising a number to a power:
>>> math.pow(2, 3)
8.0
Python also has the built-in exponentiation operator ** to do the same, but it doesn’t
automatically convert the result to a float if the base and power are both integers:
>>> 2**3
8
>>> 2.0**3
8.0
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Get a square root with sqrt():
>>> math.sqrt(100.0)
10.0
Don’t try to trick this function; it’s seen it all before:
>>> math.sqrt(-100.0)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
ValueError: math domain error
The usual trigonometric functions are all there, and I’ll just list their names here: sin(),
cos(), tan(), asin(), acos(), atan(), and atan2(). If you remember the Pythagorean
theorem (or can say it fast three times without spitting), the math library also has a
hypot() function to calculate the hypotenuse from two sides:
>>> x = 3.0
>>> y = 4.0
>>> math.hypot(x, y)
5.0
If you don’t trust all these fancy functions, you can work it out yourself:
>>> math.sqrt(x*x + y*y)
5.0
>>> math.sqrt(x**2 + y**2)
5.0
A last set of functions converts angular coordinates:
>>> math.radians(180.0)
3.141592653589793
>>> math.degrees(math.pi)
180.0
Working with Complex Numbers
Complex numbers are fully supported in the base Python language, with their familiar
notation of real and imaginary parts:
>>> # a real number
... 5
5
>>> # an imaginary number
... 8j
8j
>>> # an imaginary number
... 3 + 2j
(3+2j)
Because the imaginary number i (1j in Python) is defined as the square root of –1, we
can execute the following:
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>>> 1j * 1j
(-1+0j)
>>> (7 + 1j) * 1j
(-1+7j)
Some complex math functions are in the standard cmath module.
Calculate Accurate Floating Point with decimal
Floating-point numbers in computers are not quite like the real numbers we learned in
school. Because computer CPUs are designed for binary math, numbers that aren’t exact
powers of two often can’t be represented exactly:
>>> x = 10.0 / 3.0
>>> x
3.3333333333333335
Whoa, what’s that 5 at the end? It should be 3 all the way down. With Python’s deci
mal module, you can represent numbers to your desired level of significance. This is
especially important for calculations involving money. US currency doesn’t go lower
than a cent (a hundredth of a dollar), so if we’re calculating money amounts as dollars
and cents, we want to be accurate to the penny. If we try to represent dollars and cents
through floating-point values such as 19.99 and 0.06, we’ll lose some significance way
down in the end bits before we even begin calculating with them. How do we handle
this? Easy. We use the decimal module, instead:
>>> from decimal import Decimal
>>> price = Decimal('19.99')
>>> tax = Decimal('0.06')
>>> total = price + (price * tax)
>>> total
Decimal('21.1894')
We created the price and tax with string values to preserve their significance. The total
calculation maintained all the significant fractions of a cent, but we want to get the
nearest cent:
>>> penny = Decimal('0.01')
>>> total.quantize(penny)
Decimal('21.19')
You might get the same results with plain old floats and rounding, but not always. You
could also multiply everything by 100 and use integer cents in your calculations, but
that will bite you eventually, too. There’s a nice discussion of these issues at www.itmay‐
beahack.com.
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Perform Rational Arithmetic with fractions
You can represent numbers as a numerator divided by a denominator through the stan‐
dard Python fractions module. Here is a simple operation multiplying one-third by
two-thirds:
>>> from fractions import Fraction
>>> Fraction(1, 3) * Fraction(2, 3)
Fraction(2, 9)
Floating-point arguments can be inexact, so you can use Decimal within Fraction:
>>> Fraction(1.0/3.0)
Fraction(6004799503160661, 18014398509481984)
>>> Fraction(Decimal('1.0')/Decimal('3.0'))
Fraction(3333333333333333333333333333, 10000000000000000000000000000)
Get the greatest common divisor of two numbers with the gcd function:
>>> import fractions
>>> fractions.gcd(24, 16)
8
Use Packed Sequences with array
A Python list is more like a linked list than an array. If you want a one-dimensional
sequence of the same type, use the array type. It uses less space than a list and supports
many list methods. Create one with array( typecode , initializer ). The typecode
specifies the data type (like int or float) and the optional initializer contains initial
values, which you can specify as a list, string, or iterable.
I’ve never used this package for real work. It’s a low-level data structure, useful for things
such as image data. If you actually need an array—especially with more then one di‐
mension—to do numeric calculations, you’re much better off with NumPy, which we’ll
discuss momentarily.
Handling Simple Stats by Using statistics
Beginning with Python 3.4, statistics is a standard module. It has the usual functions:
mean, media, mode, standard deviation, variance, and so on. Input arguments are se‐
quences (lists or tuples) or iterators of various numeric data types: int, float, decimal,
and fraction. One function, mode, also accepts strings. Many more statistical functions
are available in packages such as SciPy and Pandas, featured later in this appendix.
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Matrix Multiplication
Starting with Python 3.5, you’ll see the @ character doing something out of character. It
will still be used for decorators, but it will also have a new use for matrix multiplica‐
tion. However, until it arrives, NumPy (coming right up) is your best bet.
Scientific Python
The rest of this appendix covers third-party Python packages for science and math.
Although you can install them individually, you should consider downloading all of
them at once as part of a scientific Python distribution. Here are your main choices:
Anaconda
This package is free, extensive, up-to-the-minute, supports Python 2 and 3, and
won’t clobber your existing system Python.
Enthought Canopy
This package is available in both free and commercial versions.
Python(x,y)
This is a Windows-only release.
Pyzo
This package is based on some tools from Anaconda, plus a few others.
ALGORETE Loopy
This is also based on Anaconda, with extras.
I recommend installing Anaconda. It’s big, but everything in this appendix is in there.
See Appendix D for details on using Python 3 with Anaconda. The examples in the rest
of this appendix will assume that you’ve installed the required packages, either indi‐
vidually or as part of Anaconda.
NumPy
NumPy is one of the main reasons for Python’s popularity among scientists. You’ve heard
that dynamic languages such as Python are often slower than compiled languages like
C, or even other interpreted languages such as Java. NumPy was written to provide fast
multidimensional numeric arrays, similar to scientific languages like FORTRAN. You
get the speed of C with the developer-friendliness of Python.
If you’ve downloaded one of the scientific distributions, you already have NumPy. If
not, follow the instructions on the NumPy download page.
To begin with NumPy, you should understand a core data structure, a multidimensional
array called an ndarray (for N-dimensional array) or just an array. Unlike Python’s lists
and tuples, each element needs to be of the same type. NumPy refers to an array’s number
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of dimensions as its rank. A one-dimensional array is like a row of values, a twodimensional array is like a table of rows and columns, and a three-dimensional array is
like a Rubik’s Cube. The lengths of the dimensions need not be the same.
The NumPy array and the standard Python array are not the same
thing. For the rest of this appendix, when I say array, I’m referring to
a NumPy array.
But why do you need an array?
• Scientific data often consists of large sequences of data.
• Scientific calculations on this data often use matrix math, regression, simulation,
and other techniques that process many data points at a time.
• NumPy handles arrays much faster than standard Python lists or tuples.
There are many ways to make a NumPy array.
Make an Array with array()
You can make an array from a normal list or tuple:
>>> b = np.array( [2, 4, 6, 8] )
>>> b
array([2, 4, 6, 8])
The attribute ndim returns the rank:
>>> b.ndim
1
The total number of values in the array are given by size:
>>> b.size
4
The number of values in each rank are returned by shape:
>>> b.shape
(4,)
Make an Array with arange()
NumPy’s arange() method is similar to Python’s standard range(). If you call arange()
with a single integer argument num, it returns an ndarray from 0 to num-1:
>>> import numpy as np
>>> a = np.arange(10)
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>>> a
array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
>>> a.ndim
1
>>> a.shape
(10,)
>>> a.size
10
With two values, it creates an array from the first to the last minus one:
>>> a = np.arange(7, 11)
>>> a
array([ 7, 8, 9, 10])
And you can provide a step size to use instead of one as a third argument:
>>> a = np.arange(7, 11, 2)
>>> a
array([7, 9])
So far, our examples have used integers, but floats work just fine:
>>> f = np.arange(2.0, 9.8, 0.3)
>>> f
array([ 2. , 2.3, 2.6, 2.9, 3.2, 3.5,
5.3, 5.6, 5.9, 6.2, 6.5, 6.8,
8.6, 8.9, 9.2, 9.5, 9.8])
3.8,
7.1,
4.1,
7.4,
4.4,
7.7,
4.7,
8. ,
5. ,
8.3,
And one last trick: the dtype argument tells arange what type of values to produce:
>>> g = np.arange(10, 4, -1.5, dtype=np.float)
>>> g
array([ 10. ,
8.5,
7. ,
5.5])
Make an Array with zeros(), ones(), or random()
The zeros() method returns an array in which all the values are zero. The argument
you provide is a tuple with the shape that you want. Here’s a one-dimensional array:
>>> a = np.zeros((3,))
>>> a
array([ 0., 0., 0.])
>>> a.ndim
1
>>> a.shape
(3,)
>>> a.size
3
This one is of rank two:
>>> b = np.zeros((2, 4))
>>> b
array([[ 0., 0., 0., 0.],
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>>>
2
>>>
(2,
>>>
8
[ 0.,
b.ndim
0.,
0.,
0.]])
b.shape
4)
b.size
The other special function that fills an array with the same value is ones():
>>> import numpy as np
>>> k = np.ones((3, 5))
>>> k
array([[ 1., 1., 1., 1.,
[ 1., 1., 1., 1.,
[ 1., 1., 1., 1.,
1.],
1.],
1.]])
One last initializer creates an array with random values between 0.0 and 1.0:
>>> m = np.random.random((3, 5))
>>> m
array([[ 1.92415699e-01,
4.43131404e-01,
7.99226773e-01,
1.14301942e-01,
2.85383430e-04],
[ 6.53705749e-01,
7.48034559e-01,
4.49463241e-01,
4.87906915e-01,
9.34341118e-01],
[ 9.47575562e-01,
2.21152583e-01,
2.49031209e-01,
3.46190961e-01,
8.94842676e-01]])
Change an Array’s Shape with reshape()
So far, an array doesn’t seem that different from a list or tuple. One difference is that
you can get it to do tricks, such as change its shape by using reshape():
>>> a = np.arange(10)
>>> a
array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
>>> a = a.reshape(2, 5)
>>> a
array([[0, 1, 2, 3, 4],
[5, 6, 7, 8, 9]])
>>> a.ndim
2
>>> a.shape
(2, 5)
>>> a.size
10
You can reshape the same array in different ways:
>>> a = a.reshape(5, 2)
>>> a
array([[0, 1],
[2, 3],
[4, 5],
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[6, 7],
[8, 9]])
>>> a.ndim
2
>>> a.shape
(5, 2)
>>> a.size
10
Assigning a shapely tuple to shape does the same thing:
>>> a.shape = (2, 5)
>>> a
array([[0, 1, 2, 3, 4],
[5, 6, 7, 8, 9]])
The only restriction on a shape is that the product of the rank sizes needs to equal the
total number of values (in this case, 10):
>>> a = a.reshape(3, 4)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
ValueError: total size of new array must be unchanged
Get an Element with []
A one-dimensional array works like a list:
>>> a = np.arange(10)
>>> a[7]
7
>>> a[-1]
9
However, if the array has a different shape, use comma-separated indices:
>>> a.shape = (2, 5)
>>> a
array([[0, 1, 2, 3, 4],
[5, 6, 7, 8, 9]])
>>> a[1,2]
7
That’s different from a two-dimensional Python list:
>>> l = [ [0, 1, 2, 3, 4], [5, 6, 7, 8, 9] ]
>>> l
[[0, 1, 2, 3, 4], [5, 6, 7, 8, 9]]
>>> l[1,2]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: list indices must be integers, not tuple
>>> l[1][2]
7
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One last thing: slices work, but again, only within one set of square brackets. Let’s make
our familiar test array again:
>>> a = np.arange(10)
>>> a = a.reshape(2, 5)
>>> a
array([[0, 1, 2, 3, 4],
[5, 6, 7, 8, 9]])
Use a slice to get the first row, elements from offset 2 to the end:
>>> a[0, 2:]
array([2, 3, 4])
Now, get the last row, elements up to the third from the end:
>>> a[-1, :3]
array([5, 6, 7])
You can also assign a value to more than one element with a slice. The following state‐
ment assigns the value 1000 to columns (offsets) 2 and 3 of all rows:
>>> a[:, 2:4] = 1000
>>> a
array([[
0,
1, 1000, 1000,
[
5,
6, 1000, 1000,
4],
9]])
Array Math
Making and reshaping arrays was so much fun that we almost forgot to actually do
something with them. For our first trick, we’ll use NumPy’s redefined multiplication (*)
operator to multiply all the values in a NumPy array at once:
>>> from numpy import *
>>> a = arange(4)
>>> a
array([0, 1, 2, 3])
>>> a *= 3
>>> a
array([0, 3, 6, 9])
If you tried to multiply each element in a normal Python list by a number, you’d need a
loop or a list comprehension:
>>>
>>>
[0,
>>>
>>>
[0,
plain_list = list(range(4))
plain_list
1, 2, 3]
plain_list = [num * 3 for num in plain_list]
plain_list
3, 6, 9]
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This all-at-once behavior also applies to addition, subtraction, division, and other func‐
tions in the NumPy library. For example, you can initialize all members of an array to
any value by using zeros() and +:
>>> from numpy import *
>>> a = zeros((2, 5)) + 17.0
>>> a
array([[ 17., 17., 17., 17., 17.],
[ 17., 17., 17., 17., 17.]])
Linear Algebra
NumPy includes many functions for linear algebra. For example, let’s define this system
of linear equations:
4x + 5y = 20
x + 2y = 13
How do we solve for x and y? We’ll build two arrays:
• The coefficients (multipliers for x and y)
• The dependent variables (right side of the equation)
>>> import numpy as np
>>> coefficients = np.array([ [4, 5], [1, 2] ])
>>> dependents = np.array( [20, 13] )
Now, use the solve() function in the linalg module:
>>> answers = np.linalg.solve(coefficients, dependents)
>>> answers
array([ -8.33333333, 10.66666667])
The result says that x is about –8.3 and y is about 10.6. Did these numbers solve the
equation?
>>> 4 * answers[0] + 5 * answers[1]
20.0
>>> 1 * answers[0] + 2 * answers[1]
13.0
How about that. To avoid all that typing, you can also ask NumPy to get the dot prod‐
uct of the arrays for you:
>>> product = np.dot(coefficients, answers)
>>> product
array([ 20., 13.])
The values in the product array should be close to the values in dependents if this
solution is correct. You can use the allclose() function to check whether the arrays
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are approximately equal (they might not be exactly equal because of floating-point
rounding):
>>> np.allclose(product, dependents)
True
NumPy also has modules for polynomials, Fourier transforms, statistics, and some
probability distributions.
The SciPy Library
There’s even more in a library of mathematical and statistical functions built on top of
NumPy: SciPy. The SciPy release includes NumPy, SciPy, Pandas (coming later in this
appendix), and other libraries.
SciPy includes many modules, including some for the following tasks:
• Optimization
• Statistics
• Interpolation
• Linear regression
• Integration
• Image processing
• Signal processing
If you’ve worked with other scientific computing tools, you’ll find that Python, NumPy,
and SciPy cover some of the same ground as the commercial MATLAB or open source
R.
The SciKit Library
In the same pattern of building on earlier software, SciKit is a group of scientific packages
built on SciPy. SciKit’s specialty is machine learning: it supports modeling, classification,
clustering, and various algorithms.
The IPython Library
IPython is worth your time for many reasons. Here are some of them:
• An improved interactive interpreter (an alternative to the >>> examples that we’ve
used throughout this book)
• Publishing code, plots, text, and other media in web-based notebooks
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• Support for parallel computing
Let’s look at the interpreter and notebooks.
A Better Interpreter
IPython has different versions for Python 2 and 3, and both are installed by Anaconda
or other modern scientific Python releases. Use ipython3 for the Python 3 version.
$ ipython3
Python 3.3.3 (v3.3.3:c3896275c0f6, Nov 16 2013, 23:39:35)
Type "copyright", "credits" or "license" for more information.
IPython 0.13.1 -- An enhanced Interactive Python.
?
-> Introduction and overview of IPython's features.
%quickref -> Quick reference.
help
-> Python's own help system.
object? -> Details about 'object', use 'object??' for extra details.
In [1]:
The standard Python interpreter uses the input prompts >>> and ... to indicate where
and when you should type code. IPython tracks everything you type in a list called In,
and all your output in Out. Each input can be more than one line, so you submit it by
holding the Shift key while pressing Enter. Here’s a one-line example:
In [1]: print("Hello? World?")
Hello? World?
In [2]:
In and Out are automatically numbered lists, letting you access any of the inputs you
typed or outputs you received.
If you type ? after a variable, IPython tells you its type, value, ways of making a variable
of that type, and some explanation:
In [4]: answer = 42
In [5]: answer?
Type:
int
String Form:42
Docstring:
int(x=0) -> integer
int(x, base=10) -> integer
Convert a number or string to an integer, or return 0 if no arguments
are given. If x is a number, return x.__int__(). For floating point
numbers, this truncates towards zero.
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If x is not a number or if base is given, then x must be a string,
bytes, or bytearray instance representing an integer literal in the
given base. The literal can be preceded by '+' or '-' and be surrounded
by whitespace. The base defaults to 10. Valid bases are 0 and 2-36.
Base 0 means to interpret the base from the string as an integer literal.
>>> int('0b100', base=0)
4
Name lookup is a popular feature of IDEs such as IPython. If you press the Tab key right
after some characters, IPython shows all variables, keywords, and functions that begin
with those characters. Let’s define some variables and then find everything that begins
with the letter f:
In [6]: fee = 1
In [7]: fie = 2
In [8]: fo = 3
In [9]: fum = 4
In [10]: ftab
%%file
fie
fee
filter
finally
float
fo
for
format
from
frozenset
fum
If you type fe followed by the Tab key, it expands to the variable fee, which, in this
program, is the only thing that starts with fe:
In [11]: fee
Out[11]: 1
IPython Notebook
If you prefer graphical interfaces, you might enjoy IPython’s web-based implementation.
You start from the Anaconda launcher window (Figure C-1).
The IPython Library
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Figure C-1. The Anaconda home page
To launch the notebook in a web browser, click the “launch” icon to the right of “ipythonnotebook.” Figure C-2 shows the initial display.
Figure C-2. The iPython home page
Now, click the New Notebook button. A window similar to Figure C-3 opens.
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Figure C-3. The iPython Notebook page
For a graphical version of our previous text-based example, type the same command
that we used in the previous section, as shown in Figure C-4.
Figure C-4. Entering code in iPython
Click the solid black triangle icon to run it. The result is depicted in Figure C-5.
Figure C-5. Running code in iPython
The notebook is more than just a graphical version of an improved interpreter. Besides
code, it can contain text, images, and formatted mathematical expressions.
In the row of icons at the top of the notebook, there’s a pull-down menu (Figure C-6)
that specifies how you can enter content. Here are the choices:
Code
The default, for Python code
Markdown
An alternative to HTML that serves as readable text and a preprocessor format
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Raw Text
Unformatted text Heading 1 through Heading 6: HTML <H1> through <H6> heading
tags
Figure C-6. Menu of content choices
Let’s intersperse some text with our code, making it sort of a wiki. Select Heading 1 from
the pull-down menu, type “Humble Brag Example,” and then hold the Shift key while
pressing the Enter key. You should see those three words in a large bold font. Then,
select Code from the pull-down menu and type some code like this:
print("Some people say this code is ingenious")
Again, press Shift + Enter to complete this entry. You should now see your formatted
title and code, as shown in Figure C-7.
Figure C-7. Formatted text and code
By interspersing code input, output results, text, and even images, you can create an
interactive notebook. Because it’s served over the Web, you can access it from any
browser.
You can see some notebooks converted to static HTML or in a gallery. For a specific
example, try the notebook about the passengers on the Titanic. It includes charts show‐
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ing how gender, wealth, and position on the ship affected survival. As a bonus, you can
read how to apply different machine learning techniques.
Scientists are starting to use IPython notebooks to publish their research, including all
the code and data used to reach their conclusions.
Pandas
Recently, the phrase data science has become common. Some definitions that I’ve seen
include “statistics done on a Mac,” or “statistics done in San Francisco.” However you
define it, the tools we’ve talked about in this chapter—NumPy, SciPy, and the subject of
this section, Pandas—are components of a growing popular data-science toolkit. (Mac
and San Francisco are optional.)
Pandas is a new package for interactive data analysis. It’s especially useful for real world
data manipulation, combining the matrix math of NumPy with the processing ability
of spreadsheets and relational databases. The book Python for Data Analysis: Data
Wrangling with Pandas, NumPy, and IPython by Wes McKinney (O’Reilly) covers data
wrangling with NumPy, IPython, and Pandas.
NumPy is oriented toward traditional scientific computing, which tends to manipulate
multidimensional data sets of a single type, usually floating point. Pandas is more like
a database editor, handling multiple data types in groups. In some languages, such
groups are called records or structures. Pandas defines a base data structure called a
DataFrame. This is an ordered collection of columns with names and types. It has some
resemblance to a database table, a Python named tuple, and a Python nested dictionary.
Its purpose is to simplify the handling of the kind of data you’re likely to encounter not
just in science, but also in business. In fact, Pandas was originally designed to manipulate
financial data, for which the most common alternative is a spreadsheet.
Pandas is an ETL tool for real world, messy data—missing values, oddball formats,
scattered measurements—of all data types. You can split, join, extend, fill in, convert,
reshape, slice, and load and save files. It integrates with the tools we’ve just discussed—
NumPy, SciPy, iPython—to calculate statistics, fit data to models, draw plots, publish,
and so on.
Most scientists just want to get their work done, without spending months to become
experts in esoteric computer languages or applications. With Python, they can become
productive more quickly.
Python and Scientific Areas
We’ve been looking at Python tools that could be used in almost any area of science.
What about software and documentation targeted to specific scientific domains? Here’s
a small sample of Python’s use for specific problems, and some special-purpose libraries:
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General
• Python computations in science and engineering
• A crash course in Python for scientists
Physics
• Computational physics
Biology and medicine
• Python for biologists
• Neuroimaging in Python
International conferences on Python and scientific data include the following:
• PyData
• SciPy
• EuroSciPy
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APPENDIX D
Install Python 3
By the time Python 3 is preinstalled on every machine, toasters will be replaced by 3-D
printers that crank out daily doughnuts with sprinkles. Windows doesn’t have Python
at all, and OS X, Linux, and Unix tend to have old versions. Until they catch up, you’ll
probably need to install Python 3 yourself.
The following sections describe how to carry out these tasks:
• Find out what version of Python you have on your computer, if any
• Install the standard distribution of Python 3, if you don’t have it
• Install the Anaconda distribution of scientific Python modules
• Install pip and virtualenv, if you can’t modify your system
• Install conda as an alternative to pip
Most of the examples in this book were written and tested with Python 3.3, the most
recent stable version at the time of writing. Some used 3.4, which was released during
the editing process. The What’s New in Python page presents what was added in each
version. There are many sources of Python and many ways to install a new version. In
this appendix, I’ll describe two of these ways:
• If you just want the standard interpreter and libraries, I recommend going to the
official language site.
• If you would like Python together with the standard library, and the great scientific
libraries described in Appendix C, use Anaconda.
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Install Standard Python
Go to the Python download page with your web browser. It tries to guess your operating
system and present the appropriate choices, but if it guesses wrong, you can use these:
• Python Releases for Windows
• Python Releases for Mac OS X
• Python Source Releases (Linux and Unix)
You’ll see a page similar to that shown in Figure D-1.
Figure D-1. Sample download page
Click the Download link for the most recent version. In our case, that’s 3.4.1. This takes
you to an information page like the one shown in Figure D-2.
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Figure D-2. Detail page for download
You need to scroll down the page to see the actual download link (Figure D-3).
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Figure D-3. Bottom of page offering download
Click the download link to get to the actual release-specific page (Figure D-4).
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Figure D-4. Files to download
Now, click the correct version for your computer.
Mac OS X
Click the Mac OS X 64-bit/32-bit installer link to download a Mac .dmg file. Doubleclick it after the download completes. A window with four icons opens. Right-click
Python.mpkg and then, in the dialog box that opens, click Open. Click the Continue
button three times or so to step through the legalese, and then, when the appropriate
dialog box opens, click Install. Python 3 will be installed as /usr/local/bin/python3, leav‐
ing any existing Python 2 on your computer unchanged.
Windows
For Windows, download one of these:
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• Windows x86 MSI installer (32-bit)
• Windows x86-64 MSI installer (64-bit)
To determine whether you have a 32-bit or 64-bit version of Windows, do the following:
1. Click the Start button.
2. Right-click Computer.
3. Click Properties and find the bit value.
Click the appropriate installer (.msi file). After it’s downloaded, double-click it and
follow the installer directions.
Linux or Unix
Linux and Unix users get a choice of compressed source formats:
• XZ compressed source tarball
• Gzipped source tarball
Download either one. Decompress it by using tar xJ (.xz file) or tar xz (.tgz file) and
then run the resulting shell script.
Install Anaconda
Anaconda is an all-in-one installer with an emphasis on science: it includes Python, the
standard library, and many useful third-party libraries. Until recently, it included Python
2 as its standard interpreter, although there was a workaround to install Python 3.
The new upgrade, Anaconda 2.0, installs the latest version of Python and its standard
library (3.4 when this was written). Other goodies include libraries that we’ve talked
about in this book: beautifulsoup4, flask, ipython, matplotlib, nose, numpy, pandas,
pillow, pip, scipy, tables, zmq, and many others. It includes a cross-platform instal‐
lation program called conda that improves on pip; we’ll talk about that shortly.
To install Anaconda 2, go to the download page for the Python 3 versions. Click the
appropriate link for your platform (version numbers might have changed since this was
written, but you can figure it out):
• To download for the Mac, click Anaconda3-2.0.0-MacOSX-x86_64.pkg. Doubleclick the file when it’s done downloading, and then follow the usual steps for in‐
stalling Mac software. It will install everything to the anaconda directory under
your home directory.
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• For Windows, click the 32-bit version or 64-bit version. Double-click the .exe file
after it downloads.
• For Linux, click the 32-bit version or 64-bit version. When it has downloaded,
execute it (it’s a big shell script).
Ensure that the name of the file you download starts with Anacon‐
da3. If it starts with just Anaconda, that’s the Python 2 version.
Anaconda installs everything to its own directory (anaconda under your home direc‐
tory). This means that it won’t interfere with any versions of Python that might already
be on your computer. It also means that you don’t need any special permission (names
like admin or root) to install it either.
To see what packages are included, visit the Anaconda docs page and then, in the box
at the top of the page, click “Python version: 3.4.” It listed 141 packages when I last
looked.
After installing Anaconda 2, you can see what Santa put on your computer by typing
this command:
$ ./conda list
# packages in environment at /Users/williamlubanovic/anaconda:
#
anaconda
2.0.0
np18py34_0
argcomplete
0.6.7
py34_0
astropy
0.3.2
np18py34_0
backports.ssl-match-hostname 3.4.0.2
<pip>
beautiful-soup
4.3.1
py34_0
beautifulsoup4
4.3.1
<pip>
binstar
0.5.3
py34_0
bitarray
0.8.1
py34_0
blaze
0.5.0
np18py34_0
blz
0.6.2
np18py34_0
bokeh
0.4.4
np18py34_1
cdecimal
2.3
py34_0
colorama
0.2.7
py34_0
conda
3.5.2
py34_0
conda-build
1.3.3
py34_0
configobj
5.0.5
py34_0
curl
7.30.0
2
cython
0.20.1
py34_0
datashape
0.2.0
np18py34_1
dateutil
2.1
py34_2
docutils
0.11
py34_0
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dynd-python
flask
freetype
future
greenlet
h5py
hdf5
ipython
ipython-notebook
ipython-qtconsole
itsdangerous
jdcal
jinja2
jpeg
libdynd
libpng
libsodium
libtiff
libxml2
libxslt
llvm
llvmpy
lxml
markupsafe
matplotlib
mock
multipledispatch
networkx
nose
numba
numexpr
numpy
openpyxl
openssl
pandas
patsy
pillow
pip
ply
psutil
py
pycosat
pycparser
pycrypto
pyflakes
pygments
pyparsing
pyqt
pytables
pytest
python
python-dateutil
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0.6.2
0.10.1
2.4.10
0.12.1
0.4.2
2.3.0
1.8.9
2.1.0
2.1.0
2.1.0
0.24
1.0
2.7.2
8d
0.6.2
1.5.13
0.4.5
4.0.2
2.9.0
1.1.28
3.3
0.12.4
3.3.5
0.18
1.3.1
1.0.1
0.4.3
1.8.1
1.3.3
0.13.1
2.3.1
1.8.1
2.0.2
1.0.1g
0.13.1
0.2.1
2.4.0
1.5.6
3.4
2.1.1
1.4.20
0.6.1
2.10
2.6.1
0.8.1
1.6
2.0.1
4.10.4
3.1.1
2.5.2
3.4.1
2.1
np18py34_0
py34_1
1
py34_0
py34_0
np18py34_0
2
py34_0
py34_0
py34_0
py34_0
py34_0
py34_0
1
0
1
0
0
1
2
0
py34_0
py34_0
py34_0
np18py34_1
py34_0
py34_0
py34_0
py34_0
np18py34_0
np18py34_0
py34_0
py34_0
0
np18py34_0
np18py34_0
py34_0
py34_0
py34_0
py34_0
py34_0
py34_0
py34_0
py34_0
py34_0
py34_0
py34_0
py34_0
np18py34_0
py34_0
0
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python.app
pytz
pyyaml
pyzmq
qt
readline
redis
redis-py
requests
rope
rope-py3k
runipy
scikit-image
scipy
setuptools
sip
six
sphinx
spyder
spyder-app
sqlalchemy
sqlite
ssl_match_hostname
sympy
tables
tk
tornado
ujson
werkzeug
xlrd
xlsxwriter
yaml
zeromq
zlib
1.2
2014.3
3.11
14.3.0
4.8.5
6.2
2.6.9
2.9.1
2.3.0
0.9.4
0.9.4
0.1.0
0.9.3
0.14.0
3.6
4.15.5
1.6.1
1.2.2
2.3.0rc1
2.3.0rc1
0.9.4
3.8.4.1
3.4.0.2
0.7.5
3.1.1
8.5.15
3.2.1
1.33
0.9.4
0.9.3
0.5.5
0.1.4
4.0.4
1.2.7
py34_2
py34_0
py34_0
py34_0
3
2
0
py34_0
py34_0
py34_1
<pip>
py34_0
np18py34_0
np18py34_0
py34_0
py34_0
py34_0
py34_0
py34_0
py34_0
py34_0
0
py34_0
py34_0
<pip>
0
py34_0
py34_0
py34_0
py34_0
py34_0
1
0
1
Install and Use pip and virtualenv
The pip package is the most popular way to install third-party (nonstandard) Python
packages. It has been annoying that such a useful tool isn’t part of standard Python, and
that you’ve needed to download and install it yourself. As a friend of mine used to say,
it’s a cruel hazing ritual. The good news is that pip is a standard part of Python, starting
with the 3.4 release.
Often used with pip, the virtualenv program is a way to install Python packages in a
specified directory (folder) to avoid interactions with any preexisting system Python
packages. This lets you use whatever Python goodies you want, even if you don’t have
permission to change the existing installation.
If you have Python 3 but only the Python 2 version of pip, here’s how to get the Python
3 version on Linux or OS X:
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$
$
$
$
curl
sudo
curl
sudo
-O http://python-distribute.org/distribute_setup.py
python3 distribute_setup.py
-O https://raw.github.com/pypa/pip/master/contrib/get-pip.py
python3 get-pip.py
This installs pip-3.3 in the bin directory of your Python 3 installation. Then, use
pip-3.3 to install third-party Python packages rather than Python 2’s pip.
Some good guides to pip and virtualenv are:
• A non-magical introduction to Pip and Virtualenv for Python beginners
• The hitchhiker’s guide to packaging: pip
Install and Use conda
Until recently, pip always downloaded source files rather than binaries. This can be a
problem with Python modules that are built on C libraries. Recently, the Anaconda
developers built conda to address the problems they’ve seen with pip and other tools.
pip is a Python package manager, but conda works with any software and language.
conda also avoids the need for something like virtualenv to keep installations from
stepping on one another.
If you installed the Anaconda distribution, you already have the conda program. If not,
you can get Python 3 and conda from the miniconda page. As with Anaconda, make
sure the file you download starts with Miniconda3; if it starts with Miniconda alone, it’s
the Python 2 version.
conda works with pip. Although it has its own public package repository, commands
like conda search will also search the PyPi repository. If you have problems with pip,
conda might be a good alternative.
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APPENDIX E
Answers to Exercises
Chapter 1, A Taste of Py
1.1 If you don’t already have Python 3 installed on your computer, do it now. Read Ap‐
pendix D for the details for your computer system.
1.2 Start the Python 3 interactive interpreter. Again, details are in Appendix D. It should
print a few lines about itself and then a single line starting with >>>. That’s your prompt
to type Python commands.
Here’s what it looks like on my MacBook Pro:
$ python
Python 3.3.0 (v3.3.0:bd8afb90ebf2, Sep 29 2012, 01:25:11)
[GCC 4.2.1 (Apple Inc. build 5666) (dot 3)] on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>>
1.3 Play with the interpreter a little. Use it like a calculator and type this: 8 * 9. Press the
Enter key to see the result. Python should print 72.
>>> 8 * 9
72
1.4 Type the number 47 and press the Enter key. Did it print 47 for you on the next line?
>>> 47
47
1.5 Now type print(47) and press Enter. Did that also print 47 for you on the next line?
>>> print(47)
47
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Chapter 2, Py Ingredients: Numbers, Strings, and Variables
2.1 How many seconds are in an hour? Use the interactive interpreter as a calculator and
multiply the number of seconds in a minute (60) by the number of minutes in an hour (also
60).
>>> 60 * 60
3600
2.2 Assign the result from the previous task (seconds in an hour) to a variable called
seconds_per_hour.
>>> seconds_per_hour = 60 * 60
>>> seconds_per_hour
3600
2.3 How many seconds are in a day? Use your seconds_per_hour variable.
>>> seconds_per_hour * 24
86400
2.4 Calculate seconds per day again, but this time save the result in a variable called
seconds_per_day.
>>> seconds_per_day = seconds_per_hour * 24
>>> seconds_per_day
86400
2.5 Divide seconds_per_day by seconds_per_hour. Use floating-point (/) division.
>>> seconds_per_day / seconds_per_hour
24.0
2.6 Divide seconds_per_day by seconds_per_hour, using integer (//) division. Did this
number agree with the floating-point value from the previous question, aside from the
final .0?
>>> seconds_per_day // seconds_per_hour
24
Chapter 3, Py Filling: Lists, Tuples, Dictionaries, and Sets
3.1 Create a list called years_list, starting with the year of your birth, and each year
thereafter until the year of your fifth birthday. For example, if you were born in 1980, the
list would be years_list = [1980, 1981, 1982, 1983, 1984, 1985].
If you were born in 1980, you would type:
>>> years_list = [1980, 1981, 1982, 1983, 1984, 1985]
3.2 In which of these years was your third birthday? Remember, you were 0 years of age
for your first year.
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You want offset 3. Thus, if you were born in 1980:
>>> years_list[3]
1983
3.3 In which year in years_list were you the oldest?
You want the last year, so use offset -1. You could also say 5 because you know this list
has six items, but -1 gets the last item from a list of any size. For a 1980-vintage person:
>>> years_list[-1]
1985
3.4. Make and print a list called things with these three strings as elements: "mozzarella",
"cinderella", "salmonella".
>>> things = ["mozzarella", "cinderella", "salmonella"]
>>> things
['mozzarella', 'cinderella', 'salmonella']
3.5. Capitalize the element in things that refers to a person and then print the list. Did it
change the element in the list?
This capitalizes the word, but doesn’t change it in the list:
>>> things[1].capitalize()
'Cinderella'
>>> things
['mozzarella', 'cinderella', 'salmonella']
If you want to change it in the list, you need to assign it back:
>>> things[1] = things[1].capitalize()
>>> things
['mozzarella', 'Cinderella', 'salmonella']
3.6. Make the cheesy element of things all uppercase and then print the list.
>>> things[0] = things[0].upper()
>>> things
['MOZZARELLA', 'Cinderella', 'salmonella']
3.7. Delete the disease element, collect your Nobel Prize, and then print the list.
This would remove it by value:
>>> things.remove("salmonella")
>>> things
['MOZZARELLA', 'Cinderella']
Because it was last in the list, the following would have worked also:
>>> del things[-1]
And you could have deleted by offset from the beginning:
>>> del things[2]
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3.8. Create a list called surprise with the elements "Groucho", "Chico", and "Harpo".
>>> surprise = ['Groucho', 'Chico', 'Harpo']
>>> surprise
['Groucho', 'Chico', 'Harpo']
3.9. Lowercase the last element of the surprise list, reverse it, and then capitalize it.
>>> surprise[-1] = surprise[-1].lower()
>>> surprise[-1] = surprise[-1][::-1]
>>> surprise[-1].capitalize()
'Oprah'
3.10. Make an English-to-French dictionary called e2f and print it. Here are your starter
words: dog is chien, cat is chat, and walrus is morse.
>>> e2f = {'dog': 'chien', 'cat': 'chat', 'walrus': 'morse'}
>>> e2f
{'cat': 'chat', 'walrus': 'morse', 'dog': 'chien'}
3.11. Using your three-word dictionary e2f, print the French word for walrus.
>>> e2f['walrus']
'morse'
3.12. Make a French-to-English dictionary called f2e from e2f. Use the items method.
>>> f2e = {}
>>> for english, french in e2f.items():
f2e[french] = english
>>> f2e
{'morse': 'walrus', 'chien': 'dog', 'chat': 'cat'}
3.13. Print the English equivalent of the French word chien.
>>> f2e['chien']
'dog'
3.14. Print the set of English words from e2f.
>>> set(e2f.keys())
{'cat', 'walrus', 'dog'}
3.15. Make a multilevel dictionary called life. Use these strings for the topmost keys:
'animals', 'plants', and 'other'. Make the 'animals' key refer to another dictionary
with the keys 'cats', 'octopi', and 'emus'. Make the 'cats' key refer to a list of strings
with the values 'Henri', 'Grumpy', and 'Lucy'. Make all the other keys refer to empty
dictionaries.
This is a hard one, so don’t feel bad if you peeked here first.
>>> life = {
...
'animals': {
...
'cats': [
...
'Henri', 'Grumpy', 'Lucy'
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...
...
...
...
...
...
...
>>>
],
'octopi': {},
'emus': {}
},
'plants': {},
'other': {}
}
3.16. Print the top-level keys of life.
>>> print(life.keys())
dict_keys(['animals', 'other', 'plants'])
Python 3 includes that dict_keys stuff. To print them as a plain list, use this:
>>> print(list(life.keys()))
['animals', 'other', 'plants']
By the way, you can use spaces to make your code easier to read:
>>> print ( list ( life.keys() ) )
['animals', 'other', 'plants']
3.17. Print the keys for life['animals'].
>>> print(life['animals'].keys())
dict_keys(['cats', 'octopi', 'emus'])
3.18. Print the values for life['animals']['cats'].
>>> print(life['animals']['cats'])
['Henri', 'Grumpy', 'Lucy']
Chapter 4, Py Crust: Code Structures
4.1. Assign the value 7 to the variable guess_me. Then, write the conditional tests (if,
else, and elif) to print the string 'too low' if guess_me is less than 7, 'too high' if
greater than 7, and 'just right' if equal to 7.
guess_me = 7
if guess_me < 7:
print('too low')
elif guess_me > 7:
print('too high')
else:
print('just right')
Run this program and you should see the following:
just right
4.2. Assign the value 7 to the variable guess_me and the value 1 to the variable start.
Write a while loop that compares start with guess_me. Print 'too low' if start is less
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than guess me. If start equals guess_me, print 'found it!' and exit the loop. If start
is greater than guess_me, print 'oops' and exit the loop. Increment start at the end of
the loop.
guess_me = 7
start = 1
while True:
if start < guess_me:
print('too low')
elif start == guess_me:
print('found it!')
break
elif start > guess_me:
print('oops')
break
start += 1
If you did this right, you should see this:
too low
too low
too low
too low
too low
too low
found it!
Notice that the elif start > guess_me: line could have been a simple else:, because
if start is not less than or equal to guess_me, it must be greater. At least in this universe.
4.3. Use a for loop to print the values of the list [3, 2, 1, 0].
>>> for value in [3, 2, 1, 0]:
...
print(value)
...
3
2
1
0
4.4. Use a list comprehension to make a list called even of the even numbers in range(10).
>>> even = [number for number in range(10) if number % 2 == 0]
>>> even
[0, 2, 4, 6, 8]
4.5. Use a dictionary comprehension to create the dictionary squares. Use range(10) to
return the keys, and use the square of each key as its value.
>>> squares = {key: key*key for key in range(10)}
>>> squares
{0: 0, 1: 1, 2: 4, 3: 9, 4: 16, 5: 25, 6: 36, 7: 49, 8: 64, 9: 81}
4.6. Use a set comprehension to create the set odd from the odd numbers in range(10).
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>>> odd = {number for number in range(10) if number % 2 == 1}
>>> odd
{1, 3, 9, 5, 7}
4.7. Use a generator comprehension to return the string 'Got ' and a number for the
numbers in range(10). Iterate through this by using a for loop.
>>>
...
...
Got
Got
Got
Got
Got
Got
Got
Got
Got
Got
for thing in ('Got %s' % number for number in range(10)):
print(thing)
0
1
2
3
4
5
6
7
8
9
4.8. Define a function called good() that returns the list ['Harry', 'Ron', 'Hermione'].
>>> def good():
...
return ['Harry', 'Ron', 'Hermione']
...
>>> good()
['Harry', 'Ron', 'Hermione']
4.9. Define a generator function called get_odds() that returns the odd numbers from
range(10). Use a for loop to find and print the third value returned.
>>> def get_odds():
...
for number in range(1, 10, 2):
...
yield number
...
>>> for count, number in enumerate(get_odds(), 1):
...
if count == 3:
...
print("The third odd number is", number)
...
break
...
The third odd number is 5
4.10. Define a decorator called test that prints 'start' when a function is called and
'end' when it finishes.
>>> def test(func):
...
def new_func(*args, **kwargs):
...
print('start')
...
result = func(*args, **kwargs)
...
print('end')
...
return result
...
return new_func
...
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>>>
>>> @test
... def greeting():
...
print("Greetings, Earthling")
...
>>> greeting()
start
Greetings, Earthling
end
4.11. Define an exception called OopsException. Raise this exception to see what happens.
Then, write the code to catch this exception and print 'Caught an oops'.
>>> class OopsException(Exception):
...
pass
...
>>> raise OopsException()
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
__main__.OopsException
>>>
>>> try:
...
raise OopsException
... except OopsException:
...
print('Caught an oops')
...
Caught an oops
4.12. Use zip() to make a dictionary called movies that pairs these lists: titles =
['Creature of Habit', 'Crewel Fate'] and plots = ['A nun turns into a mon
ster', 'A haunted yarn shop'].
>>> titles = ['Creature of Habit', 'Crewel Fate']
>>> plots = ['A nun turns into a monster', 'A haunted yarn shop']
>>> movies = dict(zip(titles, plots))
>>> movies
{'Crewel Fate': 'A haunted yarn shop', 'Creature of Habit': 'A nun turns
into a monster'}
Chapter 5, Py Boxes: Modules, Packages, and Programs
5.1. Make a file called zoo.py. In it, define a function called hours that prints the string
'Open 9-5 daily'. Then, use the interactive interpreter to import the zoo module and
call its hours function. Here’s zoo.py:
def hours():
print('Open 9-5 daily')
And now, let’s import it interactively:
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>>> import zoo
>>> zoo.hours()
Open 9-5 daily
5.2. In the interactive interpreter, import the zoo module as menagerie and call its hours()
function.
>>> import zoo as menagerie
>>> menagerie.hours()
Open 9-5 daily
5.3. Staying in the interpreter, import the hours() function from zoo directly and call it.
>>> from zoo import hours
>>> hours()
Open 9-5 daily
5.4. Import the hours() function as info and call it.
>>> from zoo import hours as info
>>> info()
Open 9-5 daily
5.5 Make a dictionary called plain with the key-value pairs 'a': 1, 'b': 2, and 'c':
3, and then print it.
>>> plain = {'a': 1, 'b': 2, 'c': 3}
>>> plain
{'a': 1, 'c': 3, 'b': 2}
5.6. Make an OrderedDict called fancy from the same pairs and print it. Did it print in
the same order as plain?
>>> from collections import OrderedDict
>>> fancy = OrderedDict([('a', 1), ('b', 2), ('c', 3)])
>>> fancy
OrderedDict([('a', 1), ('b', 2), ('c', 3)])
5.7. Make a defaultdict called dict_of_lists and pass it the argument list. Make the
list dict_of_lists['a'] and append the value 'something for a' to it in one assign‐
ment. Print dict_of_lists['a'].
>>> from collections import defaultdict
>>> dict_of_lists = defaultdict(list)
>>> dict_of_lists['a'].append('something for a')
>>> dict_of_lists['a']
['something for a']
Chapter 6, Oh Oh: Objects and Classes
6.1. Make a class called Thing with no contents and print it. Then, create an object
called example from this class and also print it. Are the printed values the same or different?
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>>> class Thing:
...
pass
...
>>> print(Thing)
<class '__main__.Thing'>
>>> example = Thing()
>>> print(example)
<__main__.Thing object at 0x1006f3fd0>
6.2. Make a new class called Thing2 and assign the value 'abc' to a class variable called
letters. Print letters.
>>> class Thing2:
...
letters = 'abc'
...
>>> print(Thing2.letters)
abc
6.3. Make yet another class called (of course) Thing3. This time, assign the value 'xyz' to
an instance (object) variable called letters. Print letters. Do you need to make an object
from the class to do this?
>>> class Thing3:
...
def __init__(self):
...
self.letters = 'xyz'
...
The variable letters belongs to any objects made from Thing3, not the Thing3 class
itself:
>>> print(Thing3.letters)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
AttributeError: type object 'Thing3' has no attribute 'letters'
>>> something = Thing3()
>>> print(something.letters)
xyz
6.4. Make a class called Element, with instance attributes name, symbol, and number.
Create an object called hydrogen of this class with the values 'Hydrogen', 'H', and 1.
>>> class Element:
...
def __init__(self, name, symbol, number):
...
self.name = name
...
self.symbol = symbol
...
self.number = number
...
>>> hydrogen = Element('Hydrogen', 'H', 1)
6.5. Make a dictionary with these keys and values: 'name': 'Hydrogen', 'symbol':
'H', 'number': 1. Then, create an object called hydrogen from class Element using this
dictionary.
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Start with the dictionary:
>>> el_dict = {'name': 'Hydrogen', 'symbol': 'H', 'number': 1}
This works, although it takes a bit of typing:
>>> hydrogen = Element(el_dict['name'], el_dict['symbol'], el_dict['number'])
Let’s check that it worked:
>>> hydrogen.name
'Hydrogen'
However, you can also initialize the object directly from the dictionary, because its key
names match the arguments to __init__ (refer to Chapter 3 for a discussion of keyword
arguments):
>>> hydrogen = Element(**el_dict)
>>> hydrogen.name
'Hydrogen'
6.6. For the Element class, define a method called dump() that prints the values of the
object’s attributes (name, symbol, and number). Create the hydrogen object from this new
definition and use dump() to print its attributes.
>>> class Element:
...
def __init__(self, name, symbol, number):
...
self.name = name
...
self.symbol = symbol
...
self.number = number
...
def dump(self):
...
print('name=%s, symbol=%s, number=%s' %
...
(self.name, self.symbol, self.number))
...
>>> hydrogen = Element(**el_dict)
>>> hydrogen.dump()
name=Hydrogen, symbol=H, number=1
6.7. Call print(hydrogen). In the definition of Element, change the name of method dump
to __str__, create a new hydrogen object, and call print(hydrogen) again.
>>> print(hydrogen)
<__main__.Element object at 0x1006f5310>
>>> class Element:
...
def __init__(self, name, symbol, number):
...
self.name = name
...
self.symbol = symbol
...
self.number = number
...
def __str__(self):
...
return ('name=%s, symbol=%s, number=%s' %
...
(self.name, self.symbol, self.number))
...
>>> hydrogen = Element(**el_dict)
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>>> print(hydrogen)
name=Hydrogen, symbol=H, number=1
__str__() is one of Python’s magic methods. The print function calls an object’s
__str__() method to get its string representation. If it doesn’t have a __str__() method,
it gets the default method from its parent Object class, which returns a string like
<__main__.Element object at 0x1006f5310>.
6.8. Modify Element to make the attributes name, symbol, and number private. Define a
getter property for each to return its value.
>>> class Element:
...
def __init__(self, name, symbol, number):
...
self.__name = name
...
self.__symbol = symbol
...
self.__number = number
...
@property
...
def name(self):
...
return self.__name
...
@property
...
def symbol(self):
...
return self.__symbol
...
@property
...
def number(self):
...
return self.__number
...
>>> hydrogen = Element('Hydrogen', 'H', 1)
>>> hydrogen.name
'Hydrogen'
>>> hydrogen.symbol
'H'
>>> hydrogen.number
1
6.9 Define three classes: Bear, Rabbit, and Octothorpe. For each, define only one meth‐
od: eats(). This should return 'berries' (Bear), 'clover' (Rabbit), and 'campers'
(Octothorpe). Create one object from each and print what it eats.
>> class Bear:
...
def eats(self):
...
return 'berries'
...
>>> class Rabbit:
...
def eats(self):
...
return 'clover'
...
>>> class Octothorpe:
...
def eats(self):
...
return 'campers'
...
>>> b = Bear()
>>> r = Rabbit()
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>>> o = Octothorpe()
>>> print(b.eats())
berries
>>> print(r.eats())
clover
>>> print(o.eats())
campers
6.10. Define these classes: Laser, Claw, and SmartPhone. Each has only one method:
does(). This returns 'disintegrate' (Laser), 'crush' (Claw), or 'ring' (Smart
Phone). Then, define the class Robot that has one instance (object) of each of these. Define
a does() method for the Robot that prints what its component objects do.
>>> class Laser:
...
def does(self):
...
return 'disintegrate'
...
>>> class Claw:
...
def does(self):
...
return 'crush'
...
>>> class SmartPhone:
...
def does(self):
...
return 'ring'
...
>>> class Robot:
...
def __init__(self):
...
self.laser = Laser()
...
self.claw = Claw()
...
self.smartphone = SmartPhone()
...
def does(self):
...
return '''I have many attachments:
... My laser, to %s.
... My claw, to %s.
... My smartphone, to %s.''' % (
...
self.laser.does(),
...
self.claw.does(),
...
self.smartphone.does() )
...
>>> robbie = Robot()
>>> print( robbie.does() )
I have many attachments:
My laser, to disintegrate.
My claw, to crush.
My smartphone, to ring.
Chapter 7, Mangle Data Like a Pro
7.1. Create a Unicode string called mystery and assign it the value '\U0001f4a9'. Print
mystery. Look up the Unicode name for mystery.
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>>> import unicodedata
>>> mystery = '\U0001f4a9'
>>> mystery
'💩'
>>> unicodedata.name(mystery)
'PILE OF POO'
Oh my. What else have they got in there?
7.2. Encode mystery, this time using UTF-8, into the bytes variable pop_bytes. Print
pop_bytes.
>>> pop_bytes = mystery.encode('utf-8')
>>> pop_bytes
b'\xf0\x9f\x92\xa9'
7.3. Using UTF-8, decode pop_bytes into the string variable pop_string. Print
pop_string. Is pop_string equal to mystery?
>>> pop_string = pop_bytes.decode('utf-8')
>>> pop_string
'💩'
>>> pop_string == mystery
True
7.4. Write the following poem by using old-style formatting. Substitute the strings 'roast
beef', 'ham', 'head', and 'clam' into this string:
My kitty cat likes %s,
My kitty cat likes %s,
My kitty cat fell on his %s
And now thinks he's a %s.
>>>
...
...
...
...
...
>>>
>>>
poem = '''
My kitty cat likes %s,
My kitty cat likes %s,
My kitty cat fell on his %s
And now thinks he's a %s.
'''
args = ('roast beef', 'ham', 'head', 'clam')
print(poem % args)
My kitty cat likes roast beef,
My kitty cat likes ham,
My kitty cat fell on his head
And now thinks he's a clam.
7.5. Write a form letter by using new-style formatting. Save the following string as let
ter (you’ll use it in the next exercise):
Dear {salutation} {name},
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Thank you for your letter. We are sorry that our {product} {verbed} in your
{room}. Please note that it should never be used in a {room}, especially
near any {animals}.
Send us your receipt and {amount} for shipping and handling. We will send
you another {product} that, in our tests, is {percent}% less likely to
have {verbed}.
Thank you for your support.
Sincerely,
{spokesman}
{job_title}
>>>
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
letter = '''
Dear {salutation} {name},
Thank you for your letter. We are sorry that our {product} {verb} in your
{room}. Please note that it should never be used in a {room}, especially
near any {animals}.
Send us your receipt and {amount} for shipping and handling. We will send
you another {product} that, in our tests, is {percent}% less likely to
have {verbed}.
Thank you for your support.
Sincerely,
{spokesman}
{job_title}
'''
7.6. Make a dictionary called response with values for the string keys 'salutation',
'name', 'product', 'verbed' (past tense verb), 'room', 'animals', 'percent', 'spokes
man', and 'job_title'. Print letter with the values from response.
>>> response = {
...
'salutation': 'Colonel',
...
'name': 'Hackenbush',
...
'product': 'duck blind',
...
'verbed': 'imploded',
...
'room': 'conservatory',
...
'animals': 'emus',
...
'amount': '$1.38',
...
'percent': '1',
...
'spokesman': 'Edgar Schmeltz',
...
'job_title': 'Licensed Podiatrist'
...
}
...
>>> print( letter.format(**response) )
Dear Colonel Hackenbush,
Thank you for your letter. We are sorry that our duck blind imploded in your
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conservatory. Please note that it should never be used in a conservatory,
especially near any emus.
Send us your receipt and $1.38 for shipping and handling. We will send
you another duck blind that, in our tests, is 1% less likely to have imploded.
Thank you for your support.
Sincerely,
Edgar Schmeltz
Licensed Podiatrist
7.7. When you’re working with text, regular expressions come in very handy. We’ll apply
them in a number of ways to our featured text sample. It’s a poem titled “Ode on the
Mammoth Cheese,” written by James McIntyre in 1866 in homage to a seven-thousandpound cheese that was crafted in Ontario and sent on an international tour. If you’d rather
not type all of it, use your favorite search engine and cut and paste the words into your
Python program. Or, just grab it from Project Gutenberg. Call the text string mammoth.
>>>
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
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|
mammoth = '''
We have seen thee, queen of cheese,
Lying quietly at your ease,
Gently fanned by evening breeze,
Thy fair form no flies dare seize.
All gaily dressed soon you'll go
To the great Provincial show,
To be admired by many a beau
In the city of Toronto.
Cows numerous as a swarm of bees,
Or as the leaves upon the trees,
It did require to make thee please,
And stand unrivalled, queen of cheese.
May you not receive a scar as
We have heard that Mr. Harris
Intends to send you off as far as
The great world's show at Paris.
Of the youth beware of these,
For some of them might rudely squeeze
And bite your cheek, then songs or glees
We could not sing, oh! queen of cheese.
We'rt
You'd
Folks
About
'''
thou suspended from balloon,
cast a shade even at noon,
would think it was the moon
to fall and crush them soon.
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7.8 Import the re module to use Python’s regular expression functions. Use re.fin
dall() to print all the words that begin with 'c'.
We’ll define the variable pat for the pattern and then search for it in mammoth:
>>> import re
>>> re = r'\bc\w*'
>>> re.findall(pat, mammoth)
['cheese', 'city', 'cheese', 'cheek', 'could', 'cheese', 'cast', 'crush']
The \b means to begin at a boundary between a word and a nonword. Use this to specify
either the beginning or end of a word. The literal c is the first letter of the words we’re
looking for. The \w means any word character, which includes letters, digits, and un‐
derscores (_). The * means zero or more of these word characters. Together, this finds
words that begin with c, including 'c' itself. If you didn’t use a raw string (with an r
right before the starting quote), Python would interpret \b as a backspace and the search
would mysteriously fail:
>>> pat = '\bc\w*'
>>> re.findall(pat, mammoth)
[]
7.9 Find all four-letter words that begin with c.
>>> pat = r'\bc\w{3}\b'
>>> re.findall(pat, mammoth)
['city', 'cast']
You need that final \b to indicate the end of the word. Otherwise, you’ll get the first four
letters of all words that begin with c and have at least four letters:
>>> pat = r'\bc\w{3}'
>>> re.findall(pat, mammoth)
['chee', 'city', 'chee', 'chee', 'coul', 'chee', 'cast', 'crus']
7.10. Find all the words that end with r.
This is a little tricky. We get the right result for words that end with r:
>>> pat = r'\b\w*r\b'
>>> re.findall(pat,mammoth)
['your', 'fair', 'Or', 'scar', 'Mr', 'far', 'For', 'your', 'or']
However, the results aren’t so good for words that end with l:
>>> pat = r'\b\w*l\b'
>>> re.findall(pat,mammoth)
['All', 'll', 'Provincial', 'fall']
But what’s that ll doing there? The \w pattern only matches letters, numbers, and un‐
derscores—not ASCII apostrophes. As a result, it grabs the final ll from you'll. We
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can handle this by adding an apostrophe to the set of characters to match. Our first try
fails:
>>> >>> pat = r'\b[\w']*l\b'
File "<stdin>", line 1
pat = r'\b[\w']*l\b'
Python points to the vicinity of the error, but it might take a while to see that the mistake
was that the pattern string is surrounded by the same apostrophe/quote character. One
way to solve this is to escape it with a backslash:
>>> pat = r'\b[\w\']*l\b'
>>> re.findall(pat, mammoth)
['All', "you'll", 'Provincial', 'fall']
Another way is to surround the pattern string with double quotes:
>>> pat = r"\b[\w']*l\b"
>>> re.findall(pat, mammoth)
['All', "you'll", 'Provincial', 'fall']
7.11. Find all the words that contain exactly three vowels in a row.
Begin with a word boundary, any number of word characters, three vowels, and then
any non-vowel characters to the end of the word:
>>> pat = r'\b\w*[aeiou]{3}[^aeiou]\w*\b'
>>> re.findall(pat, mammoth)
['queen', 'quietly', 'beau\nIn', 'queen', 'squeeze', 'queen']
This looks right, except for that 'beau\nIn' string. We searched mammoth as a single
multiline string. Our [^aeiou] matches any non-vowels, including \n (line feed, which
marks the end of a text line). We need to add one more thing to the ignore set: \s matches
any space characters, including \n:
>>> pat = r'\b\w*[aeiou]{3}[^aeiou\s]\w*\b'
>>> re.findall(pat, mammoth)
['queen', 'quietly', 'queen', 'squeeze', 'queen']
We didn’t find beau this time, so we need one more tweak to the pattern: match any
number (even zero) of non-vowels after the three vowels. Our previous pattern always
matched one non-vowel.
>>> pat = r'\b\w*[aeiou]{3}[^aeiou\s]*\w*\b'
>>> re.findall(pat, mammoth)
['queen', 'quietly', 'beau', 'queen', 'squeeze', 'queen']
What does all of this show? Among other things, that regular expressions can do a lot,
but they can be very tricky to get right.
7.12. Use unhexlify() to convert this hex string (combined from two strings to fit on a
page) to a bytes variable called gif:
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'47494638396101000100800000000000ffffff21f9' +
'0401000000002c000000000100010000020144003b'
>>>
>>>
...
>>>
>>>
42
import binascii
hex_str = '47494638396101000100800000000000ffffff21f9' + \
'0401000000002c000000000100010000020144003b'
gif = binascii.unhexlify(hex_str)
len(gif)
7.13. The bytes in gif define a one-pixel transparent GIF file, one of the most common
graphics file formats. A legal GIF starts with the string GIF89a. Does gif match this?
>>> gif[:6] == b'GIF89a'
True
Notice that we needed to use a b to define a byte string rather than a Unicode character
string. You can compare bytes with bytes, but you cannot compare bytes with strings:
>>> gif[:6] == 'GIF89a'
False
>>> type(gif)
<class 'bytes'>
>>> type('GIF89a')
<class 'str'>
>>> type(b'GIF89a')
<class 'bytes'>
7.14. The pixel width of a GIF is a 16-bit big-endian integer starting at byte offset 6, and
the height is the same size, starting at offset 8. Extract and print these values for gif. Are
they both 1?
>>>
>>>
>>>
(1,
import struct
width, height = struct.unpack('<HH', gif[6:10])
width, height
1)
Chapter 8, Data Has to Go Somewhere
8.1. Assign the string 'This is a test of the emergency text system' to the vari‐
able test1, and write test1 to a file called test.txt.
>>> test1 = 'This is a test of the emergency text system'
>>> len(test1)
43
Here’s how to do it by using open, write, and close:
>>> outfile = open('test.txt', 'wt')
>>> outfile.write(test1)
43
>>> outfile.close()
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Or, you can use with and avoid calling close (Python does it for you):
>>> with open('test.txt', 'wt') as outfile:
...
outfile.write(test1)
...
43
8.2. Open the file test.txt and read its contents into the string test2. Are test1 and test2
the same?
>>> with open('test.txt', 'rt') as infile:
...
test2 = infile.read()
...
>>> len(test2)
43
>>> test1 == test2
True
8.3. Save these text lines to a file called test.csv. Notice that if the fields are separated by
commas, you need to surround a field with quotes if it contains a comma.
author,book
J R R Tolkien,The Hobbit
Lynne Truss,"Eats, Shoots & Leaves"
>>>
...
...
...
>>>
...
...
73
text = '''author,book
J R R Tolkien,The Hobbit
Lynne Truss,"Eats, Shoots & Leaves"
'''
with open('test.csv', 'wt') as outfile:
outfile.write(text)
8.4. Use the csv module and its DictReader() method to read test.csv to the variable
books. Print the values in books. Did DictReader() handle the quotes and commas in
the second book’s title?
>>> with open('test.csv', 'rt') as infile:
...
books = csv.DictReader(infile)
...
for book in books:
...
print(book)
...
{'book': 'The Hobbit', 'author': 'J R R Tolkien'}
{'book': 'Eats, Shoots & Leaves', 'author': 'Lynne Truss'}
8.5. Create a CSV file called books.csv by using these lines:
title,author,year
The Weirdstone of Brisingamen,Alan Garner,1960
Perdido Street Station,China Miéville,2000
Thud!,Terry Pratchett,2005
The Spellman Files,Lisa Lutz,2007
Small Gods,Terry Pratchett,1992
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>>>
...
...
...
...
...
...
>>>
...
...
201
text = '''title,author,year
The Weirdstone of Brisingamen,Alan Garner,1960
Perdido Street Station,China Miéville,2000
Thud!,Terry Pratchett,2005
The Spellman Files,Lisa Lutz,2007
Small Gods,Terry Pratchett,1992
'''
with open('books.csv', 'wt') as outfile:
outfile.write(text)
8.6. Use the sqlite3 module to create a SQLite database called books.db and a table
called books with these fields: title (text), author (text), and year (integer).
>>> import sqlite3
>>> db = sqlite3.connect('books.db')
>>> curs = db.cursor()
>>> curs.execute('''create table book (title text, author text, year int)''')
<sqlite3.Cursor object at 0x1006e3b90>
>>> db.commit()
8.7. Read the data from books.csv and insert them into the book table.
>>> import csv
>>> import sqlite3
>>> ins_str = 'insert into book values(?, ?, ?)'
>>> with open('books.csv', 'rt') as infile:
...
books = csv.DictReader(infile)
...
for book in books:
...
curs.execute(ins_str, (book['title'], book['author'], book['year']))
...
<sqlite3.Cursor object at 0x1007b21f0>
<sqlite3.Cursor object at 0x1007b21f0>
<sqlite3.Cursor object at 0x1007b21f0>
<sqlite3.Cursor object at 0x1007b21f0>
<sqlite3.Cursor object at 0x1007b21f0>
>>> db.commit()
8.8. Select and print the title column from the book table in alphabetical order.
>>> sql = 'select title from book order by title asc'
>>> for row in db.execute(sql):
...
print(row)
...
('Perdido Street Station',)
('Small Gods',)
('The Spellman Files',)
('The Weirdstone of Brisingamen',)
('Thud!',)
If you just wanted to print the title value without that tuple stuff (parentheses and
comma), try this:
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>>> for row in db.execute(sql):
...
print(row[0])
...
Perdido Street Station
Small Gods
The Spellman Files
The Weirdstone of Brisingamen
Thud!
If you want to ignore the initial 'The' in titles, you need a little extra SQL fairy dust:
>>> sql = '''select title from book order by
... case when (title like "The %") then substr(title, 5) else title end'''
>>> for row in db.execute(sql):
...
print(row[0])
...
Perdido Street Station
Small Gods
The Spellman Files
Thud!
The Weirdstone of Brisingamen
8.9. Select and print all columns from the book table in order of publication.
>>> for row in db.execute('select * from book order by year'):
...
print(row)
...
('The Weirdstone of Brisingamen', 'Alan Garner', 1960)
('Small Gods', 'Terry Pratchett', 1992)
('Perdido Street Station', 'China Miéville', 2000)
('Thud!', 'Terry Pratchett', 2005)
('The Spellman Files', 'Lisa Lutz', 2007)
To print all the fields in each row, just separate with a comma and space:
>>> for row in db.execute('select * from book order by year'):
...
print(*row, sep=', ')
...
The Weirdstone of Brisingamen, Alan Garner, 1960
Small Gods, Terry Pratchett, 1992
Perdido Street Station, China Miéville, 2000
Thud!, Terry Pratchett, 2005
The Spellman Files, Lisa Lutz, 2007
8.10. Use the sqlalchemy module to connect to the sqlite3 database books.db that you just
made in exercise 8.6. As in 8.8, select and print the title column from the book table in
alphabetical order.
>>>
>>>
>>>
>>>
>>>
...
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conn = sqlalchemy.create_engine('sqlite:///books.db')
sql = 'select title from book order by title asc'
rows = conn.execute(sql)
for row in rows:
print(row)
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...
('Perdido Street Station',)
('Small Gods',)
('The Spellman Files',)
('The Weirdstone of Brisingamen',)
('Thud!',)
8.11. Install the Redis server (see Appendix D) and the Python redis library (pip install
redis) on your machine. Create a Redis hash called test with the fields count (1) and
name ('Fester Bestertester'). Print all the fields for test.
>>> import redis
>>> conn = redis.Redis()
>>> conn.delete('test')
1
>>> conn.hmset('test', {'count': 1, 'name': 'Fester Bestertester'})
True
>>> conn.hgetall('test')
{b'name': b'Fester Bestertester', b'count': b'1'}
8.12. Increment the count field of test and print it.
>>> conn.hincrby('test', 'count', 3)
4
>>> conn.hget('test', 'count')
b'4'
Chapter 9, The Web, Untangled
9.1. If you haven’t installed flask yet, do so now. This will also install werkzeug, jinja2,
and possibly other packages.
9.2. Make a skeleton website, using Flask’s debug/reload development web server. Ensure
that the server starts up for hostname localhost on default port 5000. If your machine is
already using port 5000 for something else, use another port number.
Here’s flask1.py:
from flask import Flask
app = Flask(__name__)
app.run(port=5000, debug=True)
Gentlemen, start your engines:
$ python flask1.py
* Running on http://127.0.0.1:5000/
* Restarting with reloader
9.3. Add a home() function to handle requests for the home page. Set it up to return the
string It's alive!.
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What should we call this one, flask2.py?
from flask import Flask
app = Flask(__name__)
@app.route('/')
def home():
return "It's alive!"
app.run(debug=True)
Start the server:
$ python flask2.py
* Running on http://127.0.0.1:5000/
* Restarting with reloader
Finally, access the home page via a browser, command-line HTTP program such as curl
or wget, or even telnet:
$ curl http://localhost:5000/
It's alive!
9.4. Create a Jinja2 template file called home.html with the following contents:
I'm of course referring to {{thing}}, which is {{height}} feet tall and {{color}}.
Make a directory called templates and create the file home.html with the contents just
shown. If your Flask server is still running from the previous examples, it will detect
the new content and restart itself.
9.5. Modify your server’s home() function to use the home.html template. Provide it with
three GET parameters: thing, height, and color.
Here comes flask3.py:
from flask import Flask, request, render_template
app = Flask(__name__)
@app.route('/')
def home():
thing = request.values.get('thing')
height = request.values.get('height')
color = request.values.get('color')
return render_template('home.html',
thing=thing, height=height, color=color)
app.run(debug=True)
Go to this address in your web client:
http://localhost:5000/?thing=Octothorpe&height=7&color=green
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You should see the following:
I'm of course referring to Octothorpe, which is 7 feet tall and green.
Chapter 10, Systems
10.1. Write the current date as a string to the text file today.txt.
>>>
>>>
>>>
>>>
...
>>>
from datetime import date
now = date.today()
now_str = now.isoformat()
with open('today', 'wt') as output:
print(now_str, file=output)
Instead of print, you could have also said something like output.write(now_str).
Using print adds the final newline.
10.2. Read the text file today.txt into the string today_string.
>>> with open('today', 'rt') as input:
...
today_string = input.read()
...
>>> today_string
'2014-02-04\n'
10.3. Parse the date from today_string.
>>> fmt = '%Y-%m-%d\n'
>>> datetime.strptime(today_string, fmt)
datetime.datetime(2014, 2, 4, 0, 0)
If you wrote that final newline to the file, you need to match it in the format string.
10.4. List the files in your current directory.
If your current directory is ohmy and contains three files named after animals, it might
look like this:
>>> import os
>>> os.listdir('.')
['bears', 'lions', 'tigers']
10.5. List the files in your parent directory.
If your parent directory contained two files plus the current ohmy directory, it might
look like this:
>>> import os
>>> os.listdir('..')
['ohmy', 'paws', 'whiskers']
10.6. Use multiprocessing to create three separate processes. Make each one wait a ran‐
dom number of seconds between zero and one, print the current time, and then exit.
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Save this as multi_times.py:
import multiprocessing
def now(seconds):
from datetime import datetime
from time import sleep
sleep(seconds)
print('wait', seconds, 'seconds, time is', datetime.utcnow())
if __name__ == '__main__':
import random
for n in range(3):
seconds = random.random()
proc = multiprocessing.Process(target=now, args=(seconds,))
proc.start()
$ python multi_times.py
wait 0.4670532005508353 seconds, time is 2014-06-03 05:14:22.930541
wait 0.5908421960431798 seconds, time is 2014-06-03 05:14:23.054925
wait 0.8127669040699719 seconds, time is 2014-06-03 05:14:23.275767
10.7. Create a date object of your day of birth.
Let’s say that you were born on August 14, 1982:
>>> my_day = date(1982, 8, 14)
>>> my_day
datetime.date(1982, 8, 14)
10.8. What day of the week was your day of birth?
>>> my_day.weekday()
5
>>> my_day.isoweekday()
6
With weekday(), Monday is 0 and Sunday is 6. With isoweekday(), Monday is 1 and
Sunday is 7. Therefore, this date was a Saturday.
10.9. When will you be (or when were you) 10,000 days old?
>>> from datetime import timedelta
>>> party_day = my_day + timedelta(days=10000)
>>> party_day
datetime.date(2009, 12, 30)
If that was your birthday, you probably missed an excuse for a party.
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11.1. Use a plain socket to implement a current-time service. When a client sends the
string 'time' to the server, return the current date and time as an ISO string.
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Here’s one way to write the server, udp_time_server.py:
from datetime import datetime
import socket
address = ('localhost', 6789)
max_size = 4096
print('Starting the server at', datetime.now())
print('Waiting for a client to call.')
server = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
server.bind(address)
while True:
data, client_addr = server.recvfrom(max_size)
if data == b'time':
now = str(datetime.utcnow())
data = now.encode('utf-8')
server.sendto(data, client_addr)
print('Server sent', data)
server.close()
And the client, udp_time_client.py:
import socket
from datetime import datetime
from time import sleep
address
max_size
= ('localhost', 6789)
= 4096
print('Starting the client at', datetime.now())
client = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
while True:
sleep(5)
client.sendto(b'time', address)
data, server_addr = client.recvfrom(max_size)
print('Client read', data)
client.close()
I put in a sleep(5) call at the top of the client loop to make the data exchange less
supersonic. Start the server in one window:
$ python udp_time_server.py
Starting the server at 2014-06-02 20:28:47.415176
Waiting for a client to call.
Start the client in another window:
$ python udp_time_client.py
Starting the client at 2014-06-02 20:28:51.454805
After five seconds, you’ll start getting output in both windows. Here are the first three
lines from the server:
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Server sent b'2014-06-03 01:28:56.462565'
Server sent b'2014-06-03 01:29:01.463906'
Server sent b'2014-06-03 01:29:06.465802'
And here are the first three from the client:
Client read b'2014-06-03 01:28:56.462565'
Client read b'2014-06-03 01:29:01.463906'
Client read b'2014-06-03 01:29:06.465802'
Both of these programs run forever, so you’ll need to cancel them manually.
11.2. Use ZeroMQ REQ and REP sockets to do the same thing.
Here’s zmq_time_server.py:
import zmq
from datetime import datetime
host = '127.0.0.1'
port = 6789
context = zmq.Context()
server = context.socket(zmq.REP)
server.bind("tcp://%s:%s" % (host, port))
print('Server started at', datetime.utcnow())
while True:
# Wait for next request from client
message = server.recv()
if message == b'time':
now = datetime.utcnow()
reply = str(now)
server.send(bytes(reply, 'utf-8'))
print('Server sent', reply)
And here’s zmq_time_client.py:
import zmq
from datetime import datetime
from time import sleep
host = '127.0.0.1'
port = 6789
context = zmq.Context()
client = context.socket(zmq.REQ)
client.connect("tcp://%s:%s" % (host, port))
print('Client started at', datetime.utcnow())
while True:
sleep(5)
request = b'time'
client.send(request)
reply = client.recv()
print("Client received %s" % reply)
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With plain sockets, you need to start the server first. With ZeroMQ, you can start either
the server or client first.
$ python zmq_time_server.py
Server started at 2014-06-03 01:39:36.933532
$ python zmq_time_client.py
Client started at 2014-06-03 01:39:42.538245
After 15 seconds or so, you should have some lines from the server:
Server sent 2014-06-03 01:39:47.539878
Server sent 2014-06-03 01:39:52.540659
Server sent 2014-06-03 01:39:57.541403
Here’s what you should see from the client:
Client received b'2014-06-03 01:39:47.539878'
Client received b'2014-06-03 01:39:52.540659'
Client received b'2014-06-03 01:39:57.541403'
11.3. Try the same with XMLRPC.
The server, xmlrpc_time_server.py:
from xmlrpc.server import SimpleXMLRPCServer
def now():
from datetime import datetime
data = str(datetime.utcnow())
print('Server sent', data)
return data
server = SimpleXMLRPCServer(("localhost", 6789))
server.register_function(now, "now")
server.serve_forever()
And the client, xmlrpc_time_client.py:
import xmlrpc.client
from time import sleep
proxy = xmlrpc.client.ServerProxy("http://localhost:6789/")
while True:
sleep(5)
data = proxy.now()
print('Client received', data)
Start the server:
$ python xmlrpc_time_server.py
Start the client:
$ python xmlrpc_time_client.py
Wait 15 seconds or so. Here are the first three lines of server output:
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Server sent
127.0.0.1 Server sent
127.0.0.1 Server sent
127.0.0.1 -
2014-06-03 02:14:52.299122
- [02/Jun/2014 21:14:52] "POST / HTTP/1.1" 200 2014-06-03 02:14:57.304741
- [02/Jun/2014 21:14:57] "POST / HTTP/1.1" 200 2014-06-03 02:15:02.310377
- [02/Jun/2014 21:15:02] "POST / HTTP/1.1" 200 -
And here are the first three lines from the client:
Client received 2014-06-03 02:14:52.299122
Client received 2014-06-03 02:14:57.304741
Client received 2014-06-03 02:15:02.310377
11.4. You may have seen the old I Love Lucy television episode in which Lucy and Ethel
worked in a chocolate factory (it’s a classic). The duo fell behind as the conveyor belt that
supplied the confections for them to process began operating at an ever-faster rate. Write
a simulation that pushes different types of chocolates to a Redis list, and Lucy is a client
doing blocking pops of this list. She needs 0.5 seconds to handle a piece of chocolate. Print
the time and type of each chocolate as Lucy gets it, and how many remain to be handled.
redis_choc_supply.py supplies the infinite treats:
import redis
import random
from time import sleep
conn = redis.Redis()
varieties = ['truffle', 'cherry', 'caramel', 'nougat']
conveyor = 'chocolates'
while True:
seconds = random.random()
sleep(seconds)
piece = random.choice(varieties)
conn.rpush(conveyor, piece)
redis_lucy.py might look like this:
import redis
from datetime import datetime
from time import sleep
conn = redis.Redis()
timeout = 10
conveyor = 'chocolates'
while True:
sleep(0.5)
msg = conn.blpop(conveyor, timeout)
remaining = conn.llen(conveyor)
if msg:
piece = msg[1]
print('Lucy got a', piece, 'at', datetime.utcnow(),
', only', remaining, 'left')
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Start them in either order. Because Lucy takes a half second to handle each, and they’re
being produced every half second on average, it’s a race to keep up. The more of a head
start that you give to the conveyor belt, the harder you make Lucy’s life.
$ python redis_choc_supply.py&
$ python
Lucy got
Lucy got
Lucy got
Lucy got
Lucy got
Lucy got
Lucy got
Lucy got
Lucy got
Lucy got
Lucy got
Lucy got
Lucy got
redis_lucy.py
a b'nougat' at 2014-06-03 03:15:08.721169 , only 4 left
a b'cherry' at 2014-06-03 03:15:09.222816 , only 3 left
a b'truffle' at 2014-06-03 03:15:09.723691 , only 5 left
a b'truffle' at 2014-06-03 03:15:10.225008 , only 4 left
a b'cherry' at 2014-06-03 03:15:10.727107 , only 4 left
a b'cherry' at 2014-06-03 03:15:11.228226 , only 5 left
a b'cherry' at 2014-06-03 03:15:11.729735 , only 4 left
a b'truffle' at 2014-06-03 03:15:12.230894 , only 6 left
a b'caramel' at 2014-06-03 03:15:12.732777 , only 7 left
a b'cherry' at 2014-06-03 03:15:13.234785 , only 6 left
a b'cherry' at 2014-06-03 03:15:13.736103 , only 7 left
a b'caramel' at 2014-06-03 03:15:14.238152 , only 9 left
a b'cherry' at 2014-06-03 03:15:14.739561 , only 8 left
Poor Lucy.
11.5. Using the poem from exercise 7.7, use ZeroMQ to publish it, one word at a time. Write
a ZeroMQ consumer that prints every word that starts with a vowel, and another that
prints every word that contains five letters. Ignore punctuation characters.
Here’s the server, poem_pub.py, which plucks each word from the poem and publishes
it to the topic vowels if it starts with a vowel, and the topic five if it has five letters.
Some words might be in both topics, some in neither.
import string
import zmq
host = '127.0.0.1'
port = 6789
ctx = zmq.Context()
pub = ctx.socket(zmq.PUB)
pub.bind('tcp://%s:%s' % (host, port))
with open('mammoth.txt', 'rt') as poem:
words = poem.read()
for word in words.split():
word = word.strip(string.punctuation)
data = word.encode('utf-8')
if word.startswith(('a','e','i','o','u','A','e','i','o','u')):
pub.send_multipart([b'vowels', data])
if len(word) == 5:
pub.send_multipart([b'five', data])
The client, poem_sub.py, subscribes to the topics vowels and five and prints the topic
and word:
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import string
import zmq
host = '127.0.0.1'
port = 6789
ctx = zmq.Context()
sub = ctx.socket(zmq.SUB)
sub.connect('tcp://%s:%s' % (host, port))
sub.setsockopt(zmq.SUBSCRIBE, b'vowels')
sub.setsockopt(zmq.SUBSCRIBE, b'five')
while True:
topic, word = sub.recv_multipart()
print(topic, word)
If you start these and run them, they almost work. Your code looks fine but nothing
happens. You need to read the ZeroMQ guide to learn about the slow joiner problem:
even if you start the client before the server, the server begins pushing data immediately
after starting, and the client takes a little time to connect to the server. If you’re publishing
a constant stream of something and don’t really care when the subscribers jump in, it’s
no problem. But in this case, the data stream is so short that it’s flown past before the
subscriber blinks, like a fastball past a batter.
The easiest way to fix this is to make the publisher sleep a second after it calls bind()
and before it starts sending messages. Call this version poem_pub_sleep.py:
import string
import zmq
from time import sleep
host = '127.0.0.1'
port = 6789
ctx = zmq.Context()
pub = ctx.socket(zmq.PUB)
pub.bind('tcp://%s:%s' % (host, port))
sleep(1)
with open('mammoth.txt', 'rt') as poem:
words = poem.read()
for word in words.split():
word = word.strip(string.punctuation)
data = word.encode('utf-8')
if word.startswith(('a','e','i','o','u','A','e','i','o','u')):
print('vowels', data)
pub.send_multipart([b'vowels', data])
if len(word) == 5:
print('five', data)
pub.send_multipart([b'five', data])
Start the subscriber and then the sleepy publisher:
$ python poem_sub.py
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$ python poem_pub_sleep.py
Now, the subscriber has time to grab its two topics. Here are the first few lines of its
output:
b'five' b'queen'
b'vowels' b'of'
b'five' b'Lying'
b'vowels' b'at'
b'vowels' b'ease'
b'vowels' b'evening'
b'five' b'flies'
b'five' b'seize'
b'vowels' b'All'
b'five' b'gaily'
b'five' b'great'
b'vowels' b'admired'
If you can’t add a sleep() to your publisher, you can synchronize publisher and sub‐
scriber programs by using REQ and REP sockets. See the publisher.py and subscriber.py
examples on GitHub.
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APPENDIX F
Cheat Sheets
I find myself looking up certain things a little too often. Here are some tables that I hope
you’ll find useful.
Operator Precedence
This table is a remix of the official documentation on precedence in Python 3, with the
highest precedence operators at the top.
Operator
Description and examples
[ v1, … ], { v1, …}, { k1: v1, …}, (…)
List/set/dict/generator creation or comprehension, parenthesized
expression
seq [ n ], seq [ n : m ], func ( args… ), obj .attr Index, slice, function call, attribute reference
**
Exponentiation
`+`x, `-`x, `~`x
Positive, negative, bitwise not
*, /, //, %
Multiplication, float division, int division, remainder
+, -
Addition, subtraction
<<, >>
Bitwise left, right shifts
&
Bitwise and
|
Bitwise or
in, not in, is, is not, <, <=, >, >=, !=, == Membership and equality tests
not x
Boolean (logical) not
and
Boolean and
or
Boolean or
if … else
Conditional expression
lambda
lambda expression
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String Methods
Python offers both string methods (can be used with any str object) and a string
module with some useful definitions. Let’s use these test variables:
>>> s = "OH, my paws and whiskers!"
>>> t = "I'm late!"
Change Case
>>> s.capitalize()
'Oh, my paws and whiskers!'
>>> s.lower()
'oh, my paws and whiskers!'
>>> s.swapcase()
'oh, MY PAWS AND WHISKERS!'
>>> s.title()
'Oh, My Paws And Whiskers!'
>>> s.upper()
'OH, MY PAWS AND WHISKERS!'
Search
>>> s.count('w')
2
>>> s.find('w')
9
>>> s.index('w')
9
>>> s.rfind('w')
16
>>> s.rindex('w')
16
>>> s.startswith('OH')
True
Modify
>>> ''.join(s)
'OH, my paws and whiskers!'
>>> ' '.join(s)
'O H ,
m y
p a w s
a n d
w h i s k e r s !'
>>> ' '.join((s, t))
"OH, my paws and whiskers! I'm late!"
>>> s.lstrip('HO')
', my paws and whiskers!'
>>> s.replace('H', 'MG')
'OMG, my paws and whiskers!'
>>> s.rsplit()
['OH,', 'my', 'paws', 'and', 'whiskers!']
>>> s.rsplit(' ', 1)
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Appendix F: Cheat Sheets
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['OH, my paws and', 'whiskers!']
>>> s.split()
['OH,', 'my', 'paws', 'and', 'whiskers!']
>>> s.split(' ')
['OH,', 'my', 'paws', 'and', 'whiskers!']
>>> s.splitlines()
['OH, my paws and whiskers!']
>>> s.strip()
'OH, my paws and whiskers!'
>>> s.strip('s!')
'OH, my paws and whisker'
Format
>>> s.center(30)
' OH, my paws and whiskers!
'
>>> s.expandtabs()
'OH, my paws and whiskers!'
>>> s.ljust(30)
'OH, my paws and whiskers!
'
>>> s.rjust(30)
'
OH, my paws and whiskers!'
String Type
>>> s.isalnum()
False
>>> s.isalpha()
False
>>> s.isprintable()
True
>>> s.istitle()
False
>>> s.isupper()
False
>>> s.isdecimal()
False
>>> s.isnumeric()
False
String Module Attributes
These are class attributes that are used as constant definitions.
Attribute
Example
ascii_letters
'abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ'
ascii_lowercase 'abcdefghijklmnopqrstuvwxyz'
ascii_uppercase 'ABCDEFGHIJKLMNOPQRSTUVWXYZ'
digits
'0123456789'
String Module Attributes
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431
Attribute
Example
hexdigits
'0123456789abcdefABCDEF'
octdigits
'01234567'
punctuation
‘'!"#$%&\'()*+,-./:;<=>[email protected][\]^_\{|}~’
printable
‘’0123456789abcdefghijklmnopqrstuvwxyz’ + ‘ABCDEFGHIJKLMNOPQRSTUVWXYZ’ + '!"#$%&\'()*
+,-./:;<=>[email protected][\]^_`{|}~’ + ' \t\n\r\x0b\x0c'
whitespace
' \t\n\r\x0b\x0c'
Fin
This page intentionally left blank.
No, wait…it isn’t.
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Index
Symbols
!= (inequality operator), 72
# (hash character), 69
% (modulus (remainder) operator), 19, 23
& (set intersection operator), 63
() (parentheses), 52
* (asterisk), 6, 32, 91
* (multiplication operator), 19
** (exponentiation operator), 19, 92
+ (addition operator), 19, 31
+ (plus sign), 20
+= (merge operator), 46
- (subtraction operator), 19, 64
-i flag, 319
. (full stop), 158
.bat files, 6
/ (floating point division operator), 19, 21
// (integer (truncating) division, 19, 21
2D graphics, 337
3D graphics, 343
<= operator, 64
= (assignment operator), 17
== (equality operator), 72
>= operator, 65
>>> prompt, 14, 15
[ key ], 55, 58
[ offset ], 43, 45
[ ] (square brackets), 4, 32, 42
\ (backslash), 30
\ (continuation character), 70
\' (literal single quote), 31
\n (new line), 30
\t (tab), 31
\” (literal double quote), 31
_ (underscores), 18, 103
__init__(), 125
{ } (curly brackets), 4, 54, 61, 154
| operator, 64
A
abspath() function, 244
accumulate() function, 119
activestate, 121
addition, 20
addition operator (+), 19
aggregation, 140
algebra, 376
algorithms
data structures and, 328
definition of, 10, 319
alias, 112
Amazon Web Services, 300
ampersand (&), 63
Anaconda
installing, 390
listing packages, 391
anchors, 162
animation, 343
We’d like to hear your suggestions for improving our indexes. Send email to [email protected]
433
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anonymous functions, 96
Apache web server, 232
API (application programming interface)
definition of, 195
mashups and, 291
vs. web pages, 236
append() function, 46, 46
arange() function, 371
arguments
default values for, 90
definition of, 87
keyword, 89, 92
positional, 89
self argument, 130, 134
arithmetic calculations, 19, 369
array() function, 371
arrays
changing with reshape(), 373
getting elements from, 374
making with arange(), 371
making with array(), 371
making with zeros()/ones()/random(), 372
math functions and, 375
ASCII standard, 145
assertions, 311
assignment
assignment operator (=), 17
vs. copying, 17, 50, 59
associative arrays (see dictionaries)
asterisk (*), 6, 32, 91
asynchronous vs. synchronous, 262
asyncio module, 271
attributes
accessing, 131
definition of, 123
finding, 130
in XML markup format, 184
private, 133
audio, 348
authentication, 218
automatic echoing, 28
Avro, 191
B
back pressure, 275
background programs, 273
backslash (\), 30, 70
base classes, 126
bases, 24
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|
bash shell, definition of, 6
basic multilingual planes, 146
batch files, definition of, 6
batch operations, 263
Berners-Lee, Tim, 217
best practices
code, debugging, 317–324
code, finding, 304
code, optimizing, 326–330
code, source control for, 330–333
code, testing, 309–317
integrated development environments
(IDEs), 306
logging, 324–326
naming and documenting, 307
packages, installing, 304
resources, 333–335
big data, 297
big endian format, 166
binary (base2), 24
binary data
bit-level integer operators, 169
bytes and bytearrays, 164
converting bytes/strings, 169
converting with struct, 165
endianness and sign bits, 163
interchange formats for, 191
practice exercise answers, 407
practice exercises, 170
reading binary files, 178
structured binary files, 192
third-party tools for, 168
writing binary files, 177
binascii module, 169
binio, 168
bit-level integer operators, 169
bits
definition of, 145
in Redis, 212
bitstring, 168
Blender, 345
blocking pop operations, 272
bokeh, 347
boolean operators, 73, 88
booleans
conversion to integers, 25
definition of, 15
object type and, 16
Bottle framework, 226
Index
www.it-ebooks.info
break checkers, 77, 78
break statements, 75, 78
breakpoints, 322
broadcasts, 276
browsers, 217, 218
business applications
benefits of Python for, 351
business tasks, 353
challenges of, 351
data security, 358
financial, 357
maps, 358
Microsoft Office Suite, 352
processing business data, 353
bytecode, 330
bytes
bytearrays and, 164
converting to strings, 169
definition of, 145
C
C Extensions, 329
C libraries, 394
cache servers, 205
caches, in Redis, 214
caching, 218
calculations, 19
calendars/clocks
alternative modules for, 258
challenges of date representation, 250
datetime module, 251
reading/writing dates and times, 255
time module, 254
call() function, 248
callback design, 270
callback functions, 97
capitalization, 37
case, 37
case sensitivity/insensitivity, 195
Central Processing Unit (see CPU)
CERN (European Organization for Nuclear Re‐
search), 217
chain() function, 119
characters
counting, 35
delimiter, 181
escape, 30, 163
extracting, 32
fill character, 156
special characters, 159
Unicode table of, 146
chdir() function, 246
check_output() function, 248
Cheese Shop, 121, 304
child classes, 126
chip (see CPU (Central Processing Unit))
chmod() command, 243
chown() command, 244
classes
adding methods, 128
calling parent methods, 129
class methods, 134
composition and, 140
creating new from existing, 126
defining, 124
defining empty, 124
definition of term, 124
finding attributes/methods, 130
getter/setter methods, 131
method types, 134
named tuples, 141
overriding methods, 127
polymorphism and, 135
practice exercise answers, 403
practice exercises, 143
private attributes, 133
self argument, 130
special methods, 137
vs. modules, 141
vs. type, 18
clear() function, 57
client-server patterns, 276
client-server systems, 218, 281
(see also World Wide Web)
closures, 95
cloud computing, 298
cmd shell, definition of, 6
code
code counts, 328
debugging, 317–324
finding, 304
optimizing, 326–330
pseudocode, 319
source control for, 330–333
testing, 309–317
code structures
commenting, 69
comparative statements, 71
Index
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435
comprehensions, 81–85
continuing lines, 70
decorators, 99, 132
exceptions, 103
for loops, 77–81
functions, 85–97
generators, 98
hierarchical arrangement of, 110
iterating over, 119
namespaces, 101
PEP-8 style, 71
practice exercise answers, 399
practice exercises, 106
scope, 101
while loops, 75
white space and, 69
command-line arguments, 110
comments, definition of, 69
Common Gateway Interface (CGI), 224
communication, 261
(see also concurrency; networking)
comparisons
conditional, 72
exceptions during, 71
making multiple, 73
operators for, 72
statements for, 71
testing equality, 73
compilation, definition of, 7
completed lists, 275
complex numbers, 367
composition, 140
comprehensions
definition of, 81
dictionary comprehensions, 83
generator comprehensions, 84
list comprehensions, 81
set comprehensions, 84
computed values, 133
computer graphics, 337
concurrency
additional techniques for, 275
asyncio module, 271
definition of, 261
documentation on, 262
green threads, 267
practice exercise answers, 420
practice exercises, 302
processes and, 264
436
processes requiring, 262
queues and, 263
Redis, 271
threads and, 265
twisted framework, 270
conda, installing, 394
conditional comparisons, 72
configuration files, 190
connect() function, 195, 283
connection pools, 199, 201
constraints, 193
construct, 168
context managers, 178
continuation character (\), 70
continue statement, 76, 78
continuous integration, 317
cookies
definition of, 219
http package for, 220
copy() function, 50, 59, 243
coroutines, 267
count() function, 49
count/count prefix, 167
counter() function, 116
CPU (Central Processing Unit)
CPU-bound programs, 9, 262
static vs. dynamic programs and, 10
CRUD (create, replace, update, delete), 195
CSV (comma-separated values) format, 181
csv module, 181
ctime() function, 254
curly brackets ({ }), 4, 54, 61, 154
cursor() function, 196
cycle() function, 119
Cython, 329
D
daemon mode, 233
data feed, XML format for, 184
data munging, 354
data science, 383
data storage
disk vs. RAM, 173
file input/output, 173–180
full-text databases, 214
NoSQL data stores, 204–214
practice exercise answers, 413
practice exercises, 215
relational databases, 193–204
| Index
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structured binary files, 192
structured text files, 181–192
data streams, 77
data structure servers, 206
data structures
algorithms and, 328
comparing, 66
converting binary data to, 165
creating complex, 66
creating from one or more iterators, 81
custom (see objects)
definition of, 4
dictionaries
adding/changing items, 55
assignment vs. copying, 59
combining, 56
conversion to, 54
creating, 54
definition of, 3, 53
deleting all items, 57
deleting individual items, 57
getting all key-value pairs, 59
getting all keys, 59
getting all values, 59
getting items by [ key ], 58
mutability of, 53
square and curly brackets in, 4
testing for keys, 58
vs. sets, 60
FIFO (first-in, first-out) queue, 48, 263
iterating unknown sizes, 77–79
LIFO (last- in, first-out) queue, 48
lists
adding items to end of, 46
assigning to multiple variables, 50
benefits of, 42
changing values, 45
conversion to, 43
converting to strings, 49
counting occurrences of values, 49
creating, 42
deleting items by offset, 47
deleting items by value, 47
determining length of, 50
elements in, 41, 46
extracting items by offset range, 45
extracting values from, 43
finding item’s offset by value, 48
getting/deleting items, 47
inserting items, 46
list order, 42
merging, 46
mutability of, 41
of lists, 44
reordering items, 49
testing for values, 48
vs. tuples, 53
objects (see objects)
practice exercise answers, 396
practice exercises, 67
sets
checking for set values, 63
conversion to, 61
creating, 61
null/empty, 60
testing for values, 62
vs. dictionaries, 60
tuples
advantages of, 53
conversion to lists, 43
creating, 52
elements in, 41
immutability of, 41, 52
tuple unpacking, 52
vs. lists, 53
data types
binary (see binary data)
booleans
conversion to integers, 25
definition of, 15
object type and, 15
converting to lists, 43
floats
definition of, 15
example of, 19
integers
arithmetic operations, 20
bases, 24
conversion to float, 27
definition of, 15, 19
int() function, 25
integer overflow, 27
literal, 19, 21
negative, 20
positive, 20
precedence rules, 23
type conversion to, 25
math operators for, 19
Index
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437
mutable vs. immutable values, 16, 28
practice exercise answers, 396
practice exercises, 39
strings
breaking into smaller strings, 35
combining, 31, 36
conversion to, 30, 49
conversion to integers, 25
counting characters in, 35
creating with quotes, 28
definition of, 15, 27
documentation on, 39
duplicating, 32
empty strings, 30
escape characters, 30
extracting characters from, 32
extracting substrings, 33
handling case and alignment, 37
immutability of, 28, 45
literal, 31
manipulating, 36, 145
(see also text strings)
multiline, 28
substitutions, 38
vs. lists and tuples, 41
variables, names and objects, 15–19
data wrangling, 354
databases
full-text, 214
multiple meanings of, 194
NoSQL data stores, 204–214
relational, 193–204
web framework support for, 234
dates/times, 250
(see also calendars/clocks)
datetime module, 251
DB-API, 195
DDL (data definition language), 194
debugging
best practices and, 317
with pdb, 318
decimal (base 10), 24
decimal point numbers (see floats)
deck (see deque)
declarative languages, 194
decoding, definition of, 5
decorators, 99, 132
defaultdict() function, 114
Defused XML, 190
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del statement, 47, 57
delimiters, 181
deque, 118
derived classes, 126
dialects, 194, 198
dict (see dictionaries)
dict() function, 54, 116
dictionaries
adding/changing items, 55
assignment vs. copying, 59
building with comprehensions, 83
combining, 56
conversion to, 54
creating, 54
definition of, 3, 53
deleting all items, 57
deleting individual items, 57
empty, 74
gathering keyword arguments into, 92
getting all key-value pairs, 59
getting all keys, 59
getting all values, 59
getting items by [ key ], 58
iteration and, 77
mutability of, 53
square and curly brackets in, 4, 61
testing for keys, 58
vs. sets, 60
dict_keys objects, 59
difference() function, 64
directories
changing current, 246
creating, 245
creating subdirectories, 245
definition of, 244
deleting, 245
displaying current working, 247
listing contents of, 245
listing matching files, 246
distributed computing
definition of, 261
fallacies of, 298
MapReduce and, 297
version control systems, 330
division, 21
DML (data manipulation language), 194
docstrings, 92, 310
doctest, 315
documentation, 92, 307
Index
www.it-ebooks.info
Domain Name System (DNS), 290
duck typing, 135
dynamic languages, definition of, 8
E
echoing, 28
elements, in tuples and lists, 41, 46
ElementTree library, 184
elif (else if) statements, 72
else statement, 71, 76
email modules, 291
embedded mode, 233
empty sets, 60
empty values, 74
encode() function, 149
encoding, 292
end tags, 184
endian specifiers, 167
endianness, 163
enumerate() function, 113
epoch values, 211, 254
equality operator (==), 72
error handling, 20
(see also exceptions)
escape characters, 30, 163
escape sequences, 181
ETL (extract, transform, load), 354
event loops, 267
event-based programming, 267
exceptions
after string copy/paste, 149
creating your own, 105
definition of, 20, 103
during comparisons, 71
during type conversion, 25
exception handlers, 103
logging error messages, 324
logic errors, 321
(see also debugging)
while extracting values, 44
execute permissions, 243
execute() function, 196
executemany() function, 196
exists() function, 242
expiration dates, 214
exponentiation operator (**), 19, 92
expression language, 199, 201
extend() function, 46
F
fabric package, 294
false values, 74, 88
fanin pattern, 276
fanout pattern, 276
FastCGI, 225
feeds, XML format for, 184
fetchall() function, 196
fetchmany() function, 196
fetchone() function, 196
FIFO (first-in, first-out) queue, 48, 263
file handling
changing file names, 243
changing ownership, 244
changing permissions, 243
checking for existence of files, 242
checking type of, 242
copying files, 243
creating files, 241
deleting files, 244
getting pathnames, 244
getting symlink pathnames, 244
linking files, 243
file input/output
basics of, 173
changing positions in file, 179
closing files automatically, 178
reading binary files, 178
reading text files, 176
writing binary files, 177
writing text files, 174
file system, definition of, 244
File Transfer Protocol (FTP), 291
files
.bat, 6
configuration, 190
creating, 245
fabric files, 295
flat, 173
listing matching, 246
log files, 325
opening, 173
structured text files, 181–192
fill character, 156
financial applications, 357
findall() function, 158
fire and forget technique, 275
Flask framework, 228
flat files, 173
Index
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439
float() function, 27
floating point division operator (/), 19, 21
floats (floating point numbers)
calculating, 368
conversion to integers, 25
definition of, 15
example of, 19
type conversion to, 27
folders, definition of, 244
for loops
benefits of, 77
canceling, 78
check break, 78
continuing, 78
generating number sequences, 80
iterating over multiple sequences, 79
itertools and, 119
foreground programs, 273
foreign keys, 194
formatting
curly brackets/format, 154
old and new styles, 152
string % data, 152
frameworks, 223, 225
full-text search, 214
functions
anonymous, 96
arguments and, 87
as first-class citizens, 93
callback functions, 97
closures, 95
default parameter values, 90
defining, 86
definition of term, 2, 35, 85
docstrings, 92
inner functions, 95
keyword arguments and, 89, 92
math, 365
monkey-patching functions, 268
positional arguments and, 89, 91
running as separate process, 249
G
games, 348
generator comprehensions, 84
generators, 98
GET command, 219
get() function, 58
gethostbyname() function, 290
440
getoutput() function, 247
getstatusoutput() function, 248
getter methods, 131
get_description() function, 111
gevent library, 235, 267
Git, 330
github, 121
glob() function, 246
Global Interpreter Lock (GIL), 267
global namespace, 101
global variables, 101
glue code, 8
Google, 299
graphical user interfaces (GUIs), 342
graphics
2D, 337
3D, 343
graphs, 346
green threads, 267
group ids, 247
GTK+ user interface, 343
H
h5py module, 192
hachoir, 168
Hadoop streaming, 298
hard links, 243
hash character (#), 69
hashes (see dictionaries)
hashmaps (see dictionaries)
HDF5 format, 192
HEAD command, 219
hex (base 16), 24
Houdini, 345
HTML (Hypertext Markup Language), 185, 217
HTTP (Hypertext Transfer Protocol)
client-server communication via, 217, 218
GET command, 219
HEAD command, 219
POST command, 220
response headers, 220
statelessness of, 218
status codes, 221
text-based protocol of, 219
verbs in, 219
http package, 220
human-readable code, 69, 92
| Index
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I
I/O bound, 262
IDLE, 306
if statement, 71
ImageMagick, 341
images, 338
(see also graphics)
immutable, definition of, 16
import alias, 200
import statement, 110
in statement, 48, 58, 62, 72
indentation, 69, 71
index() function, 48
indexing, 193
inequality operator (!=), 72
inheritance, 126, 140
inner functions, 95
input() function, 75
insert() function, 46
installation
Anaconda, 390
conda, 394
Linux/Unix, 390
Mac OS X, 389
overview of, 385
pip, 393
standard Python, 386
standard vs. scientific libraries, 385
Windows, 389
instance method, 134
instantiation, 125
int() function, 116
size of int, 26
type conversion with, 25
integer (truncating) division (//), 19, 21
integers
arithmetic operations, 20
bases, 24
bit-level operators, 169
definition of, 15, 19
int() function, 25
integer overflow, 27
literal, 19, 21
negative, 20
object type and, 16
positive, 20
precedence rules, 23
type conversion to, 25
integrated development environments (IDEs),
306
interactive interpreter
automatic value printing feature, 11
debugging and, 319
string echoes by, 28
internationalization, 257
Internet Message Access Protocol (IMAP), 291
interpreters, definition of, 8
intersection() function, 63
IP (Internet Protocol) layer, 280
IP addresses, 281
IPython, 307, 377
is operator, 88
isfile() function, 242
islink() function, 243
isoformat() function, 252
issubset() function, 64
issuperset() function, 65
items
adding to end of lists, 46
adding/changing in dictionaries, 55
adding/deleting, 118
changing by [ offset ], 45
counting, 116
deleting all items, 57
deleting by offset, 47
deleting by value, 47
deleting individual, 57
extracting by offset range, 45
finding offset of by value, 48
getting by using [ offset ], 43
getting items by [ key ], 58
getting/deleting, 47
inserting by offset, 46
reordering, 49
returning number of, 50
items() function, 59, 78
iteration
creating data structures with, 81
for statement, 77
of text files, 177
over code structures, 119
over keys, 77
over multiple sequences, 79
over values, 78
tuples vs. strings, 77
itertools, 119
Index
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441
J
job queues, 263
join() function, 36, 49
joins, 193
JSON (JavaScript Object Notation), 4, 185
justification, 37
K
kernel, definition of, 247
key-value pairs, 54, 59, 204
keys
copying, 56
definition of, 3, 53
deleting, 57
expiration of, 214
foreign, 194
getting all, 59
handling missing, 114
iterating over, 77
ordering, 118
primary, 193
requirements for, 56, 60
testing for, 58
keys() function, 59, 77
keyword arguments, 89, 92
Kivy user interface, 343
L
lambda function, 96
language interpreters, 225
languages
declarative, 194
expression, 199, 201
web server interpreters, 225
layers, 280
layout, 37
left justification, 37
len() function, 35, 50
libraries
C libraries, 394
ElementTree, 184
gevent, 235, 267
installation of, 385
installing third-party, 390, 393
IPython library, 377
message passing, 289
Python standard library, 114–121
442
Python’s standard web libraries, 220
scapy library, 289
SciKit library, 377
SciPy library, 377
security issues with, 189
special-purpose scientific, 383
third-party, 121
xml.dom, 185
xml.sax, 185
yaml library, 188
ZeroMQ library, 285
LIFO (last-in, first-out) queue, 48
link() function, 243
list() function, 42, 59, 116
listdir() function, 245
lists
adding items to end of, 46
assigning to multiple variables, 50
benefits of, 42
building with list comprehensions, 81
changing values, 45
concurrency and, 275
conversion to, 43
converting to strings, 49
counting occurrences of values, 49
creating, 42
definition of, 3, 35
deleting items by offset, 47
deleting items by value, 47
determining length of, 50
elements in, 41, 46
empty, 74
extracting items by offset range, 45
extracting values from, 43
finding item’s offset by value, 48
getting/deleting items, 47
in Redis, 208
inserting items, 46
list order, 42
merging, 46
of lists, 44
reordering items, 49
testing for values, 48
vs. tuples, 53
literal double quote (\”), 31
literal integers, 19, 21
literal single quote (\'), 31
localhost, 223
locks, 267
| Index
www.it-ebooks.info
logging, 324–326
log files, 325
root loggers, 325
logic errors, 321
loopback interface, 281
loops
definition of, 2
event loops, 267
for loops, 77, 119
infinite loops, 75
optional else statement, 76, 78
skipping ahead, 76
while, 75
lowercase, 37
M
machine languages, definition of, 7
Mac’s Activity Monitor (OS X), 247
magic methods, 137
make() function, 245
mangling, 134
MapReduce, 297
maps, 358
markup formats, 183
marshalling, 292
mashups, 291
match() function, 157
math functions, 365
math operators, 19
matplotlib, 346
matrix multiplication, 370
Maya, 345
Mercurial, 330
merge operator (+=), 46
messages
message passing libraries, 289
queue transport of, 263
XML format for, 184
methods
adding, 128
calling, 129
class methods, 134
definition of, 123
finding, 130
getter/setter, 131
instance methods, 134
magic, 137
method types, 134
overriding, 127
special, 137
static methods, 135
Microsoft Excel, 192
Microsoft Office Suite, 352
microthreads, 268
MIME types, 221
modules
asyncio, 271
binascii module, 169
csv module, 181
date/time modules, 258
datetime module, 251
definition of, 110
email modules, 291
for business tasks, 353
graphics-related, 337
h5py, 192
importing, 110
importing individual parts of, 112
importing with another name, 112
json module, 185
locating standard, 114
multiprocessing module, 249, 264
os module, 247
pickle module, 191
PyTables, 193
referring to other modules, 110
requests modules, 222
search path, 113
statistics module, 369
struct module, 165
subprocess module, 247
threading module, 265
time module, 254
unicodedata module, 146
vs. classes and objects, 141
modulus (remainder) operator (%), 19, 23
monkey-patching functions, 268
MsgPack, 191
multiline strings, 28
multiplication, 20, 370
multiplication operator (*), 19
multiprocessing module, 249, 264
multitasking, 262
music, 348
mutable, definition of, 16
MySQL, 198
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NumPy, 329, 370
N
N-dimensional arrays, 370
named tuples, 141
names/naming
avoiding conflicts, 111
best practices, 307
hidden attributes, 133
mangling, 134
of variables, 18
purpose of, 17
(see also variables)
reserved words to avoid, 19
namespaces, 101
negative integers, 20
nesting, 184
networking
cloud computing, 298
definition of, 261
Domain Name System (DNS) and, 290
email modules, 291
File Transfer Protocol (FTP), 291
for big data, 297
Internet services, 290
message passing libraries for, 289
patterns and, 276
practice exercise answers, 420
practice exercises, 302
publish-subscribe model, 276
remote processing, 292
scapy library, 289
sockets and, 281
Transmission Control Protocol/Internet
Protocol (TCP/IP), 280
web services/APIs, 291
ZeroMQ library, 278, 285
new line (\n), 30
nginx web server, 234
None value, 88
nose, 316
NoSQL data stores
additional servers/APIs, 214
benefits of, 204
dbm family, 204
memcached, 205
Redis, 206–214
null sets, 60, 74
nulls, definition of term, 193
number crunching, 262
number sequences, generating, 80
444
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O
objects
accessing attributes, 131
adding methods, 128
calling parent methods, 129
composition and, 140
contents of, 123
creating custom, 124
date/time objects, 251
definition of, 15, 93, 123
finding attributes/methods, 130
inheritance and, 126
method types, 134
named tuples, 141
overriding methods, 127
polymorphism in, 135
practice exercise answers, 403
practice exercises, 143
private attributes, 133
restoring in binary format, 191
self argument, 130
special methods, 137
type and, 16
vs. classes, 124
vs. modules, 141
octal (base 8), 24
octothorpe character (#), 69
offset
accessing values by, 3
adding items by, 46
adding items by with insert(), 46
changing items by [ offset ], 45
deleting items by, 47
extracting items by offset range, 45
extracting values by [ offset ], 43
getting items by, 47
specifying, 32
ones() function, 372
open() function, 241
OpenStack, 301
operators
for comparisons, 72
math, 19
precedence of, 429
optional else statement, 76, 78
options, 190
OrderedDict() function, 118
Index
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ORM (Object Relational Model) layer, 199, 202
os module, 247
out of band communication, 273
P
p-code, 330
pack() function, 166
packages
definition of, 113
fabric package, 294
http package, 220
in Python 3, 220, 262
installing, 304
installing from source, 306
package managers, 305
third-party for science and math, 370
urllib package, 220
xlrd package, 192
packed sequences, 369
packets, 280
Panda3D, 343
Pandas, 383
parameters
default values, 90
definition of, 85
parent classes, 126
parentheses (), 52
parsers, 190
pathnames, 242, 244
pattern specifiers, 161
patterns, 276, 288
pending lists, 275
PEP-8, 71, 309
performance, 261
(see also concurrency; networking)
permissions, 243
persistence, 173, 205
PHP language, 8
pickle module, 191
Pike, Rob, 148
Pillow, 338
pip
installing, 393
using, 305
placeholders, 197
planes, 146
plots, 346
plus sign (+), 20, 31
pointers, lack of, 91
polymorphism, 135
pools, 199, 201
pop() function, 47, 118
popleft() function, 118
poplib library, 291
ports, 281, 290
positional arguments, 89, 91
positive integers, 20
POST command, 220
Post Office Protocol 3 (POP3), 291
PostgreSQL, 198
pound character (#), 69
pprint() function, 120
precedence, 73
precedence rules, 23
precision value, 156
pretty printer, 120
primary keys, 193
print() function
alternative to, 120
example of, 5
testing code and, 309
vs. automatic echoing, 29
within interactive interpreter, 12
process ids, 247
processes
concurrency and, 264
creating with multiprocessing module, 249
creating with subprocess module, 247
definition of, 247
killing, 249
viewing current, 247
programs
background programs, 273
basic development of, 13
basic features of, 2
command-line arguments, 110
CPU-bound, 9, 262
event-based, 267
exceptions, 20
(see also exceptions)
foreground programs, 273
I/O bound, 262
modules, 110–113
packages, 113
practice exercise answers, 402
practice exercises, 121
processes and, 247
readability and writability of, 9, 92
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sequential, 261
standalone, 109
static vs. dynamic, 10
thread-safe, 266
prompts
>>> prompt, 14, 15
definition of, 11
proper subsets, 65
properties
computed values and, 133
defining, 132
getting/setting attribute values, 131
Protocol Buffers, 191
protocols
definition of term, 218
layers and, 280
UDP/TCP, 280
pseudocode, 319
public IP addresses, 281
publish-subscribe (pub-sub) pattern, 209, 276
pull pattern, 276
push pattern, 276
PyCharm, 306
pyflakes, 309
pylint, 309
PyPI, 121, 304
PyPy, 330
PyTables module, 193
Python 3
applications in business, 351–363
applications in math and statistics, 365–384
applications in the arts and music, 337–349
benefits of, 9
drawbacks of, 9
installation
Anaconda, 390
conda, 394
Linux/Unix, 390
Mac OS X, 389
overview of, 385
pip, 393
standard Python, 386
standard vs. scientific libraries, 385
virtualenv, 393
Windows, 389
introduction to
basic program features, 2
data structures, 4
dictionaries, 3
446
lists, 3
practice exercise answers, 395
practice exercises, 14
syntax, 2
operator precedence in, 429
packages in, 220, 262
philosophy of, 13
real-world uses of, 6
resources for learning, 333–335
running
interactive interpreter, 11
main methods of, 11
using Python files, 12
versions in use, 385
vs. other languages, 6
vs. Python 2, 10, 28
Python debugger (pdb), 318
Python Image Library (PIL), 338
Python standard library
adding/deleting items, 118
alternatives to, 121
benefits of, 114
counting items, 116
finding code, 304
graphics-related modules, 337
handling missing keys, 114
importing code from, 4
iterating over code structures, 119
math and statistics in, 365
order by key, 118
pretty printer, 120
Q
Qt user interface, 342
queries, 194
queues, 263
quotes
literal single or double, 31
single vs. double vs. triple, 28
R
random() function, 372
range() function, 80
raw strings, 163
read() function, 176, 178
read, definition of, 173
Read-Evaluate-Print Loop (REPL), 309
read/write permissions, 243
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readline() function, 176
readlines() function, 176
readthedocs, 121
realpath() function, 244
Redis
benefits of, 206
bits, 212
caches/expiration, 214
concurrency and, 271
hashes in, 209
lists in, 208, 272
publish-subscribe system with, 276
sets in, 210
sorted sets in, 211
strings in, 206
references, definition of, 17
regressions, 311
regular expressions
all matches, 158
basics of, 156
exact matches, 157
first match, 158
pattern specifiers, 161
replace at match, 159
special characters, 159
specifying match output, 163
split at matches, 158
relational databases
benefits of, 193
DB-API, 195
keys and indexing in, 193
MySQL, 198
PostgreSQL, 198
SQL (structured query language), 194
SQLAlchemy, 198
SQLite, 196
tables in, 193
remote processing
alternative products for, 297
benefits of, 292
fabric package for, 294
Remote Procedure Calls (RPCs), 292
Salt, 297
remove() function, 47, 244
rename() function, 243
replace() function, 33, 38
Representational State Transfer (REST), 236
request-reply patterns, 276
request-reply technique, 275
requests module, 222
requests, definition of, 218
reserved words, 19
reshape() function, 373
resources, 333–335
response headers, 220
responses, definition of, 218
Respresentational State Transfer (REST), 292
results, definition of, 85
reverse() function, 119
right justification, 37
rmdir() function, 245
robustness, 261
(see also concurrency; networking)
Ronacher, Armin, 228
root loggers, 325
Ruby language, 8
S
Salt, 297
scapy library, 289
SCGI, 225
schemas, 193
SciKit library, 377
SciPy library, 377
scope, 101
scripting languages (see dynamic languages)
scripts
definition of, 6
dynamic languages and, 8
shell scripts, 6
search() function, 158
Secure Shell (SSH), 294
security issues
business data, 358
debug in production web servers, 229
passwords, 297
pickle module, 192
Python libraries vulnerability, 189
SQL injection, 197
seek() function, 179
seeking, drawbacks of, 297
self argument, 130, 134
sentinel values, 273, 323
separators, 35, 181
sequences
adding/deleting items, 118
creating with generators, 98
escape sequences, 181
Index
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447
generating number sequences, 80
iterating over multiple, 79
lists (see lists)
packed, 369
strings as, 28
tuples (see tuples)
sequential programs, 261
serialization, 191, 292
sessions, 218
set intersection operator (&), 63
set() function, 61
setdefault() function, 114
setlocale() function, 257
sets
building with set comprehensions, 84
checking for combinations of set values, 63
conversion to, 61
creating, 61
empty, 74
in Redis, 210
null/empty, 60
sorted, 211
testing for values, 62
vs. dictionaries, 60
setter methods, 131
settings, 190
sh (see bash shell)
sharp character (#), 69
shell programs
definition of, 6
for Linux/Unix, 6
for Windows, 6
shell scripts, drawbacks of, 6
sign bits, 163
Simple API for XML (SAX), 185
Simple Mail Transfer Protocol (SMTP), 291
simplicity, 261
(see also concurrency; networking)
singletons, 141
slice, 33, 45
slices
example of, 5
extracting items by offset range, 45
reversing a string with, 119
social media sites, 292
sockets
binding to, 282
client-server exchange using, 281
complications resulting from, 284
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creating, 282
IP addresses and, 281
TCP and, 283
software locks, 267
sort() function, 49
sorted() function, 49
source control systems
Git, 330
importance of, 330
Mercurial, 330
spaces, 31
Spark, 298
special characters, 159
special methods, 137
split() function, 35, 43, 49, 158
spreadsheets, 192
SQL (structured query language), 194
SQL expression language, 199
SQL injection, 197
SQLAlchemy
applicable levels for, 199
engine layer, 200
Expression Language, 201
initial connection string, 199
installing, 198
ORM (Object Relational Model) layer, 202
SQLite, 196
square brackets ([ ]), 4, 32, 42, 61
stack, definition of, 48
standalone programs, 109
start tags, 184
statelessness, 218
statements, 194
static languages, definition of, 7
static methods, 135
statistics module, 369
status codes, 221
str() function, 30
streaming
benefits of, 297
Hadoop streaming, 298
iterators and, 77
Spark, 298
TCP for, 280
strftime() function, 252, 256
string % data conversion types, 152
strings
breaking into smaller strings, 35
combining, 31, 36
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conversion to, 30, 49
conversion to integers, 25
converting to binary data, 169
counting characters in, 35
creating multiline, 28
creating with quotes, 28
definition of, 15, 27
docstrings, 92
documentation on, 39
duplicating, 32
empty, 74
empty strings, 30
escape characters, 30
extracting characters from, 32
extracting substrings, 33
handling case and alignment, 37
immutability of, 28, 45
in Redis, 206
iteration and, 77
literal, 31
manipulating, 36, 145
(see also text strings)
raw strings, 163
string methods, 430
string module attributes, 431
substitutions, 38
vs. lists and tuples, 41
strongly typed, definition of, 16
strptime() function, 252, 257
struct module, 165
structured binary files, 192
structured text files
binary interchange formats, 191
configuration files, 190
CSV (comma-separated values), 181
distinguishing formats, 181
HTML (Hypertext Markup Language), 185
JSON (JavaScript Object Notation), 185
security concerns, 189
serialization of, 191
XML (Extensible Markup Language), 183
YAML (YAML Ain’t Markup Language), 188
sub() function, 159
subclasses, 126
subdirectories, creating, 245
subprocess module, 247
subsets, proper, 65
substitutions, 38
substrings, extracting, 33
subtraction, 20
subtraction operator (-), 19, 64
super() function, 129
superclasses, 126
supersets, 65
symbolic links, 243, 244
symlink() function, 243
symmetric_difference() function, 64
synchronous vs. asynchronous, 262
syntax
curly brackets ({ }), 4, 54, 61
definition of, 2
in C/C++, 6
in Python, 2
reserved words for, 19
square brackets ([ ]), 4, 61
system functions
accessing system information, 247
calendars/clocks, 250–259
directories, 244–246
file handling, 241–244
practice exercise answers, 419
practice exercises, 259
programs/processes, 247–250
T
tab (\t), 31
table scans, 194
tables, 193
tags
in XML format, 183
nesting of, 184
start/end, 184
Task Manager, 247
task queues, 263
tell() function, 179
telnet, 219
terminate() function, 249, 266
testing
additional frameworks for, 317
continuous integration, 317
doctest, 315
nose, 316
Python code checkers, 309
Read-Evaluate-Print Loop (REPL) and, 309
unit tests, 228
unittest, 311
text
aligning, 31, 37
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449
changing case of, 37
forcing new line, 30
text strings
formatting
curly brackets/format, 154
old and new styles, 152
string % data, 152
practice exercise answers, 407
practice exercises, 170
regular expressions
all matches, 158
basics of, 156
exact matches, 157
first match, 158
pattern specifiers, 161
replace at match, 159
special characters, 159
specifying match output, 163
split at matches, 158
Unicode
decoding, 151
encode() function, 149
Python 3 strings, 146
Unicode charts, 147
unicodedata module, 146
UTF-8 encoding/decoding, 148
vs. ASCII format, 145
Thompson, Ken, 148
thread-safe programs, 266
threads
best application of, 266
challenges of, 266
concurrency and, 265
green threads, 267
microthreads, 268
termination of, 266
Thrift, 191
throttling, 275
time module, 254
time() function, 254
timing, measurement of, 327
tokens, 292
topics, definition of, 276
Transmission Control Protocol/Internet Proto‐
col (TCP/IP), 218, 223, 280, 283
true values, 74
truncating integer division, 21
tuples
advantages of, 53
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conversion to lists, 43
creating, 52
elements in, 41
empty, 74
immutability of, 41, 52
iteration and, 77
lack of comprehensions, 84
named tuples, 141
tuple unpacking, 52
vs. lists, 53
twisted framework, 270
type
definition of, 7, 15
strongly typed, 16
type conversion, 25
vs. class, 18
typing, duck typing, 135
U
UDP (User Datagram Protocol), 280, 283
underscores (_)
double, 103
in names, 18
Unicode
decoding, 151
encode() function, 149
practice exercise answers, 407
practice exercises, 170
Python 3 strings, 146
standard for, 28
Unicode charts, 147
unicodedata module, 146
UTF-8 encoding/decoding, 148
vs. ASCII format, 145
union() function, 64
unit tests, 228
unittest, 311
Unix
domain sockets, 282
epoch values, 211, 254
shell rules, 246
time representation, 254
unpack() function, 166
unpacking, tuples, 52
update() function, 56
uppercase, 37
URL (Uniform Resource Locator), 217
urllib package, 220
user ids, 247
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UTF-8 dynamic encoding, 148
V
values
changing, 45
checking, 71
combinations of set values, 63
computed values, 133
copying, 51, 56
counting occurrences of, 49
default, 90
definition of, 3
deleting, 57
deleting items by, 47
discarding duplicate, 61
extracting, 43
false values, 74, 88
finding item’s offset by, 48
getting all, 59
getting/setting for attributes, 131
in XML markup format, 184
iterating over, 78
keys and, 53
mutable vs. immutable, 16
None, 88
precision value, 156
sentinel values, 273, 323
testing for, 48, 62
true values, 74
values() function, 59, 78
van Rossum, Guido, 69, 271
variables
arithmetic operations and, 21
assigning lists to, 50
assignment of, 17
defining, 17
definition of term, 7, 15
global, 101
naming of, 18
temporary, 22
ventilator pattern, 288
verbs, 219
version control, 330
virtual machines, 330
virtualenv, installing, 393
visualization, 346
volumes, definition of, 244
W
Web Server Gateway Interface (WSGI), 225
web services
connecting to, 5
networking and, 291
while loops, 75
white space, 69
wild-card symbols
* (asterisk), 6, 32, 158
. (full stop), 158
.* (full stop asterisk), 158
Unix shell rules and, 246
Window’s Task Manager, 247
with statement, 178
words, reserved for Python syntax, 19
work queues, 263
working directory, 247
working lists, 275
World Wide Web
basic design of, 217
history of, 217
practice exercise answers, 417
practice exercises, 239
web clients
data interchange through, 218
Python’s standard web libraries, 220
requests module, 222
telnet text-based interaction with, 219
web servers
bare-bones Python HTTP server, 223
Bottle framework, 226
Flask framework, 228
Flask non-Python, 232
for database-backed websites, 234
variety of frameworks available for, 223,
225
web server gateway interface, 224
WORM (write once/read many) applications,
192
write() function, 174, 177
write, definition of, 173
write/read permissions, 243
WxPthon user interface, 343
X
xlrd package, 192
XML (Extensible Markup Language) format,
183
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xml.dom library, 185
xml.sax library, 185
Y
YAML (YAML Ain’t Markup Language), 188
Z
zero float, 74
zero integer, 74
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ZeroMQ library
alternatives to, 289
benefits of, 285
connecting multiple clients/services, 288
documentation for, 285
network patterns in, 285
publication-subscription model in, 278
zeros() function, 372
zip() function, 79
zsets, 211
Index
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About the Author
Bill Lubanovic has developed software with Unix since 1977, GUIs since 1981, databases
since 1990, and the Web since 1993.
In 1982, at a startup named Intran, he developed MetaForm, one of the first commercial
GUIs (before the Mac or Windows), on one of the first graphic workstations. In the
early 1990s, while at Northwest Airlines, he wrote a graphic yield management system
that generated millions of dollars in revenue; established a presence for the company
on the Internet; and wrote its first Internet marketing test. Later, he cofounded an ISP
(Tela) in 1994, and a web development company (Mad Scheme) in 1999.
Recently, he developed core services and distributed systems with a remote team for a
Manhattan startup. Currently, he’s integrating OpenStack services for a supercomputer
company.
Bill enjoys life in Minnesota with his wonderful wife Mary, children Tom and Karin,
and cats Inga, Chester, and Lucy.
Colophon
The animal on the cover of Introducing Python is an Asiatic Python (Python reticula‐
tus). This snake is not quite as scary as it looks: it is nonvenomous and only occasionally
attacks humans. Approaching lengths of seven meters (and sometimes over nine), this
can be the longest snake or reptile in the world, but most individuals are closer to three
or four meters long. The Latin terminology in its name refers to the netlike appearance
of its patterns and coloring. The size and color of the shapes varies widely, particularly
according to location, but diamonds are common on the python’s back. Their appear‐
ance often allows them to blend in well with their surroundings in the wild.
The Asiatic python lives throughout Southeast Asia. There may be three subspecies with
distinct geographical boundaries, but the scientific community does not formally rec‐
ognize this idea. Pythons are most commonly found in rainforests, woodlands, grass‐
lands, and bodies of water, since their swimming skills are superb. They have even been
known to swim out to remote islands. Their diet consists mainly of mammals and birds.
This is an increasingly popular species to have in captivity, due to their striking appear‐
ance and good behavior, but this species can be unpredictable. There are documented
instances of Asiatic pythons eating or otherwise killing people. Exact length measure‐
ments are very hard to take on a fairly aggressive snake, so only figures on dead or
anesthetized Asiatic pythons are really considered accurate. While many captive py‐
thons are obese, ones in the wild tend to be lighter, and they may be more fairly described
as erratic rather than genuinely dangerous.
Many of the animals on O’Reilly covers are endangered; all of them are important to
the world. To learn more about how you can help, go to http://animals.oreilly.com.
www.it-ebooks.info
The cover image is from Johnson’s Natural History. The cover font is Adobe ITC Gara‐
mond. The text font is Adobe Minion Pro; the heading font is Adobe Myriad Condensed;
and the code font is Dalton Maag’s Ubuntu Mono.
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