Python Essentials
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Python Programming
Python
Programming
Object-Oriented Programming
Exception Handling
Data Types
Python Libraries
Functions
Decorators
Comparison Operators
About this ebook
- Learn the essentials of Python programming to get you up and coding effectively
- Get up-to-speed with the most important built-in data structures in Python, using sequences, sets, and mappings
- Explore typical use cases for various features in Python through this compact guide
This book is designed for Python 2 developers who want to get to grips with Python 3 in a short period of time. It covers the key features of Python, assuming you are familiar with the fundamentals of Python 2.
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Reviews for Python Essentials
7 ratings1 review
- Rating: 5 out of 5 stars5/5Juste Excellent!!!!!!!!!!!!!!!
c'est le livre qu'il faut pour commencer et se faire une idée assez large de la puissance de Python.
encore merci
Book preview
Python Essentials - Steven F. Lott
Table of Contents
Python Essentials
Credits
About the Author
About the Reviewers
www.PacktPub.com
Support files, eBooks, discount offers, and more
Why subscribe?
Free access for Packt account holders
Preface
What this book covers
What you need for this book
Who this book is for
Conventions
Reader feedback
Customer support
Downloading the example code
Errata
Piracy
Questions
1. Getting Started
Installation or upgrade
Installing Python on Windows
Considering some alternatives
Upgrading to Python 3.4 in Mac OS X
Adding the Tkinter package
Upgrading to Python 3.4 in Linux
Using the Read-Evaluate-Print Loop (REPL)
Confirming that things are working
Doing simple arithmetic
Assigning results to variables
Using import to add features
Interacting with the help subsystem
Using the pydoc program
Creating simple script files
Simplified syntax rules
The Python ecosystem
The idea of extensibility via add-ons
Using the Python Package Index – PyPI
Using pip to gather modules
Using easy_install to add modules
Installing modules manually
Looking at other Python interpreters
Summary
2. Simple Data Types
Introducing the built-in operators
Making comparisons
Using integers
Using the bit-oriented operators
Using rational numbers
Using decimal numbers
Using floating-point numbers
Using complex numbers
The numeric tower
The math libraries
Using bits and Boolean values
Working with sequences
Slicing and dicing a sequence
Using string and bytes values
Writing string literals
Using raw string literals
Using byte string literals
Using the string operators
Converting between Unicode and bytes
Using string methods
Accessing the details of a string
Parsing strings into substrings
Using the tuple collection
The None object
The consequences of immutability
Using the built-in conversion functions
Summary
3. Expressions and Output
Expressions, operators, and data types
Using operators on non-numeric data
The print() function
Examining syntax rules
Splitting, partitioning, and joining strings
Using the format() method to make more readable output
Summary of the standard string libraries
Using the re module to parse strings
Using regular expressions
Creating a regular expression string
Working with Unicode, ASCII, and bytes
Using the locale module for personalization
Summary
4. Variables, Assignment and Scoping Rules
Simple assignment and variables
Multiple assignment
Using repeated assignment
Using the head, *tail assignment
Augmented assignment
The input() function
Python language concepts
Object types versus variable declarations
Avoiding confusion when naming variables
Garbage collection via reference counting
The little-used del statement
The Python namespace concept
Globals and locals
Summary
5. Logic, Comparisons, and Conditions
Boolean data and the bool() function
Comparison operators
Combining comparisons to simplify the logic
Testing float values
Comparing object IDs with the is operator
Equality and object hash values
Logic operators – and, or, not, if-else
Short-circuit (or non-strict) evaluation
The if-elif-else statement
Adding elif clauses
The pass statement as a placeholder
The assert statement
The logic of the None object
Summary
6. More Complex Data Types
The mutability and immutability distinction
Using the list collection
Using list operators
Mutating a list with subscripts
Mutating a list with method functions
Accessing a list
Using collection functions
Using the set collection
Using set operators
Mutating a set with method functions
Using augmented assignment with sets
Accessing a set with operators and method functions
Mappings
Using dictionary operators
Using dictionary mutators
Using methods for accessing items in a mapping
Using extensions from the collections module
Processing collections with the for statement
Using literal lists in a for statement
Using the range() and enumerate() functions
Iterating with the while statement
The continue and break statements
Breaking early from a loop
Using the else clause on a loop
Summary
7. Basic Function Definitions
Looking at the five kinds of callables
Defining functions with positional parameters
Defining multiple parameters
Using the return statement
Evaluating a function with positional or keyword arguments
Writing a function's docstring
Mutable and immutable argument values
Defining optional parameters via default values
A warning about mutable default values
Using the everything else
notations of * and **
Using sequences and dictionaries to fill in *args and *kw
Nested function definitions
Working with namespaces
Assigning a global variable
Assigning a non-local variable
Defining lambdas
Writing additional function annotations
Summary
8. More Advanced Functions
Using the for statement with iterable collections
Iterators and iterable collections
Consequences and next steps
Using generator expressions and comprehensions
Limitations of generator expressions
Using multiple loops and conditions
Writing comprehensions
Defining generator functions with the yield statement
Using the higher-order functions
Writing our own higher-order functions
Using the built-in reductions – max, min, and reduce
Three ways to sort a sequence
Sorting via a key function
Sorting via wrapping and unwrapping
Functional programming design patterns
Summary
9. Exceptions
The core exception concept
Examining the exception object
Using the try and except statements
Using nested try statements
Matching exception classes in an except clause
Matching more general exceptions
The empty except clause
Creating our own exceptions
Using a finally clause
Use cases for exceptions
Issuing warnings instead of exceptions
Permission versus forgiveness – a Pythonic approach
Summary
10. Files, Databases, Networks, and Contexts
The essential file concept
Opening text files
Filtering text lines
Working with raw bytes
Using file-like objects
Using a context manager via the with statement
Closing file-like objects with contextlib
Using the shelve module as a database
Using the sqlite database
Using object-relational mapping
Web services and Internet protocols
Physical format considerations
Summary
11. Class Definitions
Creating a class
Writing the suite of statements in a class
Using instance variables and methods
Pythonic object-oriented programming
Trying to do type casting
Designing for encapsulation and privacy
Using properties
Using inheritance to simplify class definitions
Using multiple inheritance and the mixin design pattern
Using class methods and attributes
Using mutable class variables
Writing static methods
Using __slots__ to save storage
The ABCs of abstract base classes
Writing a callable class
Summary
12. Scripts, Modules, Packages, Libraries, and Applications
Script file rules
Running a script by the filename
Running a script by its module name
Running a script using OS shell rules
Choosing good script names
Creating a reusable module
Creating a hybrid library/application module
Creating a package
Designing alternative implementations
Seeing the package search path
Summary
13. Metaprogramming and Decorators
Simple metaprogramming with decorators
Defining our own decorator
More complex metaprogramming with metaclasses
Summary
14. Fit and Finish – Unit Testing, Packaging, and Documentation
Writing docstrings
Writing unit tests with doctest
Using the unittest library for testing
Combining doctest and unittest
Using other add-on test libraries
Logging events and conditions
Configuring the logging system
Writing documentation with RST markup
Creating HTML documentation from an RST source
Using the Sphinx tool
Organizing Python code
Summary
15. Next Steps
Leveraging the standard library
Leveraging PyPI – the Python Package Index
Types of applications
Building CLI applications
Getting command-line arguments with argparse
Using the cmd module for interactive applications
Building GUI applications
Using more sophisticated packages
Building web applications
Using a web framework
Building a RESTful web service with Flask
Plugging into a MapReduce framework
Summary
Index
Python Essentials
Python Essentials
Copyright © 2015 Packt Publishing
All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.
Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the author, nor Packt Publishing, and its dealers and distributors will be held liable for any damages caused or alleged to be caused directly or indirectly by this book.
Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.
First published: June 2015
Production reference: 1250615
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ISBN 978-1-78439-034-1
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Credits
Author
Steven F. Lott
Reviewers
Amoatey Harrison
Alessio Di Lorenzo
Dr. Philip Polstra
Commissioning Editor
Edward Gordon
Acquisition Editor
Subho Gupta
Content Development Editor
Adrian Raposo
Technical Editors
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Copy Editors
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Project Coordinator
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Proofreader
Safis Editing
Indexer
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Graphics
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Production Coordinator
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Cover Work
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About the Author
Steven F. Lott has been programming since the 70s, when computers were large, expensive, and rare. As a contract software developer and architect, he has worked on hundreds of projects, from very small to very large. He's been using Python to solve business problems for over 10 years.
He's particularly adept at struggling with thorny data representation problems.
He has also authored Mastering Object-oriented Python by Packt Publishing.
He is currently a technomad who lives in various places on the east coast of the US. His technology blog can be found at http://slott-softwarearchitect.blogspot.com.
About the Reviewers
Amoatey Harrison is a Python programmer with a passion for building software systems to solve problems. When he is not programming, he plays video games, swims, or simply hangs out with friends.
After graduating from the Kwame Nkrumah University of Science and Technology with a degree in computer engineering, he is currently doing his national service at the GCB Bank head office in Accra, Ghana. He also helped review a book on Python programming, Functional Python Programming, Packt Publishing, which was published in January 2015.
He would like to think of himself as a cool nerd.
Alessio Di Lorenzo is a marine biologist and has an MSc in geographical information systems (GIS) and remote sensing. Since 2006, he has been dealing with the analysis and development of GIS applications dedicated to the study and spread of environmental and epidemiological data. He is experienced in the use of the main proprietary and open source GIS software and programming languages.
He has coauthored OpenLayers Starter and reviewed ArcPy and ArcGIS – Geospatial Analysis with Python, both by Packt Publishing.
Dr. Philip Polstra (known as Dr. Phil to his friends) is an associate professor of digital forensics in the Department of Math and Digital Sciences at Bloomsburg University of Pennsylvania. He teaches forensics, information security, and penetration testing. His research over the last few years has been on the use of microcontrollers and small computer boards (such as the BeagleBone Black) for forensics and penetration testing.
He is an internationally recognized hardware hacker. His work has been presented at numerous conferences across the globe, including repeat performances at Black Hat, DEFCON, 44CON, B-sides, GrrCON, ForenSecure, and other top conferences. He has also provided training on forensics and security, both in person and online via http://www.pentesteracademy.com and other training sites.
He has published a number of books, including Hacking and Penetration Testing with Low Power Devices, Syngress, and Linux Forensics from A to Z, PAP. He has also been a technical editor or reviewer on numerous books.
When not teaching or speaking at a conference, he is known to build electronics with his children, fly airplanes, and also teach others how to fly and build airplanes. His latest happenings can be found on his blog at http://philpolstra.com.
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Preface
Python programming should be expressive and elegant. In order for this to be true, the language itself must be easy to learn and easy to use. Any practical language—and its associated libraries—can present a daunting volume of information. In order to help someone learn Python, we've identified and described those features that seem essential.
Learning a language can be a long voyage. We'll pass numerous islands, archipelagos, inlets, and estuaries along the route. Our objective is to point out the key features that will be passed during the initial stages of this journey.
The concepts of data structures and algorithms are ever-present considerations in programming. Our overall approach is to introduce the various Python data structures first. As part of working with a given class of objects, the language statements are introduced later. One of Python's significant advantages over other languages is the rich collection of built-in data types. Selecting an appropriate representation of data can lead to elegant, high-performance applications.
An essential aspect of Python is its overall simplicity. There are very few operators and very few different kinds of statements. Much of the code we write can be generic with respect to the underlying data type. This allows us to easily exchange different data structure implementations as part of making tradeoffs between storage, performance, accuracy, and other considerations.
Some subject areas could take us well beyond the basics. Python's object-oriented programming features are rich enough to easily fill several large volumes. If we're also interested in functional programming features, we can study these in far more depth elsewhere. We'll touch only briefly on these subjects.
What this book covers
Chapter 1, Getting Started, addresses installing or upgrading Python. We explore Python's Read-Evaluate-Print Loop (REPL) as a way to interact with the language. We'll use this interactive Python mode as a way to explore most of the language features.
Chapter 2, Simple Data Types, introduces a few features concerning numbers and some simple collections. We'll look at Python's Unicode strings as well as byte strings, including some of the conversions between strings and numbers.
Chapter 3, Expressions and Output, provides more details on Python expression syntax and how the various numeric types relate to each other. We'll look at the coercion rules and the numeric tower. We'll look at the print() function, which is a common tool for looking at output.
Chapter 4, Variables, Assignment and Scoping Rules, shows how we assign names to objects. We look at a number of different assignment statements available in Python. We also explore the input() function, which parallels the print() function.
Chapter 5, Logic, Comparisons, and Conditions, shows the logical operators and literals that Python uses. We'll look at the comparison operators and how we use them. We'll look closely at the if statement.
Chapter 6, More Complex Data Types, shows the core features of the list, set, and dict built-in types. We use the for statement to work with these collections. We also use functions such as sum(), map(), and filter().
Chapter 7, Basic Function Definitions, introduces the syntax for the def statement as well as the return statement. Python offers a wide variety of ways to provide argument values to functions; we show a few of the alternatives.
Chapter 8, More Advanced Functions, extends the basic function definitions to include the yield statement. This allows us to write generator functions that will iterate over a sequence of data values. We look at a few functional programming features available via built-in functions as well as the modules in the Python Standard Library.
Chapter 9, Exceptions, shows how we handle and raise exceptions. This allows us to write programs which are considerably more flexible. A simple happy path
can handle the bulk of the processing, and exception clauses can handle rare or unexpected alternative paths.
Chapter 10, Files, Databases, Networks, and Contexts, will introduce a number of features related to persistent storage. We'll look at Python's use of files and file-like objects. We'll also extend the concept of persistence to include some database features available in the Python Standard Library. This chapter will also include a review of the with statement for context management.
Chapter 11, Class Definitions, demonstrates the class statement and the essentials of object-oriented programming. We look at the basics of inheritance and how to define class-level (static) methods.
Chapter 12, Scripts, Modules, Packages, Libraries, and Applications, shows different ways in which we can create Python code files. We'll look at the formal structures of script, module, and package. We'll also look at informal concepts such as application, library, and framework.
Chapter 13, Metaprogramming and Decorators, introduces two concepts that can help us write Python code that manipulates Python code. Python makes metaprogramming relatively simple; we can leverage this to simplify certain types of programming where a common aspect doesn't fit neatly into a class hierarchy or library of functions.
Chapter 14, Fit and Finish – Unit Testing, Packaging, and Documentation, moves beyond the Python language into the idea of creating a complete, polished product. Any well-written program should include test cases and documentation. We show common ways to make sure this is done properly.
Chapter 15, Next Steps, will demonstrate four simple kinds of applications. We'll look at the command-line interface (CLI), graphic user interface (GUI), simple Web frameworks, as well as MapReduce applications.
What you need for this book
We're going to focus on Python 3, exclusively. Many computers will have Python 2 already installed, which means an upgrade is required. Some computers don't have Python installed at all, which means that a fresh installation of Python 3 will be necessary. The details are the subject of Chapter 1, Getting Started.
It's important to note that Python 2 can't easily be used to run all of the examples. Python 2 may work for many of the examples, but it's not our focus.
In order to install software, you'll generally need administrative rights on the computer you intend to use. For a home computer, this is generally true. For computers supplied through work or school, administrative passwords may be required.
You may also want to have a proper programmer's text editor. Default text editing applications such as Windows Notepad or Mac OS X TextEdit can be used, but aren't ideal. There are numerous free text editors available: feel free to download several to locate the one that feels most comfortable for you.
Who this book is for
This book is for programmers who want to learn Python quickly. It shows key features of Python, assuming a background in programming. The focus is on essential features: the approach is broad but relatively shallow. We'll provide pointers and direction for additional study and research, assuming that the reader is willing and able to follow those pointers.
In many data-intensive industries, a great deal of big data analysis is done with Python and toolsets such as Apache Hadoop. In this case, the users of Python will be statisticians, data scientists, or analysts. Their interest isn't in Python itself, but in using Python to process collections of data. This book is designed to provide language fundamentals for data scientists.
This book can be used by students who are learning Python. Since this book doesn't cover the computer science foundations of programming, an additional text would be helpful.
Conventions
In this book, you will find a number of text styles that distinguish between different kinds of information. Here are some examples of these styles and an explanation of their meaning.
Code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles are shown as follows: We've built an ArgumentParser method using all of the default parameters.
A block of code is set as follows:
def prod(sequence):
p= 1
for item in sequence:
p *= item
return p
When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:
def prod(sequence):
p= 1
for item in sequence:
p *= item
return
Any command-line input or output is written as follows:
MacBookPro-SLott:Code slott$ python3 -m test_all
New terms and important words are shown in bold. Words that you see on the screen, for example, in menus or dialog boxes, appear in the text like this: Clicking on Continue will step through the Read Me, License, Destination Select, and Installation Type windows.
Note
Warnings or important notes appear in a box like this.
Tip
Tips and tricks appear like this.
Reader feedback
Feedback from our readers is always welcome. Let us know what you think about this book—what you liked or disliked. Reader feedback is important for us as it helps us develop titles that you will really get the most out of.
To send us general feedback, simply e-mail <feedback@packtpub.com>, and mention the book's title in the subject of your message.
If there is a topic that you have expertise in and you are interested in either writing or contributing to a book, see our author guide at www.packtpub.com/authors.
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Downloading the example code
You can download the example code files from your account at http://www.packtpub.com for all the Packt Publishing books you have purchased. If you purchased this book elsewhere, you can visit http://www.packtpub.com/support and register to have the files e-mailed directly to you.
Errata
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To view the previously submitted errata, go to https://www.packtpub.com/books/content/support and enter the name of the book in the search field. The required information will appear under the Errata section.
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Questions
If you have a problem with any aspect of this book, you can contact us at <questions@packtpub.com>, and we will do our best to address the problem.
Chapter 1. Getting Started
Python comes on some computers as part of the OS. On other computers, we'll need to add the Python program and related tools. The installation is pretty simple, but we'll review the details to be sure that everyone has a common foundation.
Once we have Python, we'll need to confirm that Python is present. In some cases, we'll have more than one version of Python available. We need to be sure that we're using Python 3.4 or newer. To confirm that Python's available, we'll do a few interactions at Python's >>> prompt.
To extend our foundation for the remaining chapters, we'll look at a few essential rules of Python syntax. This isn't complete, but it will help us write scripts and learn the language. After we've had more chances to work with simple and compound statements, the detailed syntax rules will make sense.
We'll also look at the Python ecosystem
, starting with the built-in standard library. We'll emphasize the standard library throughout this book for two reasons. First, it's immense—much of what we need is already on our computer. Second, and more important, studying this library is the best way to learn the finer points of Python programming.
Beyond the built-in library, we'll take a look at the Python Package Index (PyPI). If we can't find the right module in the standard library, the second place to look for extensions is PyPI—https://pypi.python.org.
Installation or upgrade
To work with Python on Windows, we must install Python. For Mac OS X and Linux, a version of Python is already present; we'll often want to add a newer version to the preinstalled Python.
There are two significantly different flavors of Python available:
Python 2.x
Python 3.x
This book is about Python 3.4. We won't cover Python 2.x at all. There are several visible differences. What's important is that Python 2.x is a bit of a mess under the hood. Python 3 reflects some fundamental improvements. The improvements came at the cost of a few areas where the two versions of the language had to be made incompatible.
The Python community is continuing to keep Python 2.x around. Doing this is a help to people who are stuck with old software. For the most part, developers are moving forward with Python 3 because it's a clear improvement.
Before we get started, it's important to know if Python is already installed. The general test to see if Python is already installed is to get an OS command prompt. For Windows, use Command Prompt; for Mac OS X or Linux, use the Terminal tool. We'll show Mac OS X prompts from the Mac OS X Terminal. It looks like this:
MacBookPro-SLott:~ slott$ python3 Python 3.3.4 (v3.3.4:7ff62415e426, Feb 9 2014, 00:29:34) [GCC 4.2.1 (Apple Inc. build 5666) (dot 3)] on darwin Type help
, copyright
, credits
or license
for more information. >>>
We've shown the OS