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- Herausgeber: Packt Publishing
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
Become an ace Python programmer by learning best coding practices and advance-level concepts with Python 3.5
About This Book
- Based on the latest stable version of Python (version 3.5)
- Creating well manageable code that will run in various environments with different sets of dependencies
- Packed with advanced concepts and best practices to write efficient Python code
Who This Book Is For
The book would appeal to web developers and Python programmers who want to start using version 3.5 and write code efficiently. Basic knowledge of Python programming is expected.
What You Will Learn
- Conventions and best practices that are widely adopted in the python community
- Package python code effectively for community and production use
- Easy and lightweight ways to automate code deployment on remote systems
- Improve your code's quality, reliability, and performance
- Write concurrent code in python
- Extend python with code written in different languages
In Detail
Python is a dynamic programming language, used in a wide range of domains by programmers who find it simple, yet powerful. Even if you find writing Python code easy, writing code that is efficient and easy to maintain and reuse is a challenge.
The focus of the book is to familiarize you with common conventions, best practices, useful tools and standards used by python professionals on a daily basis when working with code.
You will begin with knowing new features in Python 3.5 and quick tricks for improving productivity. Next, you will learn advanced and useful python syntax elements brought to this new version. Using advanced object-oriented concepts and mechanisms available in python, you will learn different approaches to implement metaprogramming. You will learn to choose good names, write packages, and create standalone executables easily.
You will also be using some powerful tools such as buildout and vitualenv to release and deploy the code on remote servers for production use. Moving on, you will learn to effectively create Python extensions with C, C++, cython, and pyrex. The important factors while writing code such as code management tools, writing clear documentation, and test-driven development are also covered.
You will now dive deeper to make your code efficient with general rules of optimization, strategies for finding bottlenecks, and selected tools for application optimization.
By the end of the book, you will be an expert in writing efficient and maintainable code.
Style and approach
An easy-to-follow guide that covers industry followed best practices in Python programming
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Seitenzahl: 721
Veröffentlichungsjahr: 2016
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Table of Contents
Expert Python Programming Second Edition
Expert Python Programming Second Edition
Copyright © 2016 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 authors, 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: September 2008
Second edition: May 2016
Production reference: 1160516
Published by Packt Publishing Ltd.
Livery Place
35 Livery Street
Birmingham B3 2PB, UK.
ISBN 978-1-78588-685-0
www.packtpub.com
Credits
Authors
Michał Jaworski
Tarek Ziadé
Reviewer
Facundo Batista
Commissioning Editor
Kunal Parikh
Acquisition Editor
Meeta Rajani
Technical Editor
Pankaj Kadam
Copy Editor
Laxmi Subramanian
Proofreader
Safis Editing
Indexer
Rekha Nair
Graphics
Jason Monteiro
Production Coordinator
Aparna Bhagat
Cover Work
Aparna Bhagat
About the Authors
Michał Jaworski has 7 years of experience in Python. He is also the creator of graceful, which is a REST framework built on top of falcon. He has been in various roles at different companies: from an ordinary full-stack developer through software architect to VP of engineering in a fast-paced start-up company. He is currently a lead backend engineer in TV Store team at Opera Software. He is highly experienced in designing high-performance distributed services. He is also an active contributor to some of the popular Python open source projects.
Tarek Ziadé is an engineering manager at Mozilla, working with a team specialized in building web services in Python at scale for Firefox. He's contributed to the Python packaging effort and has worked with a lot of different Python web frameworks since Zope in the early days.
Tarek has also created Afpy, the French Python User Group, and has written two books on Python in French. He has delivered numerous talks and tutorials in French at international events such as Solutions Linux, PyCon, OSCON, and EuroPython.
About the Reviewer
Facundo Batista is a specialist in the Python programming language, with more than 15 years of experience with it. He is a core developer of the language, and a member by merit of the Python Software Foundation. He also received the 2009 Community Service Award for organizing PyCon Argentina and the Argentinian Python community as well as contributions to the standard library and work in translating the Python documentation.
He delivers talks in the main Python conferences in Argentina and other countries (The United States and Europe). In general, he has strong distributed collaborative experience from being involved in FLOSS development and working with people around the globe for more than 10 years.
He worked as a telecommunication engineer at Telefónica Móviles and Ericsson, and as a Python expert at Cyclelogic (developer in chief) and Canonical (senior software developer, his current position).
He also loves playing tennis, and is a father of two wonderful children.
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Preface
Python rocks!
From the earliest version in the late 1980s to the current version, it has evolved with the same philosophy: providing a multiparadigm programming language with readability and productivity in mind.
People used to see Python as yet another scripting language and wouldn't feel right about using it to build large systems. However, over the years and thanks to some pioneer companies, it became obvious that Python could be used to build almost any kind of system.
In fact, many developers that come from another language are charmed by Python and make it their language of choice.
This is something you are probably aware of if you have bought this book, so there's no need to convince you about the merits of the language any further.
This book is written to express many years of experience of building all kinds of applications with Python, from small system scripts done in a couple of hours to very large applications written by dozens of developers over several years.
It describes the best practices used by developers when working with Python.
This book covers some topics that do not focus on the language itself but rather on the tools and techniques used to work with it.
In other words, this book describes how an advanced Python developer works every day.
What this book covers
Chapter 1, Current Status of Python, showcases the current state of the Python language and its community. It shows how Python is constantly changing, why it is changing, and also why these facts are important for anyone who wants to call themselves a Python professional. This chapter also features the most popular and canonical ways of working in Python—popular productivity tools and conventions that are de facto standards now.
Chapter 2, Syntax Best Practices – below the Class Level, presents iterators, generators, descriptors, and so on, in an advanced way. It also covers useful notes about Python idioms and internal CPython types implementations with their computational complexities as a rationale for showcased idioms.
Chapter 3, Syntax Best Practices – above the Class Level, explains syntax best practices, but focuses above the class level. It covers more advanced object-oriented concepts and mechanisms available in Python. This knowledge is required in order to understand the last section of the chapter, which presents different approaches to metaprogramming in Python.
Chapter 4, Choosing Good Names, involves choosing good names. It is an extension to PEP 8 with naming best practices, but also gives tips on designing good APIs.
Chapter 5, Writing a Package, explains how to create the Python package and which tools to use in order to properly distribute it on the official Python Package Index or any other package repository. Information about packages is supplemented with a brief review of the tools that allow you to create standalone executables from Python sources.
Chapter 6, Deploying Code, aims mostly at Python web developers and backend engineers, because it deals with code deployments. It explains how Python applications should be built in order to be easily deployed to remote servers and what tools you can use in order to automate that process. This chapter dovetails with Chapter 5, Writing a Package, because it shows how packages and private package repositories can be used to streamline your application deployments.
Chapter 7, Python Extensions in Other Languages, explains why writing C extensions for Python might be a good solution sometimes. It also shows that it is not as hard as it seems to be as long as the proper tools are used.
Chapter 8, Managing Code, gives some insight into how a project code base can be managed and explains how to set up various continuous development processes.
Chapter 9, Documenting Your Project, covers documentation and provides tips on technical writing and how Python projects should be documented.
Chapter 10, Test-Driven Development, explains the basic principles of test-driven development and the tools that can be used in this development methodology.
Chapter 11, Optimization – General Principles and Profiling Techniques, explains optimization. It provides profiling techniques and an optimization strategy guideline.
Chapter 12, Optimization – Some Powerful Techniques, extends Chapter 11, Optimization – General Principles and Profiling Techniques, by providing some common solutions to the performance problems that are often found in Python programs.
Chapter 13, Concurrency, introduces the vast topic of concurrency in Python. It explains what concurrency is, when it might be necessary to write concurrent applications, and what are the main approaches to concurrency for Python programmers.
Chapter 14, Useful Design Patterns, concludes the book with a set of useful design patterns and example implementations in Python.
What you need for this book
This book is written for developers who work under any operating system for which Python 3 is available.
This is not a book for beginners, so I assume you have Python installed in your environment or know how to install it. Anyway, this book takes into account the fact that not everyone needs to be fully aware of the latest Python features or officially recommended tools. This is why the first chapter provides a recap of common utilities (such as virtual environments and pip) that are now considered standard tools of professional Python developers.
Who this book is for
This book is written for Python developers who wish to go further in mastering Python. And by developers I mean mostly professionals, so programmers who write software in Python for a living. This is because it focuses mostly on tools and practices that are crucial for creating performant, reliable, and maintainable software in Python.
It does not mean that hobbyists won't find anything interesting. This book should be great for anyone who is interested in learning advance-level concepts with Python. Anyone who has basic Python skills should be able to follow the content of the book, although it might require some additional effort from less experienced programmers. It should also be a good introduction to Python 3.5 for those who are still a bit behind and continue to use Python in version 2.7 or older.
Finally, the groups that should benefit most from reading this book are web developers and backend engineers. This is because of two topics featured in here that are especially important in their areas of work: reliable code deployments and concurrency.
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: "Use the str.encode(encoding, errors) method, which encodes the string using a registered codec for encoding."
A block of code is set as follows:
When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:
Any command-line input or output is written as follows:
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 the Next button moves you to the next screen."
Note
Warnings or important notes appear in a box like this.
Tip
Tips and tricks appear like this.
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Errata
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Questions
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Chapter 1. Current Status of Python
Python is good for developers.
No matter what operating system you or your customers are running, it will work. Unless you are coding platform-specific things, or using a platform-specific library, you can work on Linux and deploy on other systems, for example. However, that's not uncommon anymore (Ruby, Java, and many other languages work in the same way). Combined with the other qualities that we will discover throughout this book, Python becomes a smart choice for a company's primary development language.
This book is focused on the latest version of Python, 3.5, and all code examples are written in this version of the language unless another version is explicitly mentioned. Because this release is not yet widely used, this chapter contains some description of the current status quo of Python 3 to introduce readers to it, as well as some introductory information on modern approaches to development in Python. This chapter covers the following topics:
A book always starts with some appetizers. So, if you are already familiar with Python (especially with the latest 3.x branch) and know how to properly isolate environments for development purposes, you can skip the first two sections of this chapter and just read the other sections quickly. They describe some tools and resources that are not essential but can highly improve productivity in Python. Be sure to read the section on application-level environment isolation and pip, though, as their installation is mandatory for the rest of the book.
Where are we now and where we are going?
Python history starts somewhere in the late 1980s, but its 1.0 release date was in the year 1994, so it is not a very young language. There could be a whole timeline of major Python releases mentioned here, but what really matters is a single date: December 3, 2008 – the release date of Python 3.0.
At the time of writing, seven years have passed since the first Python 3 release. It is also four years since the creation of PEP 404—the official document that "un-released" Python 2.8 and officially closed the 2.x branch. Although a lot of time has passed, there is a specific dichotomy in the Python community—while the language develops very fast, there is a large group of its users that do not want to move forward with it.
Why and how does Python change?
The answer is simple—Python changes because there is such a need. The competition does not sleep. Every few months a new language pops out out of nowhere claiming to solve problems of all its predecessors. Most projects like these lose developers' attention very quickly and their popularity is driven by a sudden hype.
Anyway, this is a sign of some bigger issue. People design new languages because they find the existing ones unsuitable for solving their problems in the best ways possible. It would be silly not to recognize such a need. Also, more and more wide spread usage of Python shows that it could, and should, be improved in many places.
Lots of improvements in Python are often driven by the needs of particular fields where it is used. The most significant one is web development, which necessitated improvements to deal with concurrency in Python.
Some changes are just caused by the age and maturity of the Python project. Throughout the years, it has collected some of the clutter in the form of de-organized and redundant standard library modules or some bad design decisions. First, the Python 3 release aimed to bring major clean-up and refreshment to the language, but time showed that this plan backfired a bit. For a long time, it was treated by many developers only like curiosity, but, hopefully, this is changing.
Getting up to date with changes – PEP documents
The Python community has a well-established way of dealing with changes. While speculative Python language ideas are mostly discussed on specific mailing lists (<[email protected]>), nothing major ever gets changed without the existence of a new document called a PEP. A PEP is a Python Enhancement Proposal. It is a paper written that proposes a change on Python, and is a starting point for the community to discuss it. The whole purpose, format, and workflow around these documents is also standardized in the form of a Python Enhancement Proposal—precisely, PEP 1 document (http://www.python.org/dev/peps/pep-0001).
PEP documents are very important for Python and depending on the topic, they serve different purposes:
A list of all the proposed PEPs is available as in a document—PEP 0 (https://www.python.org/dev/peps/). Since they are easily accessible in one place and the actual URL is also very easy to guess, they are usually referred to by the number in the book.
Those who are wondering what the direction is in which the Python language is heading but do not have time to track a discussion on Python mailing lists, the PEP 0 document can be a great source of information. It shows which documents have already been accepted but are not yet implemented and also which are still under consideration.
PEPs also serve additional purposes. Very often, people ask questions like:
In most such cases, the extensive answer is available in specific PEP documents where such a feature has already been mentioned. There are a lot of PEP documents describing Python language features that were proposed but not accepted. These documents are left as a historical reference.
Python 3 adoption at the time of writing this book
So, is Python 3, thanks to new exciting features, well adopted among its community? Sadly, not yet. The popular page Python 3 Wall of Superpowers (https://python3wos.appspot.com) that tracks the compatibility of most popular packages with the Python 3 branch was, until not so long ago, named Python 3 Wall of Shame. This situation is changing and the table of listed packages on the mentioned page is slowly turning "more green" with every month. Still, this does not mean that all teams building their applications will shortly use only Python 3. When all popular packages are available on Python 3, the popular excuse—the packages that we use are not ported yet—will no longer be valid.
The main reason for such a situation is that porting the existing application from Python 2 to Python 3 is always a challenge. There are tools like 2to3 that can perform automated code translation but they do not ensure that the result will be 100% correct. Also, such translated code may not perform as well as in its original form without manual adjustments. The moving of existing complex code bases to Python 3 might involve tremendous effort and cost that some organizations may not be able to afford. Still such costs can be split in time. Some good software architecture design methodologies, such as service-oriented architecture or microservices, can help to achieve this goal gradually. New project components (services or microservices) can be written using the new technology and existing ones can be ported one at a time.
In the long run, moving to Python 3 can only have beneficial effects on a project. According to PEP-404, there won't be a 2.8 release in the 2.x branch of Python anymore. Also, there may be a time in the future when all major projects such as Django, Flask, and numpy will drop any 2.x compatibility and will only be available on Python 3.
My personal opinion on this topic can be considered controversial. I think that the best incentive for the community would be to completely drop Python 2 support when creating new packages. This, of course, greatly limits the reach of such software but it may be the only way to change the way of thinking of those who insist on sticking to Python 2.x.
Not only CPython
The main Python implementation is written in the C language and is called CPython. It is the one that the majority of people refer to when they talk about Python. When the language evolves, the C implementation is changed accordingly. Besides C, Python is available in a few other implementations that are trying to keep up with the mainstream. Most of them are a few milestones behind CPython, but provide a great opportunity to use and promote the language in a specific environment.
Why should I care?
There are plenty of alternative Python implementations available. The Python Wiki page on that topic (https://wiki.python.org/moin/PythonImplementations) features more than 20 different language variants, dialects, or implementations of Python interpreter built with something else than C. Some of them implement only a subset of the core language syntax, features, and built-in extensions but there is at least a few that are almost fully compatible with CPython. The most important thing to know is that while some of them are just toy projects or experiments, most of them were created to solve some real problems – problems that were either impossible to solve with CPython or required too much of the developer's effort. Examples of such problems are:
This section provides a short description of subjectively most popular and up-to-date choices that are currently available for Python programmers.
Stackless Python
Stackless Python advertises itself as an enhanced version of Python. Stackless is named so because it avoids depending on the C call stack for its own stack. It is in fact a modified CPython code that also adds some new features that were missing from core Python implementation at the time Stackless was created. The most important of them are microthreads managed by the interpreter as a cheap and lightweight alternative to ordinary threads that must depend on system kernel context switching and tasks scheduling.
The latest available versions are 2.7.9 and 3.3.5 that implement 2.7 and 3.3 versions of Python respectively. All the additional features provided by Stackless are exposed as a framework within this distribution through the built-in stackless module.
Stackless isn't the most popular alternative implementation of Python, but it is worth knowing because ideas introduced in it have had a strong impact on the language community. The core switching functionality was extracted from Stackless and published as an independent package named greenlet, which is now a basis for many useful libraries and frameworks. Also, most of its features were re-implemented in PyPy—another Python implementation that will be featured later. Refer to http://stackless.readthedocs.org/.
Jython
Jython is a Java implementation of the language. It compiles the code into Java byte code, and allows the developers to seamlessly use Java classes within their Python modules. Jython allows people to use Python as the top-level scripting language on complex application systems, for example, J2EE. It also brings Java applications into the Python world. Making Apache Jackrabbit (which is a document repository API based on JCR; see http://jackrabbit.apache.org) available in a Python program is a good example of what Jython allows.
The latest available version of Jython is Jython 2.7, and this corresponds to 2.7 version of the language. It is advertised as implementing nearly all of the core Python standard library and uses the same regression test suite. The version of Jython 3.x is under development.
The main differences of Jython as compared to CPython implementation are:
The main weakness of this implementation of the language is the lack of support for C Python Extension APIs, so no Python extensions written in C will work with Jython. This might change in the future because there are plans to support the C Python Extension API in Jython 3.x.
Some Python web frameworks such as Pylons were known to be boosting Jython development to make it available in the Java world. Refer to http://www.jython.org.
IronPython
IronPython brings Python into the .NET Framework. The project is supported by Microsoft, where IronPython's lead developers work. It is quite an important implementation for the promotion of a language. Besides Java, the .NET community is one of the biggest developer communities out there. It is also worth noting that Microsoft provides a set of free development tools that turn Visual Studio into full-fledged Python IDE. This is distributed as Visual Studio plugins named PVTS (Python Tools for Visual Studio) and is available as open source code on GitHub (http://microsoft.github.io/PTVS).
The latest stable release is 2.7.5 and it is compatible with Python 2.7. Similar to Jython, there is some development around Python 3.x implementation, but there is no stable release available yet. Despite the fact that .NET runs primarily on Microsoft Windows, it is possible to run IronPython also on Mac OS X and Linux. This is thanks to Mono, a cross platform, open source .NET implementation.
Main differences or advantages of IronPython as compared to CPython are as follows:
When speaking about weaknesses, IronPython, again, seems very similar to Jython because it does not support the C Python Extension APIs. This is important for developers who would like to use packages such as numpy that are largely based on C extensions. There is a project called ironclad (refer to https://github.com/IronLanguages/ironclad) that aims to allow using such extensions seamlessly with IronPython, albeit its last known supported release is 2.6 and development seems to have stopped at this point. Refer to http://ironpython.net/.
PyPy
PyPy is probably the most exciting implementation, as its goal is to rewrite Python into Python. In PyPy, the Python interpreter is itself written in Python. We have a C code layer carrying out the nuts-and-bolts work for the CPython implementation of Python. However, in the PyPy implementation, this C code layer is rewritten in pure Python.
This means you can change the interpreter's behavior during execution time and implement code patterns that couldn't be easily done in CPython.
PyPy currently aims to be fully compatible with Python 2.7, while PyPy3 is compatible with Python 3.2.5 version.
In the past, PyPy was interesting mostly for theoretical reasons, and it interested those who enjoyed going deep into the details of the language. It was not generally used in production, but this has changed through the years. Nowadays, many benchmarks show that surprisingly PyPy is often way faster than the CPython implementation. This project has its own benchmarking site that tracks the performance of each version measured using tens of different benchmarks (refer to http://speed.pypy.org/). It clearly shows that PyPy with JIT enabled is at least a few times faster than CPython. This and other features of PyPy makes more and more developers decide to switch to PyPy in their production environments.
The main differences of PyPy as compared to the CPython implementation are:
Like almost every other alternative Python implementation, PyPy lacks the full official support of C Python Extension API. Still it, at least, provides some sort of support for C extensions through its CPyExt subsystem, although it is poorly documented and still not feature complete. Also, there is an ongoing effort within the community in porting NumPy to PyPy because it is the most requested feature. Refer to http://pypy.org.
Modern approaches to Python development
A deep understanding of the programming language of choice is the most important thing to harness as an expert. This will always be true for any technology. Still, it is really hard to develop a good software without knowing the common tools and practices within the given language community. Python has no single feature that could not be found in some other language. So, in direct comparison of syntax, expressiveness, or performance, there will always be a solution that is better in one or more fields. But the area in which Python really stands out from the crowd is in the whole ecosystem built around the language. Its community has, for years, polished the standard practices and libraries that help to create more reliable software in a shorter time.
The most obvious and important part of the mentioned ecosystem is a huge collection of free and open source packages that solve a multitude of problems. Writing new software is always an expensive and time-consuming process. Being able to reuse the existing code instead of reinventing the wheel greatly reduces the time and costs of development. For some companies, it is the only reason their projects are economically feasible.
Due to this reason, Python developers put a lot of effort on creating tools and standards to work with open source packages created by others. Starting from virtual isolated environments, improved interactive shells and debuggers, to programs that help to discover, search, and analyze the huge collection of packages available on PyPI (Python Package Index).
Application-level isolation of Python environments
Nowadays, a lot of operating systems come with Python as a standard component. Most Linux distributions and Unix-based systems such as FreeBSD, NetBSD, OpenBSD, or OS X come with Python are either installed by default or available through system package repositories. Many of them even use it as part of some core components—Python powers the installers of Ubuntu (Ubiquity), Red Hat Linux (Anaconda), and Fedora (Anaconda again).
Due to this fact, a lot of packages from PyPI are also available as native packages managed by the system's package management tools such as apt-get (Debian, Ubuntu), rpm (Red Hat Linux), or emerge (Gentoo). Although it should be remembered that the list of available libraries is very limited and they are mostly outdated when compared to PyPI. This is the reason why pip should always be used to obtain new packages in the latest version as a recommendation of PyPA (Python Packaging Authority). Although it is an independent package starting from version 2.7.9 and 3.4 of CPython, it is bundled with every new release by default. Installing the new package is as simple as this:
Among other features, pip allows forcing specific versions of packages (using the pip install package-name==version syntax) and upgrading to the latest version available (using the ––upgrade switch). The full usage description for most of the command-line tools presented in the book can be easily obtained simply by running the command with the -h or --help switch, but here is an example session that demonstrates the most commonly used options:
