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Mastering Python
Mastering Python

Mastering Python: Master the art of writing beautiful and powerful Python by using all of the features that Python 3.5 offers

By Rick van Hattem
$35.99 $24.99
Book Apr 2016 486 pages 1st Edition
$35.99 $24.99
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Mastering Python

Chapter 1. Getting Started – One Environment per Project

There is one aspect of the Python philosophy that always has been, and always will be, the most important in the entire language—readability, or Pythonic code. This book will help you master writing Python the way it was meant to be: readable, beautiful, explicit, and as simple as possible. In short, it will be Pythonic code. That is not to say that complicated subjects will not be covered. Naturally, they will, but whenever the philosophy of Python is at stake, you will be warned when and where the technique is justified.

Most of the code within this book will function on both Python 2 and Python 3, but the main target is Python 3. There are three reasons for doing this:

  1. Python 3 was released in 2008, which is a very long time in the rapidly changing software world. It's not a new thing anymore, it's stable, it's usable, and, most importantly, it's the future.

  2. Development for Python 2 effectively stopped in 2009. Certain features have been backported from Python 3 to Python 2, but any new development will be for Python 3 first.

  3. Python 3 has become mature. While I have to admit that Python 3.2 and older versions still had a few small issues that made it hard to write code that functions on both Python 2 and 3, Python 3.3 did improve greatly in that aspect, and I consider it mature. This is evidenced by the marginally modified syntax in Python 3.4 and 3.5 and a lot of very useful features, which are covered in this book.

To summarize, Python 3 is an improvement over Python 2. I have been a skeptic for a very long time myself, but I do not see any reason not to use Python 3 for new projects, and even porting existing projects to Python 3 is generally possible with only minor changes. With cool new features such as async with in Python 3.5, you will want to upgrade just to try it.

This first chapter will show you how to properly set up an environment, create a new isolated environment, and make sure you get similar results when running the same code on different machines. Most Python programmers are already using virtualenv to create virtual Python environments, but the venv command, introduced in Python 3.3, is a very nice alternative. It is essentially a clone of the virtualenv package but is slightly simpler and bundled with Python. While its usage is mostly analogous to virtualenv, there are a few changes that are interesting to know.

Secondly, we will discuss the pip command. The pip command is automatically installed when using venv through the ensurepip package, a package introduced in Python 3.4. This package automatically bootstraps pip into an existing Python library while maintaining independent versions of Python and pip. Before Python 3.4, venv came without pip and had to be installed manually.

Finally, we will discuss how packages created with distutils can be installed. While pure Python packages are generally easy to install, it can get challenging when C modules are involved.

In this chapter, the following topics are covered:

  • Creating a virtual Python environment using venv

  • Bootstrapping pip using ensurepip

  • Installing packages based on distutils (C/C++) with pip

Creating a virtual Python environment using venv

Most Python programmers are already be familiar with venv or virtualenv, but even if you're not, it's never too late to start using it. The venv module is designed to isolate your Python environments so that you can install packages specific to your current project without polluting your global namespace. For example, having a filename such as in your current directory can seriously break your code if you expect to have the standard Python sys library—your local sys libraries will be imported before the global one, effectively hiding the system library. In addition, because the packages are installed locally, you don't need system (root/administrator) access to install them.

The result is that you can make sure you have exactly the same version of a package on both your local development machine and production machines without interfering with other packages. For example, there are many Django packages around that require specific versions of the Django project. Using venv, you can easily install Django 1.4 for project A and Django 1.8 for project B without them ever knowing that there are different versions installed in other environments. By default, the environments are even configured in such a way that the global packages are not visible. The benefit of this is that to get an exact list of all installed packages within the environment, simply a pip freeze will suffice. The downside is that some of the heavier packages (for example, numpy) will have to be installed in every separate environment. Needless to say, which choice is the best for your project depends on the project. For most projects, I would keep the default setting of not having the global packages, but when messing around with projects that have lots of C/C++ extensions, it would be convenient to simply enable the global site packages. The reason is simple; if you do not have a compiler available, installing the package locally can be difficult, while the global install has an executable for Windows or an installable package for Linux/Unix available.


The venv module ( can be seen as a slightly simplified version of the virtualenv tool (, which has been bundled with Python since version 3.3 (refer to PEP 0405 -- Python Virtual Environments:

The virtualenv package can generally be used as a drop-in replacement for venv, which is especially relevant for older Python versions (below 3.3) that do not come bundled with venv.

Creating your first venv

Creating an environment is quite easy. The basic command comes down to pyvenv PATH_TO_THE_NEW_VIRTUAL_ENVIRONMENT, so let's give it a try. Note that this command works on Linux, Unix, and Mac; the Windows command will follow shortly:

# pyvenv test_venv
# . ./test_venv/bin/activate
(test_venv) #


Some Ubuntu releases (notably 14.04 LTS) maim the Python installation by not including the full pyvenv package with ensurepip. The standard workaround is to call pyvenv --without-pip test_env, which requires a manual pip installation through the file available on the pip home page.

This creates an environment called test_venv, and the second line activates the environment.

On Windows, everything is slightly different but similar overall. By default, the pyvenv command won't be in your PATH, so running the command is slightly different. The three options are as follows:

  • Add the Python\Tools\Scripts\ directory to your PATH

  • Run the module:

    python -m venv test_venv
  • Run the script directly:

    python Python\Tools\Scripts\ test_venv

For convenience, I would recommend that you add the Scripts directory to your PATH anyhow, since many other applications/scripts (such as pip) will be installed there as well.

Here is the full example for Windows:

C:\envs>python -m venv test_venv
(test_venv) C:\envs>


When using Windows PowerShell, the environment can be activated by using test_venv\Scripts\Activate.ps1 instead. Note that you really do need backslashes here.

venv arguments

So far, we have just created a plain and regular venv, but there are a few, really useful flags for customizing your venv specifically to your needs.

First, let's look at the venv help:




It gives the virtual environment access to the system-site-packages directory


Try to use symlinks rather than copies when symlinks are not the default for the platform


Try to use copies rather than symlinks even when symlinks are the default for the platform


Delete the contents of the environment directory, if it exists, before environment creation


Upgrade the environment directory to use this version of Python, assuming that Python has been upgraded in-place


This skips installing or upgrading pip in the virtual environment (pip is bootstrapped by default)

The most important argument to note is --system-site-packages, which enables the global site packages within the environment. This means that if you have a package installed in your global Python version, it will be available within your environment as well. However, if you try to update it to a different version, it will be installed locally. Whenever possible, I would recommend disabling the --system-site-packages flag because it gives you a simple environment without too many variables. A simple update of the system packages could break your virtual environment otherwise, but worse, there is no way to know which packages are needed locally and which ones are just installed for other purposes.

To enable this for an existing environment, you can simply run the environment creation command again, but this time adding the --system-site-packages flag to enable the global site packages.

To disable it again, you can simply run the environment creation command without the flag. This will keep the locally (within the environment) installed packages available but will remove the global packages from your Python scope.


When using virtualenvwrapper, this can also be done with the toggleglobalsitepackages command from within the activated environment.

The --symlinks and --copies arguments can generally be ignored, but it is important to know the difference. These arguments decide whether the files will be copied from the base python directory or whether they will be symlinked.


Symlinks are a Linux/Unix/Mac thing; instead of copying a file it creates a symbolic link that tells the system where to find the actual file.

By default, venv will try to symlink the files, and if that fails, it will fall back to copying. Since Windows Vista and Python 3.2, this is also supported on Windows, so unless you're using a very old system, you will most likely be using symlinks in your environment. The benefit of symlinks is that it saves disk space and stays in sync with your Python installation. The downside is that if your system's Python version undergoes an upgrade, it can break the packages installed within your environment, but that can easily be fixed by reinstalling the packages using pip.

Finally, the --upgrade argument is useful if your system Python version has been upgraded in-place. The most common use case for this argument is for repairing broken environments after upgrading the system Python with a copied (as opposed to symlinked) environment.

Differences between virtualenv and venv

Since the venv module is essentially a simpler version of virtualenv, they are mostly the same, but some things are different. Also, since virtualenv is a package that is distributed separately from Python, it does have some advantages.

The following are the advantages of venv over virtualenv:

  • venv is distributed with Python 3.3 and above, so no separate install is needed

  • venv is simple and straightforward with no features besides the bare necessities

Advantages of virtualenv over venv:

  • virtualenv is distributed outside of Python, so it can be updated separately.

  • virtualenv works on old Python versions, but Python 2.6 or a higher version is recommended. However, Python 2.5 support is possible with older versions (1.9.x or lower).

  • It supports convenient wrappers, such as virtualenvwrapper (

In short, if venv is enough for you, use it. If you are using an old Python version or want some extra convenience, such as virtualenvwrapper, use virtualenv instead. Both projects essentially do the same thing, and efforts have been made to easily switch between them. The biggest and most significant difference between the two is the wide variety of Python versions that virtualenv supports.

Bootstrapping pip using ensurepip

Slowly, the pip package manager has been replacing easy_install since its introduction in 2008. Since Python 3.4, it has even become the default and is bundled with Python. Since Python 3.4 onward, it is installed by default within both the regular Python environment and that of pyvenv; before that, a manual install is required. To automatically install pip in Python 3.4 and above, the ensurepip library is used. This is a library that handles automatic installation and/or upgrades of pip, so it is at least as recent as the one bundled with ensurepip.

ensurepip usage

The usage of ensurepip is fairly straightforward. Just run python -m ensurepip to guarantee a pip version or python -m ensurepip --upgrade to make sure that pip will be at least the version that is bundled with ensurepip.

In addition to installing the regular pip shortcut, this will also install the pipX and pipX.Y links, which allow you to select a specific Python version. When using Python 2 and Python 3 simultaneously, this allows you to install packages within Python 2 and Python 3 with pip2 and pip3, respectively. This means that if you use python -m ensurepip on Python 3.5 you will get pip, pip3, and pip3.5 commands installed in your environment.

Manual pip install

The ensurepip package is great if you are using Python 3.4 or above. Below that, however, you need to install pip manually. Actually, this is surprisingly easy. It involves just two steps:

  1. Download the file:

  2. Execute the file: python


    If the ensurepip command fails due to permission errors, it can be useful to supply the --user argument. This allows you to install pip inside the user specific site packages directory, so root/admin access is not required.

Installing C/C++ packages

Most Python packages are purely Python and blissfully easy to install, just as a simple pip install packagename does the trick. However, there are cases where compilation is involved and installation goes from a simple pip install to searching for hours to see which dependencies are needed to install a certain package.

The specific error message will differ as per the project and environment, but there is a common pattern in these errors, and understanding what you are looking at can help a lot when searching for a solution.

For example, when installing pillow on a standard Ubuntu machine, you'll get a few pages full of errors, warnings, and other messages that end like this:

    x86_64-linux-gnu-gcc: error: build/temp.linux-x86_64-3.4/libImaging/Jpeg2KDecode.o: No such file or directory
    x86_64-linux-gnu-gcc: error: build/temp.linux-x86_64-3.4/libImaging/Jpeg2KEncode.o: No such file or directory
    x86_64-linux-gnu-gcc: error: build/temp.linux-x86_64-3.4/libImaging/BoxBlur.o: No such file or directory
    error: command 'x86_64-linux-gnu-gcc' failed with exit status 1

Command "python3 -c "import setuptools, tokenize;__file__='/tmp/pip-build-_f0ryusw/pillow/';exec(compile(getattr(tokenize, 'open', open)(__file__).read().replace('\r\n', '\n'), __file__, 'exec'))" install --record /tmp/pip-kmmobum2-record/install-record.txt --single-version-externally-managed --compile --install-headers include/site/python3.4/pillow" failed with error code 1 in /tmp/pip-build-_f0ryusw/pillow

Upon seeing messages like these, you might be tempted to search for one of the lines such as x86_64-linux-gnu-gcc: error: build/temp.linux-x86_64-3.4/libImaging/Jpeg2KDecode.o: No such file or directory. While this might give you some relevant results, most likely it will not. The trick with installations like these is to scroll up until you see messages about missing headers. Here is an example:

  In file included from libImaging/Imaging.h:14:0,
                   from libImaging/Resample.c:16:
  libImaging/ImPlatform.h:10:20: fatal error: Python.h: No such file or directory
   #include "Python.h"
  compilation terminated.

The key message here is that Python.h is missing. These are part of the Python headers and are needed for the compilation of most C/C++ packages within Python. Depending on the operating system, the solutions will vary—unfortunately. So, I recommend that you skip all parts of this paragraph that are not relevant for your case.

Debian and Ubuntu

In Debian and Ubuntu, the package to be installed is python3-dev or python2-dev if you're still using Python 2. The command to execute is as follows:

# sudo apt-get install python3-dev

However, this installs the development headers only. If you want the compiler and other headers bundled with the install, then the build-dep command is also very useful. Here is an example:

# sudo apt-get build-dep python3

Red Hat, CentOS, and Fedora

Red Hat, CentOS, and Fedora are rpm-based distros that use the yum package manager to install the requirements. Most development headers are available through <package-name>-devel and are easily installable as such. To install the Python 3 development headers, use this line:

# sudo apt-get install python3-devel

To make sure you have all the requirements such as development headers and compilers to build packages such as Python, the yum-builddep command is available:

# yum-builddep python3


The install procedure on OS X consists of three steps before the actual package can be installed.

First, you have to install Xcode. This can be done through the OS X App Store at

Then you have to install the Xcode command-line tools:

# xcode-select --install

Finally, you need to install the Homebrew package manager. The steps are available at, but the install command is as follows:

# /usr/bin/ruby -e "$(curl -fsSL"


Other package managers, such as Macports, are also possible, but Homebrew is currently the OS X package manager with the most active development and community.

Once all of these steps have been completed, you should have a working Homebrew installation. The working of Homebrew can be verified using the brew doctor command. If there are no major errors in the output, then you should be ready to install your first packages through brew. Now we simply need to install Python and we're done:

# brew install python3


On Windows, manual compilation of C Python packages is generally a non-trivial task to say the least. Most packages have been written with Linux/Unix systems in mind (OS X falls under the Unix category), and Windows is a nice-to-have for developers. The result is that packages are difficult to compile on Windows because there are few people testing them and many of the libraries require manual installation, making it a very tedious task. So, unless you really have to, try and stay away from manually compiling Python packages on Windows. Most packages are available as installable binary downloads with a bit of searching, and there are alternatives such as Anaconda that include binary packages for most important C Python packages.

If you still feel inclined to manually compile C Python packages, then there is another option, and it is generally an easier alternative. The Cygwin project ( attempts to make Linux applications run natively on Windows. This is generally an easier solution than making packages work with Visual Studio.

If you do wish to take the Visual Studio path, I would like to point you towards Chapter 14, Extensions in C/C++, System Calls, and C/C++ Libraries, which covers manual writing of C/C++ extensions and some information on which Visual Studio versions you need for your Python version.


With the inclusion of packages such as pip and venv, I feel that Python 3 has become a complete package that should suit most people. Beyond legacy applications, there is no real reason not to choose Python 3 anymore. The initial Python 3 release in 2008 was definitely a bit raw compared to the well-rounded Python 2.6 version released the same year, but a lot has changed in that aspect. The last major Python 2 release was Python 2.7, which was released in 2010; within the software world, that is a very, very long time. While Python 2.7 still receives maintenance, it will not receive any of the amazing new features that Python 3 is getting—features such as Unicode strings by default, dict generators (Chapter 6, Generators and Coroutines – Infinity, One Step at a Time), and async methods (Chapter 7, Async IO – Multithreading without Threads).

After finishing this chapter, you should be able to create a clean and recreatable virtual environment and know where to look if an installation of C/C++ packages fails.

Here are the most important notes for this chapter:

  • For a clean and simple environment, use venv. If compatibility with Python 2 is needed, use virtualenv.

  • If C/C++ packages fail to install, look for the error about missing includes.

The next chapter covers the Python style guide, which rules are important, and why they matter. Readability is one of the most important aspects of the Python philosophy, and you will learn methods and styles for writing cleaner and more readable Python code.

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Key benefits

  • • Become familiar with the most important and advanced parts of the Python code style
  • • Learn the trickier aspects of Python and put it in a structured context for deeper understanding of the language
  • • Offers an expert's-eye overview of how these advanced tasks fit together in Python as a whole along with practical examples


Python is a dynamic programming language. It is known for its high readability and hence it is often the first language learned by new programmers. Python being multi-paradigm, it can be used to achieve the same thing in different ways and it is compatible across different platforms. Even if you find writing Python code easy, writing code that is efficient, easy to maintain, and reuse is not so straightforward. This book is an authoritative guide that will help you learn new advanced methods in a clear and contextualised way. It starts off by creating a project-specific environment using venv, introducing you to different Pythonic syntax and common pitfalls before moving on to cover the functional features in Python. It covers how to create different decorators, generators, and metaclasses. It also introduces you to functools.wraps and coroutines and how they work. Later on you will learn to use asyncio module for asynchronous clients and servers. You will also get familiar with different testing systems such as py.test, doctest, and unittest, and debugging tools such as Python debugger and faulthandler. You will learn to optimize application performance so that it works efficiently across multiple machines and Python versions. Finally, it will teach you how to access C functions with a simple Python call. By the end of the book, you will be able to write more advanced scripts and take on bigger challenges.

What you will learn

• Create a virtualenv and start a new project • Understand how and when to use the functional programming paradigm • Get familiar with the different ways the decorators can be written in • Understand the power of generators and coroutines without digressing into lambda calculus • Create metaclasses and how it makes working with Python far easier • Generate HTML documentation out of documents and code using Sphinx • Learn how to track and optimize application performance, both memory and cpu • Use the multiprocessing library, not just locally but also across multiple machines • Get a basic understanding of packaging and creating your own libraries/applications

Product Details

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Publication date : Apr 29, 2016
Length 486 pages
Edition : 1st Edition
Language : English
ISBN-13 : 9781785289729
Category :

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Product Details

Publication date : Apr 29, 2016
Length 486 pages
Edition : 1st Edition
Language : English
ISBN-13 : 9781785289729
Category :

Table of Contents

22 Chapters
Mastering Python Chevron down icon Chevron up icon
Credits Chevron down icon Chevron up icon
About the Author Chevron down icon Chevron up icon
About the Reviewers Chevron down icon Chevron up icon Chevron down icon Chevron up icon
Preface Chevron down icon Chevron up icon
1. Getting Started – One Environment per Project Chevron down icon Chevron up icon
2. Pythonic Syntax, Common Pitfalls, and Style Guide Chevron down icon Chevron up icon
3. Containers and Collections – Storing Data the Right Way Chevron down icon Chevron up icon
4. Functional Programming – Readability Versus Brevity Chevron down icon Chevron up icon
5. Decorators – Enabling Code Reuse by Decorating Chevron down icon Chevron up icon
6. Generators and Coroutines – Infinity, One Step at a Time Chevron down icon Chevron up icon
7. Async IO – Multithreading without Threads Chevron down icon Chevron up icon
8. Metaclasses – Making Classes (Not Instances) Smarter Chevron down icon Chevron up icon
9. Documentation – How to Use Sphinx and reStructuredText Chevron down icon Chevron up icon
10. Testing and Logging – Preparing for Bugs Chevron down icon Chevron up icon
11. Debugging – Solving the Bugs Chevron down icon Chevron up icon
12. Performance – Tracking and Reducing Your Memory and CPU Usage Chevron down icon Chevron up icon
13. Multiprocessing – When a Single CPU Core Is Not Enough Chevron down icon Chevron up icon
14. Extensions in C/C++, System Calls, and C/C++ Libraries Chevron down icon Chevron up icon
15. Packaging – Creating Your Own Libraries or Applications Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon

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