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Python Guide Documentation
Release 0.0.1
Kenneth Reitz
January 11, 2018
Contents
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2
Python Development Environments
2.1 Your Development Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.2 Further Configuration of Pip and Virtualenv . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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3
Writing Great Python Code
3.1 Structuring Your Project
3.2 Code Style . . . . . . .
3.3 Reading Great Code . .
3.4 Documentation . . . . .
3.5 Testing Your Code . . .
3.6 Logging . . . . . . . . .
3.7 Common Gotchas . . .
3.8 Choosing a License . . .
4
Getting Started with Python
1.1 Picking an Python Interpreter (3 vs. 2)
1.2 Properly Installing Python . . . . . . .
1.3 Installing Python 3 on Mac OS X . . .
1.4 Installing Python 3 on Windows . . . .
1.5 Installing Python 3 on Linux . . . . . .
1.6 Installing Python 2 on Mac OS X . . .
1.7 Installing Python 2 on Windows . . . .
1.8 Installing Python 2 on Linux . . . . . .
1.9 Pipenv & Virtual Environments . . . .
1.10 Lower level: virtualenv . . . . . . . . .
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Scenario Guide for Python Applications
4.1 Network Applications . . . . . . .
4.2 Web Applications & Frameworks .
4.3 HTML Scraping . . . . . . . . . .
4.4 Command-line Applications . . . .
4.5 GUI Applications . . . . . . . . . .
4.6 Databases . . . . . . . . . . . . . .
4.7 Networking . . . . . . . . . . . . .
4.8 Systems Administration . . . . . .
4.9 Continuous Integration . . . . . . .
4.10 Speed . . . . . . . . . . . . . . . .
4.11 Scientific Applications . . . . . . .
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4.12
4.13
4.14
4.15
4.16
4.17
4.18
Image Manipulation . . . . . . .
Data Serialization . . . . . . . .
XML parsing . . . . . . . . . . .
JSON . . . . . . . . . . . . . . .
Cryptography . . . . . . . . . . .
Machine Learning . . . . . . . .
Interfacing with C/C++ Libraries
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. 93
. 95
. 95
. 96
. 97
. 98
. 100
5
Shipping Great Python Code
103
5.1 Packaging Your Code . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103
5.2 Freezing Your Code . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105
6
Additional Notes
6.1 Introduction . . . . .
6.2 The Community . . .
6.3 Learning Python . . .
6.4 Documentation . . . .
6.5 News . . . . . . . . .
6.6 Contribute . . . . . .
6.7 License . . . . . . . .
6.8 The Guide Style Guide
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Python Guide Documentation, Release 0.0.1
Greetings, Earthling! Welcome to The Hitchhiker’s Guide to Python.
This is a living, breathing guide. If you’d like to contribute, fork us on GitHub!
This handcrafted guide exists to provide both novice and expert Python developers a best practice handbook to the
installation, configuration, and usage of Python on a daily basis.
This guide is opinionated in a way that is almost, but not quite, entirely unlike Python’s official documentation. You
won’t find a list of every Python web framework available here. Rather, you’ll find a nice concise list of highly
recommended options.
Note: The use of Python 3 is highly preferred over Python 2. Consider upgrading your applications and infrastructure
if you find yourself still using Python 2 in production today. If you are using Python 3, congratulations — you are
indeed a person of excellent taste. —Kenneth Reitz
Let’s get started! But first, let’s make sure you know where your towel is.
Contents
1
Python Guide Documentation, Release 0.0.1
2
Contents
CHAPTER 1
Getting Started with Python
New to Python? Let’s properly setup up your Python environment:
Picking an Python Interpreter (3 vs. 2)
The State of Python (3 & 2)
When choosing a Python interpreter, one looming question is always present: “Should I choose Python 2 or Python
3”? The answer is a bit more subtle than one might think.
The basic gist of the state of things is as follows:
1. Most production applications today use Python 2.7.
2. Python 3 is ready for the production deployment of applications today.
3. Python 2.7 will only receive necessary security updates until 2020 6 .
4. The brand name “Python” encapsulates both Python 3 and Python 2.
Recommendations
Note: The use of Python 3 is highly preferred over Python 2. Consider upgrading your applications and infrastructure
if you find yourself still using Python 2 in production today. If you are using Python 3, congratulations — you are
indeed a person of excellent taste. —Kenneth Reitz
I’ll be blunt:
• Use Python 3 for new Python applications.
• If you’re learning Python for the first time, familiarizing yourself with Python 2.7 will be very useful, but not
more useful than learning Python 3.
• Learn both. They are both “Python”.
• Software that is already built often depends on Python 2.7.
6
https://www.python.org/dev/peps/pep-0373/#id2
3
Python Guide Documentation, Release 0.0.1
• If you are writing a new open source Python library, it’s best to write it for both Python 2 and 3 simultaneously.
Only supporting Python 3 for a new library you want to be widely adopted is a political statement and will
alienate many of your users. This is not a problem — slowly, over the next three years, this will become less the
case.
So.... 3?
If you’re choosing a Python interpreter to use, I recommend you use the newest Python 3.x, since every version brings
new and improved standard library modules, security and bug fixes.
Given such, only use Python 2 if you have a strong reason to, such as a pre-existing code-base, a Python 2 exclusive
library, simplicity/familiarity, or, of course, you absolutely love and are inspired by Python 2. No harm in that.
Check out Can I Use Python 3? to see if any software you’re depending on will block your adoption of Python 3.
Further Reading
It is possible to write code that works on Python 2.6, 2.7, and Python 3. This ranges from trivial to hard depending
upon the kind of software you are writing; if you’re a beginner there are far more important things to worry about.
Note that Python 2.6 is end-of-life upstream, so you shouldn’t try to write 2.6-compatible code unless you’re being
paid specifically to do that.
Implementations
When people speak of Python they often mean not just the language but also the CPython implementation. Python is
actually a specification for a language that can be implemented in many different ways.
CPython
CPython is the reference implementation of Python, written in C. It compiles Python code to intermediate bytecode
which is then interpreted by a virtual machine. CPython provides the highest level of compatibility with Python
packages and C extension modules.
If you are writing open-source Python code and want to reach the widest possible audience, targeting CPython is best.
To use packages which rely on C extensions to function, CPython is your only implementation option.
All versions of the Python language are implemented in C because CPython is the reference implementation.
PyPy
PyPy is a Python interpreter implemented in a restricted statically-typed subset of the Python language called RPython.
The interpreter features a just-in-time compiler and supports multiple back-ends (C, CLI, JVM).
PyPy aims for maximum compatibility with the reference CPython implementation while improving performance.
If you are looking to increase performance of your Python code, it’s worth giving PyPy a try. On a suite of benchmarks,
it’s currently over 5 times faster than CPython.
PyPy supports Python 2.7. PyPy3 1 , released in beta, targets Python 3.
1
4
http://pypy.org/compat.html
Chapter 1. Getting Started with Python
Python Guide Documentation, Release 0.0.1
Jython
Jython is a Python implementation that compiles Python code to Java bytecode which is then executed by the JVM
(Java Virtual Machine). Additionally, it is able to import and use any Java class like a Python module.
If you need to interface with an existing Java codebase or have other reasons to need to write Python code for the JVM,
Jython is the best choice.
Jython currently supports up to Python 2.7.
2
IronPython
IronPython is an implementation of Python for the .NET framework. It can use both Python and .NET framework
libraries, and can also expose Python code to other languages in the .NET framework.
Python Tools for Visual Studio integrates IronPython directly into the Visual Studio development environment, making
it an ideal choice for Windows developers.
IronPython supports Python 2.7.
3
PythonNet
Python for .NET is a package which provides near seamless integration of a natively installed Python installation with
the .NET Common Language Runtime (CLR). This is the inverse approach to that taken by IronPython (see above),
to which it is more complementary than competing with.
In conjunction with Mono, pythonnet enables native Python installations on non-Windows operating systems, such as
OS X and Linux, to operate within the .NET framework. It can be run in addition to IronPython without conflict.
Pythonnet supports from Python 2.6 up to Python 3.5.
4 5
• Properly Install Python on your system:
1.2 Properly Installing Python
There’s a good chance that you already have Python on your operating system.
If so, you do not need to install or configure anything else to use Python. Having said that, I would
strongly recommend that you install the tools and libraries described in the guides below before you start
building Python applications for real-world use. In particular, you should always install Setuptools, Pip,
and Virtualenv — they make it much easier for you to use other third-party Python libraries.
Note: The use of Python 3 is highly preferred over Python 2. Consider upgrading your applications
and infrastructure if you find yourself still using Python 2 in production today. If you are using Python 3,
congratulations — you are indeed a person of excellent taste. —Kenneth Reitz
2
3
4
5
https://hg.python.org/jython/file/412a8f9445f7/NEWS
http://ironpython.codeplex.com/releases/view/81726
https://travis-ci.org/pythonnet/pythonnet
https://ci.appveyor.com/project/TonyRoberts/pythonnet-480xs
1.2. Properly Installing Python
5
Python Guide Documentation, Release 0.0.1
Installation Guides
These guides go over the proper installation of Python for development purposes, as well as setuptools,
pip and virtualenv.
Python 3 Installation Guides
• Python 3 on MacOS.
• Python 3 on Windows.
• Python 3 on Linux.
Legacy Python 2 Installation Guides
• Python 2 on MacOS.
• Python 2 on Microsoft Windows.
• Python 2 on Linux.
Installing Python 3 on Mac OS X
The latest version of Mac OS X, High Sierra, comes with Python 2.7 out of the box.
You do not need to install or configure anything else to use Python 2. These instructions document the
installation of Python 3.
The version of Python that ships with OS X is great for learning, but it’s not good for development. The
version shipped with OS X may be out of date from the official current Python release, which is considered
the stable production version.
Doing it Right
Let’s install a real version of Python.
Before installing Python, you’ll need to install GCC. GCC can be obtained by downloading XCode, the
smaller Command Line Tools (must have an Apple account) or the even smaller OSX-GCC-Installer
package.
Note: If you already have XCode installed, do not install OSX-GCC-Installer. In combination, the
software can cause issues that are difficult to diagnose.
Note: If you perform a fresh install of XCode, you will also need to add the commandline tools by
running xcode-select --install on the terminal.
While OS X comes with a large number of UNIX utilities, those familiar with Linux systems will notice
one key component missing: a package manager. Homebrew fills this void.
To install Homebrew, open Terminal or your favorite OSX terminal emulator and run
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$ ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"
The script will explain what changes it will make and prompt you before the installation begins. Once
you’ve installed Homebrew, insert the Homebrew directory at the top of your PATH environment variable.
You can do this by adding the following line at the bottom of your ~/.profile file
export PATH=/usr/local/bin:/usr/local/sbin:$PATH
Now, we can install Python 3:
$ brew install python3
This will take a minute or two.
Pip
Homebrew installs pip3 for you.
pip3 is the alias to pip pointing to the Homebrew’d Python 3.
Working with Python 3
At this point, you have the system Python 2.7 available, potentially the Homebrew version of Python 2
installed, and the Homebrew version of Python 3 as well.
$ python
will launch the system Python interpreter.
$ python2
will launch the homebrew-installed Python 2 interpreter (if any).
$ python3
will launch the homebrew-installed Python 3 interpreter.
If the Homebrew version of Python 2 is installed then pip2 will point to Python 2. If the Homebrew
version of Python 3 is installed then pip3 will point to Python 3.
Pipenv & Virtual Environments
The next step is to install Pipenv, so you can install dependencies and manage virtual environments.
A Virtual Environment is a tool to keep the dependencies required by different projects in separate places,
by creating virtual Python environments for them. It solves the “Project X depends on version 1.x but,
Project Y needs 4.x” dilemma, and keeps your global site-packages directory clean and manageable.
For example, you can work on a project which requires Django 1.10 while also maintaining a project
which requires Django 1.8.
So, onward! To the Pipenv & Virtual Environments docs!
This page is a remixed version of another guide, which is available under the same license.
1.3. Installing Python 3 on Mac OS X
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Installing Python 3 on Windows
First, download the latest version of Python 3.6 from the official website. If you want to be sure you
are installing a fully up-to-date version, click the Downloads > Windows link from the home page of the
Python.org web site.
By design, Python installs to a directory with the version number embedded, e.g. Python version 3.6 will
install at C:\Python36\, so that you can have multiple versions of Python on the same system without
conflicts. Of course, only one interpreter can be the default application for Python file types. It also does
not automatically modify the PATH environment variable, so that you always have control over which
copy of Python is run.
Typing the full path name for a Python interpreter each time quickly gets tedious, so add the directories for
your default Python version to the PATH. Assuming that your Python installation is in C:\Python36\,
add this to your PATH:
C:\Python36\;C:\Python36\Scripts\
You can do this easily by running the following in powershell:
[Environment]::SetEnvironmentVariable("Path", "$env:Path;C:\Python36\;C:\Python36\Scripts\", "Us
This is also an option during the installation process.
The second (Scripts) directory receives command files when certain packages are installed, so it is a
very useful addition. You do not need to install or configure anything else to use Python. Having said
that, I would strongly recommend that you install the tools and libraries described in the next section
before you start building Python applications for real-world use. In particular, you should always install
Setuptools, as it makes it much easier for you to use other third-party Python libraries.
Working with Python 3
At this point, you may also have Python 2.7 installed.
$ python
This will launch the Python 2 interpreter.
$ python3
This will launch the Python 3 interpreter.
Setuptools + Pip
The two most crucial third-party Python packages are setuptools and pip, which let you download, install
and uninstall any compliant Python software product with a single command. It also enables you to add
this network installation capability to your own Python software with very little work.
All supported versions of Python 3 include pip, so just make sure it’s up to date:
python -m pip install -U pip
Pipenv & Virtual Environments
The next step is to install Pipenv, so you can install dependencies and manage virtual environments.
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A Virtual Environment is a tool to keep the dependencies required by different projects in separate places,
by creating virtual Python environments for them. It solves the “Project X depends on version 1.x but,
Project Y needs 4.x” dilemma, and keeps your global site-packages directory clean and manageable.
For example, you can work on a project which requires Django 1.10 while also maintaining a project
which requires Django 1.8.
So, onward! To the Pipenv & Virtual Environments docs!
This page is a remixed version of another guide, which is available under the same license.
Installing Python 3 on Linux
This document describes how to install Python 3.6 on Ubuntu Linux machines.
To see which version of Python 3 you have installed, open a command prompt and run
$ python3 --version
If you are using Ubuntu 16.10 or newer, then you can easily install Python 3.6 with the following commands:
$ sudo apt-get update
$ sudo apt-get install python3.6
If you’re using another version of Ubuntu (e.g. the latest LTS release), we recommend using the deadsnakes PPA to install Python 3.6:
$
$
$
$
sudo
sudo
sudo
sudo
apt-get install software-properties-common
add-apt-repository ppa:deadsnakes/ppa
apt-get update
apt-get install python3.6
If you are using other Linux distribution, chances are you already have Python 3 pre-installed as well. If
not, use your distribution’s package manager. For example on Fedora, you would use dnf :
$ sudo dnf install python3
Note that if the version of the python3 package is not recent enough for you, there may be ways
of installing more recent versions as well, depending on you distribution. For example installing the
python36 package on Fedora 25 to get Python 3.6. If you are a Fedora user, you might want to read
about multiple Python versions available in Fedora.
Working with Python 3
At this point, you may have system Python 2.7 available as well.
$ python
This will launch the Python 2 interpreter.
$ python3
This will launch the Python 3 interpreter.
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Setuptools & Pip
The two most crucial third-party Python packages are setuptools and pip.
Once installed, you can download, install and uninstall any compliant Python software product with a
single command. It also enables you to add this network installation capability to your own Python
software with very little work.
Python 2.7.9 and later (on the python2 series), and Python 3.4 and later include pip by default.
To see if pip is installed, open a command prompt and run
$ command -v pip
To install pip, follow the official pip installation guide - this will automatically install the latest version of
setuptools.
Note that on some Linux distributions including Ubuntu and Fedora the pip command is meant for
Python 2, while the pip3 command is meant for Python 3.
$ command -v pip3
However, when using virtual environments (described below), you don’t need to care about that.
Pipenv & Virtual Environments
The next step is to install Pipenv, so you can install dependencies and manage virtual environments.
A Virtual Environment is a tool to keep the dependencies required by different projects in separate places,
by creating virtual Python environments for them. It solves the “Project X depends on version 1.x but,
Project Y needs 4.x” dilemma, and keeps your global site-packages directory clean and manageable.
For example, you can work on a project which requires Django 1.10 while also maintaining a project
which requires Django 1.8.
So, onward! To the Pipenv & Virtual Environments docs!
This page is a remixed version of another guide, which is available under the same license.
Installing Python 2 on Mac OS X
Note: Check out our guide for installing Python 3 on OS X.
The latest version of Mac OS X, High Sierra, comes with Python 2.7 out of the box.
You do not need to install or configure anything else to use Python. Having said that, I would strongly
recommend that you install the tools and libraries described in the next section before you start building
Python applications for real-world use. In particular, you should always install Setuptools, as it makes it
much easier for you to install and manage other third-party Python libraries.
The version of Python that ships with OS X is great for learning, but it’s not good for development. The
version shipped with OS X may be out of date from the official current Python release, which is considered
the stable production version.
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Doing it Right
Let’s install a real version of Python.
Before installing Python, you’ll need to install a C compiler. The fastest way is to install the Xcode Command Line Tools by running xcode-select --install. You can also download the full version of
Xcode from the Mac App Store, or the minimal but unofficial OSX-GCC-Installer package.
Note: If you already have XCode installed, do not install OSX-GCC-Installer. In combination, the
software can cause issues that are difficult to diagnose.
Note: If you perform a fresh install of XCode, you will also need to add the commandline tools by
running xcode-select --install on the terminal.
While OS X comes with a large number of UNIX utilities, those familiar with Linux systems will notice
one key component missing: a decent package manager. Homebrew fills this void.
To install Homebrew, open Terminal or your favorite OSX terminal emulator and run
$ /usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/insta
The script will explain what changes it will make and prompt you before the installation begins. Once
you’ve installed Homebrew, insert the Homebrew directory at the top of your PATH environment variable.
You can do this by adding the following line at the bottom of your ~/.profile file
export PATH=/usr/local/bin:/usr/local/sbin:$PATH
Now, we can install Python 2.7:
$ brew install python
or Python 3:
$ brew install python3
This will take a minute or two.
Homebrew names the executable python2 so that you can still run the system Python via the executable
python.
$ python -V
$ python2 -V
$ python3 -V
# system Python interpreter
# Homebrew installed Python 2 interpreter
# Homebrew installed Python 3 interpreter (if installed)
Setuptools & Pip
Homebrew installs Setuptools and pip for you.
Setuptools enables you to download and install any compliant Python software over a network (usually the
Internet) with a single command (easy_install). It also enables you to add this network installation
capability to your own Python software with very little work.
pip is a tool for easily installing and managing Python packages, that is recommended over
easy_install. It is superior to easy_install in several ways, and is actively maintained.
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$ pip2 -V
$ pip3 -V
# pip pointing to the Homebrew installed Python 2 interpreter
# pip pointing to the Homebrew installed Python 3 interpreter (if installed)
Virtual Environments
A Virtual Environment (commonly referred to as a ‘virtualenv’) is a tool to keep the dependencies required
by different projects in separate places, by creating virtual Python environments for them. It solves the
“Project X depends on version 1.x but, Project Y needs 4.x” dilemma, and keeps your global site-packages
directory clean and manageable.
For example, you can work on a project which requires Django 1.10 while also maintaining a project
which requires Django 1.8.
To start using this and see more information: Virtual Environments docs.
This page is a remixed version of another guide, which is available under the same license.
Installing Python 2 on Windows
Note: Check out our guide for installing Python 3 on Windows.
First, download the latest version of Python 2.7 from the official website. If you want to be sure you
are installing a fully up-to-date version, click the Downloads > Windows link from the home page of the
Python.org web site .
The Windows version is provided as an MSI package. To install it manually, just double-click the file. The
MSI package format allows Windows administrators to automate installation with their standard tools.
By design, Python installs to a directory with the version number embedded, e.g. Python version 2.7 will
install at C:\Python27\, so that you can have multiple versions of Python on the same system without
conflicts. Of course, only one interpreter can be the default application for Python file types. It also does
not automatically modify the PATH environment variable, so that you always have control over which
copy of Python is run.
Typing the full path name for a Python interpreter each time quickly gets tedious, so add the directories for
your default Python version to the PATH. Assuming that your Python installation is in C:\Python27\,
add this to your PATH:
C:\Python27\;C:\Python27\Scripts\
You can do this easily by running the following in powershell:
[Environment]::SetEnvironmentVariable("Path", "$env:Path;C:\Python27\;C:\Python27\Scripts\", "Us
This is also an option during the installation process.
The second (Scripts) directory receives command files when certain packages are installed, so it is a
very useful addition. You do not need to install or configure anything else to use Python. Having said
that, I would strongly recommend that you install the tools and libraries described in the next section
before you start building Python applications for real-world use. In particular, you should always install
Setuptools, as it makes it much easier for you to use other third-party Python libraries.
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Setuptools + Pip
The two most crucial third-party Python packages are setuptools and pip.
Once installed, you can download, install and uninstall any compliant Python software product with a
single command. It also enables you to add this network installation capability to your own Python
software with very little work.
Python 2.7.9 and later (on the python2 series), and Python 3.4 and later include pip by default.
To see if pip is installed, open a command prompt and run
$ command -v pip
To install pip, follow the official pip installation guide - this will automatically install the latest version of
setuptools.
Virtual Environments
A Virtual Environment is a tool to keep the dependencies required by different projects in separate places,
by creating virtual Python environments for them. It solves the “Project X depends on version 1.x but,
Project Y needs 4.x” dilemma, and keeps your global site-packages directory clean and manageable.
For example, you can work on a project which requires Django 1.10 while also maintaining a project
which requires Django 1.8.
To start using this and see more information: Virtual Environments docs.
This page is a remixed version of another guide, which is available under the same license.
Installing Python 2 on Linux
Note: Check out our guide for installing Python 3 on Linux.
The latest versions of CentOS, Red Hat Enterprise Linux (RHEL) and Ubuntu come with Python 2.7 out
of the box.
To see which version of Python you have installed, open a command prompt and run
$ python2 --version
However, with the growing popularity of Python 3, some distributions, such as Fedora, don’t come with
Python 2 pre-installed. You can install the python2 package with your distribution package manager:
$ sudo dnf install python2
You do not need to install or configure anything else to use Python. Having said that, I would strongly
recommend that you install the tools and libraries described in the next section before you start building
Python applications for real-world use. In particular, you should always install Setuptools and pip, as it
makes it much easier for you to use other third-party Python libraries.
1.8. Installing Python 2 on Linux
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Setuptools & Pip
The two most crucial third-party Python packages are setuptools and pip.
Once installed, you can download, install and uninstall any compliant Python software product with a
single command. It also enables you to add this network installation capability to your own Python
software with very little work.
Python 2.7.9 and later (on the python2 series), and Python 3.4 and later include pip by default.
To see if pip is installed, open a command prompt and run
$ command -v pip
To install pip, follow the official pip installation guide - this will automatically install the latest version of
setuptools.
Virtual Environments
A Virtual Environment is a tool to keep the dependencies required by different projects in separate places,
by creating virtual Python environments for them. It solves the “Project X depends on version 1.x but,
Project Y needs 4.x” dilemma, and keeps your global site-packages directory clean and manageable.
For example, you can work on a project which requires Django 1.10 while also maintaining a project
which requires Django 1.8.
To start using this and see more information: Virtual Environments docs.
You can also use virtualenvwrapper to make it easier to manage your virtual environments.
This page is a remixed version of another guide, which is available under the same license.
• Using Virtualenvs with Pipenv:
1.9 Pipenv & Virtual Environments
This tutorial walks you through installing and using Python packages.
It will show you how to install and use the necessary tools and make strong recommendations on best
practices. Keep in mind that Python is used for a great many different purposes, and precisely how
you want to manage your dependencies may change based on how you decide to publish your software.
The guidance presented here is most directly applicable to the development and deployment of network
services (including web applications), but is also very well suited to managing development and testing
environments for any kind of project.
Note: This guide is written for Python 3, however, these instructions should work fine on Python 2.7—if
you are still using it, for some reason.
Make sure you’ve got Python & pip
Before you go any further, make sure you have Python and that it’s available from your command line.
You can check this by simply running:
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$ python --version
You should get some output like 3.6.2. If you do not have Python, please install the latest 3.x version
from python.org or refer to the Installing Python section of this guide.
Note: If you’re newcomer and you get an error like this:
>>> python
Traceback (most recent call last):
File "", line 1, in
NameError: name 'python' is not defined
It’s because this command is intended to be run in a shell (also called a terminal or console). See the
Python for Beginners getting started tutorial for an introduction to using your operating system’s shell
and interacting with Python.
Additionally, you’ll need to make sure you have pip available. You can check this by running:
$ pip --version
If you installed Python from source, with an installer from python.org, or via Homebrew you should
already have pip. If you’re on Linux and installed using your OS package manager, you may have to
install pip separately.
Installing Pipenv
Pipenv is a dependency manager for Python projects. If you’re familiar with Node.js’ npm or Ruby’s
bundler, it is similar in spirit to those tools. While pip can install Python packages, Pipenv is recommended
as it’s a higher-level tool that simplifies dependency management for common use cases.
Use pip to install Pipenv:
$ pip install --user pipenv
Note: This does a user installation to prevent breaking any system-wide packages. If pipenv isn’t
available in your shell after installation, you’ll need to add the user base‘s binary directory to your PATH.
On Linux and macOS you can find the user base binary directory by running python -m site
--user-base and adding bin to the end. For example, this will typically print ~/.local (with
~ expanded to the absolute path to your home directory) so you’ll need to add ~/.local/bin to your
PATH. You can set your PATH permanently by modifying ~/.profile.
On Windows you can find the user base binary directory by running py -m site
--user-site and replacing site-packages with Scripts. For example, this could return
C:\Users\Username\AppData\Roaming\Python36\site-packages so you would need
to set your PATH to include C:\Users\Username\AppData\Roaming\Python36\Scripts.
You can set your user PATH permanently in the Control Panel. You may need to log out for the PATH
changes to take effect.
Installing packages for your project
Pipenv manages dependencies on a per-project basis. To install packages, change into your project’s
directory (or just an empty directory for this tutorial) and run:
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$ cd myproject
$ pipenv install requests
Pipenv will install the excellent Requests library and create a Pipfile for you in your project’s directory.
The Pipfile is used to track which dependencies your project needs in case you need to re-install them,
such as when you share your project with others. You should get output similar to this (although the exact
paths shown will vary):
Creating a Pipfile for this project...
Creating a virtualenv for this project...
Using base prefix '/usr/local/Cellar/python3/3.6.2/Frameworks/Python.framework/Versions/3.6'
New python executable in ~/.local/share/virtualenvs/tmp-agwWamBd/bin/python3.6
Also creating executable in ~/.local/share/virtualenvs/tmp-agwWamBd/bin/python
Installing setuptools, pip, wheel...done.
Virtualenv location: ~/.local/share/virtualenvs/tmp-agwWamBd
Installing requests...
Collecting requests
Using cached requests-2.18.4-py2.py3-none-any.whl
Collecting idna<2.7,>=2.5 (from requests)
Using cached idna-2.6-py2.py3-none-any.whl
Collecting urllib3<1.23,>=1.21.1 (from requests)
Using cached urllib3-1.22-py2.py3-none-any.whl
Collecting chardet<3.1.0,>=3.0.2 (from requests)
Using cached chardet-3.0.4-py2.py3-none-any.whl
Collecting certifi>=2017.4.17 (from requests)
Using cached certifi-2017.7.27.1-py2.py3-none-any.whl
Installing collected packages: idna, urllib3, chardet, certifi, requests
Successfully installed certifi-2017.7.27.1 chardet-3.0.4 idna-2.6 requests-2.18.4 urllib3-1.22
Adding requests to Pipfile's [packages]...
P.S. You have excellent taste!
Using installed packages
Now that Requests is installed you can create a simple main.py file to use it:
import requests
response = requests.get('https://httpbin.org/ip')
print('Your IP is {0}'.format(response.json()['origin']))
Then you can run this script using pipenv run:
$ pipenv run python main.py
You should get output similar to this:
Your IP is 8.8.8.8
Using $ pipenv run ensures that your installed packages are available to your script. It’s also possible
to spawn a new shell that ensures all commands have access to your installed packages with $ pipenv
shell.
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Next steps
Congratulations, you now know how to install and use Python packages!
Lower level: virtualenv
virtualenv is a tool to create isolated Python environments. virtualenv creates a folder which contains all
the necessary executables to use the packages that a Python project would need.
It can be used standalone, in place of Pipenv.
Install virtualenv via pip:
$ pip install virtualenv
Test your installation
$ virtualenv --version
1. Create a virtual environment for a project:
$ cd my_project_folder
$ virtualenv my_project
virtualenv my_project will create a folder in the current directory which will contain the Python
executable files, and a copy of the pip library which you can use to install other packages. The name of
the virtual environment (in this case, it was my_project) can be anything; omitting the name will place
the files in the current directory instead.
This creates a copy of Python in whichever directory you ran the command in, placing it in a folder named
my_project.
You can also use the Python interpreter of your choice (like python2.7).
$ virtualenv -p /usr/bin/python2.7 my_project
or change the interpreter globally with an env variable in ~/.bashrc:
$ export VIRTUALENVWRAPPER_PYTHON=/usr/bin/python2.7
2. To begin using the virtual environment, it needs to be activated:
$ source my_project/bin/activate
The name of the current virtual environment will now appear on the left of the prompt (e.g.
(my_project)Your-Computer:your_project UserName$) to let you know that it’s active.
From now on, any package that you install using pip will be placed in the my_project folder, isolated
from the global Python installation.
Install packages as usual, for example:
$ pip install requests
3. If you are done working in the virtual environment for the moment, you can deactivate it:
$ deactivate
This puts you back to the system’s default Python interpreter with all its installed libraries.
To delete a virtual environment, just delete its folder. (In this case, it would be rm -rf my_project.)
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After a while, though, you might end up with a lot of virtual environments littered across your system,
and its possible you’ll forget their names or where they were placed.
Running virtualenv with the option --no-site-packages will not include the packages that are
installed globally. This can be useful for keeping the package list clean in case it needs to be accessed
later. [This is the default behavior for virtualenv 1.7 and later.]
In order to keep your environment consistent, it’s a good idea to “freeze” the current state of the environment packages. To do this, run
$ pip freeze > requirements.txt
This will create a requirements.txt file, which contains a simple list of all the packages in the
current environment, and their respective versions. You can see the list of installed packages without the
requirements format using “pip list”. Later it will be easier for a different developer (or you, if you need
to re-create the environment) to install the same packages using the same versions:
$ pip install -r requirements.txt
This can help ensure consistency across installations, across deployments, and across developers.
Lastly, remember to exclude the virtual environment folder from source control by adding it to the ignore
list (see Version Control Ignores).
virtualenvwrapper
virtualenvwrapper provides a set of commands which makes working with virtual environments much
more pleasant. It also places all your virtual environments in one place.
To install (make sure virtualenv is already installed):
$ pip install virtualenvwrapper
$ export WORKON_HOME=~/Envs
$ source /usr/local/bin/virtualenvwrapper.sh
(Full virtualenvwrapper install instructions.)
For Windows, you can use the virtualenvwrapper-win.
To install (make sure virtualenv is already installed):
$ pip install virtualenvwrapper-win
In Windows, the default path for WORKON_HOME is %USERPROFILE%Envs
Basic Usage
1. Create a virtual environment:
$ mkvirtualenv my_project
This creates the my_project folder inside ~/Envs.
2. Work on a virtual environment:
$ workon my_project
Alternatively, you can make a project, which creates the virtual environment, and also a project directory
inside $WORKON_HOME, which is cd -ed into when you workon myproject.
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$ mkproject myproject
virtualenvwrapper provides tab-completion on environment names. It really helps when you have a lot
of environments and have trouble remembering their names.
workon also deactivates whatever environment you are currently in, so you can quickly switch between
environments.
3. Deactivating is still the same:
$ deactivate
4. To delete:
$ rmvirtualenv venv
Other useful commands
lsvirtualenv List all of the environments.
cdvirtualenv Navigate into the directory of the currently activated virtual environment, so you can
browse its site-packages, for example.
cdsitepackages Like the above, but directly into site-packages directory.
lssitepackages Shows contents of site-packages directory.
Full list of virtualenvwrapper commands.
virtualenv-burrito
With virtualenv-burrito, you can have a working virtualenv + virtualenvwrapper environment in a single
command.
autoenv
When you cd into a directory containing a .env, autoenv automagically activates the environment.
Install it on Mac OS X using brew:
$ brew install autoenv
And on Linux:
$ git clone git://github.com/kennethreitz/autoenv.git ~/.autoenv
$ echo 'source ~/.autoenv/activate.sh' >> ~/.bashrc
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CHAPTER 2
Python Development Environments
This part of the guide focus on the Python development environment, and the best-practice tools that are available for
writing Python code.
Your Development Environment
Text Editors
Just about anything that can edit plain text will work for writing Python code, however, using a more powerful editor
may make your life a bit easier.
Vim
Vim is a text editor which uses keyboard shortcuts for editing instead of menus or icons. There are a couple of plugins
and settings for the Vim editor to aid Python development. If you only develop in Python, a good start is to set the
default settings for indentation and line-wrapping to values compliant with PEP 8. In your home directory, open a file
called .vimrc and add the following lines:
set
set
set
set
set
set
set
textwidth=79
shiftwidth=4
tabstop=4
expandtab
softtabstop=4
shiftround
autoindent
"
"
"
"
"
"
"
lines longer than 79 columns will be broken
operation >> indents 4 columns; << unindents 4 columns
a hard TAB displays as 4 columns
insert spaces when hitting TABs
insert/delete 4 spaces when hitting a TAB/BACKSPACE
round indent to multiple of 'shiftwidth'
align the new line indent with the previous line
With these settings, newlines are inserted after 79 characters and indentation is set to 4 spaces per tab. If you also use
Vim for other languages, there is a handy plugin called indent, which handles indentation settings for Python source
files.
There is also a handy syntax plugin called syntax featuring some improvements over the syntax file included in Vim
6.1.
These plugins supply you with a basic environment for developing in Python. To get the most out of Vim, you should
continually check your code for syntax errors and PEP8 compliance. Luckily pycodestyle and Pyflakes will do this for
you. If your Vim is compiled with +python you can also utilize some very handy plugins to do these checks from
within the editor.
For PEP8 checking and pyflakes, you can install vim-flake8. Now you can map the function Flake8 to any hotkey or
action you want in Vim. The plugin will display errors at the bottom of the screen, and provide an easy way to jump
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to the corresponding line. It’s very handy to call this function whenever you save a file. In order to do this, add the
following line to your .vimrc:
autocmd BufWritePost *.py call Flake8()
If you are already using syntastic, you can set it to run Pyflakes on write and show errors and warnings in the quickfix
window. An example configuration to do that which also shows status and warning messages in the statusbar would
be:
set
set
set
let
let
statusline+=%#warningmsg#
statusline+=%{SyntasticStatuslineFlag()}
statusline+=%*
g:syntastic_auto_loc_list=1
g:syntastic_loc_list_height=5
Python-mode
Python-mode is a complex solution for working with Python code in Vim. It has:
• Asynchronous Python code checking (pylint, pyflakes, pycodestyle, mccabe) in any combination
• Code refactoring and autocompletion with Rope
• Fast Python folding
• Virtualenv support
• Search through Python documentation and run Python code
• Auto pycodestyle error fixes
And more.
SuperTab
SuperTab is a small Vim plugin that makes code completion more convenient by using key or any other
customized keys.
Emacs
Emacs is another powerful text editor. It is fully programmable (lisp), but it can be some work to wire up correctly. A
good start if you’re already an Emacs user is Python Programming in Emacs at EmacsWiki.
1. Emacs itself comes with a Python mode.
TextMate
TextMate brings Apple’s approach to operating systems into the world of text editors. By bridging UNIX
underpinnings and GUI, TextMate cherry-picks the best of both worlds to the benefit of expert scripters
and novice users alike.
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Sublime Text
Sublime Text is a sophisticated text editor for code, markup and prose. You’ll love the slick user interface,
extraordinary features and amazing performance.
Sublime Text has excellent support for editing Python code and uses Python for its plugin API. It also has a diverse
variety of plugins, some of which allow for in-editor PEP8 checking and code “linting”.
Atom
Atom is a hackable text editor for the 21st century, built on atom-shell, and based on everything we love
about our favorite editors.
Atom is web native (HTML, CSS, JS), focusing on modular design and easy plugin development. It comes with
native package control and plethora of packages. Recommended for Python development is Linter combined with
linter-flake8.
IDEs
PyCharm / IntelliJ IDEA
PyCharm is developed by JetBrains, also known for IntelliJ IDEA. Both share the same code base and most of PyCharm’s features can be brought to IntelliJ with the free Python Plug-In. There are two versions of PyCharm: Professional Edition (Free 30-day trial) and Community Edition (Apache 2.0 License) with fewer features.
Python (on Visual Studio Code)
Python for Visual Studio is an extension for the Visual Studio Code IDE. This is a free, light weight, open source IDE,
with support for Mac, Windows, and Linux. Built using open source technologies such as Node.js and Python, with
compelling features such as Intellisense (autocompletion), local and remote debugging, linting, and the like.
MIT licensed.
Enthought Canopy
Enthought Canopy is a Python IDE which is focused towards Scientists and Engineers as it provides pre installed
libraries for data analysis.
Eclipse
The most popular Eclipse plugin for Python development is Aptana’s PyDev.
Komodo IDE
Komodo IDE is developed by ActiveState and is a commercial IDE for Windows, Mac, and Linux. KomodoEdit is the
open source alternative.
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Spyder
Spyder is an IDE specifically geared toward working with scientific Python libraries (namely Scipy). It includes
integration with pyflakes, pylint and rope.
Spyder is open-source (free), offers code completion, syntax highlighting, a class and function browser, and object
inspection.
WingIDE
WingIDE is a Python specific IDE. It runs on Linux, Windows and Mac (as an X11 application, which frustrates some
Mac users).
WingIDE offers code completion, syntax highlighting, source browser, graphical debugger and support for version
control systems.
NINJA-IDE
NINJA-IDE (from the recursive acronym: “Ninja-IDE Is Not Just Another IDE”) is a cross-platform IDE, specially
designed to build Python applications, and runs on Linux/X11, Mac OS X and Windows desktop operating systems.
Installers for these platforms can be downloaded from the website.
NINJA-IDE is open-source software (GPLv3 licence) and is developed in Python and Qt. The source files can be
downloaded from GitHub.
Eric (The Eric Python IDE)
Eric is a full featured Python IDE offering sourcecode autocompletion, syntax highlighting, support for version control
systems, python 3 support, integrated web browser, python shell, integrated debugger and a flexible plug-in system.
Written in python, it is based on the Qt gui toolkit, integrating the Scintilla editor control. Eric is an open-source
software project (GPLv3 licence) with more than ten years of active development.
Interpreter Tools
Virtual Environments
Virtual Environments provide a powerful way to isolate project package dependencies. This means that you can
use packages particular to a Python project without installing them system wide and thus avoiding potential version
conflicts.
To start using and see more information: Virtual Environments docs.
pyenv
pyenv is a tool to allow multiple versions of the Python interpreter to be installed at the same time. This solves the
problem of having different projects requiring different versions of Python. For example, it becomes very easy to
install Python 2.7 for compatibility in one project, whilst still using Python 3.4 as the default interpreter. pyenv isn’t
just limited to the CPython versions - it will also install PyPy, anaconda, miniconda, stackless, jython, and ironpython
interpreters.
pyenv works by filling a shims directory with fake versions of the Python interpreter (plus other tools like pip and
2to3). When the system looks for a program named python, it looks inside the shims directory first, and uses the
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fake version, which in turn passes the command on to pyenv. pyenv then works out which version of Python should
be run based on environment variables, .python-version files, and the global default.
pyenv isn’t a tool for managing virtual environments, but there is the plugin pyenv-virtualenv which automates the
creation of different environments, and also makes it possible to use the existing pyenv tools to switch to different
environments based on environment variables or .python-version files.
Other Tools
IDLE
IDLE is an integrated development environment that is part of Python standard library. It is completely written in
Python and uses the Tkinter GUI toolkit. Though IDLE is not suited for full-blown development using Python, it is
quite helpful to try out small Python snippets and experiment with different features in Python.
It provides the following features:
• Python Shell Window (interpreter)
• Multi window text editor that colorizes Python code
• Minimal debugging facility
IPython
IPython provides a rich toolkit to help you make the most out of using Python interactively. Its main components are:
• Powerful Python shells (terminal- and Qt-based).
• A web-based notebook with the same core features but support for rich media, text, code, mathematical expressions and inline plots.
• Support for interactive data visualization and use of GUI toolkits.
• Flexible, embeddable interpreters to load into your own projects.
• Tools for high level and interactive parallel computing.
$ pip install ipython
To download and install IPython with all it’s optional dependencies for the notebook, qtconsole, tests, and other
functionalities
$ pip install ipython[all]
BPython
bpython is an alternative interface to the Python interpreter for Unix-like operating systems. It has the following
features:
• In-line syntax highlighting.
• Readline-like autocomplete with suggestions displayed as you type.
• Expected parameter list for any Python function.
• “Rewind” function to pop the last line of code from memory and re-evaluate.
• Send entered code off to a pastebin.
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• Save entered code to a file.
• Auto-indentation.
• Python 3 support.
$ pip install bpython
ptpython
ptpython is a REPL build on top of the prompt_toolkit library. It is considered to be an alternative to BPython. Features
include:
• Syntax highlighting
• Autocompletion
• Multiline editing
• Emacs and VIM Mode
• Embedding REPL inside of your code
• Syntax Validation
• Tab pages
• Support for integrating with IPython‘s shell, by installing IPython pip install ipython and running
ptipython.
$ pip install ptpython
Further Configuration of Pip and Virtualenv
Requiring an active virtual environment for pip
By now it should be clear that using virtual environments is a great way to keep your development environment clean
and keeping different projects’ requirements separate.
When you start working on many different projects, it can be hard to remember to activate the related virtual environment when you come back to a specific project. As a result of this, it is very easy to install packages globally while
thinking that you are actually installing the package for the virtual environment of the project. Over time this can
result in a messy global package list.
In order to make sure that you install packages to your active virtual environment when you use pip install,
consider adding the following line to your ~/.bashrc file:
export PIP_REQUIRE_VIRTUALENV=true
After saving this change and sourcing the ~/.bashrc file with source ~/.bashrc, pip will no longer let you install packages if you are not in a virtual environment. If you try to use pip install outside of a virtual environment
pip will gently remind you that an activated virtual environment is needed to install packages.
$ pip install requests
Could not find an activated virtualenv (required).
You can also do this configuration by editing your pip.conf or pip.ini file. pip.conf is used by Unix and
Mac OS X operating systems and it can be found at:
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$HOME/.pip/pip.conf
Similarly, the pip.ini file is used by Windows operating systems and it can be found at:
%HOME%\pip\pip.ini
If you don’t have a pip.conf or pip.ini file at these locations, you can create a new file with the correct name
for your operating system.
If you already have a configuration file, just add the following line under the [global] settings to require an active
virtual environment:
require-virtualenv = true
If you did not have a configuration file, you will need to create a new one and add the following lines to this new file:
[global]
require-virtualenv = true
You will of course need to install some packages globally (usually ones that you use across different projects consistently) and this can be accomplished by adding the following to your ~/.bashrc file:
gpip() {
PIP_REQUIRE_VIRTUALENV="" pip "$@"
}
After saving the changes and sourcing your ~/.bashrc file you can now install packages globally by running gpip
install. You can change the name of the function to anything you like, just keep in mind that you will have to use
that name when trying to install packages globally with pip.
Caching packages for future use
Every developer has preferred libraries and when you are working on a lot of different projects, you are bound to have
some overlap between the libraries that you use. For example, you may be using the requests library in a lot of
different projects.
It is surely unnecessary to re-download the same packages/libraries each time you start working on a new project (and
in a new virtual environment as a result). Fortunately, you can configure pip in such a way that it tries to reuse already
installed packages.
On UNIX systems, you can add the following line to your .bashrc or .bash_profile file.
export PIP_DOWNLOAD_CACHE=$HOME/.pip/cache
You can set the path to anywhere you like (as long as you have write access). After adding this line, source your
.bashrc (or .bash_profile) file and you will be all set.
Another way of doing the same configuration is via the pip.conf or pip.ini files, depending on your system. If
you are on Windows, you can add the following line to your pip.ini file under [global] settings:
download-cache = %HOME%\pip\cache
Similarly, on UNIX systems you should simply add the following line to your pip.conf file under [global]
settings:
download-cache = $HOME/.pip/cache
Even though you can use any path you like to store your cache, it is recommended that you create a new folder in the
folder where your pip.conf or pip.ini file lives. If you don’t trust yourself with all of this path voodoo, just use
the values provided here and you will be fine.
2.2. Further Configuration of Pip and Virtualenv
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CHAPTER 3
Writing Great Python Code
This part of the guide focuses on the best-practices for writing Python code.
Structuring Your Project
By “structure” we mean the decisions you make concerning how your project best meets its objective. We need to
consider how to best leverage Python’s features to create clean, effective code. In practical terms, “structure” means
making clean code whose logic and dependencies are clear as well as how the files and folders are organized in the
filesystem.
Which functions should go into which modules? How does data flow through the project? What features and functions
can be grouped together and isolated? By answering questions like these you can begin to plan, in a broad sense, what
your finished product will look like.
In this section we take a closer look at Python’s module and import systems as they are the central elements to enforcing
structure in your project. We then discuss various perspectives on how to build code which can be extended and tested
reliably.
Structure of the Repository
It’s Important.
Just as Code Style, API Design, and Automation are essential for a healthy development cycle, Repository structure is
a crucial part of your project’s architecture.
When a potential user or contributor lands on your repository’s page, they see a few things:
• Project Name
• Project Description
• Bunch O’ Files
Only when they scroll below the fold will the user see your project’s README.
If your repo is a massive dump of files or a nested mess of directories, they might look elsewhere before even reading
your beautiful documentation.
Dress for the job you want, not the job you have.
Of course, first impressions aren’t everything. You and your colleagues will spend countless hours working with this
repository, eventually becoming intimately familiar with every nook and cranny. The layout of it is important.
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Sample Repository
tl;dr: This is what Kenneth Reitz recommends.
This repository is available on GitHub.
README.rst
LICENSE
setup.py
requirements.txt
sample/__init__.py
sample/core.py
sample/helpers.py
docs/conf.py
docs/index.rst
tests/test_basic.py
tests/test_advanced.py
Let’s get into some specifics.
The Actual Module
Location
Purpose
./sample/ or ./sample.py
The code of interest
Your module package is the core focus of the repository. It should not be tucked away:
./sample/
If your module consists of only a single file, you can place it directly in the root of your repository:
./sample.py
Your library does not belong in an ambiguous src or python subdirectory.
License
Location
Purpose
./LICENSE
Lawyering up.
This is arguably the most important part of your repository, aside from the source code itself. The full license text and
copyright claims should exist in this file.
If you aren’t sure which license you should use for your project, check out choosealicense.com.
Of course, you are also free to publish code without a license, but this would prevent many people from potentially
using your code.
Setup.py
Location
Purpose
./setup.py
Package and distribution management.
If your module package is at the root of your repository, this should obviously be at the root as well.
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Requirements File
Location
Purpose
./requirements.txt
Development dependencies.
A pip requirements file should be placed at the root of the repository. It should specify the dependencies required to
contribute to the project: testing, building, and generating documentation.
If your project has no development dependencies, or you prefer development environment setup via setup.py, this
file may be unnecessary.
Documentation
Location
Purpose
./docs/
Package reference documentation.
There is little reason for this to exist elsewhere.
Test Suite
For advice on writing your tests, see :doc:‘writing/tests‘.
Location
Purpose
./test_sample.py or ./tests
Package integration and unit tests.
Starting out, a small test suite will often exist in a single file:
./test_sample.py
Once a test suite grows, you can move your tests to a directory, like so:
tests/test_basic.py
tests/test_advanced.py
Obviously, these test modules must import your packaged module to test it. You can do this a few ways:
• Expect the package to be installed in site-packages.
• Use a simple (but explicit) path modification to resolve the package properly.
I highly recommend the latter. Requiring a developer to run setup.py develop to test an actively changing
codebase also requires them to have an isolated environment setup for each instance of the codebase.
To give the individual tests import context, create a tests/context.py file:
import os
import sys
sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
import sample
Then, within the individual test modules, import the module like so:
from .context import sample
This will always work as expected, regardless of installation method.
Some people will assert that you should distribute your tests within your module itself – I disagree. It often increases
complexity for your users; many test suites often require additional dependencies and runtime contexts.
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Makefile
Location
Purpose
./Makefile
Generic management tasks.
If you look at most of my projects or any Pocoo project, you’ll notice a Makefile laying around. Why? These projects
aren’t written in C... In short, make is a incredibly useful tool for defining generic tasks for your project.
Sample Makefile:
init:
pip install -r requirements.txt
test:
py.test tests
.PHONY: init test
Other generic management scripts (e.g. manage.py or fabfile.py) belong at the root of the repository as well.
Regarding Django Applications
I’ve noticed a new trend in Django applications since the release of Django 1.4. Many developers are structuring their
repositories poorly due to the new bundled application templates.
How? Well, they go to their bare and fresh repository and run the following, as they always have:
$ django-admin.py startproject samplesite
The resulting repository structure looks like this:
README.rst
samplesite/manage.py
samplesite/samplesite/settings.py
samplesite/samplesite/wsgi.py
samplesite/samplesite/sampleapp/models.py
Don’t do this.
Repetitive paths are confusing for both your tools and your developers. Unnecessary nesting doesn’t help anybody
(unless they’re nostalgic for monolithic SVN repos).
Let’s do it properly:
$ django-admin.py startproject samplesite .
Note the “.”.
The resulting structure:
README.rst
manage.py
samplesite/settings.py
samplesite/wsgi.py
samplesite/sampleapp/models.py
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Structure of Code is Key
Thanks to the way imports and modules are handled in Python, it is relatively easy to structure a Python project. Easy,
here, means that you do not have many constraints and that the module importing model is easy to grasp. Therefore,
you are left with the pure architectural task of crafting the different parts of your project and their interactions.
Easy structuring of a project means it is also easy to do it poorly. Some signs of a poorly structured project include:
• Multiple and messy circular dependencies: if your classes Table and Chair in furn.py need to import Carpenter from workers.py to answer a question such as table.isdoneby(), and if conversely the class
Carpenter needs to import Table and Chair, to answer the question carpenter.whatdo(), then you have a
circular dependency. In this case you will have to resort to fragile hacks such as using import statements inside
methods or functions.
• Hidden coupling: each and every change in Table’s implementation breaks 20 tests in unrelated test cases
because it breaks Carpenter’s code, which requires very careful surgery to adapt the change. This means you
have too many assumptions about Table in Carpenter’s code or the reverse.
• Heavy usage of global state or context: instead of explicitly passing (height, width, type, wood)
to each other, Table and Carpenter rely on global variables that can be modified and are modified on the fly
by different agents. You need to scrutinize all access to these global variables to understand why a rectangular
table became a square, and discover that remote template code is also modifying this context, messing with table
dimensions.
• Spaghetti code: multiple pages of nested if clauses and for loops with a lot of copy-pasted procedural code
and no proper segmentation are known as spaghetti code. Python’s meaningful indentation (one of its most
controversial features) make it very hard to maintain this kind of code. So the good news is that you might not
see too much of it.
• Ravioli code is more likely in Python: it consists of hundreds of similar little pieces of logic, often classes or
objects, without proper structure. If you never can remember if you have to use FurnitureTable, AssetTable or
Table, or even TableNew for your task at hand, you might be swimming in ravioli code.
Modules
Python modules are one of the main abstraction layers available and probably the most natural one. Abstraction layers
allow separating code into parts holding related data and functionality.
For example, a layer of a project can handle interfacing with user actions, while another would handle low-level
manipulation of data. The most natural way to separate these two layers is to regroup all interfacing functionality in
one file, and all low-level operations in another file. In this case, the interface file needs to import the low-level file.
This is done with the import and from ... import statements.
As soon as you use import statements you use modules. These can be either built-in modules such as os and sys,
third-party modules you have installed in your environment, or your project’s internal modules.
To keep in line with the style guide, keep module names short, lowercase, and be sure to avoid using special symbols
like the dot (.) or question mark (?). So a file name like my.spam.py is one you should avoid! Naming this way will
interfere with the way Python looks for modules.
In the case of my.spam.py Python expects to find a spam.py file in a folder named my which is not the case. There is
an example of how the dot notation should be used in the Python docs.
If you’d like you could name your module my_spam.py, but even our friend the underscore should not be seen often
in module names. However, using other characters (spaces or hyphens) in module names will prevent importing (- is
the subtract operator), so try to keep module names short so there is no need to separate words. And, most of all, don’t
namespace with underscores, use submodules instead.
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# OK
import library.plugin.foo
# not OK
import library.foo_plugin
Aside from some naming restrictions, nothing special is required for a Python file to be a module, but you need to
understand the import mechanism in order to use this concept properly and avoid some issues.
Concretely, the import modu statement will look for the proper file, which is modu.py in the same directory as
the caller if it exists. If it is not found, the Python interpreter will search for modu.py in the “path” recursively and
raise an ImportError exception if it is not found.
Once modu.py is found, the Python interpreter will execute the module in an isolated scope. Any top-level statement
in modu.py will be executed, including other imports if any. Function and class definitions are stored in the module’s
dictionary.
Then, the module’s variables, functions, and classes will be available to the caller through the module’s namespace, a
central concept in programming that is particularly helpful and powerful in Python.
In many languages, an include file directive is used by the preprocessor to take all code found in the file and
‘copy’ it into the caller’s code. It is different in Python: the included code is isolated in a module namespace, which
means that you generally don’t have to worry that the included code could have unwanted effects, e.g. override an
existing function with the same name.
It is possible to simulate the more standard behavior by using a special syntax of the import statement: from modu
import *. This is generally considered bad practice. Using import * makes code harder to read and makes
dependencies less compartmentalized.
Using from modu import func is a way to pinpoint the function you want to import and put it in the global
namespace. While much less harmful than import * because it shows explicitly what is imported in the global
namespace, its only advantage over a simpler import modu is that it will save a little typing.
Very bad
[...]
from modu import *
[...]
x = sqrt(4) # Is sqrt part of modu? A builtin? Defined above?
Better
from modu import sqrt
[...]
x = sqrt(4) # sqrt may be part of modu, if not redefined in between
Best
import modu
[...]
x = modu.sqrt(4)
# sqrt is visibly part of modu's namespace
As mentioned in the Code Style section, readability is one of the main features of Python. Readability means to avoid
useless boilerplate text and clutter, therefore some efforts are spent trying to achieve a certain level of brevity. But
terseness and obscurity are the limits where brevity should stop. Being able to tell immediately where a class or
function comes from, as in the modu.func idiom, greatly improves code readability and understandability in all but
the simplest single file projects.
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Packages
Python provides a very straightforward packaging system, which is simply an extension of the module mechanism to
a directory.
Any directory with an __init__.py file is considered a Python package. The different modules in the package are
imported in a similar manner as plain modules, but with a special behavior for the __init__.py file, which is used
to gather all package-wide definitions.
A file modu.py in the directory pack/ is imported with the statement import pack.modu. This statement will
look for an __init__.py file in pack, execute all of its top-level statements. Then it will look for a file named
pack/modu.py and execute all of its top-level statements. After these operations, any variable, function, or class
defined in modu.py is available in the pack.modu namespace.
A commonly seen issue is to add too much code to __init__.py files. When the project complexity grows, there
may be sub-packages and sub-sub-packages in a deep directory structure. In this case, importing a single item from a
sub-sub-package will require executing all __init__.py files met while traversing the tree.
Leaving an __init__.py file empty is considered normal and even a good practice, if the package’s modules and
sub-packages do not need to share any code.
Lastly, a convenient syntax is available for importing deeply nested packages: import very.deep.module as
mod. This allows you to use mod in place of the verbose repetition of very.deep.module.
Object-oriented programming
Python is sometimes described as an object-oriented programming language. This can be somewhat misleading and
needs to be clarified.
In Python, everything is an object, and can be handled as such. This is what is meant when we say, for example,
that functions are first-class objects. Functions, classes, strings, and even types are objects in Python: like any object,
they have a type, they can be passed as function arguments, and they may have methods and properties. In this
understanding, Python is an object-oriented language.
However, unlike Java, Python does not impose object-oriented programming as the main programming paradigm. It
is perfectly viable for a Python project to not be object-oriented, i.e. to use no or very few class definitions, class
inheritance, or any other mechanisms that are specific to object-oriented programming.
Moreover, as seen in the modules section, the way Python handles modules and namespaces gives the developer a
natural way to ensure the encapsulation and separation of abstraction layers, both being the most common reasons to
use object-orientation. Therefore, Python programmers have more latitude to not use object-orientation, when it is not
required by the business model.
There are some reasons to avoid unnecessary object-orientation. Defining custom classes is useful when we want to
glue together some state and some functionality. The problem, as pointed out by the discussions about functional
programming, comes from the “state” part of the equation.
In some architectures, typically web applications, multiple instances of Python processes are spawned to respond
to external requests that can happen at the same time. In this case, holding some state into instantiated objects,
which means keeping some static information about the world, is prone to concurrency problems or race-conditions.
Sometimes, between the initialization of the state of an object (usually done with the __init__() method) and the
actual use of the object state through one of its methods, the world may have changed, and the retained state may be
outdated. For example, a request may load an item in memory and mark it as read by a user. If another request requires
the deletion of this item at the same time, it may happen that the deletion actually occurs after the first process loaded
the item, and then we have to mark as read a deleted object.
This and other issues led to the idea that using stateless functions is a better programming paradigm.
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Another way to say the same thing is to suggest using functions and procedures with as few implicit contexts and sideeffects as possible. A function’s implicit context is made up of any of the global variables or items in the persistence
layer that are accessed from within the function. Side-effects are the changes that a function makes to its implicit
context. If a function saves or deletes data in a global variable or in the persistence layer, it is said to have a side-effect.
Carefully isolating functions with context and side-effects from functions with logic (called pure functions) allow the
following benefits:
• Pure functions are deterministic: given a fixed input, the output will always be the same.
• Pure functions are much easier to change or replace if they need to be refactored or optimized.
• Pure functions are easier to test with unit-tests: There is less need for complex context setup and data cleaning
afterwards.
• Pure functions are easier to manipulate, decorate, and pass around.
In summary, pure functions are more efficient building blocks than classes and objects for some architectures because
they have no context or side-effects.
Obviously, object-orientation is useful and even necessary in many cases, for example when developing graphical
desktop applications or games, where the things that are manipulated (windows, buttons, avatars, vehicles) have a
relatively long life of their own in the computer’s memory.
Decorators
The Python language provides a simple yet powerful syntax called ‘decorators’. A decorator is a function or a class that
wraps (or decorates) a function or a method. The ‘decorated’ function or method will replace the original ‘undecorated’
function or method. Because functions are first-class objects in Python, this can be done ‘manually’, but using the
@decorator syntax is clearer and thus preferred.
def foo():
# do something
def decorator(func):
# manipulate func
return func
foo = decorator(foo)
# Manually decorate
@decorator
def bar():
# Do something
# bar() is decorated
This mechanism is useful for separating concerns and avoiding external un-related logic ‘polluting’ the core logic
of the function or method. A good example of a piece of functionality that is better handled with decoration is
memoization or caching: you want to store the results of an expensive function in a table and use them directly instead
of recomputing them when they have already been computed. This is clearly not part of the function logic.
Context Managers
A context manager is a Python object that provides extra contextual information to an action. This extra information
takes the form of running a callable upon initiating the context using the with statement, as well as running a callable
upon completing all the code inside the with block. The most well known example of using a context manager is
shown here, opening on a file:
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with open('file.txt') as f:
contents = f.read()
Anyone familiar with this pattern knows that invoking open in this fashion ensures that f‘s close method will be
called at some point. This reduces a developer’s cognitive load and makes the code easier to read.
There are two easy ways to implement this functionality yourself: using a class or using a generator. Let’s implement
the above functionality ourselves, starting with the class approach:
class CustomOpen(object):
def __init__(self, filename):
self.file = open(filename)
def __enter__(self):
return self.file
def __exit__(self, ctx_type, ctx_value, ctx_traceback):
self.file.close()
with CustomOpen('file') as f:
contents = f.read()
This is just a regular Python object with two extra methods that are used by the with statement. CustomOpen is first
instantiated and then its __enter__ method is called and whatever __enter__ returns is assigned to f in the as
f part of the statement. When the contents of the with block is finished executing, the __exit__ method is then
called.
And now the generator approach using Python’s own contextlib:
from contextlib import contextmanager
@contextmanager
def custom_open(filename):
f = open(filename)
try:
yield f
finally:
f.close()
with custom_open('file') as f:
contents = f.read()
This works in exactly the same way as the class example above, albeit it’s more terse. The custom_open function
executes until it reaches the yield statement. It then gives control back to the with statement, which assigns
whatever was yield‘ed to f in the as f portion. The finally clause ensures that close() is called whether or
not there was an exception inside the with.
Since the two approaches appear the same, we should follow the Zen of Python to decide when to use which. The
class approach might be better if there’s a considerable amount of logic to encapsulate. The function approach might
be better for situations where we’re dealing with a simple action.
Dynamic typing
Python is dynamically typed, which means that variables do not have a fixed type. In fact, in Python, variables are
very different from what they are in many other languages, specifically statically-typed languages. Variables are not a
segment of the computer’s memory where some value is written, they are ‘tags’ or ‘names’ pointing to objects. It is
therefore possible for the variable ‘a’ to be set to the value 1, then to the value ‘a string’, then to a function.
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The dynamic typing of Python is often considered to be a weakness, and indeed it can lead to complexities and hardto-debug code. Something named ‘a’ can be set to many different things, and the developer or the maintainer needs to
track this name in the code to make sure it has not been set to a completely unrelated object.
Some guidelines help to avoid this issue:
• Avoid using the same variable name for different things.
Bad
a = 1
a = 'a string'
def a():
pass # Do something
Good
count = 1
msg = 'a string'
def func():
pass # Do something
Using short functions or methods helps reduce the risk of using the same name for two unrelated things.
It is better to use different names even for things that are related, when they have a different type:
Bad
items = 'a b c d' # This is a string...
items = items.split(' ') # ...becoming a list
items = set(items) # ...and then a set
There is no efficiency gain when reusing names: the assignments will have to create new objects anyway. However,
when the complexity grows and each assignment is separated by other lines of code, including ‘if’ branches and loops,
it becomes harder to ascertain what a given variable’s type is.
Some coding practices, like functional programming, recommend never reassigning a variable. In Java this is done
with the final keyword. Python does not have a final keyword and it would be against its philosophy anyway. However,
it may be a good discipline to avoid assigning to a variable more than once, and it helps in grasping the concept of
mutable and immutable types.
Mutable and immutable types
Python has two kinds of built-in or user-defined types.
Mutable types are those that allow in-place modification of the content. Typical mutables are lists and dictionaries:
All lists have mutating methods, like list.append() or list.pop(), and can be modified in place. The same
goes for dictionaries.
Immutable types provide no method for changing their content. For instance, the variable x set to the integer 6 has no
“increment” method. If you want to compute x + 1, you have to create another integer and give it a name.
my_list = [1, 2, 3]
my_list[0] = 4
print my_list # [4, 2, 3] <- The same list has changed
x = 6
x = x + 1
# The new x is another object
One consequence of this difference in behavior is that mutable types are not “stable”, and therefore cannot be used as
dictionary keys.
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Using properly mutable types for things that are mutable in nature and immutable types for things that are fixed in
nature helps to clarify the intent of the code.
For example, the immutable equivalent of a list is the tuple, created with (1, 2). This tuple is a pair that cannot be
changed in-place, and can be used as a key for a dictionary.
One peculiarity of Python that can surprise beginners is that strings are immutable. This means that when constructing
a string from its parts, it is much more efficient to accumulate the parts in a list, which is mutable, and then glue (‘join’)
the parts together when the full string is needed. One thing to notice, however, is that list comprehensions are better
and faster than constructing a list in a loop with calls to append().
One other option is using the map function, which can ‘map’ a function (‘str’) to an iterable (‘range(20)’). This results
in a map object, which you can then (‘join’) together just like the other examples. The map function can be even faster
than a list comprehension in some cases.
Bad
# create a concatenated string from 0 to 19 (e.g. "012..1819")
nums = ""
for n in range(20):
nums += str(n)
# slow and inefficient
print nums
Good
# create a concatenated string from 0 to 19 (e.g. "012..1819")
nums = []
for n in range(20):
nums.append(str(n))
print "".join(nums) # much more efficient
Better
# create a concatenated string from 0 to 19 (e.g. "012..1819")
nums = [str(n) for n in range(20)]
print "".join(nums)
Best
# create a concatenated string from 0 to 19 (e.g. "012..1819")
nums = map(str, range(20))
print "".join(nums)
One final thing to mention about strings is that using join() is not always best. In the instances where you are
creating a new string from a pre-determined number of strings, using the addition operator is actually faster, but in
cases like above or in cases where you are adding to an existing string, using join() should be your preferred
method.
foo = 'foo'
bar = 'bar'
foobar = foo + bar # This is good
foo += 'ooo' # This is bad, instead you should do:
foo = ''.join([foo, 'ooo'])
Note: You can also use the % formatting operator to concatenate a pre-determined number of strings besides
str.join() and +. However, PEP 3101, discourages the usage of the % operator in favor of the str.format()
method.
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foo = 'foo'
bar = 'bar'
foobar = '%s%s' % (foo, bar) # It is OK
foobar = '{0}{1}'.format(foo, bar) # It is better
foobar = '{foo}{bar}'.format(foo=foo, bar=bar) # It is best
Vendorizing Dependencies
Runners
Further Reading
• http://docs.python.org/2/library/
• http://www.diveintopython.net/toc/index.html
Code Style
If you ask Python programmers what they like most about Python, they will often cite its high readability. Indeed, a
high level of readability is at the heart of the design of the Python language, following the recognized fact that code is
read much more often than it is written.
One reason for the high readability of Python code is its relatively complete set of Code Style guidelines and
“Pythonic” idioms.
When a veteran Python developer (a Pythonista) calls portions of code not “Pythonic”, they usually mean that these
lines of code do not follow the common guidelines and fail to express its intent in what is considered the best (hear:
most readable) way.
On some border cases, no best way has been agreed upon on how to express an intent in Python code, but these cases
are rare.
General concepts
Explicit code
While any kind of black magic is possible with Python, the most explicit and straightforward manner is preferred.
Bad
def make_complex(*args):
x, y = args
return dict(**locals())
Good
def make_complex(x, y):
return {'x': x, 'y': y}
In the good code above, x and y are explicitly received from the caller, and an explicit dictionary is returned. The
developer using this function knows exactly what to do by reading the first and last lines, which is not the case with
the bad example.
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One statement per line
While some compound statements such as list comprehensions are allowed and appreciated for their brevity and their
expressiveness, it is bad practice to have two disjointed statements on the same line of code.
Bad
print 'one'; print 'two'
if x == 1: print 'one'
if and :
# do something
Good
print 'one'
print 'two'
if x == 1:
print 'one'
cond1 =
cond2 =
if cond1 and cond2:
# do something
Function arguments
Arguments can be passed to functions in four different ways.
1. Positional arguments are mandatory and have no default values. They are the simplest form of arguments and
they can be used for the few function arguments that are fully part of the function’s meaning and their order is
natural. For instance, in send(message, recipient) or point(x, y) the user of the function has no
difficulty remembering that those two functions require two arguments, and in which order.
In those two cases, it is possible to use argument names when calling the functions and, doing so, it is possible to
switch the order of arguments, calling for instance send(recipient=’World’, message=’Hello’) and
point(y=2, x=1) but this reduces readability and is unnecessarily verbose, compared to the more straightforward
calls to send(’Hello’, ’World’) and point(1, 2).
2. Keyword arguments are not mandatory and have default values. They are often used for optional parameters
sent to the function. When a function has more than two or three positional parameters, its signature is more
difficult to remember and using keyword arguments with default values is helpful. For instance, a more complete
send function could be defined as send(message, to, cc=None, bcc=None). Here cc and bcc are
optional, and evaluate to None when they are not passed another value.
Calling a function with keyword arguments can be done in multiple ways in Python, for example it is possible to
follow the order of arguments in the definition without explicitly naming the arguments, like in send(’Hello’,
’World’, ’Cthulhu’, ’God’), sending a blind carbon copy to God. It would also be possible to name arguments in another order, like in send(’Hello again’, ’World’, bcc=’God’, cc=’Cthulhu’). Those
two possibilities are better avoided without any strong reason to not follow the syntax that is the closest to the function
definition: send(’Hello’, ’World’, cc=’Cthulhu’, bcc=’God’).
As a side note, following YAGNI principle, it is often harder to remove an optional argument (and its logic inside the
function) that was added “just in case” and is seemingly never used, than to add a new optional argument and its logic
when needed.
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3. The arbitrary argument list is the third way to pass arguments to a function. If the function intention is better
expressed by a signature with an extensible number of positional arguments, it can be defined with the *args
constructs. In the function body, args will be a tuple of all the remaining positional arguments. For example,
send(message, *args) can be called with each recipient as an argument:send(’Hello’, ’God’,
’Mom’, ’Cthulhu’), and in the function body args will be equal to (’God’, ’Mom’, ’Cthulhu’).
However, this construct has some drawbacks and should be used with caution. If a function receives a list of arguments
of the same nature, it is often more clear to define it as a function of one argument, that argument being a list or any sequence. Here, if send has multiple recipients, it is better to define it explicitly: send(message, recipients)
and call it with send(’Hello’, [’God’, ’Mom’, ’Cthulhu’]). This way, the user of the function can
manipulate the recipient list as a list beforehand, and it opens the possibility to pass any sequence, including iterators,
that cannot be unpacked as other sequences.
4. The arbitrary keyword argument dictionary is the last way to pass arguments to functions. If the function
requires an undetermined series of named arguments, it is possible to use the **kwargs construct. In the
function body, kwargs will be a dictionary of all the passed named arguments that have not been caught by
other keyword arguments in the function signature.
The same caution as in the case of arbitrary argument list is necessary, for similar reasons: these powerful techniques
are to be used when there is a proven necessity to use them, and they should not be used if the simpler and clearer
construct is sufficient to express the function’s intention.
It is up to the programmer writing the function to determine which arguments are positional arguments and which are
optional keyword arguments, and to decide whether to use the advanced techniques of arbitrary argument passing. If
the advice above is followed wisely, it is possible and enjoyable to write Python functions that are:
• easy to read (the name and arguments need no explanations)
• easy to change (adding a new keyword argument does not break other parts of the code)
Avoid the magical wand
A powerful tool for hackers, Python comes with a very rich set of hooks and tools allowing you to do almost any kind
of tricky tricks. For instance, it is possible to do each of the following:
• change how objects are created and instantiated
• change how the Python interpreter imports modules
• it is even possible (and recommended if needed) to embed C routines in Python.
However, all these options have many drawbacks and it is always better to use the most straightforward way to achieve
your goal. The main drawback is that readability suffers greatly when using these constructs. Many code analysis
tools, such as pylint or pyflakes, will be unable to parse this “magic” code.
We consider that a Python developer should know about these nearly infinite possibilities, because it instills confidence
that no impassable problem will be on the way. However, knowing how and particularly when not to use them is very
important.
Like a kung fu master, a Pythonista knows how to kill with a single finger, and never to actually do it.
We are all responsible users
As seen above, Python allows many tricks, and some of them are potentially dangerous. A good example is that any
client code can override an object’s properties and methods: there is no “private” keyword in Python. This philosophy,
very different from highly defensive languages like Java, which give a lot of mechanisms to prevent any misuse, is
expressed by the saying: “We are all responsible users”.
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This doesn’t mean that, for example, no properties are considered private, and that no proper encapsulation is possible
in Python. Rather, instead of relying on concrete walls erected by the developers between their code and other’s,
the Python community prefers to rely on a set of conventions indicating that these elements should not be accessed
directly.
The main convention for private properties and implementation details is to prefix all “internals” with an underscore.
If the client code breaks this rule and accesses these marked elements, any misbehavior or problems encountered if
the code is modified is the responsibility of the client code.
Using this convention generously is encouraged: any method or property that is not intended to be used by client code
should be prefixed with an underscore. This will guarantee a better separation of duties and easier modification of
existing code; it will always be possible to publicize a private property, but making a public property private might be
a much harder operation.
Returning values
When a function grows in complexity it is not uncommon to use multiple return statements inside the function’s
body. However, in order to keep a clear intent and a sustainable readability level, it is preferable to avoid returning
meaningful values from many output points in the body.
There are two main cases for returning values in a function: the result of the function return when it has been processed
normally, and the error cases that indicate a wrong input parameter or any other reason for the function to not be able
to complete its computation or task.
If you do not wish to raise exceptions for the second case, then returning a value, such as None or False, indicating
that the function could not perform correctly might be needed. In this case, it is better to return as early as the incorrect
context has been detected. It will help to flatten the structure of the function: all the code after the return-because-oferror statement can assume the condition is met to further compute the function’s main result. Having multiple such
return statements is often necessary.
However, when a function has multiple main exit points for its normal course, it becomes difficult to debug the returned
result, so it may be preferable to keep a single exit point. This will also help factoring out some code paths, and the
multiple exit points are a probable indication that such a refactoring is needed.
def complex_function(a, b, c):
if not a:
return None # Raising an exception might be better
if not b:
return None # Raising an exception might be better
# Some complex code trying to compute x from a, b and c
# Resist temptation to return x if succeeded
if not x:
# Some Plan-B computation of x
return x # One single exit point for the returned value x will help
# when maintaining the code.
Idioms
A programming idiom, put simply, is a way to write code. The notion of programming idioms is discussed amply at
c2 and at Stack Overflow.
Idiomatic Python code is often referred to as being Pythonic.
Although there usually is one — and preferably only one — obvious way to do it; the way to write idiomatic Python
code can be non-obvious to Python beginners. So, good idioms must be consciously acquired.
Some common Python idioms follow:
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Unpacking
If you know the length of a list or tuple, you can assign names to its elements with unpacking. For example, since
enumerate() will provide a tuple of two elements for each item in list:
for index, item in enumerate(some_list):
# do something with index and item
You can use this to swap variables as well:
a, b = b, a
Nested unpacking works too:
a, (b, c) = 1, (2, 3)
In Python 3, a new method of extended unpacking was introduced by PEP 3132:
a, *rest = [1, 2, 3]
# a = 1, rest = [2, 3]
a, *middle, c = [1, 2, 3, 4]
# a = 1, middle = [2, 3], c = 4
Create an ignored variable
If you need to assign something (for instance, in Unpacking) but will not need that variable, use __:
filename = 'foobar.txt'
basename, __, ext = filename.rpartition('.')
Note: Many Python style guides recommend the use of a single underscore “_” for throwaway variables rather
than the double underscore “__” recommended here. The issue is that “_” is commonly used as an alias for the
gettext() function, and is also used at the interactive prompt to hold the value of the last operation. Using a
double underscore instead is just as clear and almost as convenient, and eliminates the risk of accidentally interfering
with either of these other use cases.
Create a length-N list of the same thing
Use the Python list * operator:
four_nones = [None] * 4
Create a length-N list of lists
Because lists are mutable, the * operator (as above) will create a list of N references to the same list, which is not
likely what you want. Instead, use a list comprehension:
four_lists = [[] for __ in xrange(4)]
Note: Use range() instead of xrange() in Python 3
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Create a string from a list
A common idiom for creating strings is to use str.join() on an empty string.
letters = ['s', 'p', 'a', 'm']
word = ''.join(letters)
This will set the value of the variable word to ‘spam’. This idiom can be applied to lists and tuples.
Searching for an item in a collection
Sometimes we need to search through a collection of things. Let’s look at two options: lists and sets.
Take the following code for example:
s = set(['s', 'p', 'a', 'm'])
l = ['s', 'p', 'a', 'm']
def lookup_set(s):
return 's' in s
def lookup_list(l):
return 's' in l
Even though both functions look identical, because lookup_set is utilizing the fact that sets in Python are hashtables,
the lookup performance between the two is very different. To determine whether an item is in a list, Python will have
to go through each item until it finds a matching item. This is time consuming, especially for long lists. In a set, on the
other hand, the hash of the item will tell Python where in the set to look for a matching item. As a result, the search
can be done quickly, even if the set is large. Searching in dictionaries works the same way. For more information see
this StackOverflow page. For detailed information on the amount of time various common operations take on each of
these data structures, see this page.
Because of these differences in performance, it is often a good idea to use sets or dictionaries instead of lists in cases
where:
• The collection will contain a large number of items
• You will be repeatedly searching for items in the collection
• You do not have duplicate items.
For small collections, or collections which you will not frequently be searching through, the additional time and
memory required to set up the hashtable will often be greater than the time saved by the improved search speed.
Zen of Python
Also known as PEP 20, the guiding principles for Python’s design.
>>> 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.
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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!
For some examples of good Python style, see these slides from a Python user group.
PEP 8
PEP 8 is the de-facto code style guide for Python. A high quality, easy-to-read version of PEP 8 is also available at
pep8.org.
This is highly recommended reading. The entire Python community does their best to adhere to the guidelines laid out
within this document. Some project may sway from it from time to time, while others may amend its recommendations.
That being said, conforming your Python code to PEP 8 is generally a good idea and helps make code more consistent
when working on projects with other developers. There is a command-line program, pycodestyle (previously known
as pep8), that can check your code for conformance. Install it by running the following command in your terminal:
$ pip install pycodestyle
Then run it on a file or series of files to get a report of any violations.
$ pycodestyle optparse.py
optparse.py:69:11: E401 multiple imports on one line
optparse.py:77:1: E302 expected 2 blank lines, found 1
optparse.py:88:5: E301 expected 1 blank line, found 0
optparse.py:222:34: W602 deprecated form of raising exception
optparse.py:347:31: E211 whitespace before '('
optparse.py:357:17: E201 whitespace after '{'
optparse.py:472:29: E221 multiple spaces before operator
optparse.py:544:21: W601 .has_key() is deprecated, use 'in'
The program autopep8 can be used to automatically reformat code in the PEP 8 style. Install the program with:
$ pip install autopep8
Use it to format a file in-place with:
$ autopep8 --in-place optparse.py
Excluding the --in-place flag will cause the program to output the modified code directly to the console for review.
The --aggressive flag will perform more substantial changes and can be applied multiple times for greater effect.
Conventions
Here are some conventions you should follow to make your code easier to read.
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Check if variable equals a constant
You don’t need to explicitly compare a value to True, or None, or 0 - you can just add it to the if statement. See Truth
Value Testing for a list of what is considered false.
Bad:
if attr == True:
print 'True!'
if attr == None:
print 'attr is None!'
Good:
# Just check the value
if attr:
print 'attr is truthy!'
# or check for the opposite
if not attr:
print 'attr is falsey!'
# or, since None is considered false, explicitly check for it
if attr is None:
print 'attr is None!'
Access a Dictionary Element
Don’t use the dict.has_key() method.
dict.get().
Instead, use x in d syntax, or pass a default argument to
Bad:
d = {'hello': 'world'}
if d.has_key('hello'):
print d['hello']
# prints 'world'
else:
print 'default_value'
Good:
d = {'hello': 'world'}
print d.get('hello', 'default_value') # prints 'world'
print d.get('thingy', 'default_value') # prints 'default_value'
# Or:
if 'hello' in d:
print d['hello']
Short Ways to Manipulate Lists
List comprehensions provide a powerful, concise way to work with lists. Also, the map() and filter() functions
can perform operations on lists using a different, more concise syntax.
Bad:
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# Filter elements greater than 4
a = [3, 4, 5]
b = []
for i in a:
if i > 4:
b.append(i)
Good:
a
b
#
b
= [3, 4, 5]
= [i for i in a if i > 4]
Or:
= filter(lambda x: x > 4, a)
Bad:
# Add three to all list members.
a = [3, 4, 5]
for i in range(len(a)):
a[i] += 3
Good:
a
a
#
a
= [3, 4, 5]
= [i + 3 for i in a]
Or:
= map(lambda i: i + 3, a)
Use enumerate() keep a count of your place in the list.
a = [3, 4, 5]
for i, item in enumerate(a):
print i, item
# prints
# 0 3
# 1 4
# 2 5
The enumerate() function has better readability than handling a counter manually. Moreover, it is better optimized
for iterators.
Read From a File
Use the with open syntax to read from files. This will automatically close files for you.
Bad:
f = open('file.txt')
a = f.read()
print a
f.close()
Good:
with open('file.txt') as f:
for line in f:
print line
The with statement is better because it will ensure you always close the file, even if an exception is raised inside the
with block.
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Line Continuations
When a logical line of code is longer than the accepted limit, you need to split it over multiple physical lines. The
Python interpreter will join consecutive lines if the last character of the line is a backslash. This is helpful in some
cases, but should usually be avoided because of its fragility: a white space added to the end of the line, after the
backslash, will break the code and may have unexpected results.
A better solution is to use parentheses around your elements. Left with an unclosed parenthesis on an end-of-line the
Python interpreter will join the next line until the parentheses are closed. The same behavior holds for curly and square
braces.
Bad:
my_very_big_string = """For a long time I used to go to bed early. Sometimes, \
when I had put out my candle, my eyes would close so quickly that I had not even \
time to say “I’m going to sleep.”"""
from some.deep.module.inside.a.module import a_nice_function, another_nice_function, \
yet_another_nice_function
Good:
my_very_big_string = (
"For a long time I used to go to bed early. Sometimes, "
"when I had put out my candle, my eyes would close so quickly "
"that I had not even time to say “I’m going to sleep.”"
)
from some.deep.module.inside.a.module import (
a_nice_function, another_nice_function, yet_another_nice_function)
However, more often than not, having to split a long logical line is a sign that you are trying to do too many things at
the same time, which may hinder readability.
Reading Great Code
One of the core tenets behind the design of Python is creating readable code. The motivation behind this design is
simple: The number one thing that Python programmers do is read code.
One of the secrets of becoming a great Python programmer is to read, understand, and comprehend excellent code.
Excellent code typically follows the guidelines outlined in Code Style, and does its best to express a clear and concise
intent to the reader.
Included below is a list of recommended Python projects for reading. Each one of these projects is a paragon of Python
coding.
• Howdoi Howdoi is a code search tool, written in Python.
• Flask Flask is a microframework for Python based on Werkzeug and Jinja2. It’s intended for getting started very
quickly and was developed with best intentions in mind.
• Diamond Diamond is a python daemon that collects metrics and publishes them to Graphite or other backends.
It is capable of collecting cpu, memory, network, i/o, load and disk metrics. Additionally, it features an API for
implementing custom collectors for gathering metrics from almost any source.
• Werkzeug Werkzeug started as simple collection of various utilities for WSGI applications and has become one
of the most advanced WSGI utility modules. It includes a powerful debugger, full-featured request and response
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objects, HTTP utilities to handle entity tags, cache control headers, HTTP dates, cookie handling, file uploads,
a powerful URL routing system and a bunch of community-contributed addon modules.
• Requests Requests is an Apache2 Licensed HTTP library, written in Python, for human beings.
• Tablib Tablib is a format-agnostic tabular dataset library, written in Python.
Todo
Include code examples of exemplary code from each of the projects listed. Explain why it is excellent code. Use
complex examples.
Todo
Explain techniques to rapidly identify data structures, algorithms and determine what the code is doing.
Documentation
Readability is a primary focus for Python developers, in both project and code documentation. Following some simple
best practices can save both you and others a lot of time.
Project Documentation
A README file at the root directory should give general information to both users and maintainers of a project. It
should be raw text or written in some very easy to read markup, such as reStructuredText or Markdown. It should
contain a few lines explaining the purpose of the project or library (without assuming the user knows anything about
the project), the URL of the main source for the software, and some basic credit information. This file is the main
entry point for readers of the code.
An INSTALL file is less necessary with Python. The installation instructions are often reduced to one command, such
as pip install module or python setup.py install and added to the README file.
A LICENSE file should always be present and specify the license under which the software is made available to the
public.
A TODO file or a TODO section in README should list the planned development for the code.
A CHANGELOG file or section in README should compile a short overview of the changes in the code base for the
latest versions.
Project Publication
Depending on the project, your documentation might include some or all of the following components:
• An introduction should show a very short overview of what can be done with the product, using one or two
extremely simplified use cases. This is the thirty-second pitch for your project.
• A tutorial should show some primary use cases in more detail. The reader will follow a step-by-step procedure
to set-up a working prototype.
• An API reference is typically generated from the code (see docstrings). It will list all publicly available interfaces, parameters, and return values.
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• Developer documentation is intended for potential contributors. This can include code convention and general
design strategy of the project.
Sphinx
Sphinx is far and away the most popular Python documentation tool. Use it. It converts reStructuredText markup
language into a range of output formats including HTML, LaTeX (for printable PDF versions), manual pages, and
plain text.
There is also great, free hosting for your Sphinx docs: Read The Docs. Use it. You can configure it with commit
hooks to your source repository so that rebuilding your documentation will happen automatically.
When run, Sphinx will import your code and using Python’s introspection features it will extract all function, method
and class signatures. It will also extract the accompanying docstrings, and compile it all into well structured and easily
readable documentation for your project.
Note: Sphinx is famous for its API generation, but it also works well for general project documentation. This Guide
is built with Sphinx and is hosted on Read The Docs
reStructuredText
Most Python documentation is written with reStructuredText. It’s like Markdown with all the optional extensions built
in.
The reStructuredText Primer and the reStructuredText Quick Reference should help you familiarize yourself with its
syntax.
Code Documentation Advice
Comments clarify the code and they are added with purpose of making the code easier to understand. In Python,
comments begin with a hash (number sign) (#). In Python, docstrings describe modules, classes, and functions:
def square_and_rooter(x):
"""Return the square root of self times self."""
...
In general, follow the comment section of PEP 8#comments (the “Python Style Guide”). More information about
docstrings can be found at PEP 0257#specification (The Docstring Conventions Guide).
Commenting Sections of Code
Do not use triple-quote strings to comment code. This is not a good practice, because line-oriented command-line tools
such as grep will not be aware that the commented code is inactive. It is better to add hashes at the proper indentation
level for every commented line. Your editor probably has the ability to do this easily, and it is worth learning the
comment/uncomment toggle.
Docstrings and Magic
Some tools use docstrings to embed more-than-documentation behavior, such as unit test logic. Those can be nice, but
you won’t ever go wrong with vanilla “here’s what this does.”
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Tools like Sphinx will parse your docstrings as reStructuredText and render it correctly as HTML. This makes it very
easy to embed snippets of example code in a project’s documentation.
Additionally, Doctest will read all embedded docstrings that look like input from the Python commandline (prefixed
with “>>>”) and run them, checking to see if the output of the command matches the text on the following line. This
allows developers to embed real examples and usage of functions alongside their source code, and as a side effect, it
also ensures that their code is tested and works.
def my_function(a, b):
"""
>>> my_function(2, 3)
6
>>> my_function('a', 3)
'aaa'
"""
return a * b
Docstrings versus Block comments
These aren’t interchangeable. For a function or class, the leading comment block is a programmer’s note. The
docstring describes the operation of the function or class:
# This function slows down program execution for some reason.
def square_and_rooter(x):
"""Returns the square root of self times self."""
...
Unlike block comments, docstrings are built into the Python language itself. This means you can use all of Python’s
powerful introspection capabilities to access docstrings at runtime, compared with comments which are optimised out.
Docstrings are accessible from both the __doc__ dunder attribute for almost every Python object, as well as with the
built in help() function.
While block comments are usually used to explain what a section of code is doing, or the specifics of an algorithm,
docstrings are more intended for explaining to other users of your code (or you in 6 months time) how a particular
function can be used and the general purpose of a function, class, or module.
Writing Docstrings
Depending on the complexity of the function, method, or class being written, a one-line docstring may be perfectly
appropriate. These are generally used for really obvious cases, such as:
def add(a, b):
"""Add two numbers and return the result."""
return a + b
The docstring should describe the function in a way that is easy to understand. For simple cases like trivial functions
and classes, simply embedding the function’s signature (i.e. add(a, b) -> result) in the docstring is unnecessary. This
is because with Python’s inspect module, it is already quite easy to find this information if needed, and it is also readily
available by reading the source code.
In larger or more complex projects however, it is often a good idea to give more information about a function, what it
does, any exceptions it may raise, what it returns, or relevant details about the parameters.
For more detailed documentation of code a popular style is the one used for the Numpy project, often called Numpy
style docstrings. While it can take up more lines than the previous example, it allows the developer to include a lot
more information about a method, function, or class.
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def random_number_generator(arg1, arg2):
"""
Summary line.
Extended description of function.
Parameters
---------arg1 : int
Description of arg1
arg2 : str
Description of arg2
Returns
------int
Description of return value
"""
return 42
The sphinx.ext.napoleon plugin allows Sphinx to parse this style of docstrings, making it easy to incorporate NumPy
style docstrings into your project.
At the end of the day, it doesn’t really matter what style is used for writing docstrings, their purpose is to serve as
documentation for anyone who may need to read or make changes to your code. As long as it is correct, understandable
and gets the relevant points across then it has done the job it was designed to do.
For further reading on docstrings, feel free to consult PEP 257
Other Tools
You might see these in the wild. Use Sphinx.
Pycco Pycco is a “literate-programming-style documentation generator” and is a port of the node.js Docco. It makes
code into a side-by-side HTML code and documentation.
Ronn Ronn builds Unix manuals. It converts human readable textfiles to roff for terminal display, and also to HTML
for the web.
Epydoc Epydoc is discontinued. Use Sphinx instead.
MkDocs MkDocs is a fast and simple static site generator that’s geared towards building project documentation with
Markdown.
Testing Your Code
Testing your code is very important.
Getting used to writing testing code and running this code in parallel is now considered a good habit. Used wisely,
this method helps you define more precisely your code’s intent and have a more decoupled architecture.
Some general rules of testing:
• A testing unit should focus on one tiny bit of functionality and prove it correct.
• Each test unit must be fully independent. Each test must be able to run alone, and also within the test suite, regardless of the order that they are called. The implication of this rule is that each test must be loaded with a fresh
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dataset and may have to do some cleanup afterwards. This is usually handled by setUp() and tearDown()
methods.
• Try hard to make tests that run fast. If one single test needs more than a few milliseconds to run, development
will be slowed down or the tests will not be run as often as is desirable. In some cases, tests can’t be fast because
they need a complex data structure to work on, and this data structure must be loaded every time the test runs.
Keep these heavier tests in a separate test suite that is run by some scheduled task, and run all other tests as often
as needed.
• Learn your tools and learn how to run a single test or a test case. Then, when developing a function inside a
module, run this function’s tests frequently, ideally automatically when you save the code.
• Always run the full test suite before a coding session, and run it again after. This will give you more confidence
that you did not break anything in the rest of the code.
• It is a good idea to implement a hook that runs all tests before pushing code to a shared repository.
• If you are in the middle of a development session and have to interrupt your work, it is a good idea to write a
broken unit test about what you want to develop next. When coming back to work, you will have a pointer to
where you were and get back on track faster.
• The first step when you are debugging your code is to write a new test pinpointing the bug. While it is not
always possible to do, those bug catching tests are among the most valuable pieces of code in your project.
• Use long and descriptive names for testing functions. The style guide here is slightly different than that of
running code, where short names are often preferred. The reason is testing functions are never called explicitly. square() or even sqr() is ok in running code, but in testing code you would have names such as
test_square_of_number_2(), test_square_negative_number(). These function names are
displayed when a test fails, and should be as descriptive as possible.
• When something goes wrong or has to be changed, and if your code has a good set of tests, you or other
maintainers will rely largely on the testing suite to fix the problem or modify a given behavior. Therefore the
testing code will be read as much as or even more than the running code. A unit test whose purpose is unclear
is not very helpful in this case.
• Another use of the testing code is as an introduction to new developers. When someone will have to work on the
code base, running and reading the related testing code is often the best thing that they can do to start. They will
or should discover the hot spots, where most difficulties arise, and the corner cases. If they have to add some
functionality, the first step should be to add a test to ensure that the new functionality is not already a working
path that has not been plugged into the interface.
The Basics
Unittest
unittest is the batteries-included test module in the Python standard library. Its API will be familiar to anyone
who has used any of the JUnit/nUnit/CppUnit series of tools.
Creating test cases is accomplished by subclassing unittest.TestCase.
import unittest
def fun(x):
return x + 1
class MyTest(unittest.TestCase):
def test(self):
self.assertEqual(fun(3), 4)
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As of Python 2.7 unittest also includes its own test discovery mechanisms.
unittest in the standard library documentation
Doctest
The doctest module searches for pieces of text that look like interactive Python sessions in docstrings, and then
executes those sessions to verify that they work exactly as shown.
Doctests have a different use case than proper unit tests: they are usually less detailed and don’t catch special cases or
obscure regression bugs. They are useful as an expressive documentation of the main use cases of a module and its
components. However, doctests should run automatically each time the full test suite runs.
A simple doctest in a function:
def square(x):
"""Return the square of x.
>>> square(2)
4
>>> square(-2)
4
"""
return x * x
if __name__ == '__main__':
import doctest
doctest.testmod()
When running this module from the command line as in python module.py, the doctests will run and complain
if anything is not behaving as described in the docstrings.
Tools
py.test
py.test is a no-boilerplate alternative to Python’s standard unittest module.
$ pip install pytest
Despite being a fully-featured and extensible test tool, it boasts a simple syntax. Creating a test suite is as easy as
writing a module with a couple of functions:
# content of test_sample.py
def func(x):
return x + 1
def test_answer():
assert func(3) == 5
and then running the py.test command
$ py.test
=========================== test session starts ============================
platform darwin -- Python 2.7.1 -- pytest-2.2.1
collecting ... collected 1 items
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test_sample.py F
================================= FAILURES =================================
_______________________________ test_answer ________________________________
>
E
E
def test_answer():
assert func(3) == 5
assert 4 == 5
+ where 4 = func(3)
test_sample.py:5: AssertionError
========================= 1 failed in 0.02 seconds =========================
is far less work than would be required for the equivalent functionality with the unittest module!
py.test
Hypothesis
Hypothesis is a library which lets you write tests that are parametrized by a source of examples. It then generates
simple and comprehensible examples that make your tests fail, letting you find more bugs with less work.
$ pip install hypothesis
For example, testing lists of floats will try many examples, but report the minimal example of each bug (distinguished
exception type and location):
@given(lists(floats(allow_nan=False, allow_infinity=False), min_size=1))
def test_mean(xs):
mean = sum(xs) / len(xs)
assert min(xs) <= mean(xs) <= max(xs)
Falsifying example: test_mean(
xs=[1.7976321109618856e+308, 6.102390043022755e+303]
)
Hypothesis is practical as well as very powerful, and will often find bugs that escaped all other forms of testing. It
integrates well with py.test, and has a strong focus on usability in both simple and advanced scenarios.
hypothesis
tox
tox is a tool for automating test environment management and testing against multiple interpreter configurations
$ pip install tox
tox allows you to configure complicated multi-parameter test matrices via a simple ini-style configuration file.
tox
Unittest2
unittest2 is a backport of Python 2.7’s unittest module which has an improved API and better assertions over the one
available in previous versions of Python.
If you’re using Python 2.6 or below, you can install it with pip
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$ pip install unittest2
You may want to import the module under the name unittest to make porting code to newer versions of the module
easier in the future
import unittest2 as unittest
class MyTest(unittest.TestCase):
...
This way if you ever switch to a newer Python version and no longer need the unittest2 module, you can simply change
the import in your test module without the need to change any other code.
unittest2
mock
unittest.mock is a library for testing in Python. As of Python 3.3, it is available in the standard library.
For older versions of Python:
$ pip install mock
It allows you to replace parts of your system under test with mock objects and make assertions about how they have
been used.
For example, you can monkey-patch a method:
from mock import MagicMock
thing = ProductionClass()
thing.method = MagicMock(return_value=3)
thing.method(3, 4, 5, key='value')
thing.method.assert_called_with(3, 4, 5, key='value')
To mock classes or objects in a module under test, use the patch decorator. In the example below, an external search
system is replaced with a mock that always returns the same result (but only for the duration of the test).
def mock_search(self):
class MockSearchQuerySet(SearchQuerySet):
def __iter__(self):
return iter(["foo", "bar", "baz"])
return MockSearchQuerySet()
# SearchForm here refers to the imported class reference in myapp,
# not where the SearchForm class itself is imported from
@mock.patch('myapp.SearchForm.search', mock_search)
def test_new_watchlist_activities(self):
# get_search_results runs a search and iterates over the result
self.assertEqual(len(myapp.get_search_results(q="fish")), 3)
Mock has many other ways you can configure it and control its behavior.
mock
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Logging
The logging module has been a part of Python’s Standard Library since version 2.3. It is succinctly described in
PEP 282. The documentation is notoriously hard to read, except for the basic logging tutorial.
Logging serves two purposes:
• Diagnostic logging records events related to the application’s operation. If a user calls in to report an error, for
example, the logs can be searched for context.
• Audit logging records events for business analysis. A user’s transactions can be extracted and combined with
other user details for reports or to optimize a business goal.
... or Print?
The only time that print is a better option than logging is when the goal is to display a help statement for a command
line application. Other reasons why logging is better than print:
• The log record, which is created with every logging event, contains readily available diagnostic information
such as the file name, full path, function, and line number of the logging event.
• Events logged in included modules are automatically accessible via the root logger to your application’s logging
stream, unless you filter them out.
• Logging can be selectively silenced by using the method logging.Logger.setLevel() or disabled by
setting the attribute logging.Logger.disabled to True.
Logging in a Library
Notes for configuring logging for a library are in the logging tutorial. Because the user, not the library, should dictate
what happens when a logging event occurs, one admonition bears repeating:
Note: It is strongly advised that you do not add any handlers other than NullHandler to your library’s loggers.
Best practice when instantiating loggers in a library is to only create them using the __name__ global variable: the
logging module creates a hierarchy of loggers using dot notation, so using __name__ ensures no name collisions.
Here is an example of best practice from the requests source – place this in your __init__.py
import logging
logging.getLogger(__name__).addHandler(logging.NullHandler())
Logging in an Application
The twelve factor app, an authoritative reference for good practice in application development, contains a section on
logging best practice. It emphatically advocates for treating log events as an event stream, and for sending that event
stream to standard output to be handled by the application environment.
There are at least three ways to configure a logger:
• Using an INI-formatted file:
– Pro:
possible to update configuration while
logging.config.listen() to listen on a socket.
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– Con: less control (e.g. custom subclassed filters or loggers) than possible when configuring a logger
in code.
• Using a dictionary or a JSON-formatted file:
– Pro: in addition to updating while running, it is possible to load from a file using the json module,
in the standard library since Python 2.6.
– Con: less control than when configuring a logger in code.
• Using code:
– Pro: complete control over the configuration.
– Con: modifications require a change to source code.
Example Configuration via an INI File
Let us say the file is named logging_config.ini. More details for the file format are in the logging configuration
section of the logging tutorial.
[loggers]
keys=root
[handlers]
keys=stream_handler
[formatters]
keys=formatter
[logger_root]
level=DEBUG
handlers=stream_handler
[handler_stream_handler]
class=StreamHandler
level=DEBUG
formatter=formatter
args=(sys.stderr,)
[formatter_formatter]
format=%(asctime)s %(name)-12s %(levelname)-8s %(message)s
Then use logging.config.fileConfig() in the code:
import logging
from logging.config import fileConfig
fileConfig('logging_config.ini')
logger = logging.getLogger()
logger.debug('often makes a very good meal of %s', 'visiting tourists')
Example Configuration via a Dictionary
As of Python 2.7, you can use a dictionary with configuration details. PEP 391 contains a list of the mandatory and
optional elements in the configuration dictionary.
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import logging
from logging.config import dictConfig
logging_config = dict(
version = 1,
formatters = {
'f': {'format':
'%(asctime)s %(name)-12s %(levelname)-8s %(message)s'}
},
handlers = {
'h': {'class': 'logging.StreamHandler',
'formatter': 'f',
'level': logging.DEBUG}
},
root = {
'handlers': ['h'],
'level': logging.DEBUG,
},
)
dictConfig(logging_config)
logger = logging.getLogger()
logger.debug('often makes a very good meal of %s', 'visiting tourists')
Example Configuration Directly in Code
import logging
logger = logging.getLogger()
handler = logging.StreamHandler()
formatter = logging.Formatter(
'%(asctime)s %(name)-12s %(levelname)-8s %(message)s')
handler.setFormatter(formatter)
logger.addHandler(handler)
logger.setLevel(logging.DEBUG)
logger.debug('often makes a very good meal of %s', 'visiting tourists')
Common Gotchas
For the most part, Python aims to be a clean and consistent language that avoids surprises. However, there are a few
cases that can be confusing to newcomers.
Some of these cases are intentional but can be potentially surprising. Some could arguably be considered language
warts. In general, what follows is a collection of potentially tricky behavior that might seem strange at first glance, but
is generally sensible once you’re aware of the underlying cause for the surprise.
Mutable Default Arguments
Seemingly the most common surprise new Python programmers encounter is Python’s treatment of mutable default
arguments in function definitions.
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What You Wrote
def append_to(element, to=[]):
to.append(element)
return to
What You Might Have Expected to Happen
my_list = append_to(12)
print my_list
my_other_list = append_to(42)
print my_other_list
A new list is created each time the function is called if a second argument isn’t provided, so that the output is:
[12]
[42]
What Does Happen
[12]
[12, 42]
A new list is created once when the function is defined, and the same list is used in each successive call.
Python’s default arguments are evaluated once when the function is defined, not each time the function is called (like
it is in say, Ruby). This means that if you use a mutable default argument and mutate it, you will and have mutated
that object for all future calls to the function as well.
What You Should Do Instead
Create a new object each time the function is called, by using a default arg to signal that no argument was provided
(None is often a good choice).
def append_to(element, to=None):
if to is None:
to = []
to.append(element)
return to
Do not forget, you are passing a list object as the second argument.
When the Gotcha Isn’t a Gotcha
Sometimes you can specifically “exploit” (read: use as intended) this behavior to maintain state between calls of a
function. This is often done when writing a caching function.
Late Binding Closures
Another common source of confusion is the way Python binds its variables in closures (or in the surrounding global
scope).
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What You Wrote
def create_multipliers():
return [lambda x : i * x for i in range(5)]
What You Might Have Expected to Happen
for multiplier in create_multipliers():
print multiplier(2)
A list containing five functions that each have their own closed-over i variable that multiplies their argument, producing:
0
2
4
6
8
What Does Happen
8
8
8
8
8
Five functions are created; instead all of them just multiply x by 4.
Python’s closures are late binding. This means that the values of variables used in closures are looked up at the time
the inner function is called.
Here, whenever any of the returned functions are called, the value of i is looked up in the surrounding scope at call
time. By then, the loop has completed and i is left with its final value of 4.
What’s particularly nasty about this gotcha is the seemingly prevalent misinformation that this has something to do
with lambdas in Python. Functions created with a lambda expression are in no way special, and in fact the same
exact behavior is exhibited by just using an ordinary def:
def create_multipliers():
multipliers = []
for i in range(5):
def multiplier(x):
return i * x
multipliers.append(multiplier)
return multipliers
What You Should Do Instead
The most general solution is arguably a bit of a hack. Due to Python’s aforementioned behavior concerning evaluating
default arguments to functions (see Mutable Default Arguments), you can create a closure that binds immediately to
its arguments by using a default arg like so:
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def create_multipliers():
return [lambda x, i=i : i * x for i in range(5)]
Alternatively, you can use the functools.partial function:
from functools import partial
from operator import mul
def create_multipliers():
return [partial(mul, i) for i in range(5)]
When the Gotcha Isn’t a Gotcha
Sometimes you want your closures to behave this way. Late binding is good in lots of situations. Looping to create
unique functions is unfortunately a case where they can cause hiccups.
Bytecode (.pyc) Files Everywhere!
By default, when executing Python code from files, the Python interpreter will automatically write a bytecode version
of that file to disk, e.g. module.pyc.
These .pyc files should not be checked into your source code repositories.
Theoretically, this behavior is on by default, for performance reasons. Without these bytecode files present, Python
would re-generate the bytecode every time the file is loaded.
Disabling Bytecode (.pyc) Files
Luckily, the process of generating the bytecode is extremely fast, and isn’t something you need to worry about while
developing your code.
Those files are annoying, so let’s get rid of them!
$ export PYTHONDONTWRITEBYTECODE=1
With the $PYTHONDONTWRITEBYTECODE environment variable set, Python will no longer write these files to disk,
and your development environment will remain nice and clean.
I recommend setting this environment variable in your ~/.profile.
Removing Bytecode (.pyc) Files
Here’s nice trick for removing all of these files, if they already exist:
$ find . -type f -name "*.py[co]" -delete -or -type d -name "__pycache__" -delete
Run that from the root directory of your project, and all .pyc files will suddenly vanish. Much better.
Version Control Ignores
If you still need the .pyc files for performance reasons, you can always add them to the ignore files of your version
control repositories. Popular version control systems have the ability to use wildcards defined in a file to apply special
rules.
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An ignore file will make sure the matching files don’t get checked into the repository. Git uses .gitignore while
Mercurial uses .hgignore.
At the minimum your ignore files should look like this.
syntax:glob
*.py[cod]
__pycache__/
# This line is not needed for .gitignore files.
# Will match .pyc, .pyo and .pyd files.
# Exclude the whole folder
You may wish to include more files and directories depending on your needs. The next time you commit to the
repository, these files will not be included.
Choosing a License
Your source publication needs a license. In the US, if no license is specified, users have no legal right to download,
modify, or distribute. Furthermore, people can’t contribute to your code unless you tell them what rules to play by.
Choosing a license is complicated, so here are some pointers:
Open source. There are plenty of open source licenses available to choose from.
In general, these licenses tend to fall into one of two categories:
1. licenses that focus more on the user’s freedom to do with the software as they please (these are the more
permissive open source licenses such as the MIT, BSD, & Apache).
2. licenses that focus more on making sure that the code itself — including any changes made to it and distributed
along with it — always remains free (these are the less permissive free software licenses such as the GPL and
LGPL).
The latter are less permissive in the sense that they don’t permit someone to add code to the software and distribute it
without also including the source code for their changes.
To help you choose one for your project, there’s a license chooser, use it.
More Permissive
• PSFL (Python Software Foundation License) – for contributing to Python itself
• MIT / BSD / ISC
– MIT (X11)
– New BSD
– ISC
• Apache
Less Permissive:
• LGPL
• GPL
– GPLv2
– GPLv3
A good overview of licenses with explanations of what one can, cannot, and must do using a particular software can
be found at tl;drLegal.
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This part of the guide focuses on tool and module advice based on different scenarios.
Network Applications
HTTP
The Hypertext Transfer Protocol (HTTP) is an application protocol for distributed, collaborative, hypermedia information systems. HTTP is the foundation of data communication for the World Wide Web.
Requests
Python’s standard urllib2 module provides most of the HTTP capabilities you need, but the API is thoroughly broken.
It was built for a different time — and a different web. It requires an enormous amount of work (even method overrides)
to perform the simplest of tasks.
Requests takes all of the work out of Python HTTP — making your integration with web services seamless. There’s
no need to manually add query strings to your URLs, or to form-encode your POST data. Keep-alive and HTTP
connection pooling are 100% automatic, powered by urllib3, which is embedded within Requests.
• Documentation
• PyPi
• GitHub
Distributed Systems
ZeroMQ
ØMQ (also spelled ZeroMQ, 0MQ or ZMQ) is a high-performance asynchronous messaging library aimed at use in
scalable distributed or concurrent applications. It provides a message queue, but unlike message-oriented middleware,
a ØMQ system can run without a dedicated message broker. The library is designed to have a familiar socket-style
API.
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RabbitMQ
RabbitMQ is an open source message broker software that implements the Advanced Message Queuing Protocol
(AMQP). The RabbitMQ server is written in the Erlang programming language and is built on the Open Telecom
Platform framework for clustering and failover. Client libraries to interface with the broker are available for all major
programming languages.
• Homepage
• GitHub Organization
Web Applications & Frameworks
As a powerful scripting language adapted to both fast prototyping and bigger projects, Python is widely used in web
application development.
Context
WSGI
The Web Server Gateway Interface (or “WSGI” for short) is a standard interface between web servers and Python
web application frameworks. By standardizing behavior and communication between web servers and Python web
frameworks, WSGI makes it possible to write portable Python web code that can be deployed in any WSGI-compliant
web server. WSGI is documented in PEP 3333.
Frameworks
Broadly speaking, a web framework consists of a set of libraries and a main handler within which you can build custom
code to implement a web application (i.e. an interactive web site). Most web frameworks include patterns and utilities
to accomplish at least the following:
URL Routing Matches an incoming HTTP request to a particular piece of Python code to be invoked
Request and Response Objects Encapsulate the information received from or sent to a user’s browser
Template Engine Allows for separating Python code implementing an application’s logic from the HTML (or other)
output that it produces
Development Web Server Runs an HTTP server on development machines to enable rapid development; often automatically reloads server-side code when files are updated
Django
Django is a “batteries included” web application framework, and is an excellent choice for creating content-oriented
websites. By providing many utilities and patterns out of the box, Django aims to make it possible to build complex,
database-backed web applications quickly, while encouraging best practices in code written using it.
Django has a large and active community, and many pre-built re-usable modules that can be incorporated into a new
project as-is, or customized to fit your needs.
There are annual Django conferences in the United States, Europe, and Australia.
The majority of new Python web applications today are built with Django.
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Flask
Flask is a “microframework” for Python, and is an excellent choice for building smaller applications, APIs, and web
services.
Building an app with Flask is a lot like writing standard Python modules, except some functions have routes attached
to them. It’s really beautiful.
Rather than aiming to provide everything you could possibly need, Flask implements the most commonly-used core
components of a web application framework, like URL routing, request and response objects, and templates.
If you use Flask, it is up to you to choose other components for your application, if any. For example, database access
or form generation and validation are not built-in functions of Flask.
This is great, because many web applications don’t need those features. For those that do, there are many Extensions
available that may suit your needs. Or, you can easily use any library you want yourself!
Flask is default choice for any Python web application that isn’t a good fit for Django.
Tornado
Tornado is an asyncronous web framework for Python that has its own event loop. This allows it to natively support
WebSockets, for example. Well-written Tornado applications are known to have excellent performance characteristics.
I do not recommend using Tornado unless you think you need it.
Pyramid
Pyramid is a very flexible framework with a heavy focus on modularity. It comes with a small number of libraries
(“batteries”) built-in, and encourages users to extend its base functionality.
Pyramid does not have a large user base, unlike Django and Flask. It’s a capable framework, but not a very popular
choice for new Python web applications today.
Web Servers
Nginx
Nginx (pronounced “engine-x”) is a web server and reverse-proxy for HTTP, SMTP and other protocols. It is known
for its high performance, relative simplicity, and compatibility with many application servers (like WSGI servers).
It also includes handy features like load-balancing, basic authentication, streaming, and others. Designed to serve
high-load websites, Nginx is gradually becoming quite popular.
WSGI Servers
Stand-alone WSGI servers typically use less resources than traditional web servers and provide top performance 3 .
3
Benchmark of Python WSGI Servers
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Gunicorn
Gunicorn (Green Unicorn) is a pure-python WSGI server used to serve Python applications. Unlike other Python web
servers, it has a thoughtful user-interface, and is extremely easy to use and configure.
Gunicorn has sane and reasonable defaults for configurations. However, some other servers, like uWSGI, are tremendously more customizable, and therefore, are much more difficult to effectively use.
Gunicorn is the recommended choice for new Python web applications today.
Waitress
Waitress is a pure-python WSGI server that claims “very acceptable performance”. Its documentation is not very
detailed, but it does offer some nice functionality that Gunicorn doesn’t have (e.g. HTTP request buffering).
Waitress is gaining popularity within the Python web development community.
uWSGI
uWSGI is a full stack for building hosting services. In addition to process management, process monitoring, and
other functionality, uWSGI acts as an application server for various programming languages and protocols - including
Python and WSGI. uWSGI can either be run as a stand-alone web router, or be run behind a full web server (such as
Nginx or Apache). In the latter case, a web server can configure uWSGI and an application’s operation over the uwsgi
protocol. uWSGI’s web server support allows for dynamically configuring Python, passing environment variables and
further tuning. For full details, see uWSGI magic variables.
I do not recommend using uWSGI unless you know why you need it.
Server Best Practices
The majority of self-hosted Python applications today are hosted with a WSGI server such as Gunicorn, either directly
or behind a lightweight web server such as nginx.
The WSGI servers serve the Python applications while the web server handles tasks better suited for it such as static
file serving, request routing, DDoS protection, and basic authentication.
Hosting
Platform-as-a-Service (PaaS) is a type of cloud computing infrastructure which abstracts and manages infrastructure,
routing, and scaling of web applications. When using a PaaS, application developers can focus on writing application
code rather than needing to be concerned with deployment details.
Heroku
Heroku offers first-class support for Python 2.7–3.5 applications.
Heroku supports all types of Python web applications, servers, and frameworks. Applications can be developed on
Heroku for free. Once your application is ready for production, you can upgrade to a Hobby or Professional application.
Heroku maintains detailed articles on using Python with Heroku, as well as step-by-step instructions on how to set up
your first application.
Heroku is the recommended PaaS for deploying Python web applications today.
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Eldarion
Eldarion (formely known as Gondor) is a PaaS powered by Kubernetes, CoreOS, and Docker. They support any WSGI
application and have a guide on deploying Django projects.
Templating
Most WSGI applications are responding to HTTP requests to serve content in HTML or other markup languages.
Instead of generating directly textual content from Python, the concept of separation of concerns advises us to use
templates. A template engine manages a suite of template files, with a system of hierarchy and inclusion to avoid
unnecessary repetition, and is in charge of rendering (generating) the actual content, filling the static content of the
templates with the dynamic content generated by the application.
As template files are sometimes written by designers or front-end developers, it can be difficult to handle increasing
complexity.
Some general good practices apply to the part of the application passing dynamic content to the template engine, and
to the templates themselves.
• Template files should be passed only the dynamic content that is needed for rendering the template. Avoid the
temptation to pass additional content “just in case”: it is easier to add some missing variable when needed than
to remove a likely unused variable later.
• Many template engines allow for complex statements or assignments in the template itself, and many allow some
Python code to be evaluated in the templates. This convenience can lead to uncontrolled increase in complexity,
and often make it harder to find bugs.
• It is often necessary to mix JavaScript templates with HTML templates. A sane approach to this design is to
isolate the parts where the HTML template passes some variable content to the JavaScript code.
Jinja2
Jinja2 is a very well-regarded template engine.
It uses a text-based template language and can thus be used to generate any type markup, not just HTML. It allows
customization of filters, tags, tests and globals. It features many improvements over Django’s templating system.
Here some important html tags in Jinja2:
{# This is a comment #}
{# The next tag is a variable output: #}
{{title}}
{# Tag for a block, can be replaced through inheritance with other html code #}
{% block head %}
This is the head!
{% endblock %}
{# Output of an array as an iteration #}
{% for item in list %}
{{ item }}
{% endfor %}
The next listings is an example of a web site in combination with the Tornado web server. Tornado is not very
complicated to use.
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# import Jinja2
from jinja2 import Environment, FileSystemLoader
# import Tornado
import tornado.ioloop
import tornado.web
# Load template file templates/site.html
TEMPLATE_FILE = "site.html"
templateLoader = FileSystemLoader( searchpath="templates/" )
templateEnv = Environment( loader=templateLoader )
template = templateEnv.get_template(TEMPLATE_FILE)
# List for famous movie rendering
movie_list = [[1,"The Hitchhiker's Guide to the Galaxy"],[2,"Back to future"],[3,"Matrix"]]
# template.render() returns a string which contains the rendered html
html_output = template.render(list=movie_list,
title="Here is my favorite movie list")
# Handler for main page
class MainHandler(tornado.web.RequestHandler):
def get(self):
# Returns rendered template string to the browser request
self.write(html_output)
# Assign handler to the server root (127.0.0.1:PORT/)
application = tornado.web.Application([
(r"/", MainHandler),
])
PORT=8884
if __name__ == "__main__":
# Setup the server
application.listen(PORT)
tornado.ioloop.IOLoop.instance().start()
The base.html file can be used as base for all site pages which are for example implemented in the content block.
{{title}} - My Webpage
{# In the next line the content from the site.html template will be added #}
{% block content %}{% endblock %}
The next listing is our site page (site.html) loaded in the Python app which extends base.html. The content
block is automatically set into the corresponding block in the base.html page.
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{% extends "base.html" %}
{% block content %}
{{title}}
{{ list_title }}
{% for item in list %}
- {{ item[0]}} : {{ item[1]}}
{% endfor %}
{% endblock %}
Jinja2 is the recommended templating library for new Python web applications.
Chameleon
Chameleon Page Templates are an HTML/XML template engine implementation of the Template Attribute Language
(TAL), TAL Expression Syntax (TALES), and Macro Expansion TAL (Metal) syntaxes.
Chameleon is available for Python 2.5 and up (including 3.x and pypy), and is commonly used by the Pyramid Framework.
Page Templates add within your document structure special element attributes and text markup. Using a set of simple
language constructs, you control the document flow, element repetition, text replacement and translation. Because of
the attribute-based syntax, unrendered page templates are valid HTML and can be viewed in a browser and even edited
in WYSIWYG editors. This can make round-trip collaboration with designers and prototyping with static files in a
browser easier.
The basic TAL language is simple enough to grasp from an example:
Hello, World!
The pattern for text insertion is common enough that if you do not require strict
validity in your unrendered templates, you can replace it with a more terse and readable syntax that uses the pattern
${expression}, as follows:
Hello, ${world}!
${row.capitalize()} ${col}
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But keep in mind that the full Default Text syntax also allows for default
content in the unrendered template.
Being from the Pyramid world, Chameleon is not widely used.
Mako
Mako is a template language that compiles to Python for maximum performance. Its syntax and api is borrowed from
the best parts of other templating languages like Django and Jinja2 templates. It is the default template language
included with the Pylons and Pyramid web frameworks.
An example template in Mako looks like:
<%inherit file="base.html"/>
<%
rows = [[v for v in range(0,10)] for row in range(0,10)]
%>
% for row in rows:
${makerow(row)}
% endfor
<%def name="makerow(row)">
% for name in row:
${name} \
% endfor
To render a very basic template, you can do the following:
from mako.template import Template
print(Template("hello ${data}!").render(data="world"))
Mako is well respected within the Python web community.
References
HTML Scraping
Web Scraping
Web sites are written using HTML, which means that each web page is a structured document. Sometimes it would be
great to obtain some data from them and preserve the structure while we’re at it. Web sites don’t always provide their
data in comfortable formats such as csv or json.
This is where web scraping comes in. Web scraping is the practice of using a computer program to sift through a web
page and gather the data that you need in a format most useful to you while at the same time preserving the structure
of the data.
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lxml and Requests
lxml is a pretty extensive library written for parsing XML and HTML documents very quickly, even handling messed
up tags in the process. We will also be using the Requests module instead of the already built-in urllib2 module due to
improvements in speed and readability. You can easily install both using pip install lxml and pip install
requests.
Let’s start with the imports:
from lxml import html
import requests
Next we will use requests.get to retrieve the web page with our data, parse it using the html module and save
the results in tree:
page = requests.get('http://econpy.pythonanywhere.com/ex/001.html')
tree = html.fromstring(page.content)
(We need to use page.content rather than page.text because html.fromstring implicitly expects bytes
as input.)
tree now contains the whole HTML file in a nice tree structure which we can go over two different ways: XPath and
CSSSelect. In this example, we will focus on the former.
XPath is a way of locating information in structured documents such as HTML or XML documents. A good introduction to XPath is on W3Schools .
There are also various tools for obtaining the XPath of elements such as FireBug for Firefox or the Chrome Inspector.
If you’re using Chrome, you can right click an element, choose ‘Inspect element’, highlight the code, right click again
and choose ‘Copy XPath’.
After a quick analysis, we see that in our page the data is contained in two elements - one is a div with title ‘buyername’ and the other is a span with class ‘item-price’:
Carson Busses
$29.95
Knowing this we can create the correct XPath query and use the lxml xpath function like this:
#This will create a list of buyers:
buyers = tree.xpath('//div[@title="buyer-name"]/text()')
#This will create a list of prices
prices = tree.xpath('//span[@class="item-price"]/text()')
Let’s see what we got exactly:
print 'Buyers: ', buyers
print 'Prices: ', prices
Buyers: ['Carson Busses', 'Earl E. Byrd', 'Patty Cakes',
'Derri Anne Connecticut', 'Moe Dess', 'Leda Doggslife', 'Dan Druff',
'Al Fresco', 'Ido Hoe', 'Howie Kisses', 'Len Lease', 'Phil Meup',
'Ira Pent', 'Ben D. Rules', 'Ave Sectomy', 'Gary Shattire',
'Bobbi Soks', 'Sheila Takya', 'Rose Tattoo', 'Moe Tell']
Prices: ['$29.95', '$8.37', '$15.26', '$19.25', '$19.25',
'$13.99', '$31.57', '$8.49', '$14.47', '$15.86', '$11.11',
'$15.98', '$16.27', '$7.50', '$50.85', '$14.26', '$5.68',
'$15.00', '$114.07', '$10.09']
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Congratulations! We have successfully scraped all the data we wanted from a web page using lxml and Requests. We
have it stored in memory as two lists. Now we can do all sorts of cool stuff with it: we can analyze it using Python or
we can save it to a file and share it with the world.
Some more cool ideas to think about are modifying this script to iterate through the rest of the pages of this example
dataset, or rewriting this application to use threads for improved speed.
If you want to learn how to import data using python - this DataCamp course on Importing Data is a great place to
start.
Command-line Applications
Command-line applications, also referred to as Console Applications, are computer programs designed to be used
from a text interface, such as a shell. Command-line applications usually accept various inputs as arguments, often
referred to as parameters or sub-commands, as well as options, often referred to as flags or switches.
Some popular command-line applications include:
• Grep - A plain-text data search utility
• curl - A tool for data transfer with URL syntax
• httpie - A command line HTTP client, a user-friendly cURL replacement
• git - A distributed version control system
• mercurial - A distributed version control system primarily written in Python
Clint
clint is a Python module which is filled with very useful tools for developing command-line applications. It supports
features such as; CLI colors and indents, simple and powerful column printer, iterator based progress bars and implicit
argument handling.
Click
click is a Python package for creating command-line interfaces in a composable way with as little code as possible.
This “Command-line Interface Creation Kit” is highly configurable but comes with good defaults out of the box.
docopt
docopt is a lightweight, highly Pythonic package that allows creating command-line interfaces easily and intuitively,
by parsing POSIX-style usage instructions.
Plac
Plac is a simple wrapper over the Python standard library argparse, which hides most of its complexity by using a
declarative interface: the argument parser is inferred rather than written down by imperatively. This module targets
especially unsophisticated users, programmers, sys-admins, scientists and in general people writing throw-away scripts
for themselves, who choose to create a command-line interface because it is quick and simple.
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Cliff
Cliff is a framework for building command-line programs. It uses setuptools entry points to provide subcommands,
output formatters, and other extensions. The framework is meant to be used to create multi-level commands such as
subversion and git, where the main program handles some basic argument parsing and then invokes a sub-command
to do the work.
Cement
Cement is an advanced CLI Application Framework. Its goal is to introduce a standard, and feature-full platform for
both simple and complex command line applications as well as support rapid development needs without sacrificing
quality. Cement is flexible, and it’s use cases span from the simplicity of a micro-framework to the complexity of a
meg-framework.
GUI Applications
Alphabetical list of GUI Applications.
Camelot
Camelot provides components for building applications on top of Python, SQLAlchemy and Qt. It is inspired by the
Django admin interface.
The main resource for information is the website: http://www.python-camelot.com and the mailing list
https://groups.google.com/forum/#!forum/project-camelot
Cocoa
Note: The Cocoa framework is only available on OS X. Don’t pick this if you’re writing a cross-platform application!
GTk
PyGTK provides Python bindings for the GTK+ toolkit. Like the GTK+ library itself, it is currently licensed under the
GNU LGPL. It is worth noting that PyGTK only currently supports the Gtk-2.X API (NOT Gtk-3.0). It is currently
recommended that PyGTK not be used for new projects and that existing applications be ported from PyGTK to
PyGObject.
PyGObject aka (PyGi)
PyGObject provides Python bindings, which gives access to the entire GNOME software platform. It is fully compatible with GTK+ 3. Here is a tutorial to get started with Python GTK+ 3 Tutorial.
API Reference
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Kivy
Kivy is a Python library for development of multi-touch enabled media rich applications. The aim is to allow for quick
and easy interaction design and rapid prototyping, while making your code reusable and deployable.
Kivy is written in Python, based on OpenGL and supports different input devices such as: Mouse, Dual Mouse, TUIO,
WiiMote, WM_TOUCH, HIDtouch, Apple’s products and so on.
Kivy is actively being developed by a community and is free to use. It operates on all major platforms (Linux, OSX,
Windows, Android).
The main resource for information is the website: http://kivy.org
PyObjC
Note: Only available on OS X. Don’t pick this if you’re writing a cross-platform application.
PySide
PySide is a Python binding of the cross-platform GUI toolkit Qt.
pip install pyside
https://wiki.qt.io/Category:LanguageBindings::PySide::Downloads
PyQt
Note: If your software does not fully comply with the GPL you will need a commercial license!
PyQt provides Python bindings for the Qt Framework (see below).
http://www.riverbankcomputing.co.uk/software/pyqt/download
PyjamasDesktop (pyjs Desktop)
PyjamasDesktop is a port of Pyjamas. PyjamasDesktop is application widget set for desktop and a cross-platform
framework. (After release v0.6 PyjamasDesktop is a part of Pyjamas (Pyjs)). Briefly, it allows the exact same Python
web application source code to be executed as a standalone desktop application.
Python Wiki for PyjamasDesktop.
The main website; pyjs Desktop.
Qt
Qt is a cross-platform application framework that is widely used for developing software with a GUI but can also be
used for non-GUI applications.
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Toga
Toga is a Python native, OS native, cross platform GUI toolkit. Toga consists of a library of base components with a
shared interface to simplify platform-agnostic GUI development.
Toga is available on Mac OS, Windows, Linux (GTK), and mobile platforms such as Android and iOS.
Tk
Tkinter is a thin object-oriented layer on top of Tcl/Tk. It has the advantage of being included with the Python
standard library, making it the most convenient and compatible toolkit to program with.
Both Tk and Tkinter are available on most Unix platforms, as well as on Windows and Macintosh systems. Starting
with the 8.0 release, Tk offers native look and feel on all platforms.
There’s a good multi-language Tk tutorial with Python examples at TkDocs. There’s more information available on
the Python Wiki.
wxPython
wxPython is a GUI toolkit for the Python programming language. It allows Python programmers to create programs
with a robust, highly functional graphical user interface, simply and easily. It is implemented as a Python extension
module (native code) that wraps the popular wxWidgets cross platform GUI library, which is written in C++.
Install (Stable) wxPython go to http://www.wxpython.org/download.php#stable and download the appropriate package for your OS.
Databases
DB-API
The Python Database API (DB-API) defines a standard interface for Python database access modules. It’s documented
in PEP 249. Nearly all Python database modules such as sqlite3, psycopg and mysql-python conform to this interface.
Tutorials that explain how to work with modules that conform to this interface can be found here and here.
SQLAlchemy
SQLAlchemy is a commonly used database toolkit. Unlike many database libraries it not only provides an ORM layer
but also a generalized API for writing database-agnostic code without SQL.
$ pip install sqlalchemy
Records
Records is minimalist SQL library, designed for sending raw SQL queries to various databases. Data can be used
programmatically, or exported to a number of useful data formats.
$ pip install records
Also included is a command-line tool for exporting SQL data.
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Django ORM
The Django ORM is the interface used by Django to provide database access.
It’s based on the idea of models, an abstraction that makes it easier to manipulate data in Python.
The basics:
• Each model is a Python class that subclasses django.db.models.Model.
• Each attribute of the model represents a database field.
• Django gives you an automatically-generated database-access API; see Making queries.
peewee
peewee is another ORM with a focus on being lightweight with support for Python 2.6+ and 3.2+ which supports
SQLite, MySQL and Postgres by default. The model layer is similar to that of the Django ORM and it has SQL-like
methods to query data. While SQLite, MySQL and Postgres are supported out-of-the-box, there is a collection of
add-ons available.
PonyORM
PonyORM is an ORM that takes a different approach to querying the database. Instead of writing an SQL-like language
or boolean expressions, Python’s generator syntax is used. There’s also an graphical schema editor that can generate
PonyORM entities for you. It supports Python 2.6+ and Python 3.3+ and can connect to SQLite, MySQL, Postgres &
Oracle
SQLObject
SQLObject is yet another ORM. It supports a wide variety of databases: Common database systems MySQL, Postgres
and SQLite and more exotic systems like SAP DB, SyBase and MSSQL. It only supports Python 2 from Python 2.6
upwards.
Networking
Twisted
Twisted is an event-driven networking engine. It can be used to build applications around many different networking protocols, including http servers and clients, applications using SMTP, POP3, IMAP or SSH protocols, instant
messaging and much more.
PyZMQ
PyZMQ is the Python binding for ZeroMQ, which is a high-performance asynchronous messaging library. One great
advantage of ZeroMQ is that it can be used for message queuing without a message broker. The basic patterns for this
are:
• request-reply: connects a set of clients to a set of services. This is a remote procedure call and task distribution
pattern.
• publish-subscribe: connects a set of publishers to a set of subscribers. This is a data distribution pattern.
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• push-pull (or pipeline): connects nodes in a fan-out / fan-in pattern that can have multiple steps, and loops. This
is a parallel task distribution and collection pattern.
For a quick start, read the ZeroMQ guide.
gevent
gevent is a coroutine-based Python networking library that uses greenlets to provide a high-level synchronous API on
top of the libev event loop.
Systems Administration
Fabric
Fabric is a library for simplifying system administration tasks. While Chef and Puppet tend to focus on managing
servers and system libraries, Fabric is more focused on application level tasks such as deployment.
Install Fabric:
$ pip install fabric
The following code will create two tasks that we can use: memory_usage and deploy. The former will output the
memory usage on each machine. The latter will ssh into each server, cd to our project directory, activate the virtual
environment, pull the newest codebase, and restart the application server.
from fabric.api import cd, env, prefix, run, task
env.hosts = ['my_server1', 'my_server2']
@task
def memory_usage():
run('free -m')
@task
def deploy():
with cd('/var/www/project-env/project'):
with prefix('. ../bin/activate'):
run('git pull')
run('touch app.wsgi')
With the previous code saved in a file named fabfile.py, we can check memory usage with:
$ fab memory_usage
[my_server1] Executing task 'memory'
[my_server1] run: free -m
[my_server1] out:
total
[my_server1] out: Mem:
6964
[my_server1] out: -/+ buffers/cache:
[my_server1] out: Swap:
0
used
1897
1509
0
free
5067
5455
0
shared
0
buffers
166
cached
222
[my_server2]
[my_server2]
[my_server2]
[my_server2]
[my_server2]
[my_server2]
used
902
148
1
free
764
1517
894
shared
0
buffers
180
cached
572
Executing task 'memory'
run: free -m
out:
total
out: Mem:
1666
out: -/+ buffers/cache:
out: Swap:
895
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and we can deploy with:
$ fab deploy
Additional features include parallel execution, interaction with remote programs, and host grouping.
Fabric Documentation
Salt
Salt is an open source infrastructure management tool. It supports remote command execution from a central point
(master host) to multiple hosts (minions). It also supports system states which can be used to configure multiple servers
using simple template files.
Salt supports Python versions 2.6 and 2.7 and can be installed via pip:
$ pip install salt
After configuring a master server and any number of minion hosts, we can run arbitrary shell commands or use prebuilt modules of complex commands on our minions.
The following command lists all available minion hosts, using the ping module.
$ salt '*' test.ping
The host filtering is accomplished by matching the minion id, or using the grains system. The grains system uses static
host information like the operating system version or the CPU architecture to provide a host taxonomy for the Salt
modules.
The following command lists all available minions running CentOS using the grains system:
$ salt -G 'os:CentOS' test.ping
Salt also provides a state system. States can be used to configure the minion hosts.
For example, when a minion host is ordered to read the following state file, it will install and start the Apache server:
apache:
pkg:
- installed
service:
- running
- enable: True
- require:
- pkg: apache
State files can be written using YAML, the Jinja2 template system or pure Python.
Salt Documentation
Psutil
Psutil is an interface to different system information (e.g. CPU, memory, disks, network, users and processes).
Here is an example to be aware of some server overload. If any of the tests (net, CPU) fail, it will send an email.
# Functions to get system values:
from psutil import cpu_percent, net_io_counters
# Functions to take a break:
from time import sleep
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# Package for email services:
import smtplib
import string
MAX_NET_USAGE = 400000
MAX_ATTACKS = 4
attack = 0
counter = 0
while attack <= MAX_ATTACKS:
sleep(4)
counter = counter + 1
# Check the cpu usage
if cpu_percent(interval = 1) > 70:
attack = attack + 1
# Check the net usage
neti1 = net_io_counters()[1]
neto1 = net_io_counters()[0]
sleep(1)
neti2 = net_io_counters()[1]
neto2 = net_io_counters()[0]
# Calculate the bytes per second
net = ((neti2+neto2) - (neti1+neto1))/2
if net > MAX_NET_USAGE:
attack = attack + 1
if counter > 25:
attack = 0
counter = 0
# Write a very important email if attack is higher than 4
TO = "you@your_email.com"
FROM = "webmaster@your_domain.com"
SUBJECT = "Your domain is out of system resources!"
text = "Go and fix your server!"
BODY = string.join(("From: %s" %FROM,"To: %s" %TO,"Subject: %s" %SUBJECT, "",text), "\r\n")
server = smtplib.SMTP('127.0.0.1')
server.sendmail(FROM, [TO], BODY)
server.quit()
A full terminal application like a widely extended top which is based on psutil and with the ability of a client-server
monitoring is glance.
Ansible
Ansible is an open source system automation tool. The biggest advantage over Puppet or Chef is it does not require an
agent on the client machine. Playbooks are Ansible’s configuration, deployment, and orchestration language and are
written in YAML with Jinja2 for templating.
Ansible supports Python versions 2.6 and 2.7 and can be installed via pip:
$ pip install ansible
Ansible requires an inventory file that describes the hosts to which it has access. Below is an example of a host and
playbook that will ping all the hosts in the inventory file.
Here is an example inventory file: hosts.yml
[server_name]
127.0.0.1
Here is an example playbook: ping.yml
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--- hosts: all
tasks:
- name: ping
action: ping
To run the playbook:
$ ansible-playbook ping.yml -i hosts.yml --ask-pass
The Ansible playbook will ping all of the servers in the hosts.yml file. You can also select groups of servers using
Ansible. For more information about Ansible, read the Ansible Docs.
An Ansible tutorial is also a great and detailed introduction to getting started with Ansible.
Chef
Chef is a systems and cloud infrastructure automation framework that makes it easy to deploy servers and applications
to any physical, virtual, or cloud location. In case this is your choice for configuration management, you will primarily
use Ruby to write your infrastructure code.
Chef clients run on every server that is part of your infrastructure and these regularly check with your Chef server to
ensure your system is always aligned and represents the desired state. Since each individual server has its own distinct
Chef client, each server configures itself and this distributed approach makes Chef a scalable automation platform.
Chef works by using custom recipes (configuration elements), implemented in cookbooks. Cookbooks, which are
basically packages for infrastructure choices, are usually stored in your Chef server. Read the Digital Ocean tutorial
series on chef to learn how to create a simple Chef Server.
To create a simple cookbook the knife command is used:
knife cookbook create cookbook_name
Getting started with Chef is a good starting point for Chef Beginners and many community maintained cookbooks that
can serve as a good reference or tweaked to serve your infrastructure configuration needs can be found on the Chef
Supermarket.
• Chef Documentation
Puppet
Puppet is IT Automation and configuration management software from Puppet Labs that allows System Administrators
to define the state of their IT Infrastructure, thereby providing an elegant way to manage their fleet of physical and
virtual machines.
Puppet is available both as an Open Source and an Enterprise variant. Modules are small, shareable units of code
written to automate or define the state of a system. Puppet Forge is a repository for modules written by the community
for Open Source and Enterprise Puppet.
Puppet Agents are installed on nodes whose state needs to be monitored or changed. A designated server known as
the Puppet Master is responsible for orchestrating the agent nodes.
Agent nodes send basic facts about the system such as to the operating system, kernel, architecture, ip address, hostname etc. to the Puppet Master. The Puppet Master then compiles a catalog with information provided by the agents
on how each node should be configured and sends it to the agent. The agent enforces the change as prescribed in the
catalog and sends a report back to the Puppet Master.
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Facter is an interesting tool that ships with Puppet that pulls basic facts about the system. These facts can be referenced
as a variable while writing your Puppet modules.
$ facter kernel
Linux
$ facter operatingsystem
Ubuntu
Writing Modules in Puppet is pretty straight forward. Puppet Manifests together form Puppet Modules. Puppet
manifest end with an extension of .pp. Here is an example of ‘Hello World’ in Puppet.
notify { 'This message is getting logged into the agent node':
#As nothing is specified in the body the resource title
#the notification message by default.
}
Here is another example with system based logic. Note how the operating system fact is being used as a variable
prepended with the $ sign. Similarly, this holds true for other facts such as hostname which can be referenced by
$hostname
notify{ 'Mac Warning':
message => $operatingsystem ? {
'Darwin' => 'This seems to be a Mac.',
default => 'I am a PC.',
},
}
There are several resource types for Puppet but the package-file-service paradigm is all you need for undertaking
majority of the configuration management. The following Puppet code makes sure that the OpenSSH-Server package
is installed in a system and the sshd service is notified to restart everytime the sshd configuration file is changed.
package { 'openssh-server':
ensure => installed,
}
file { '/etc/ssh/sshd_config':
source
=> 'puppet:///modules/sshd/sshd_config',
owner
=> 'root',
group
=> 'root',
mode
=> '640',
notify
=> Service['sshd'], # sshd will restart
# whenever you edit this
# file
require => Package['openssh-server'],
}
service { 'sshd':
ensure
=> running,
enable
=> true,
hasstatus => true,
hasrestart=> true,
}
For more information, refer to the Puppet Labs Documentation
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Blueprint
Todo
Write about Blueprint
Buildout
Buildout is an open source software build tool. Buildout is created using the Python programming language. It
implements a principle of separation of configuration from the scripts that do the setting up. Buildout is primarily used
to download and set up dependencies in Python eggs format of the software being developed or deployed. Recipes for
build tasks in any environment can be created, and many are already available.
Shinken
Shinken is a modern, Nagios compatible monitoring framework written in Python. Its main goal is to give users a
flexible architecture for their monitoring system that is designed to scale to large environments.
Shinken is backwards-compatible with the Nagios configuration standard, and plugins.It works on any operating system, and architecture that supports Python which includes Windows, GNU/Linux, and FreeBSD.
Continuous Integration
Note: For advice on writing your tests, see Testing Your Code.
Why?
Martin Fowler, who first wrote about Continuous Integration (short: CI) together with Kent Beck, describes the CI as
follows:
Continuous Integration is a software development practice where members of a team integrate their work
frequently, usually each person integrates at least daily - leading to multiple integrations per day. Each
integration is verified by an automated build (including test) to detect integration errors as quickly as
possible. Many teams find that this approach leads to significantly reduced integration problems and
allows a team to develop cohesive software more rapidly.
Jenkins
Jenkins CI is an extensible continuous integration engine. Use it.
Buildbot
Buildbot is a Python system to automate the compile/test cycle to validate code changes.
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Tox
tox is an automation tool providing packaging, testing and deployment of Python software right from the console or
CI server. It is a generic virtualenv management and test command line tool which provides the following features:
• Checking that packages install correctly with different Python versions and interpreters
• Running tests in each of the environments, configuring your test tool of choice
• Acting as a front-end to Continuous Integration servers, reducing boilerplate and merging CI and shell-based
testing.
Travis-CI
Travis-CI is a distributed CI server which builds tests for open source projects for free. It provides multiple workers
to run Python tests on and seamlessly integrates with GitHub. You can even have it comment on your Pull Requests
whether this particular changeset breaks the build or not. So if you are hosting your code on GitHub, travis-ci is a
great and easy way to get started with Continuous Integration.
In order to get started, add a .travis.yml file to your repository with this example content:
language: python
python:
- "2.6"
- "2.7"
- "3.2"
- "3.3"
# command to install dependencies
script: python tests/test_all_of_the_units.py
branches:
only:
- master
This will get your project tested on all the listed Python versions by running the given script, and will only build the
master branch. There are a lot more options you can enable, like notifications, before and after steps and much more.
The travis-ci docs explain all of these options, and are very thorough.
In order to activate testing for your project, go to the travis-ci site and login with your GitHub account. Then activate
your project in your profile settings and you’re ready to go. From now on, your project’s tests will be run on every
push to GitHub.
Speed
CPython, the most commonly used implementation of Python, is slow for CPU bound tasks. PyPy is fast.
Using a slightly modified version of David Beazley’s CPU bound test code (added loop for multiple tests), you can
see the difference between CPython and PyPy’s processing.
# PyPy
$ ./pypy -V
Python 2.7.1 (7773f8fc4223, Nov 18 2011, 18:47:10)
[PyPy 1.7.0 with GCC 4.4.3]
$ ./pypy measure2.py
0.0683999061584
0.0483210086823
0.0388588905334
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0.0440690517426
0.0695300102234
# CPython
$ ./python -V
Python 2.7.1
$ ./python measure2.py
1.06774401665
1.45412397385
1.51485204697
1.54693889618
1.60109114647
Context
The GIL
The GIL (Global Interpreter Lock) is how Python allows multiple threads to operate at the same time. Python’s
memory management isn’t entirely thread-safe, so the GIL is required to prevent multiple threads from running the
same Python code at once.
David Beazley has a great guide on how the GIL operates. He also covers the new GIL in Python 3.2. His results show
that maximizing performance in a Python application requires a strong understanding of the GIL, how it affects your
specific application, how many cores you have, and where your application bottlenecks are.
C Extensions
The GIL
Special care must be taken when writing C extensions to make sure you register your threads with the interpreter.
C Extensions
Cython
Cython implements a superset of the Python language with which you are able to write C and C++ modules for Python.
Cython also allows you to call functions from compiled C libraries. Using Cython allows you to take advantage of
Python’s strong typing of variables and operations.
Here’s an example of strong typing with Cython:
def primes(int kmax):
"""Calculation of prime numbers with additional
Cython keywords"""
cdef int n, k, i
cdef int p[1000]
result = []
if kmax > 1000:
kmax = 1000
k = 0
n = 2
while k < kmax:
i = 0
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while i < k and n % p[i] != 0:
i = i + 1
if i == k:
p[k] = n
k = k + 1
result.append(n)
n = n + 1
return result
This implementation of an algorithm to find prime numbers has some additional keywords compared to the next one,
which is implemented in pure Python:
def primes(kmax):
"""Calculation of prime numbers in standard Python syntax"""
p = range(1000)
result = []
if kmax > 1000:
kmax = 1000
k = 0
n = 2
while k < kmax:
i = 0
while i < k and n % p[i] != 0:
i = i + 1
if i == k:
p[k] = n
k = k + 1
result.append(n)
n = n + 1
return result
Notice that in the Cython version you declare integers and integer arrays to be compiled into C types while also
creating a Python list:
def primes(int kmax):
"""Calculation of prime numbers with additional
Cython keywords"""
cdef int n, k, i
cdef int p[1000]
result = []
def primes(kmax):
"""Calculation of prime numbers in standard Python syntax"""
p = range(1000)
result = []
What is the difference? In the upper Cython version you can see the declaration of the variable types and the integer
array in a similar way as in standard C. For example cdef int n,k,i in line 3. This additional type declaration (i.e. integer)
allows the Cython compiler to generate more efficient C code from the second version. While standard Python code
is saved in *.py files, Cython code is saved in *.pyx files.
What’s the difference in speed? Let’s try it!
import time
#activate pyx compiler
import pyximport
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pyximport.install()
#primes implemented with Cython
import primesCy
#primes implemented with Python
import primes
print "Cython:"
t1= time.time()
print primesCy.primes(500)
t2= time.time()
print "Cython time: %s" %(t2-t1)
print ""
print "Python"
t1= time.time()
print primes.primes(500)
t2= time.time()
print "Python time: %s" %(t2-t1)
These lines both need a remark:
import pyximport
pyximport.install()
The pyximport module allows you to import *.pyx files (e.g., primesCy.pyx) with the Cython-compiled version
of the primes function. The pyximport.install() command allows the Python interpreter to start the Cython compiler
directly to generate C-code, which is automatically compiled to a *.so C-library. Cython is then able to import this
library for you in your Python code, easily and efficiently. With the time.time() function you are able to compare the
time between these 2 different calls to find 500 prime numbers. On a standard notebook (dual core AMD E-450 1.6
GHz), the measured values are:
Cython time: 0.0054 seconds
Python time: 0.0566 seconds
And here the output of an embedded ARM beaglebone machine:
Cython time: 0.0196 seconds
Python time: 0.3302 seconds
Pyrex
Shedskin?
Concurrency
Concurrent.futures
The concurrent.futures module is a module in the standard library that provides a “high-level interface for asynchronously executing callables”. It abstracts away a lot of the more complicated details about using multiple threads
or processes for concurrency, and allows the user to focus on accomplishing the task at hand.
The concurrent.futures module exposes two main classes, the ThreadPoolExecutor and the ProcessPoolExecutor. The
ThreadPoolExecutor will create a pool of worker threads that a user can submit jobs to. These jobs will then be
executed in another thread when the next worker thread becomes available.
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The ProcessPoolExecutor works in the same way, except instead of using multiple threads for its workers, it will use
multiple processes. This makes it possible to side-step the GIL, however because of the way things are passed to
worker processes, only picklable objects can be executed and returned.
Because of the way the GIL works, a good rule of thumb is to use a ThreadPoolExecutor when the task being executed
involves a lot of blocking (i.e. making requests over the network) and to use a ProcessPoolExecutor executor when
the task is computationally expensive.
There are two main ways of executing things in parallel using the two Executors. One way is with the map(func,
iterables) method. This works almost exactly like the builtin map() function, except it will execute everything in
parallel. :
from concurrent.futures import ThreadPoolExecutor
import requests
def get_webpage(url):
page = requests.get(url)
return page
pool = ThreadPoolExecutor(max_workers=5)
my_urls = ['http://google.com/']*10
# Create a list of urls
for page in pool.map(get_webpage, my_urls):
# Do something with the result
print(page.text)
For even more control, the submit(func, *args, **kwargs) method will schedule a callable to be executed ( as
func(*args, **kwargs)) and returns a Future object that represents the execution of the callable.
The Future object provides various methods that can be used to check on the progress of the scheduled callable. These
include:
cancel() Attempt to cancel the call.
cancelled() Return True if the call was successfully cancelled.
running() Return True if the call is currently being executed and cannot be cancelled.
done() Return True if the call was successfully cancelled or finished running.
result() Return the value returned by the call. Note that this call will block until the scheduled callable returns by
default.
exception() Return the exception raised by the call. If no exception was raised then this returns None. Note that this
will block just like result().
add_done_callback(fn) Attach a callback function that will be executed (as fn(future)) when the scheduled callable
returns.
from concurrent.futures import ProcessPoolExecutor, as_completed
def is_prime(n):
if n % 2 == 0:
return n, False
sqrt_n = int(n**0.5)
for i in range(3, sqrt_n + 1, 2):
if n % i == 0:
return n, False
return n, True
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PRIMES = [
112272535095293,
112582705942171,
112272535095293,
115280095190773,
115797848077099,
1099726899285419]
futures = []
with ProcessPoolExecutor(max_workers=4) as pool:
# Schedule the ProcessPoolExecutor to check if a number is prime
# and add the returned Future to our list of futures
for p in PRIMES:
fut = pool.submit(is_prime, p)
futures.append(fut)
# As the jobs are completed, print out the results
for number, result in as_completed(futures):
if result:
print("{} is prime".format(number))
else:
print("{} is not prime".format(number))
The concurrent.futures module contains two helper functions for working with Futures. The as_completed(futures)
function returns an iterator over the list of futures, yielding the futures as they complete.
The wait(futures) function will simply block until all futures in the list of futures provided have completed.
For more information, on using the concurrent.futures module, consult the official documentation.
Threading
The standard library comes with a threading module that allows a user to work with multiple threads manually.
Running a function in another thread is as simple as passing a callable and it’s arguments to Thread‘s constructor and
then calling start():
from threading import Thread
import requests
def get_webpage(url):
page = requests.get(url)
return page
some_thread = Thread(get_webpage, 'http://google.com/')
some_thread.start()
To wait until the thread has terminated, call join():
some_thread.join()
After calling join(), it is always a good idea to check whether the thread is still alive (because the join call timed out):
if some_thread.is_alive():
print("join() must have timed out.")
else:
print("Our thread has terminated.")
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Because multiple threads have access to the same section of memory, sometimes there might be situations where two
or more threads are trying to write to the same resource at the same time or where the output is dependent on the
sequence or timing of certain events. This is called a data race or race condition. When this happens, the output will
be garbled or you may encounter problems which are difficult to debug. A good example is this stackoverflow post.
The way this can be avoided is by using a Lock that each thread needs to acquire before writing to a shared resource.
Locks can be acquired and released through either the contextmanager protocol (with statement), or by using acquire()
and release() directly. Here is a (rather contrived) example:
from threading import Lock, Thread
file_lock = Lock()
def log(msg):
with file_lock:
open('website_changes.log', 'w') as f:
f.write(changes)
def monitor_website(some_website):
"""
Monitor a website and then if there are any changes,
log them to disk.
"""
while True:
changes = check_for_changes(some_website)
if changes:
log(changes)
websites = ['http://google.com/', ... ]
for website in websites:
t = Thread(monitor_website, website)
t.start()
Here, we have a bunch of threads checking for changes on a list of sites and whenever there are any changes, they
attempt to write those changes to a file by calling log(changes). When log() is called, it will wait to acquire the lock
with with file_lock:. This ensures that at any one time, only one thread is writing to the file.
Spawning Processes
Multiprocessing
Scientific Applications
Context
Python is frequently used for high-performance scientific applications. It is widely used in academia and scientific
projects because it is easy to write and performs well.
Due to its high performance nature, scientific computing in Python often utilizes external libraries, typically written
in faster languages (like C, or FORTRAN for matrix operations). The main libraries used are NumPy, SciPy and
Matplotlib. Going into detail about these libraries is beyond the scope of the Python guide. However, a comprehensive
introduction to the scientific Python ecosystem can be found in the Python Scientific Lecture Notes
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IPython
IPython is an enhanced version of Python interpreter, which provides features of great interest to scientists. The inline
mode allows graphics and plots to be displayed in the terminal (Qt based version). Moreover, the notebook mode supports literate programming and reproducible science generating a web-based Python notebook. This notebook allows
you to store chunks of Python code along side the results and additional comments (HTML, LaTeX, Markdown). The
notebook can then be shared and exported in various file formats.
Libraries
NumPy
NumPy is a low level library written in C (and FORTRAN) for high level mathematical functions. NumPy cleverly
overcomes the problem of running slower algorithms on Python by using multidimensional arrays and functions that
operate on arrays. Any algorithm can then be expressed as a function on arrays, allowing the algorithms to be run
quickly.
NumPy is part of the SciPy project, and is released as a separate library so people who only need the basic requirements
can use it without installing the rest of SciPy.
NumPy is compatible with Python versions 2.4 through to 2.7.2 and 3.1+.
Numba
Numba is a NumPy aware Python compiler (just-in-time (JIT) specializing compiler) which compiles annotated Python
(and NumPy) code to LLVM (Low Level Virtual Machine) through special decorators. Briefly, Numba uses a system
that compiles Python code with LLVM to code which can be natively executed at runtime.
SciPy
SciPy is a library that uses NumPy for more mathematical functions. SciPy uses NumPy arrays as the basic data structure, and comes with modules for various commonly used tasks in scientific programming, including linear algebra,
integration (calculus), ordinary differential equation solving and signal processing.
Matplotlib
Matplotlib is a flexible plotting library for creating interactive 2D and 3D plots that can also be saved as manuscriptquality figures. The API in many ways reflects that of MATLAB, easing transition of MATLAB users to Python.
Many examples, along with the source code to re-create them, are available in the matplotlib gallery.
Pandas
Pandas is data manipulation library based on Numpy which provides many useful functions for accessing, indexing,
merging and grouping data easily. The main data structure (DataFrame) is close to what could be found in the R
statistical package; that is, heterogeneous data tables with name indexing, time series operations and auto-alignment
of data.
Rpy2
Rpy2 is a Python binding for the R statistical package allowing the execution of R functions from Python and passing
data back and forth between the two environments. Rpy2 is the object oriented implementation of the Rpy bindings.
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PsychoPy
PsychoPy is a library for cognitive scientists allowing the creation of cognitive psychology and neuroscience experiments. The library handles presentation of stimuli, scripting of experimental design and data collection.
Resources
Installation of scientific Python packages can be troublesome, as many of these packages are implemented as Python
C extensions which need to be compiled. This section lists various so-called scientific Python distributions which
provide precompiled and easy-to-install collections of scientific Python packages.
Unofficial Windows Binaries for Python Extension Packages
Many people who do scientific computing are on Windows, yet many of the scientific computing packages are notoriously difficult to build and install on this platform. Christoph Gohlke however, has compiled a list of Windows
binaries for many useful Python packages. The list of packages has grown from a mainly scientific Python resource to
a more general list. If you’re on Windows, you may want to check it out.
Anaconda
Continuum Analytics offers the Anaconda Python Distribution which includes all the common scientific Python packages as well as many packages related to data analytics and big data. Anaconda itself is free, and Continuum sells a
number of proprietary add-ons. Free licenses for the add-ons are available for academics and researchers.
Canopy
Canopy is another scientific Python distribution, produced by Enthought. A limited ‘Canopy Express’ variant is
available for free, but Enthought charges for the full distribution. Free licenses are available for academics.
Image Manipulation
Most image processing and manipulation techniques can be carried out effectively using two libraries: Python Imaging
Library (PIL) and OpenSource Computer Vision (OpenCV).
A brief description of both is given below.
Python Imaging Library
The Python Imaging Library, or PIL for short, is one of the core libraries for image manipulation in Python. Unfortunately, its development has stagnated, with its last release in 2009.
Luckily for you, there’s an actively-developed fork of PIL called Pillow - it’s easier to install, runs on all operating
systems, and supports Python 3.
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Installation
Before installing Pillow, you’ll have to install Pillow’s prerequisites. Find the instructions for your platform in the
Pillow installation instructions.
After that, it’s straightforward:
$ pip install Pillow
Example
from PIL import Image, ImageFilter
#Read image
im = Image.open( 'image.jpg' )
#Display image
im.show()
#Applying a filter to the image
im_sharp = im.filter( ImageFilter.SHARPEN )
#Saving the filtered image to a new file
im_sharp.save( 'image_sharpened.jpg', 'JPEG' )
#Splitting the image into its respective bands, i.e. Red, Green,
#and Blue for RGB
r,g,b = im_sharp.split()
#Viewing EXIF data embedded in image
exif_data = im._getexif()
exif_data
There are more examples of the Pillow library in the Pillow tutorial.
OpenSource Computer Vision
OpenSource Computer Vision, more commonly known as OpenCV, is a more advanced image manipulation and
processing software than PIL. It has been implemented in several languages and is widely used.
Installation
In Python, image processing using OpenCV is implemented using the cv2 and NumPy modules. The installation
instructions for OpenCV should guide you through configuring the project for yourself.
NumPy can be downloaded from the Python Package Index(PyPI):
$ pip install numpy
Example
from cv2 import *
import numpy as np
#Read Image
img = cv2.imread('testimg.jpg')
#Display Image
cv2.imshow('image',img)
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cv2.waitKey(0)
cv2.destroyAllWindows()
#Applying Grayscale filter to image
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
#Saving filtered image to new file
cv2.imwrite('graytest.jpg',gray)
There are more Python-implemented examples of OpenCV in this collection of tutorials.
Data Serialization
What is data serialization?
Data serialization is the concept of converting structured data into a format that allows it to be shared or stored in
such a way that its original structure to be recovered. In some cases, the secondary intention of data serialization is to
minimize the size of the serialized data which then minimizes disk space or bandwidth requirements.
Pickle
The native data serialization module for Python is called Pickle.
Here’s an example:
import pickle
#Here's an example dict
grades = { 'Alice': 89, 'Bob': 72, 'Charles': 87 }
#Use dumps to convert the object to a serialized string
serial_grades = pickle.dumps( grades )
#Use loads to de-serialize an object
received_grades = pickle.loads( serial_grades )
Protobuf
If you’re looking for a serialization module that has support in multiple languages, Google’s Protobuf library is an
option.
XML parsing
untangle
untangle is a simple library which takes an XML document and returns a Python object which mirrors the nodes and
attributes in its structure.
For example, an XML file like this:
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can be loaded like this:
import untangle
obj = untangle.parse('path/to/file.xml')
and then you can get the child elements name like this:
obj.root.child['name']
untangle also supports loading XML from a string or an URL.
xmltodict
xmltodict is another simple library that aims at making XML feel like working with JSON.
An XML file like this:
elements
more elements
element as well
can be loaded into a Python dict like this:
import xmltodict
with open('path/to/file.xml') as fd:
doc = xmltodict.parse(fd.read())
and then you can access elements, attributes and values like this:
doc['mydocument']['@has'] # == u'an attribute'
doc['mydocument']['and']['many'] # == [u'elements', u'more elements']
doc['mydocument']['plus']['@a'] # == u'complex'
doc['mydocument']['plus']['#text'] # == u'element as well'
xmltodict also lets you roundtrip back to XML with the unparse function, has a streaming mode suitable for handling
files that don’t fit in memory and supports namespaces.
JSON
The json library can parse JSON from strings or files. The library parses JSON into a Python dictionary or list. It can
also convert Python dictionaries or lists into JSON strings.
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Parsing JSON
Take the following string containing JSON data:
json_string = '{"first_name": "Guido", "last_name":"Rossum"}'
It can be parsed like this:
import json
parsed_json = json.loads(json_string)
and can now be used as a normal dictionary:
print(parsed_json['first_name'])
"Guido"
You can also convert the following to JSON:
d = {
'first_name': 'Guido',
'second_name': 'Rossum',
'titles': ['BDFL', 'Developer'],
}
print(json.dumps(d))
'{"first_name": "Guido", "last_name": "Rossum", "titles": ["BDFL", "Developer"]}'
simplejson
The JSON library was added to Python in version 2.6. If you’re using an earlier version of Python, the simplejson
library is available via PyPI.
simplejson mimics the json standard library. It is available so that developers that use older versions of Python can use
the latest features available in the json lib.
You can start using simplejson when the json library is not available by importing simplejson under a different name:
import simplejson as json
After importing simplejson as json, the above examples will all work as if you were using the standard json library.
Cryptography
Cryptography
Cryptography is an actively developed library that provides cryptographic recipes and primitives. It supports Python
2.6-2.7, Python 3.3+ and PyPy.
Cryptography is divided into two layers of recipes and hazardous materials (hazmat). The recipes layer provides
simple API for proper symmetric encryption and the hazmat layer provides low-level cryptographic primitives.
Installation
$ pip install cryptography
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Example
Example code using high level symmetric encryption recipe:
from cryptography.fernet import Fernet
key = Fernet.generate_key()
cipher_suite = Fernet(key)
cipher_text = cipher_suite.encrypt(b"A really secret message. Not for prying eyes.")
plain_text = cipher_suite.decrypt(cipher_text)
PyCrypto
PyCrypto is another library, which provides secure hash functions and various encryption algorithms. It supports
Python version 2.1 through 3.3.
Installation
$ pip install pycrypto
Example
from Crypto.Cipher import AES
# Encryption
encryption_suite = AES.new('This is a key123', AES.MODE_CBC, 'This is an IV456')
cipher_text = encryption_suite.encrypt("A really secret message. Not for prying eyes.")
# Decryption
decryption_suite = AES.new('This is a key123', AES.MODE_CBC, 'This is an IV456')
plain_text = decryption_suite.decrypt(cipher_text)
Machine Learning
Python has a vast number of libraries for data analysis, statistics and Machine Learning itself, making it a language of
choice for many data scientists.
Some widely used packages for Machine Learning and other Data Science applications are enlisted below.
Scipy Stack
The Scipy stack consists of a bunch of core helper packages used in data science, for statistical analysis and visualising
data. Because of its huge number of functionalities and ease of use, the Stack is considered a must-have for most data
science applications.
The Stack consists of the following packages (link to documentation given):
1. NumPy
2. SciPy library
3. Matplotlib
4. IPython
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5. pandas
6. Sympy
7. nose
The stack also comes with Python bundled in, but has been excluded from the above list.
Installation
For installing the full stack, or individual packages, you can refer to the instructions given here.
NB: Anaconda is highly preferred and recommended for installing and maintaining data science packages seamlessly.
scikit-learn
Scikit is a free and open-source machine learning library for Python. It offers off-the-shelf functions to implement
many algorithms like linear regression, classifiers, SVMs, k-means, Neural Networks etc. It also has a few sample
datasets which can be directly used for training and testing.
Because of its speed, robustness and easiness to use, it’s one of the most widely-used libraries for many Machine
Learning applications.
Installation
Through PyPI:
pip install -U scikit-learn
Through conda:
conda install scikit-learn
scikit-learn also comes in shipped with Anaconda (mentioned above). For more installation instructions, refer to this
link.
Example
For this example, we train a simple classifier on the Iris dataset, which comes bundled in with scikit-learn.
The dataset takes four features of flowers: sepal length, sepal width, petal length and petal width, and classifies them
into three flower species (labels): setosa, versicolor or virginica. The labels have been represented as numbers in the
dataset: 0 (setosa), 1 (versicolor) and 2 (virginica).
We shuffle the Iris dataset, and divide it into separate training and testing sets: keeping the last 10 data points for
testing and rest for training. We then train the classifier on the training set, and predict on the testing set.
from sklearn.datasets import load_iris
from sklearn import tree
from sklearn.metrics import accuracy_score
import numpy as np
#loading the iris dataset
iris = load_iris()
x = iris.data #array of the data
y = iris.target #array of labels (i.e answers) of each data entry
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#getting label names i.e the three flower species
y_names = iris.target_names
#taking random indices to split the dataset into train and test
test_ids = np.random.permutation(len(x))
#splitting data and labels into train and test
#keeping last 10 entries for testing, rest for training
x_train = x[test_ids[:-10]]
x_test = x[test_ids[-10:]]
y_train = y[test_ids[:-10]]
y_test = y[test_ids[-10:]]
#classifying using decision tree
clf = tree.DecisionTreeClassifier()
#training (fitting) the classifier with the training set
clf.fit(x_train, y_train)
#predictions on the test dataset
pred = clf.predict(x_test)
print pred #predicted labels i.e flower species
print y_test #actual labels
print (accuracy_score(pred, y_test))*100 #prediction accuracy
Since we’re splitting randomly and the classifier trains on every iteration, the accuracy may vary. Running the above
code gives:
[0 1 1 1 0 2 0 2 2 2]
[0 1 1 1 0 2 0 2 2 2]
100.0
The first line contains the labels (i.e flower species) of the testing data as predicted by our classifier, and the second
line contains the actual flower species as given in the dataset. We thus get an accuracy of 100% this time.
More on scikit-learn can be read in the documentation.
Interfacing with C/C++ Libraries
C Foreign Function Interface
CFFI provides a simple to use mechanism for interfacing with C from both CPython and PyPy. It supports two modes:
an inline ABI compatibility mode (example provided below), which allows you to dynamically load and run functions
from executable modules (essentially exposing the same functionality as LoadLibrary or dlopen), and an API mode,
which allows you to build C extension modules.
ABI Interaction
1
2
3
from cffi import FFI
ffi = FFI()
ffi.cdef("size_t strlen(const char*);")
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4
5
6
7
clib = ffi.dlopen(None)
length = clib.strlen("String to be evaluated.")
# prints: 23
print("{}".format(length))
ctypes
ctypes is the de facto library for interfacing with C/C++ from CPython, and it provides not only full access to the
native C interface of most major operating systems (e.g., kernel32 on Windows, or libc on *nix), but also provides
support for loading and interfacing with dynamic libraries, such as DLLs or shared objects at runtime. It does bring
along with it a whole host of types for interacting with system APIs, and allows you to rather easily define your own
complex types, such as structs and unions, and allows you to modify things such as padding and alignment, if needed.
It can be a bit crufty to use, but in conjunction with the struct module, you are essentially provided full control over
how your data types get translated into something usable by a pure C(++) method.
Struct Equivalents
MyStruct.h
1
2
3
4
struct my_struct {
int a;
int b;
};
MyStruct.py
1
2
3
4
import ctypes
class my_struct(ctypes.Structure):
_fields_ = [("a", c_int),
("b", c_int)]
SWIG
SWIG, though not strictly Python focused (it supports a large number of scripting languages), is a tool for generating
bindings for interpreted languages from C/C++ header files. It is extremely simple to use: the consumer simply needs
to define an interface file (detailed in the tutorial and documentations), include the requisite C/C++ headers, and run
the build tool against them. While it does have some limits, (it currently seems to have issues with a small subset of
newer C++ features, and getting template-heavy code to work can be a bit verbose), it provides a great deal of power
and exposes lots of features to Python with little effort. Additionally, you can easily extend the bindings SWIG creates
(in the interface file) to overload operators and built-in methods, effectively re- cast C++ exceptions to be catchable by
Python, etc.
Example: Overloading __repr__
MyClass.h
1
2
3
4
5
6
7
#include
class MyClass {
private:
std::string name;
public:
std::string getName();
};
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myclass.i
1
%include "string.i"
2
3
4
5
6
7
%module myclass
%{
#include
#include "MyClass.h"
%}
8
9
10
11
12
13
14
%extend MyClass {
std::string __repr__()
{
return $self->getName();
}
}
15
16
%include "MyClass.h"
Boost.Python
Boost.Python requires a bit more manual work to expose C++ object functionality, but it is capable of providing all
the same features SWIG does and then some, to include providing wrappers to access PyObjects in C++, extracting
SWIG- wrapper objects, and even embedding bits of Python into your C++ code.
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CHAPTER 5
Shipping Great Python Code
This part of the guide focuses on deploying your Python code.
Packaging Your Code
Package your code to share it with other developers. For example to share a library for other developers to use in their
application, or for development tools like ‘py.test’.
An advantage of this method of distribution is its well established ecosystem of tools such as PyPI and pip, which
make it easy for other developers to download and install your package either for casual experiments, or as part of
large, professional systems.
It is a well-established convention for Python code to be shared this way. If your code isn’t packaged on PyPI, then
it will be harder for other developers to find it, and to use it as part of their existing process. They will regard such
projects with substantial suspicion of being either badly managed or abandoned.
The downside of distributing code like this is that it relies on the recipient understanding how to install the required
version of Python, and being able and willing to use tools such as pip to install your code’s other dependencies. This is
fine when distributing to other developers, but makes this method unsuitable for distributing applications to end-users.
The Python Packaging Guide provides an extensive guide on creating and maintaining Python packages.
Alternatives to Packaging
To distribute applications to end-users, you should freeze your application.
On Linux, you may also want to consider creating a Linux distro package (e.g. a .deb file for Debian or Ubuntu.)
For Python Developers
If you’re writing an open source Python module, PyPI , more properly known as The Cheeseshop, is the place to host
it.
Pip vs. easy_install
Use pip. More details here
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Personal PyPI
If you want to install packages from a source other than PyPI, (say, if your packages are proprietary), you can do it by
hosting a simple http server, running from the directory which holds those packages which need to be installed.
Showing an example is always beneficial
For example, if you want to install a package called MyPackage.tar.gz, and assuming this is your directory
structure:
• archive
– MyPackage
* MyPackage.tar.gz
Go to your command prompt and type:
$ cd archive
$ python -m SimpleHTTPServer 9000
This runs a simple http server running on port 9000 and will list all packages (like MyPackage). Now you can install
MyPackage using any Python package installer. Using Pip, you would do it like:
$ pip install --extra-index-url=http://127.0.0.1:9000/ MyPackage
Having a folder with the same name as the package name is crucial here. I got fooled by that, one time. But if you
feel that creating a folder called MyPackage and keeping MyPackage.tar.gz inside that, is redundant, you can
still install MyPackage using:
$ pip install
http://127.0.0.1:9000/MyPackage.tar.gz
pypiserver
Pypiserver is a minimal PyPI compatible server. It can be used to serve a set of packages to easy_install or pip. It
includes helpful features like an administrative command (-U) which will update all its packages to their latest versions
found on PyPI.
S3-Hosted PyPi
One simple option for a personal PyPi server is to use Amazon S3. A prerequisite for this is that you have an Amazon
AWS account with an S3 bucket.
1. Install all your requirements from PyPi or another source
2. Install pip2pi
• pip install git+https://github.com/wolever/pip2pi.git
3. Follow pip2pi README for pip2tgz and dir2pi commands
• pip2tgz packages/ YourPackage (or pip2tgz packages/ -r requirements.txt)
• dir2pi packages/
4. Upload the new files
• Use a client like Cyberduck to sync the entire packages folder to your s3 bucket
• Make sure you upload packages/simple/index.html as well as all new files and directories
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5. Fix new file permissions
• By default, when you upload new files to the S3 bucket, they will have the wrong permissions set.
• Use the Amazon web console to set the READ permission of the files to EVERYONE.
• If you get HTTP 403 when trying to install a package, make sure you’ve set the permissions correctly.
6. All done
• You can now install your package with pip install --index-url=http://your-s3-bucket/packages/simple
YourPackage
For Linux Distributions
Creating a Linux distro package is arguably the “right way” to distribute code on Linux.
Because a distribution package doesn’t include the Python interpreter, it makes the download and install about 2MB
smaller than freezing your application.
Also, if a distribution releases a new security update for Python, then your application will automatically start using
that new version of Python.
The bdist_rpm command makes producing an RPM file for use by distributions like Red Hat or SuSE trivially easy.
However, creating and maintaining the different configurations required for each distribution’s format (e.g. .deb for
Debian/Ubuntu, .rpm for Red Hat/Fedora, etc) is a fair amount of work. If your code is an application that you plan to
distribute on other platforms, then you’ll also have to create and maintain the separate config required to freeze your
application for Windows and OSX. It would be much less work to simply create and maintain a single config for one
of the cross platform freezing tools, which will produce stand-alone executables for all distributions of Linux, as well
as Windows and OSX.
Creating a distribution package is also problematic if your code is for a version of Python that isn’t currently supported
by a distribution. Having to tell some versions of Ubuntu end-users that they need to add the ‘dead-snakes’ PPA using
sudo apt-repository commands before they can install your .deb file makes for an extremely hostile user experience.
Not only that, but you’d have to maintain a custom equivalent of these instructions for every distribution, and worse,
have your users read, understand, and act on them.
Having said all that, here’s how to do it:
• Fedora
• Debian and Ubuntu
• Arch
Useful Tools
• fpm
• alien
• dh-virtualenv (for APT/DEB omnibus packaging)
Freezing Your Code
“Freezing” your code is creating a single-file executable file to distribute to end-users, that contains all of your application code as well as the Python interpreter.
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Applications such as ‘Dropbox’, ‘Eve Online’, ‘Civilization IV’, and BitTorrent clients do this.
The advantage of distributing this way is that your application will “just work”, even if the user doesn’t already have
the required version of Python (or any) installed. On Windows, and even on many Linux distributions and OS X, the
right version of Python will not already be installed.
Besides, end-user software should always be in an executable format. Files ending in .py are for software engineers
and system administrators.
One disadvantage of freezing is that it will increase the size of your distribution by about 2–12MB. Also, you will be
responsible for shipping updated versions of your application when security vulnerabilities to Python are patched.
Alternatives to Freezing
Packaging your code is for distributing libraries or tools to other developers.
On Linux, an alternative to freezing is to create a Linux distro package (e.g. .deb files for Debian or Ubuntu, or .rpm
files for Red Hat and SuSE.)
Todo
Fill in “Freezing Your Code” stub
Comparison of Freezing Tools
Solutions and platforms/features supported:
SoluWintion
dows
bbFreeze yes
py2exe
yes
pyInyes
staller
cx_Freeze yes
py2app no
Linux OS
X
yes
yes
no
no
yes
yes
Python
3
no
yes
yes
License
MIT
MIT
GPL
One-file
mode
no
yes
yes
Zipfile
import
yes
yes
no
Eggs pkg_resources
support
yes
yes
no
no
yes
no
yes
no
yes
yes
PSF
MIT
no
no
yes
yes
yes
yes
yes
yes
no
yes
Note: Freezing Python code on Linux into a Windows executable was only once supported in PyInstaller and later
dropped..
Note: All solutions need MS Visual C++ dll to be installed on target machine, except py2app. Only Pyinstaller makes
self-executable exe that bundles the dll when passing --onefile to Configure.py.
Windows
bbFreeze
Prerequisite is to install Python, Setuptools and pywin32 dependency on Windows.
1. Install bbfreeze:
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$ pip install bbfreeze
2. Write most basic bb_setup.py
from bbfreeze import Freezer
freezer = Freezer(distdir='dist')
freezer.addScript('foobar.py', gui_only=True)
freezer()
Note: This will work for the most basic one file scripts. For more advanced freezing you will have to provide include
and exclude paths like so
freezer = Freezer(distdir='dist', includes=['my_code'], excludes=['docs'])
3. (Optionally) include icon
freezer.setIcon('my_awesome_icon.ico')
4. Provide the Microsoft Visual C runtime DLL for the freezer. It might be possible to append your sys.path with
Microsoft Visual Studio path but I find it easier to drop msvcp90.dll in the same folder where your script resides.
5. Freeze!
$ python bb_setup.py
py2exe
Prerequisite is to install Python on Windows.
1. Download and install http://sourceforge.net/projects/py2exe/files/py2exe/
2. Write setup.py (List of configuration options):
from distutils.core import setup
import py2exe
setup(
windows=[{'script': 'foobar.py'}],
)
3. (Optionally) include icon
4. (Optionally) one-file mode
5. Generate .exe into dist directory:
$ python setup.py py2exe
6. Provide the Microsoft Visual C runtime DLL. Two options: globally install dll on target machine or distribute
dll alongside with .exe.
PyInstaller
Prerequisite is to have installed Python, Setuptools and pywin32 dependency on Windows.
• Most basic tutorial
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• Manual
OS X
py2app
PyInstaller
PyInstaller can be used to build Unix executables and windowed apps on Mac OS X 10.6 (Snow Leopard) or newer.
To install PyInstaller, use pip:
$ pip install pyinstaller
To create a standard Unix executable, from say script.py, use:
$ pyinstaller script.py
This creates,
• a script.spec file, analogous to a make file
• a build folder, that holds some log files
• a dist folder, that holds the main executable script, and some dependent Python libraries,
all in the same folder as script.py. PyInstaller puts all the Python libraries used in script.py into the dist
folder, so when distributing the executable, distribute the whole dist folder.
The script.spec file can be edited to customise the build, with options such as
• bundling data files with the executable
• including run-time libraries (.dll or .so files) that PyInstaller can’t infer automatically
• adding Python run-time options to the executable,
Now script.spec can be run with pyinstaller (instead of using script.py again):
$ pyinstaller script.spec
To create a standalone windowed OS X application, use the --windowed option
$ pyinstaller --windowed script.spec
This creates a script.app in the dist folder. Make sure to use GUI packages in your Python code, like PyQt or
PySide, to control the graphical parts of the app.
There are several options in script.spec related to Mac OS X app bundles here. For example, to specify an icon
for the app, use the icon=\path\to\icon.icns option.
Linux
bbFreeze
PyInstaller
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CHAPTER 6
Additional Notes
This part of the guide, which is mostly prose, begins with some background information about Python, then focuses
on next steps.
Introduction
From the official Python website:
Python is a general-purpose, high-level programming language similar to Tcl, Perl, Ruby, Scheme, or Java. Some of
its main key features include:
• very clear, readable syntax
Python’s philosophy focuses on readability, from code blocks delineated with significant whitespace to intuitive
keywords in place of inscrutable punctuation.
• extensive standard libraries and third party modules for virtually any task
Python is sometimes described with the words “batteries included” because of its extensive standard library,
which includes modules for regular expressions, file IO, fraction handling, object serialization, and much more.
Additionally, the Python Package Index is available for users to submit their packages for widespread use,
similar to Perl’s CPAN. There is a thriving community of very powerful Python frameworks and tools like the
Django web framework and the NumPy set of math routines.
• integration with other systems
Python can integrate with Java libraries, enabling it to be used with the rich Java environment that corporate
programmers are used to. It can also be extended by C or C++ modules when speed is of the essence.
• ubiquity on computers
Python is available on Windows, *nix, and Mac. It runs wherever the Java virtual machine runs, and the reference
implementation CPython can help bring Python to wherever there is a working C compiler.
• friendly community
Python has a vibrant and large community which maintains wikis, conferences, countless repositories, mailing
lists, IRC channels, and so much more. Heck, the Python community is even helping to write this guide!
About This Guide
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Purpose
The Hitchhiker’s Guide to Python exists to provide both novice and expert Python developers a best practice handbook
for the installation, configuration, and usage of Python on a daily basis.
By the Community
This guide is architected and maintained by Kenneth Reitz in an open fashion. This is a community-driven effort that
serves one purpose: to serve the community.
For the Community
All contributions to the Guide are welcome, from Pythonistas of all levels. If you think there’s a gap in what the Guide
covers, fork the Guide on GitHub and submit a pull request.
Contributions are welcome from everyone, whether they’re an old hand or a first-time Pythonista, and the authors
to the Guide will gladly help if you have any questions about the appropriateness, completeness, or accuracy of a
contribution.
To get started working on The Hitchhiker’s Guide, see the Contribute page.
The Community
BDFL
Guido van Rossum, the creator of Python, is often referred to as the BDFL — the Benevolent Dictator For Life.
Python Software Foundation
The mission of the Python Software Foundation is to promote, protect, and advance the Python programming language,
and to support and facilitate the growth of a diverse and international community of Python programmers.
Learn More about the PSF.
PEPs
PEPs are Python Enhancement Proposals. They describe changes to Python itself, or the standards around it.
There are three different types of PEPs (as defined by PEP 1):
Standards Describes a new feature or implementation.
Informational Describes a design issue, general guidelines, or information to the community.
Process Describes a process related to Python.
Notable PEPs
There are a few PEPs that could be considered required reading:
• PEP 8: The Python Style Guide. Read this. All of it. Follow it.
• PEP 20: The Zen of Python. A list of 19 statements that briefly explain the philosophy behind Python.
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• PEP 257: Docstring Conventions. Gives guidelines for semantics and conventions associated with Python
docstrings.
You can read more at The PEP Index.
Submitting a PEP
PEPs are peer-reviewed and accepted/rejected after much discussion. Anyone can write and submit a PEP for review.
Here’s an overview of the PEP acceptance workflow:
Python Conferences
The major events for the Python community are developer conferences. The two most notable conferences are PyCon,
which is held in the US, and its European sibling, EuroPython.
A comprehensive list of conferences is maintained at pycon.org.
Python User Groups
User Groups are where a bunch of Python developers meet to present or talk about Python topics of interest. A list of
local user groups is maintained at the Python Software Foundation Wiki.
Learning Python
Beginner
The Python Tutorial
This is the official tutorial. It covers all the basics, and offers a tour of the language and the standard library. Recommended for those who need a quick-start guide to the language.
The Python Tutorial
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Python for Beginners
thepythonguru.com is a tutorial focuses on beginner programmers. It covers many python concepts in depth. It also
teaches you some advance constructs of python like lambda expression, regular expression. At last it finishes off with
tutorial “How to access MySQL db using python”
Python for beginners
Learn Python for Data Science Interactively
If you prefer an online interactive environment to learn Python for Data Science, this free python tutorial by DataCamp
is a great way to get started. If you’re already somewhat advanced and interested in machine learning, check out this
course on Supervised Learning with scikit-learn, by one the core developers of scikit-learn.
Python for data science
Learn Python Interactive Tutorial
Learnpython.org is an easy non-intimidating way to get introduced to Python. The website takes the same approach
used on the popular Try Ruby website, it has an interactive Python interpreter built into the site that allows you to go
through the lessons without having to install Python locally.
Learn Python
If you want a more traditional book, Python For You and Me is an excellent resource for learning all aspects of the
language.
Python for You and Me Learn Python Interactively with DataCamp!
Learn Python Step by Step
Techbeamers.com provides step-by-step tutorials to teach Python. Each tutorial is supplemented with logically added
coding snippets and equips with a follow-up quiz on the subject learned. There is a section for Python interview
questions to help job seekers. You can also read essential Python tips and learn best coding practices for writing
quality code. Here, you’ll get the right platform to learn Python quickly.
Learn Python Basic to Advanced
Online Python Tutor
Online Python Tutor gives you a visual step by step representation of how your program runs. Python Tutor helps
people overcome a fundamental barrier to learning programming by understanding what happens as the computer
executes each line of a program’s source code.
Online Python Tutor
Invent Your Own Computer Games with Python
This beginner’s book is for those with no programming experience at all. Each chapter has the source code to a
small game, using these example programs to demonstrate programming concepts to give the reader an idea of what
programs “look like”.
Invent Your Own Computer Games with Python
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Hacking Secret Ciphers with Python
This book teaches Python programming and basic cryptography for absolute beginners. The chapters provide the
source code for various ciphers, as well as programs that can break them.
Hacking Secret Ciphers with Python
Learn Python the Hard Way
This is an excellent beginner programmer’s guide to Python. It covers “hello world” from the console to the web.
Learn Python the Hard Way
Crash into Python
Also known as Python for Programmers with 3 Hours, this guide gives experienced developers from other languages
a crash course on Python.
Crash into Python
Dive Into Python 3
Dive Into Python 3 is a good book for those ready to jump in to Python 3. It’s a good read if you are moving from
Python 2 to 3 or if you already have some experience programming in another language.
Dive Into Python 3
Think Python: How to Think Like a Computer Scientist
Think Python attempts to give an introduction to basic concepts in computer science through the use of the Python
language. The focus was to create a book with plenty of exercises, minimal jargon and a section in each chapter
devoted to the subject of debugging.
While exploring the various features available in the Python language the author weaves in various design patterns and
best practices.
The book also includes several case studies which have the reader explore the topics discussed in the book in greater
detail by applying those topics to real-world examples. Case studies include assignments in GUI and Markov Analysis.
Think Python
Python Koans
Python Koans is a port of Edgecase’s Ruby Koans. It uses a test-driven approach, q.v. TEST DRIVEN DESIGN
SECTION to provide an interactive tutorial teaching basic Python concepts. By fixing assertion statements that fail in
a test script, this provides sequential steps to learning Python.
For those used to languages and figuring out puzzles on their own, this can be a fun, attractive option. For those new
to Python and programming, having an additional resource or reference will be helpful.
Python Koans
More information about test driven development can be found at these resources:
Test Driven Development
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A Byte of Python
A free introductory book that teaches Python at the beginner level, it assumes no previous programming experience.
A Byte of Python for Python 2.x A Byte of Python for Python 3.x
Learn to Program in Python with Codeacademy
A Codeacademy course for the absolute Python beginner. This free and interactive course provides and teaches the
basics (and beyond) of Python programming whilst testing the user’s knowledge in between progress. This course also
features a built-in interpreter for receiving instant feedback on your learning.
Learn to Program in Python with Codeacademy
Intermediate
Effective Python
This book contains 59 specific ways to improve writing Pythonic code. At 227 pages, it is a very brief overview
of some of the most commons adapations programmers need to make to become efficient intermediate level Python
programmers.
Effective Python
Advanced
Pro Python
This book is for intermediate to advanced Python programmers who are looking to understand how and why Python
works the way it does and how they can take their code to the next level.
Pro Python
Expert Python Programming
Expert Python Programming deals with best practices in programming Python and is focused on the more advanced
crowd.
It starts with topics like decorators (with caching, proxy, and context manager case-studies), method resolution order,
using super() and meta-programming, and general PEP 8 best practices.
It has a detailed, multi-chapter case study on writing and releasing a package and eventually an application, including a chapter on using zc.buildout. Later chapters detail best practices such as writing documentation, test-driven
development, version control, optimization and profiling.
Expert Python Programming
A Guide to Python’s Magic Methods
This is a collection of blog posts by Rafe Kettler which explain ‘magic methods’ in Python. Magic methods are
surrounded by double underscores (i.e. __init__) and can make classes and objects behave in different and magical
ways.
A Guide to Python’s Magic Methods
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Note: The Rafekettler.com is currently down, you can go to their Github version directly. Here you can find a PDF
version: A Guide to Python’s Magic Methods (repo on GitHub)
For Engineers and Scientists
A Primer on Scientific Programming with Python
A Primer on Scientific Programming with Python, written by Hans Petter Langtangen, mainly covers Python’s usage
in the scientific field. In the book, examples are chosen from mathematics and the natural sciences.
A Primer on Scientific Programming with Python
Numerical Methods in Engineering with Python
Numerical Methods in Engineering with Python, written by Jaan Kiusalaas, puts the emphasis on numerical methods
and how to implement them in Python.
Numerical Methods in Engineering with Python
Miscellaneous topics
Problem Solving with Algorithms and Data Structures
Problem Solving with Algorithms and Data Structures covers a range of data structures and algorithms. All concepts
are illustrated with Python code along with interactive samples that can be run directly in the browser.
Problem Solving with Algorithms and Data Structures
Programming Collective Intelligence
Programming Collective Intelligence introduces a wide array of basic machine learning and data mining methods. The
exposition is not very mathematically formal, but rather focuses on explaining the underlying intuition and shows how
to implement the algorithms in Python.
Programming Collective Intelligence
Transforming Code into Beautiful, Idiomatic Python
Transforming Code into Beautiful, Idiomatic Python is a video by Raymond Hettinger. Learn to take better advantage
of Python’s best features and improve existing code through a series of code transformations, “When you see this, do
that instead.”
Transforming Code into Beautiful, Idiomatic Python
Fullstack Python
Fullstack Python offers a complete top-to-bottom resource for web development using Python.
From setting up the webserver, to designing the front-end, choosing a database, optimizing/scaling, etc.
As the name suggests, it covers everything you need to build and run a complete web app from scratch.
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Fullstack Python
References
Python in a Nutshell
Python in a Nutshell, written by Alex Martelli, covers most cross-platform Python’s usage, from its syntax to built-in
libraries to advanced topics such as writing C extensions.
Python in a Nutshell
The Python Language Reference
This is Python’s reference manual, it covers the syntax and the core semantics of the language.
The Python Language Reference
Python Essential Reference
Python Essential Reference, written by David Beazley, is the definitive reference guide to Python. It concisely explains
both the core language and the most essential parts of the standard library. It covers Python 3 and 2.6 versions.
Python Essential Reference
Python Pocket Reference
Python Pocket Reference, written by Mark Lutz, is an easy to use reference to the core language, with descriptions of
commonly used modules and toolkits. It covers Python 3 and 2.6 versions.
Python Pocket Reference
Python Cookbook
Python Cookbook, written by David Beazley and Brian K. Jones, is packed with practical recipes. This book covers
the core python language as well as tasks common to a wide variety of application domains.
Python Cookbook
Writing Idiomatic Python
“Writing Idiomatic Python”, written by Jeff Knupp, contains the most common and important Python idioms in a
format that maximizes identification and understanding. Each idiom is presented as a recommendation of a way to
write some commonly used piece of code, followed by an explanation of why the idiom is important. It also contains
two code samples for each idiom: the “Harmful” way to write it and the “Idiomatic” way.
For Python 2.7.3+
For Python 3.3+
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Documentation
Official Documentation
The official Python Language and Library documentation can be found here:
• Python 2.x
• Python 3.x
Read the Docs
Read the Docs is a popular community project that hosts documentation for open source software. It holds documentation for many Python modules, both popular and exotic.
Read the Docs
pydoc
pydoc is a utility that is installed when you install Python. It allows you to quickly retrieve and search for documentation from your shell. For example, if you needed a quick refresher on the time module, pulling up documentation
would be as simple as
$ pydoc time
The above command is essentially equivalent to opening the Python REPL and running
>>> help(time)
News
Planet Python
This is an aggregate of Python news from a growing number of developers.
Planet Python
/r/python
/r/python is the Reddit Python community where users contribute and vote on Python-related news.
/r/python
Talk Python Podcast
The #1 Python-focused podcast covering the people and ideas in Python.
Talk Python To Me
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Python Bytes Podcast
A short-form Python podcast covering recent developer headlines.
Python Bytes
Pycoder’s Weekly
Pycoder’s Weekly is a free weekly Python newsletter for Python developers by Python developers (Projects, Articles,
News, and Jobs).
Pycoder’s Weekly
Python Weekly
Python Weekly is a free weekly newsletter featuring curated news, articles, new releases, jobs, etc. related to Python.
Python Weekly
Python News
Python News is the news section in the official Python web site (www.python.org). It briefly highlights the news from
the Python community.
Python News
Import Python Weekly
Weekly Python Newsletter containing Python Articles, Projects, Videos, Tweets delivered in your inbox. Keep Your
Python Programming Skills Updated.
Import Python Weekly Newsletter
Awesome Python Newsletter
A weekly overview of the most popular Python news, articles and packages.
Awesome Python Newsletter
Note: Notes defined within all diatonic and chromatic musical scales have been intentionally excluded from this list
of additional notes. Additionally, this note.
Contribution notes and legal information (for those interested).
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Contribute
Python-guide is under active development, and contributors are welcome.
If you have a feature request, suggestion, or bug report, please open a new issue on GitHub. To submit patches,
please send a pull request on GitHub. Once your changes get merged back in, you’ll automatically be added to the
Contributors List.
Style Guide
For all contributions, please follow the The Guide Style Guide.
Todo List
If you’d like to contribute, there’s plenty to do. Here’s a short todo list.
• Establish “use this” vs “alternatives are....” recommendations
Todo
Write about Blueprint
(The original entry is located in /home/docs/checkouts/readthedocs.org/user_builds/pythonguide/checkouts/latest/docs/scenarios/admin.rst, line 371.)
Todo
Fill in “Freezing Your Code” stub
(The original entry is located in /home/docs/checkouts/readthedocs.org/user_builds/pythonguide/checkouts/latest/docs/shipping/freezing.rst, line 39.)
Todo
Include code examples of exemplary code from each of the projects listed. Explain why it is excellent
code. Use complex examples.
(The original entry is located in /home/docs/checkouts/readthedocs.org/user_builds/pythonguide/checkouts/latest/docs/writing/reading.rst, line 59.)
Todo
Explain techniques to rapidly identify data structures, algorithms and determine what the code is doing.
(The original entry is located in /home/docs/checkouts/readthedocs.org/user_builds/pythonguide/checkouts/latest/docs/writing/reading.rst, line 61.)
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License
The Guide is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported license.
The Guide Style Guide
As with all documentation, having a consistent format helps make the document more understandable. In order to
make The Guide easier to digest, all contributions should fit within the rules of this style guide where appropriate.
The Guide is written as reStructuredText.
Note: Parts of The Guide may not yet match this style guide. Feel free to update those parts to be in sync with The
Guide Style Guide
Note: On any page of the rendered HTML you can click “Show Source” to see how authors have styled the page.
Relevancy
Strive to keep any contributions relevant to the purpose of The Guide.
• Avoid including too much information on subjects that don’t directly relate to Python development.
• Prefer to link to other sources if the information is already out there. Be sure to describe what and why you are
linking.
• Cite references where needed.
• If a subject isn’t directly relevant to Python, but useful in conjunction with Python (e.g., Git, GitHub, Databases),
reference by linking to useful resources, and describe why it’s useful to Python.
• When in doubt, ask.
Headings
Use the following styles for headings.
Chapter title:
#########
Chapter 1
#########
Page title:
===================
Time is an Illusion
===================
Section headings:
Lunchtime Doubly So
-------------------
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Sub section headings:
Very Deep
~~~~~~~~~
Prose
Wrap text lines at 78 characters. Where necessary, lines may exceed 78 characters, especially if wrapping would make
the source text more difficult to read.
Use of the serial comma (also known as the Oxford comma) is 100% non-optional. Any attempt to submit content
with a missing serial comma will result in permanent banishment from this project, due to complete and total lack of
taste.
Banishment? Is this a joke? Hopefully we will never have to find out.
Code Examples
Wrap all code examples at 70 characters to avoid horizontal scrollbars.
Command line examples:
.. code-block:: console
$ run command --help
$ ls ..
Be sure to include the $ prefix before each line.
Python interpreter examples:
Label the example::
.. code-block:: python
>>> import this
Python examples:
Descriptive title::
.. code-block:: python
def get_answer():
return 42
Externally Linking
• Prefer labels for well known subjects (ex: proper nouns) when linking:
Sphinx_ is used to document Python.
.. _Sphinx: http://sphinx.pocoo.org
• Prefer to use descriptive labels with inline links instead of leaving bare links:
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Read the `Sphinx Tutorial `_
• Avoid using labels such as “click here”, “this”, etc. preferring descriptive labels (SEO worthy) instead.
Linking to Sections in The Guide
To cross-reference other parts of this documentation, use the :ref: keyword and labels.
To make reference labels more clear and unique, always add a -ref suffix:
.. _some-section-ref:
Some Section
------------
Notes and Warnings
Make use of the appropriate admonitions directives when making notes.
Notes:
.. note::
The Hitchhiker’s Guide to the Galaxy has a few things to say
on the subject of towels. A towel, it says, is about the most
massively useful thing an interstellar hitch hiker can have.
Warnings:
.. warning:: DON'T PANIC
TODOs
Please mark any incomplete areas of The Guide with a todo directive. To avoid cluttering the Todo List, use a single
todo for stub documents or large incomplete sections.
.. todo::
Learn the Ultimate Answer to the Ultimate Question
of Life, The Universe, and Everything
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Index
E
environment variable
PATH, 7, 8, 11, 12
P
PATH, 7, 8, 11, 12
Python Enhancement Proposals
PEP 0257#specification, 51
PEP 1, 110
PEP 20, 45, 110
PEP 249, 77
PEP 257, 53, 111
PEP 282, 58
PEP 3101, 39
PEP 3132, 44
PEP 3333, 66
PEP 391, 59
PEP 8, 21, 46, 110, 114
PEP 8#comments, 51
123
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