Python

From What is Python?:

Python is an interpreted, interactive, object-oriented programming language. It incorporates modules, exceptions, dynamic typing, very high level dynamic data types, and classes. It supports multiple programming paradigms beyond object-oriented programming, such as procedural and functional programming. Python combines remarkable power with very clear syntax. It has interfaces to many system calls and libraries, as well as to various window systems, and is extensible in C or C++. It is also usable as an extension language for applications that need a programmable interface. Finally, Python is portable: it runs on many Unix variants including Linux and macOS, and on Windows.

Installation

Python 3

Python 3 is the latest and actively developed version of the language. See What's New in Python to see the latest changes in Python 3.

To install the current release of Python 3, install the python package.

If you would like to build the latest RC/betas from source, visit Python Downloads. The Arch User Repository also contains good PKGBUILDs. If you do decide to build the RC, note that the binary (by default) installs to /usr/local/bin/python3.x. As an alternative which does not require superuser capabilities and installs to the home directory, consider using pyenv.

Python 2

Warning: Python 2 reached its end of life on January 1st of 2020. A small number of packages still depend on Python 2 but that version of Python will not be maintained further. See: Sunsetting Python 2

Python 2 is an older version of the language. Python 3 and Python 2 are incompatible. For an overview of the differences, see the historical version of the Python2orPython3 document.

Although Python 2 is no longer actively maintained, there are some packages that still depend on it. Python 2 may also be useful for developers maintaining, using or porting legacy Python 2 software.

To get the last version of Python 2, install the python2 package.

Python 2 will happily run alongside Python 3. You need to specify python2 in order to run this version.

Any program requiring Python 2 needs to use /usr/bin/python2, instead of /usr/bin/python, which points to Python 3. However, many legacy Python 2 scripts incorrectly specify python in their shebang line. To change this, open the program or script in a text editor and change the first line. The line may show one of the following:

#!/usr/bin/env python

or

#!/usr/bin/python

In either case, change python to python2 and the program will then use Python 2 instead of Python 3.

Another way to force the use of python2 without altering the scripts is to call it explicitly with python2:

$ python2 my_script.py

Finally, you may not be able to control the script calls, but there is a way to trick the environment. It only works if the scripts use #!/usr/bin/env python. It will not work with #!/usr/bin/python. This trick relies on env searching for the first corresponding entry in the PATH variable.

First create a dummy folder:

$ mkdir ~/bin

Then add a symlink python to python2 and the configuration scripts in it:

$ ln -s /usr/bin/python2 ~/bin/python
$ ln -s /usr/bin/python2-config ~/bin/python-config

Finally put the new folder at the beginning of your PATH variable:

$ export PATH=~/bin:$PATH

Note: This method of changing environment variables is not permanent and is only active in the current terminal session.

To check which python interpreter is being used by env, use the following command:

$ command -v python

A similar approach in tricking the environment, which also relies on #!/usr/bin/env python to be called by the script in question, is to use a virtual environment.

Alternative implementations

The python package installs CPython, the reference implementation of Python. However, there are also other implementations available. These implementations are usually based on older versions of Python and are not fully compatible with CPython.

Implementations available on Arch Linux include:

PyPy — A Python implementation written in Python. It has speed and memory usage advantages compared to CPython.

https://www.pypy.org || pypy, pypy3

Jython — An implementation of the Python language written in Java. It can be used to embed Python scripting into Java programs or use Java libraries in Python programs.

https://www.jython.org/ || jython

micropython — Python for microcontrollers. It includes a small subset of the Python standard library and is optimized to run on microcontrollers and in constrained environments.

https://micropython.org/ || micropython^AUR

IronPython — An implementation of the Python programming language which is tightly integrated with .NET. It can use .NET libraries and allows .NET programs to use Python libraries.

https://ironpython.net || ironpython-git^AUR

More implementations exist. Some, such as Stackless, Pyston and Cinder are used internally at large technology companies. Others are historically notable but are no longer maintained due to improvements in the most popular implementations.

Alternative shells

The python package includes an interactive Python shell/REPL which can be launched with the python command. The following shells are also available:

bpython — A fancy interface for the Python interpreter.

https://bpython-interpreter.org/ || bpython

IPython — A powerful interactive Python shell.

https://ipython.org/ || ipython

Jupyter — A web-based computation application powered by IPython.

https://jupyter.org/ || jupyterlab, jupyter-notebook

ptpython — An advanced Python REPL built with prompt-toolkit.

https://github.com/jonathanslenders/ptpython || ptpython^AUR

Old versions

Old versions of Python are available via the AUR and may be useful for historical curiosity, old applications that do not run on current versions, or for testing Python programs intended to run on a distribution that comes with an older version:

Python 3.9: python39^AUR
Python 3.8: python38^AUR
Python 3.7: python37^AUR
Python 3.6: python36^AUR

Extra modules/libraries for old versions of Python may be found on the AUR by searching for python<version without period>, e.g. searching for python37 for 3.7 modules.

Package management

There are several ways to install Python packages on Arch Linux:

Official repositories and AUR — A large number of popular packages are available in the Arch repositories. This is the preferred way to install system-wide packages.
pip(1) — The official package installer for Python. You can use pip to install packages from the Python Package Index and other indexes.

https://pip.pypa.io/ || python-pip

pipx — Closely related to pip, but creates, for the user running it, an isolated environment for each application and its associated packages, preventing conflicts with system packages. Focused on packages that can be run from the command line directly as applications. You can use pipx to install packages from the Python Package Index and other indexes.

https://pypa.github.io/pipx/ || python-pipx

Anaconda — An open source package management system and environment management system, originally created for Python programs. You can use Conda to install packages from the Anaconda repositories.

https://docs.conda.io/projects/conda/ || anaconda^AUR

Miniconda — A lightweight alternative to Anaconda which installs the package manager but does not install scientific computing packages by default.

https://docs.conda.io/en/latest/miniconda.html || miniconda3^AUR

When installing packages using pip, it is recommended to use a virtual environment to prevent conflicts with system packages in /usr. Alternatively, pip install --user can be used to install packages into the user scheme instead of /usr. pipx and Conda integrate environment management into their workflows.

See the Python Packaging User Guide for the official best practices for package management.

Historically, easy_install (part of python-setuptools) was used to install packages distributed as Eggs. easy_install and Eggs have been replaced with pip and Wheels. See pip vs easy_install and Wheel vs Egg for more information.

Note: There are also tools integrating pip with pacman by automatically generating PKGBUILDs for specified PyPI packages: see Creating packages#PKGBUILD generators.

Tip: pipenv provides a single CLI for Pipfile, pip and virtualenv. It is available as python-pipenv.

Widget bindings

The following widget toolkit bindings are available:

Tkinter — The standard Python interface to the Tk GUI toolkit.

https://docs.python.org/3/library/tkinter.html || python

Qt for Python (PySide2) — The official Python bindings for Qt5.

https://www.qt.io/qt-for-python || pyside2, pyside2-tools

Qt for Python (PySide6) — The official Python bindings for Qt6.

https://www.qt.io/qt-for-python || pyside6, pyside6-tools^AUR

pyQt — A set of Python bindings for Qt.

https://riverbankcomputing.com/software/pyqt/intro || python-pyqt5

PyGObject — Python bindings for GObject based libraries such as GTK, GStreamer, WebKitGTK, GLib, and GIO.

https://pygobject.readthedocs.io/ || python-gobject

wxPython — A cross-platform GUI toolkit for Python which wraps wxWidgets.

https://wxpython.org/ || python-wxpython

To use these with Python, you may also need to install the associated widget toolkit packages (e.g. tk must also be installed to use Tkinter).

Tips and tricks

Virtual environment

Python provides tools to create isolated virtual environments into which packages may be installed without conflicting with other virtual environments or the system packages. Virtual environments can also run applications with different versions of Python on the same system.

See Python/Virtual environment for details.

Tab completion in Python shell

Tab completion is available in the interactive shell by default. Note that the readline completer will only complete names in the global namespace. You can use python-jedi for a richer tab completion experience [1].