Getting Started

These instructions cover how to get a working copy of the source code and a compiled version of the CPython interpreter (CPython is the version of Python available from It also gives an overview of the directory structure of the CPython source code.

OpenHatch also has a great setup guide for Python for people who are completely new to contributing to open source.

Getting Set Up

Version Control Setup

CPython is developed using git. The git command line program is named git; this is also used to refer to git itself. git is easily available for all common operating systems. As the CPython repo is hosted on GitHub, please refer to either the GitHub setup instructions or the git project instructions for step-by-step installation directions. You may also want to consider a graphical client such as TortoiseGit or GitHub Desktop.

Once you installed Git, you should set up your name and email and an SSH key as this will allow you to interact with GitHub without typing a username and password each time you execute a command, such as git pull, git push, or git fetch. On Windows, you should also enable autocrlf.

Getting the Source Code

In order to get a copy of the source code you should fork the Python repository on GitHub, create a local clone of your personal fork, and configure the remotes.

You will only need to execute these steps once:

  1. Go to

  2. Press Fork on the top right.

  3. When asked where to fork the repository, choose to fork it to your username.

  4. Your fork will be created at<username>/cpython.

  5. Clone your GitHub fork (replace <username> with your username):

    $ git clone<username>/cpython.git

    (You can use both SSH-based or HTTPS-based URLs.)

  6. Configure an upstream remote:

    $ cd cpython
    $ git remote add upstream
  7. Verify that your setup is correct:

    $ git remote -v
    origin<your-username>/devguide.git (fetch)
    origin<your-username>/devguide.git (push)
    upstream (fetch)
    upstream (push)

If you did everything correctly, you should now have a copy of the code in the cpython dir and two remotes that refer to your own GitHub fork (origin) and the official CPython repository (upstream).

If you want a working copy of an already-released version of Python, i.e., a version in maintenance mode, you can checkout a release branch. For instance, to checkout a working copy of Python 3.5, do git checkout 3.5.

You will need to re-compile CPython when you do such an update.

Do note that CPython will notice that it is being run from a working copy. This means that if you edit CPython’s source code in your working copy, changes to Python code will be picked up by the interpreter for immediate use and testing. (If you change C code, you will need to recompile the affected files as described below.)

Patches for the documentation can be made from the same repository; see Documenting Python.

Compiling (for debugging)

CPython provides several compilation flags which help with debugging various things. While all of the known flags can be found in the Misc/SpecialBuilds.txt file, the most critical one is the Py_DEBUG flag which creates what is known as a “pydebug” build. This flag turns on various extra sanity checks which help catch common issues. The use of the flag is so common that turning on the flag is a basic compile option.

You should always develop under a pydebug build of CPython (the only instance of when you shouldn’t is if you are taking performance measurements). Even when working only on pure Python code the pydebug build provides several useful checks that one should not skip.

Build dependencies

The core CPython interpreter only needs a C compiler to be built; if you get compile errors with a C89 or C99-compliant compiler, please open a bug report. However, some of the extension modules will need development headers for additional libraries (such as the zlib library for compression). Depending on what you intend to work on, you might need to install these additional requirements so that the compiled interpreter supports the desired features.

For UNIX based systems, we try to use system libraries whenever available. This means optional components will only build if the relevant system headers are available. The best way to obtain the appropriate headers will vary by distribution, but the appropriate commands for some popular distributions are below.

On Fedora, Red Hat Enterprise Linux and other yum based systems:

$ sudo yum install yum-utils
$ sudo yum-builddep python3

On Fedora and other DNF based systems:

$ sudo dnf install dnf-plugins-core  # install this to use 'dnf builddep'
$ sudo dnf builddep python3

On Debian, Ubuntu, and other apt based systems, try to get the dependencies for the Python you’re working on by using the apt command.

First, make sure you have enabled the source packages in the sources list. You can do this by adding the location of the source packages, including URL, distribution name and component name, to /etc/apt/sources.list. Take Ubuntu Xenial for example:

deb-src xenial main

For other distributions, like Debian, change the URL and names to correspond with the specific distribution.

Then you should update the packages index:

$ sudo apt-get update

Now you can install the build dependencies via apt:

$ sudo apt-get build-dep python3.5

If that package is not available for your system, try reducing the minor version until you find a package that is available.

On Mac OS X systems, use the C compiler and other development utilities provided by Apple’s Xcode Developer Tools. The Developer Tools are not shipped with OS X.

For OS X 10.9 and later, the Developer Tools can be downloaded and installed automatically; you do not need to download the complete Xcode application. If necessary, run the following:

$ xcode-select --install

This will also ensure that the system header files are installed into /usr/include.

For older releases of OS X, you will need to download either the correct version of the Command Line Tools, if available, or install them from the full Xcode app or package for that OS X release. Older versions may be available either as a no-cost download through Apple’s App Store or from the Apple Developer web site.

Also note that OS X does not include several libraries used by the Python standard library, including libzma, so expect to see some extension module build failures unless you install local copies of them. As of OS X 10.11, Apple no longer provides header files for the deprecated system version of OpenSSL which means that you will not be able to build the _ssl extension. One solution is to install these libraries from a third-party package manager, like Homebrew or MacPorts, and then add the appropriate paths for the header and library files to your configure command. For example,

with Homebrew:

$ brew install openssl xz

and configure:

$ CPPFLAGS="-I$(brew --prefix openssl)/include" \
  LDFLAGS="-L$(brew --prefix openssl)/lib" \
  ./configure --with-pydebug

and make:

$ make -s -j2

or MacPorts:

$ sudo port install openssl xz

and configure:

$ CPPFLAGS="-I/opt/local/include" \
  LDFLAGS="-L/opt/local/lib" \
  ./configure --with-pydebug

and make:

$ make -s -j2

This will build CPython with only warnings and errors being printed to stderr and utilize up to 2 CPU cores. If you are using a multi-core machine with more than 2 cores (or a single-core machine), you can adjust the number passed into the -j flag to match the number of cores you have.

Do take note of what modules were not built as stated at the end of your build. More than likely you are missing a dependency for the module(s) that were not built, and so you can install the dependencies and re-run both configure and make (if available for your OS). Otherwise the build failed and thus should be fixed (at least with a bug being filed on the issue tracker).

There will sometimes be optional modules added for a new release which won’t yet be identified in the OS level build dependencies. In those cases, just ask for assistance on the core-mentorship list. If working on bug fixes for Python 2.7, use python in place of python3 in the above commands.

Explaining how to build optional dependencies on a UNIX based system without root access is beyond the scope of this guide.


While you need a C compiler to build CPython, you don’t need any knowledge of the C language to contribute! Vast areas of CPython are written completely in Python: as of this writing, CPython contains slightly more Python code than C.


The basic steps for building Python for development is to configure it and then compile it.

Configuration is typically:

./configure --with-pydebug

More flags are available to configure, but this is the minimum you should do to get a pydebug build of CPython.

Once configure is done, you can then compile CPython with:

make -s -j2

This will build CPython with only warnings and errors being printed to stderr and utilize up to 2 CPU cores. If you are using a multi-core machine with more than 2 cores (or a single-core machine), you can adjust the number passed into the -j flag to match the number of cores you have.

Do take note of what modules were not built as stated at the end of your build. More than likely you are missing a dependency for the module(s) that were not built, and so you can install the dependencies and re-run both configure and make (if available for your OS). Otherwise the build failed and thus should be fixed (at least with a bug being filed on the issue tracker).

Once CPython is done building you will then have a working build that can be run in-place; ./python on most machines (and what is used in all examples), ./python.exe wherever a case-insensitive filesystem is used (e.g. on OS X by default), in order to avoid conflicts with the Python directory. There is normally no need to install your built copy of Python! The interpreter will realize where it is being run from and thus use the files found in the working copy. If you are worried you might accidentally install your working copy build, you can add --prefix=/tmp/python to the configuration step. When running from your working directory, it is best to avoid using the --enable-shared flag to configure; unless you are very careful, you may accidentally run with code from an older, installed shared Python library rather than from the interpreter you just built.


If you are using clang to build CPython, some flags you might want to set to quiet some standard warnings which are specifically superfluous to CPython are -Wno-unused-value -Wno-empty-body -Qunused-arguments. You can set your CFLAGS environment variable to these flags when running configure.

If you are using clang with ccache, turn off the noisy parentheses-equality warnings with the -Wno-parentheses-equality flag. These warnings are caused by clang not having enough information to detect that extraneous parentheses in expanded macros are valid, because the preprocessing is done separately by ccache.

If you are using LLVM 2.8, also use the -no-integrated-as flag in order to build the ctypes module (without the flag the rest of CPython will still build properly).


Python 3.6 and later can use Microsoft Visual Studio 2017. You can download and use any of the free or paid versions of Visual Studio 2017.

When installing Visual Studio 2017, select the Python workload and the optional Python native development component to obtain all of the necessary build tools. If you do not already have git installed, you can find git for Windows on the Individual components tab of the installer.

Your first build should use the command line to ensure any external dependencies are downloaded:


After this build succeeds, you can open the PCBuild\pcbuild.sln solution in Visual Studio to continue development.

See the readme for more details on what other software is necessary and how to build.


Python 2.7 uses Microsoft Visual Studio 2008, which is most easily obtained through an MSDN subscription. To use the build files in the PCbuild directory you will also need Visual Studio 2010, see the 2.7 readme for more details. If you have VS 2008 but not 2010 you can use the build files in the PC/VS9.0 directory, see the VS9 readme for details.

Regenerate configure

If a change is made to Python which relies on some POSIX system-specific functionality (such as using a new system call), it is necessary to update the configure script to test for availability of the functionality.

Python’s configure script is generated from using Autoconf. Instead of editing configure, edit and then run autoreconf to regenerate configure and a number of other files (such as pyconfig.h).

When submitting a patch with changes made to, you should also include the generated files.

Note that running autoreconf is not the same as running autoconf. For example, autoconf by itself will not regenerate autoreconf runs autoconf and a number of other tools repeatedly as is appropriate.

Python’s script typically requires a specific version of Autoconf. At the moment, this reads: AC_PREREQ(2.65).

If the system copy of Autoconf does not match this version, you will need to install your own copy of Autoconf.

Troubleshooting the build

This section lists some of the common problems that may arise during the compilation of Python, with proposed solutions.

Avoiding re-creating auto-generated files

Under some circumstances you may encounter Python errors in scripts like Parser/ or Python/ while running make. Python auto-generates some of its own code, and a full build from scratch needs to run the auto-generation scripts. However, this makes the Python build require an already installed Python interpreter; this can also cause version mismatches when trying to build an old (2.x) Python with a new (3.x) Python installed, or vice versa.

To overcome this problem, auto-generated files are also checked into the Git repository. So if you don’t touch the auto-generation scripts, there’s no real need to auto-generate anything.

Editors and Tools

Python is used widely enough that practically all code editors have some form of support for writing Python code. Various coding tools also include Python support.

For editors and tools which the core developers have felt some special comment is needed for coding in Python, see Additional Resources.

Directory Structure

There are several top-level directories in the CPython source tree. Knowing what each one is meant to hold will help you find where a certain piece of functionality is implemented. Do realize, though, there are always exceptions to every rule.

The official documentation. This is what uses. See also Building the documentation.
Contains the EBNF grammar file for Python.
Contains all interpreter-wide header files.
The part of the standard library implemented in pure Python.
Mac-specific code (e.g., using IDLE as an OS X application).
Things that do not belong elsewhere. Typically this is varying kinds of developer-specific documentation.
The part of the standard library (plus some other code) that is implemented in C.
Code for all built-in types.
Windows-specific code.
Build files for the version of MSVC currently used for the Windows installers provided on
Code related to the parser. The definition of the AST nodes is also kept here.
Source code for C executables, including the main function for the CPython interpreter (in versions prior to Python 3.5, these files are in the Modules directory).
The code that makes up the core CPython runtime. This includes the compiler, eval loop and various built-in modules.
Various tools that are (or have been) used to maintain Python.