1. 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 http://www.python.org/). 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.
1.1. Getting Set Up¶
1.1.1. Version Control Setup¶
CPython is developed using Mercurial. The Mercurial
command line program is named
hg; this is also used to refer to Mercurial
itself. Mercurial is easily available for common Unix systems by way of the
standard package manager; under Windows, you might want to use the
TortoiseHg graphical client, but the build system
hg.exe to be on your PATH.
1.1.2. Getting the Source Code¶
One should always work from a working copy of the CPython source code. While it may be tempting to work from the copy of Python you already have installed on your machine, it is very likely that you will be working from out-of-date code as the Python core developers are constantly updating and fixing things in their VCS. It also means you will have better tool support through the VCS as it will provide a diff tool, etc.
hg clone https://hg.python.org/cpython
If you want a working copy of an already-released version of Python, i.e., a version in maintenance mode, you can update your working copy. For instance, to update your working copy to Python 3.5, do:
hg update 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.
1.1.3. 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
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.
188.8.131.52. 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
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
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 version that you’re working on e.g.:
$ 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
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
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,
$ brew install openssl xz $ CPPFLAGS="-I$(brew --prefix openssl)/include" \ LDFLAGS="-L$(brew --prefix openssl)/lib" \ ./configure --with-pydebug
$ sudo port install openssl xz $ CPPFLAGS="-I/opt/local/include" \ LDFLAGS="-L/opt/local/lib" \ ./configure --with-pydebug
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
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:
More flags are available to
configure, but this is the minimum you should
do to get a pydebug build of CPython.
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
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
./python.exe wherever a case-insensitive filesystem is used
(e.g. on OS X by default), in order to avoid conflicts with the
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
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
If you are using clang with ccache, turn off the noisy
parentheses-equality warnings with the
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
ctypes module (without the flag the rest of CPython will
still build properly).
Python 3.5 and later use Microsoft Visual Studio 2015. You can download and use any of the free or paid versions of Visual Studio 2015. Installing the latest updates is also recommended. 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.
1.1.4. Troubleshooting the build¶
This section lists some of the common problems that may arise during the compilation of Python, with proposed solutions.
184.108.40.206. Avoiding re-creating auto-generated files¶
Under some circumstances you may encounter Python errors in scripts like
Python/makeopcodetargets.py while running
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
To overcome this problem, auto-generated files are also checked into the
Mercurial repository. So if you don’t touch the auto-generation scripts, there’s
no real need to auto-generate anything. However, as Mercurial doesn’t preserve
timestamps well, a special build target
touch was added (the
build target is not designed for git clones and does not support them). Run:
Before running the compilation
make. This will tweak the timestamps of the
auto-generated files in a way that makes it unnecessary to create them anew and
henceforth the compilation should not require an installed Python interpreter.
1.2. 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.
1.3. 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 http://docs.python.org/ 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 python.org.
- 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.