.. figure:: https://github.com/pybind/pybind11/raw/master/docs/pybind11-logo.png
:alt: pybind11 logo
**pybind11 — Seamless operability between C++11 and Python**
|Latest Documentation Status| |Stable Documentation Status| |Gitter chat| |GitHub Discussions| |CI| |Build status|
|Repology| |PyPI package| |Conda-forge| |Python Versions|
`Setuptools example <https://github.com/pybind/python_example>`_
• `Scikit-build example <https://github.com/pybind/scikit_build_example>`_
• `CMake example <https://github.com/pybind/cmake_example>`_
.. start
**pybind11** is a lightweight header-only library that exposes C++ types
in Python and vice versa, mainly to create Python bindings of existing
C++ code. Its goals and syntax are similar to the excellent
`Boost.Python <http://www.boost.org/doc/libs/1_58_0/libs/python/doc/>`_
library by David Abrahams: to minimize boilerplate code in traditional
extension modules by inferring type information using compile-time
introspection.
The main issue with Boost.Python—and the reason for creating such a
similar project—is Boost. Boost is an enormously large and complex suite
of utility libraries that works with almost every C++ compiler in
existence. This compatibility has its cost: arcane template tricks and
workarounds are necessary to support the oldest and buggiest of compiler
specimens. Now that C++11-compatible compilers are widely available,
this heavy machinery has become an excessively large and unnecessary
dependency.
Think of this library as a tiny self-contained version of Boost.Python
with everything stripped away that isn't relevant for binding
generation. Without comments, the core header files only require ~4K
lines of code and depend on Python (3.6+, or PyPy) and the C++
standard library. This compact implementation was possible thanks to
some C++11 language features (specifically: tuples, lambda functions and
variadic templates). Since its creation, this library has grown beyond
Boost.Python in many ways, leading to dramatically simpler binding code in many
common situations.
Tutorial and reference documentation is provided at
`pybind11.readthedocs.io <https://pybind11.readthedocs.io/en/latest>`_.
A PDF version of the manual is available
`here <https://pybind11.readthedocs.io/_/downloads/en/latest/pdf/>`_.
And the source code is always available at
`github.com/pybind/pybind11 <https://github.com/pybind/pybind11>`_.
Core features
-------------
pybind11 can map the following core C++ features to Python:
- Functions accepting and returning custom data structures per value,
reference, or pointer
- Instance methods and static methods
- Overloaded functions
- Instance attributes and static attributes
- Arbitrary exception types
- Enumerations
- Callbacks
- Iterators and ranges
- Custom operators
- Single and multiple inheritance
- STL data structures
- Smart pointers with reference counting like ``std::shared_ptr``
- Internal references with correct reference counting
- C++ classes with virtual (and pure virtual) methods can be extended
in Python
- Integrated NumPy support (NumPy 2 requires pybind11 2.12+)
Goodies
-------
In addition to the core functionality, pybind11 provides some extra
goodies:
- Python 3.6+, and PyPy3 7.3 are supported with an implementation-agnostic
interface (pybind11 2.9 was the last version to support Python 2 and 3.5).
- It is possible to bind C++11 lambda functions with captured
variables. The lambda capture data is stored inside the resulting
Python function object.
- pybind11 uses C++11 move constructors and move assignment operators
whenever possible to efficiently transfer custom data types.
- It's easy to expose the internal storage of custom data types through
Pythons' buffer protocols. This is handy e.g. for fast conversion
between C++ matrix classes like Eigen and NumPy without expensive
copy operations.
- pybind11 can automatically vectorize functions so that they are
transparently applied to all entries of one or more NumPy array
arguments.
- Python's slice-based access and assignment operations can be
supported with just a few lines of code.
- Everything is contained in just a few header files; there is no need
to link against any additional libraries.
- Binaries are generally smaller by a factor of at least 2 compared to
equivalent bindings generated by Boost.Python. A recent pybind11
conversion of PyRosetta, an enormous Boost.Python binding project,
`reported <https://graylab.jhu.edu/Sergey/2016.RosettaCon/PyRosetta-4.pdf>`_
a binary size reduction of **5.4x** and compile time reduction by
**5.8x**.
- Function signatures are precomputed at compile time (using
``constexpr``), leading to smaller binaries.
- With little extra effort, C++ types can be pickled and unpickled
similar to regular Python objects.
Supported compilers
-------------------
1. Clang/LLVM 3.3 or newer (for Apple Xcode's clang, this is 5.0.0 or
newer)
2. GCC 4.8 or newer
3. Microsoft Visual Studio 2017 or newer
4. Intel classic C++ compiler 18 or newer (ICC 20.2 tested in CI)
5. Cygwin/GCC (previously tested on 2.5.1)
6. NVCC (CUDA 11.0 tested in CI)
7. NVIDIA PGI (20.9 tested in CI)
About
-----
This project was created by `Wenzel
Jakob <http://rgl.epfl.ch/people/wjakob>`_. Significant features and/or
improvements to the code were contributed by Jonas Adler, Lori A. Burns,
Sylvain Corlay, Eric Cousineau, Aaron Gokaslan, Ralf Grosse-Kunstleve, Trent Houliston, Axel
Huebl, @hulucc, Yannick Jadoul, Sergey Lyskov, Johan Mabille, Tomasz Miąsko,
Dean Moldovan, Ben Pritchard, Jason Rhinelander, Boris Schäling, Pim
Schellart, Henry Schreiner, Ivan Smirnov, Boris Staletic, and Patrick Stewart.
We thank Google for a generous financial contribution to the continuous
integration infrastructure used by this project.
Contributing
~~~~~~~~~~~~
See the `contributing
guide <https://github.com/pybind/pybind11/blob/master/.github/CONTRIBUTING.md>`_
for information on building and contributing to pybind11.
License
~~~~~~~
pybind11 is provided under a BSD-style license that can be found in the
`LICENSE <https://github.com/pybind/pybind11/blob/master/LICENSE>`_
file. By using, distributing, or contributing to this project, you agree
to the terms and conditions of this license.
.. |Latest Documentation Status| image:: https://readthedocs.org/projects/pybind11/badge?version=latest
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"description": ".. figure:: https://github.com/pybind/pybind11/raw/master/docs/pybind11-logo.png\n :alt: pybind11 logo\n\n**pybind11 \u2014 Seamless operability between C++11 and Python**\n\n|Latest Documentation Status| |Stable Documentation Status| |Gitter chat| |GitHub Discussions| |CI| |Build status|\n\n|Repology| |PyPI package| |Conda-forge| |Python Versions|\n\n`Setuptools example <https://github.com/pybind/python_example>`_\n\u2022 `Scikit-build example <https://github.com/pybind/scikit_build_example>`_\n\u2022 `CMake example <https://github.com/pybind/cmake_example>`_\n\n.. start\n\n\n**pybind11** is a lightweight header-only library that exposes C++ types\nin Python and vice versa, mainly to create Python bindings of existing\nC++ code. Its goals and syntax are similar to the excellent\n`Boost.Python <http://www.boost.org/doc/libs/1_58_0/libs/python/doc/>`_\nlibrary by David Abrahams: to minimize boilerplate code in traditional\nextension modules by inferring type information using compile-time\nintrospection.\n\nThe main issue with Boost.Python\u2014and the reason for creating such a\nsimilar project\u2014is Boost. Boost is an enormously large and complex suite\nof utility libraries that works with almost every C++ compiler in\nexistence. This compatibility has its cost: arcane template tricks and\nworkarounds are necessary to support the oldest and buggiest of compiler\nspecimens. Now that C++11-compatible compilers are widely available,\nthis heavy machinery has become an excessively large and unnecessary\ndependency.\n\nThink of this library as a tiny self-contained version of Boost.Python\nwith everything stripped away that isn't relevant for binding\ngeneration. Without comments, the core header files only require ~4K\nlines of code and depend on Python (3.6+, or PyPy) and the C++\nstandard library. This compact implementation was possible thanks to\nsome C++11 language features (specifically: tuples, lambda functions and\nvariadic templates). Since its creation, this library has grown beyond\nBoost.Python in many ways, leading to dramatically simpler binding code in many\ncommon situations.\n\nTutorial and reference documentation is provided at\n`pybind11.readthedocs.io <https://pybind11.readthedocs.io/en/latest>`_.\nA PDF version of the manual is available\n`here <https://pybind11.readthedocs.io/_/downloads/en/latest/pdf/>`_.\nAnd the source code is always available at\n`github.com/pybind/pybind11 <https://github.com/pybind/pybind11>`_.\n\n\nCore features\n-------------\n\n\npybind11 can map the following core C++ features to Python:\n\n- Functions accepting and returning custom data structures per value,\n reference, or pointer\n- Instance methods and static methods\n- Overloaded functions\n- Instance attributes and static attributes\n- Arbitrary exception types\n- Enumerations\n- Callbacks\n- Iterators and ranges\n- Custom operators\n- Single and multiple inheritance\n- STL data structures\n- Smart pointers with reference counting like ``std::shared_ptr``\n- Internal references with correct reference counting\n- C++ classes with virtual (and pure virtual) methods can be extended\n in Python\n- Integrated NumPy support (NumPy 2 requires pybind11 2.12+)\n\nGoodies\n-------\n\nIn addition to the core functionality, pybind11 provides some extra\ngoodies:\n\n- Python 3.6+, and PyPy3 7.3 are supported with an implementation-agnostic\n interface (pybind11 2.9 was the last version to support Python 2 and 3.5).\n\n- It is possible to bind C++11 lambda functions with captured\n variables. The lambda capture data is stored inside the resulting\n Python function object.\n\n- pybind11 uses C++11 move constructors and move assignment operators\n whenever possible to efficiently transfer custom data types.\n\n- It's easy to expose the internal storage of custom data types through\n Pythons' buffer protocols. This is handy e.g.\u00a0for fast conversion\n between C++ matrix classes like Eigen and NumPy without expensive\n copy operations.\n\n- pybind11 can automatically vectorize functions so that they are\n transparently applied to all entries of one or more NumPy array\n arguments.\n\n- Python's slice-based access and assignment operations can be\n supported with just a few lines of code.\n\n- Everything is contained in just a few header files; there is no need\n to link against any additional libraries.\n\n- Binaries are generally smaller by a factor of at least 2 compared to\n equivalent bindings generated by Boost.Python. A recent pybind11\n conversion of PyRosetta, an enormous Boost.Python binding project,\n `reported <https://graylab.jhu.edu/Sergey/2016.RosettaCon/PyRosetta-4.pdf>`_\n a binary size reduction of **5.4x** and compile time reduction by\n **5.8x**.\n\n- Function signatures are precomputed at compile time (using\n ``constexpr``), leading to smaller binaries.\n\n- With little extra effort, C++ types can be pickled and unpickled\n similar to regular Python objects.\n\nSupported compilers\n-------------------\n\n1. Clang/LLVM 3.3 or newer (for Apple Xcode's clang, this is 5.0.0 or\n newer)\n2. GCC 4.8 or newer\n3. Microsoft Visual Studio 2017 or newer\n4. Intel classic C++ compiler 18 or newer (ICC 20.2 tested in CI)\n5. Cygwin/GCC (previously tested on 2.5.1)\n6. NVCC (CUDA 11.0 tested in CI)\n7. NVIDIA PGI (20.9 tested in CI)\n\nAbout\n-----\n\nThis project was created by `Wenzel\nJakob <http://rgl.epfl.ch/people/wjakob>`_. Significant features and/or\nimprovements to the code were contributed by Jonas Adler, Lori A. Burns,\nSylvain Corlay, Eric Cousineau, Aaron Gokaslan, Ralf Grosse-Kunstleve, Trent Houliston, Axel\nHuebl, @hulucc, Yannick Jadoul, Sergey Lyskov, Johan Mabille, Tomasz Mi\u0105sko,\nDean Moldovan, Ben Pritchard, Jason Rhinelander, Boris Sch\u00e4ling, Pim\nSchellart, Henry Schreiner, Ivan Smirnov, Boris Staletic, and Patrick Stewart.\n\nWe thank Google for a generous financial contribution to the continuous\nintegration infrastructure used by this project.\n\n\nContributing\n~~~~~~~~~~~~\n\nSee the `contributing\nguide <https://github.com/pybind/pybind11/blob/master/.github/CONTRIBUTING.md>`_\nfor information on building and contributing to pybind11.\n\nLicense\n~~~~~~~\n\npybind11 is provided under a BSD-style license that can be found in the\n`LICENSE <https://github.com/pybind/pybind11/blob/master/LICENSE>`_\nfile. 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