PYBIND11(1)

NAME
pybind11 - pybind11 Documentation [image: pybind11 logo] [image]

pybind11 (v3) — Seamless interoperability between C++ and Python

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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 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 (CPython 3.8+, PyPy, or GraalPy) 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. A PDF version of the manual is available here. And the source code is always available at github.com/pybind/pybind11.

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+)

    In addition to the core functionality, pybind11 provides some extra goodies:

•   CPython 3.8+, PyPy3 7.3.17+, and GraalPy 24.1+ are supported with an implementation-agnostic interface (see older versions for older CPython and PyPy versions).

•   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 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.

    pybind11 is exercised in continuous integration across a range of operating systems, Python versions, C++ standards, and toolchains. For an up-to-date view of the combinations we currently test, please see the pybind11 GitHub Actions logs.

    The test matrix naturally evolves over time as older platforms and compilers fall out of use and new ones are added by the community. Closely related versions of a tested compiler or platform will often work as well in practice, but we cannot promise to validate every possible combination. If a configuration you rely on is missing from the matrix or regresses, issues and pull requests to extend coverage are very welcome. At the same time, we need to balance the size of the test matrix with the available CI resources, such as GitHub's limits on concurrent jobs under the free tier.

    This project was created by Wenzel Jakob. 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, Dustin Spicuzza, Boris Staletic, Ethan Steinberg, Patrick Stewart, Ivor Wanders, and Xiaofei Wang.

    We thank Google for a generous financial contribution to the continuous integration infrastructure used by this project.

CONTRIBUTING

See the contributing guide for information on building and contributing to pybind11.

LICENSE

pybind11 is provided under a BSD-style license that can be found in the LICENSE file. By using, distributing, or contributing to this project, you agree to the terms and conditions of this license.

Contents:

  Changelog

Starting with version 1.8.0, pybind11 releases use a semantic versioning policy.

Changes will be added here periodically from the "Suggested changelog entry" block in pull request descriptions.

  Version 3.0.2 (February 16, 2026)

New Features:

•   Added helper functions to py::array that return shape and strides as std::span when available. #5974

    Bug fixes:

•   Added fallback locking for Python 3.13t where PyCriticalSection_BeginMutex is unavailable. #5981

•   Fixed race condition in py::make_key_iterator with free-threaded Python. #5971

•   MSVC 19.16 and earlier were blocked from using std::launder due to internal compiler errors. #5968

•   Internals destructors were updated to check the owning interpreter before clearing Python objects. #5965

•   Internals shutdown handling was refined in two iterations before release: an initial finalization-time cleanup was followed by a safety adjustment to avoid late-shutdown py::cast segfaults. #5958 #5972

•   Fixed ambiguous str(handle) construction for object-derived types like kwargs or dict by templatizing the constructor with SFINAE. #5949

•   Fixed concurrency consistency for internals_pp_manager under multiple-interpreters. #5947

•   Fixed MSVC LNK2001 in C++20 builds when /GL (whole program optimization) is enabled. #5939

•   Added per-interpreter storage for gil_safe_call_once_and_store to make it safe under multi-interpreters. #5933

•   A workaround for a GCC -Warray-bounds false positive in argument_vector was added. #5908

•   Corrected a mistake where support for __index__ was added, but the type hints did not reflect acceptance of SupportsIndex objects. Also fixed a long-standing bug: the complex-caster did not accept __index__ in convert mode. #5891

•   Fixed *args/**kwargs return types. Added type hinting to py::make_tuple. #5881

•   Fixed compiler error in type_caster_generic when casting a T implicitly convertible from T*. #5873

•   Updated py::native_enum bindings to unregister enum types on destruction, preventing a use-after-free when returning a destroyed enum instance. #5871

•   Fixed undefined behavior that occurred when importing pybind11 modules from non-main threads created by C API modules or embedded python interpreters. #5870

•   Fixed dangling pointer in internals::registered_types_cpp_fast. #5867

•   Added support for std::shared_ptr<T> when loading module-local or conduit types from other modules. #5862

•   Fixed thread-safety issues if types were concurrently registered while get_local_type_info() was called in free threaded Python. #5856

•   Fixed py::float_ casting and py::int_ and py::float_ type hints. #5839

•   Fixed two smart_holder bugs in shared_ptr and unique_ptr adoption with multiple/virtual inheritance:

•   shared_ptr to-Python caster was updated to register the correct subobject pointer (fixes #5786).

•   unique_ptr adoption was updated to own the proper object start while aliasing subobject pointers for registration, which fixed MSVC crashes during destruction. #5836

•   Constrained accessor::operator= templates to avoid obscuring special members. #5832

•   Fixed crash that can occur when finalizers acquire and release the GIL. #5828

•   Fixed compiler detection in pybind11/detail/pybind11_namespace_macros.h for clang-cl on Windows, to address warning suppression macros. #5816

•   Fixed compatibility with CMake policy CMP0190 by not always requiring a Python interpreter when cross-compiling. #5829

•   Added a static assertion to disallow keep_alive and call_guard on properties. #5533

    Internal:

•   CMake policy limit was set to 4.1. #5944

•   Improved performance of function calls between Python and C++ by switching to the "vectorcall" calling protocol. #5948

•   Many C-style casts were replaced with C++-style casts. #5930

•   Added cast_sources abstraction to type_caster_generic. #5866

•   Improved the performance of from-Python conversions of legacy pybind11 enum objects bound by py::enum_. #5860

•   Reduced size overhead by deduplicating functions' readable signatures and type information. #5857

•   Used new Python 3.14 C APIs when available. #5854

•   Improved performance of function dispatch and type casting by porting two-level type info lookup strategy from nanobind. #5842

•   Updated .gitignore to exclude __pycache__/ directories. #5838

•   Changed internals to use thread_local instead of thread_specific_storage for increased performance. #5834

•   Reduced function call overhead by using thread_local for loader_life_support when possible. #5830

•   Removed heap allocation for the C++ argument array when dispatching functions with 6 or fewer arguments. #5824

    Documentation:

•   Fixed docstring for long double complex types to use numpy.clongdouble instead of the deprecated numpy.longcomplex (removed in NumPy 2.0). #5952

•   The "Supported compilers" and "Supported platforms" sections in the main README.rst were replaced with a new "Supported platforms & compilers" section that points to the CI test matrix as the living source of truth. #5910

•   Fixed documentation formatting. #5903

•   Updated upgrade notes for py::native_enum. #5885

•   Clarified in the docs to what extent bindings are global. #5859

    Tests:

•   Fixed deadlock in a free-threading test by releasing the GIL while waiting on synchronization. #5973

•   Calls to env.deprecated_call() were replaced with direct calls to pytest.deprecated_call(). #5893

•   Updated pytest configuration to use log_level instead of log_cli_level. #5890

    CI:

•   Added CI tests for windows-11-arm with clang/MSVC (currently python 3.13), windows-11-arm with clang/mingw (currently python 3.12). #5932

•   These clang-tidy rules were added: readability-redundant-casting, readability-redundant-inline-specifier, readability-redundant-member-init #5924

•   Replaced deprecated macos-13 runners with macos-15-intel in CI. #5916

•   Restored runs-on: windows-latest in CI. #5835

  Version 3.0.1 (August 22, 2025)

Bug fixes:

•   Fixed compilation error in type_caster_enum_type when casting pointer-to-enum types. Added pointer overload to handle dereferencing before enum conversion. #5776

•   Implement binary version of make_index_sequence to reduce template depth requirements for functions with many parameters. #5751

•   Subinterpreter-specific exception handling code was removed to resolve segfaults. #5795

•   Fixed issue that caused PYBIND11_MODULE code to run again if the module was re-imported after being deleted from sys.modules. #5782

•   Prevent concurrent creation of sub-interpreters as a workaround for stdlib concurrency issues in Python 3.12. #5779

•   Fixed potential crash when using cpp_function objects with sub-interpreters. #5771

•   Fixed non-entrant check in implicitly_convertible(). #5777

•   Support C++20 on platforms that have older c++ runtimes. #5761

•   Fix compilation with clang on msys2. #5757

•   Avoid nullptr dereference warning with GCC 13.3.0 and python 3.11.13. #5756

•   Fix potential warning about number of threads being too large. #5807

•   Fix gcc 11.4+ warning about serial compilation using CMake. #5791

    Documentation:

•   Improve buffer_info type checking in numpy docs. #5805

•   Replace robotpy-build with semiwrap in the binding tool list. #5804

•   Show nogil in most examples. #5770

•   Fix py::trampoline_self_life_support visibility in docs. #5766

    Tests:

•   Avoid a spurious warning about DOWNLOAD_CATCH being manually specified. #5803

•   Fix an IsolatedConfig test. #5768

    CI:

•   Add CI testing for Android. #5714

    Internal:

•   Rename internal variables to avoid the word slots (reads better). #5793

  Version 3.0.0 (July 10, 2025)

Pybind11 3.0 includes an ABI bump, the first required bump in many years on Unix (Windows has had required bumps more often). This release contains the smart-holder branch, multi-phase init and subinterpreter support, py::native_enum, an interface to warnings, typing improvements, and more. CMake now defaults to FindPython mode. Please check our upgrade guide for more info on upgrading!

Support for Python 3.14, 3.14t, GraalPy, and PyPy 3.11 has been added, while legacy support for Python 3.7, PyPy 3.8/3.9, and CMake <3.15 has been removed. Most deprecated features have been kept for this release, but anything producing a warning in 3.0 may be removed in a future 3.x version. We also now have a deprecation page.

New Features:

•   The smart_holder branch has been merged, enabling py::class_<T, py::smart_holder>, which handles two-way conversion with std::unique_ptr<T> and std::shared_ptr<T> (simultaneously), disowning a Python object being passed to std::unique_ptr<T>, trampoline objects, and std::enable_shared_from_this. #5542

•   Added support for std::shared_ptr<const T> in py::init() when using py::smart_holder, complementing existing support for std::unique_ptr<const T>. #5731

•   Support const-only smart pointers. #5718

•   Eliminate cross-DSO RTTI reliance from trampoline_self_life_support functionality, smart_holder deleter detection, and other smart_holder bookkeeping. Resolves platform-specific issues on macOS related to cross-DSO dynamic_cast and typeid mismatches. #5728 (replaces #5700)

•   Changed PYBIND11_MODULE macro implementation to perform multi-phase module initialization (PEP 489) behind the scenes. #5574 and avoid destruction #5688

•   Support for sub-interpreters (both isolated (with separate GILs) and legacy (with a global GIL). Add the py::multiple_interpreters::per_interpreter_gil() tag (or, py::multiple_interpreters::shared_gil() for legacy interpreter support) to PYBIND11_MODULE calls (as the third parameter) to indicate that a module supports running with sub-interpreters. #5564

•   Rename macro PYBIND11_SUBINTERPRETER_SUPPORT -> PYBIND11_HAS_SUBINTERPRETER_SUPPORT to meet naming convention. #5682

•   Allow subinterpreter support to be disabled if defined to 0. This is mostly an emergency workaround, and is not exposed in CMake. #5708 and #5710

•   Modify internals pointer-to-pointer implementation to not use thread_local (better iOS support). #5709

•   Support implementations without subinterpreter support. #5732

•   Changed PYBIND11_EMBEDDED_MODULE macro implementation to perform multi-phase module initialization (PEP 489) behind the scenes and to support py::mod_gil_not_used(), py::multiple_interpreters::per_interpreter_gil() and py::multiple_interpreters::shared_gil(). #5665 and consolidate code #5670.

•   Added API in pybind11/subinterpreter.h for embedding sub-interpreters (requires Python 3.12+). #5666

•   py::native_enum was added, for conversions between Python's native (stdlib) enum types and C++ enums. #5555

•   Add class doc string to py::native_enum. #5617.

•   Fix signature for functions with a native_enum in the signature. #5619

•   Support py::numpy_scalar<> / py::make_scalar() for NumPy types. #5726

•   A py::release_gil_before_calling_cpp_dtor option (for py::class_) was added to resolve the long-standing issue #1446. #5522

•   Add dtype::normalized_num and dtype::num_of. #5429

•   Add support for array_t<handle> and array_t<object>. #5427

•   Added py::warnings namespace with py::warnings::warn and py::warnings::new_warning_type that provides the interface for Python warnings. #5291

•   stl.h list|set|map_caster were made more user friendly: it is no longer necessary to explicitly convert Python iterables to tuple(), set(), or map() in many common situations. #4686

•   The array_caster in pybind11/stl.h was enhanced to support value types that are not default-constructible. #5305

•   pybind11/conduit/pybind11_platform_abi_id.h was factored out, to maximize reusability of PYBIND11_PLATFORM_ABI_ID (for other Python/C++ binding systems). Separately, a note was added to explain that the conduit feature only covers from-Python-to-C++ conversions. #5375 | #5740

•   Added support for finding pybind11 using pkgconf distributed on pypi. #5552

•   Support --extension-suffix on the pybind11 command. #5360

•   Add semi-public API: pybind11::detail::is_holder_constructed and update example for pybind11::custom_type_setup in documentation. #5669

•   Added py::scoped_critical_section to support free-threaded mode. #5684 | #5706

    New Features / fixes (typing):

•   Added option for different arg/return type hints to type_caster. Updated stl/filesystem to use correct arg/return type hints. Updated pybind11::typing to use correct arg/return type hints for nested types. #5450

•   Updated type hint for py::capsule to type.CapsuleType. #5567

•   Adds support for typing.SupportsInt and typing.SupportsFloat. Update Final to be narrower type hint. Make std::function match Callable type. Fix io_name bug in attr_with_type_hint. #5540

•   Rework of arg/return type hints to support .noconvert(). #5486

•   Add attr_with_type for declaring attribute types and Final, ClassVar type annotations. #5460

•   Allow annotate methods with py::pos_only when only have the self argument. Make arguments for auto-generated dunder methods positional-only. #5403

•   Added py::Args and py::KWArgs to enable custom type hinting of *args and **kwargs (see PEP 484). #5357

•   Switched to numpy.typing.NDArray and numpy.typing.ArrayLike. #5212

•   Use numpy.object_ instead of object. #5571

•   Fix module type hint. #5469

•   Fix Buffer type hint. #5662

•   Added support for collections.abc in type hints and convertible checks of STL casters and py::buffer. #5566

•   Fix typing and collections.abc type hint ambiguity. #5663

•   Add typing_extensions alternatives for all types that need them. #5693

    Removals:

•   Remove support for pybind11 v2 internals versions (4, 5, 6). (The internals version number has been bumped for pybind11 v3.) #5512 | #5530

•   Remove make_simple_namespace (added in 2.8.0, deprecated in 2.8.1). #5597

•   Legacy-mode option PYBIND11_NUMPY_1_ONLY has been removed. #5595

•   Add a deprecation warning to .get_type (deprecated in pybind11 2.6 in 2020). #5596

    Bug fixes:

•   Set __file__ on submodules. #5584. Except on embedded modules. #5650

•   pybind11-bound functions are now pickleable. #5580

•   Fix bug in attr_with_type_hint to allow objects to be in attr_with_type_hint. #5576

•   A -Wmaybe-uninitialized warning suppression was added in pybind11/eigen/matrix.h. #5516

•   PYBIND11_WARNING_POP was incorrectly defined as PYBIND11_PRAGMA(clang diagnostic push). #5448

•   PYBIND11_PLATFORM_ABI_ID (which is used in composing PYBIND11_INTERNALS_ID) was modernized to reflect actual ABI compatibility more accurately. #4953 | #5439

•   Fix buffer protocol implementation. #5407

•   Fix iterator increment operator does not skip first item. #5400

•   When getting or deleting an element in a container bound by bind_map, print the key in KeyError if it does not exist. #5397

•   pybind11::builtin_exception is now explicitly exported when linked to libc++. #5390

•   Allow subclasses of py::args and py::kwargs. #5381

•   Disable false-positive GCC 12 Bound Check warning. #5355

•   Update the dict when restoring pickles, instead of assigning a replacement dict. #5658

•   Properly define _DEBUG macro to 1 instead of defining it without value. #5639

•   Fix a missing time cast causing a compile error for newer ICC. #5621

•   Change the behavior of the default constructor of py::slice to be equivalent to :: in Python. #5620

    Bug fixes and features (CMake):

•   Enable FindPython mode by default, with a COMPAT mode that sets some of the old variables to ease transition. #5553

•   Add an author warning that auto-calculated PYTHON_MODULE_EXTENSION may not respect SETUPTOOLS_EXT_SUFFIX during cross-compilation. #5495

•   Don't strip with CMAKE_BUILD_TYPE None. #5392

•   Fix an issue with NO_EXTRAS adding pybind11::windows_extras anyway. #5378

•   Fix issue with NEW/OLD message showing up. #5656

•   Use CMake's warnings as errors if available (CMake 3.24+). #5612

•   Add support for running pybind11's tests via presets in CMake 3.25+. #5655 and support --fresh. #5668

•   Experimental CMake support for Android. #5733

•   Presets now generate compile_commands.json. #5685

    Bug fixes (free-threading):

•   Fix data race in free threaded CPython when accessing a shared static variable. #5494

•   A free-threading data race in all_type_info() was fixed. #5419

•   Added exception translator specific mutex used with try_translate_exceptions in the free-threaded build for internal locking. #5362

    Internals:

•   Consolidated all PYBIND11_HAS_... feature macros into pybind11/detail/common.h to streamline backward compatibility checks and simplify internal refactoring. This change ensures consistent macro availability regardless of header inclusion order. #5647

•   pybind11/gil_simple.h was factored out from pybind11/gil.h, so that it can easily be reused. #5614

•   Use CPython macros to construct PYBIND11_VERSION_HEX. #5683

    Documentation:

•   Improved reference_internal policy documentation. #5528

•   A new "Double locking, deadlocking, GIL" document was added. #5394

•   Add documenting for free-threading and subinterpreters. #5659

    Tests:

•   Download the final Catch2 2.x release if Catch download is requested. #5568

•   Explicitly used signed char for two numpy dtype tests. As seen when compiling using clang on Linux with the -funsigned-char flag. #5545

•   CI testing now includes -Wwrite-strings -Wunreachable-code -Wpointer-arith -Wredundant-decls in some jobs. #5523

•   Add nightly wheels to scientific-python's nightly wheelhouse. #5675

•   Expect free-threaded warning when loading a non-free-threaded module. #5680

•   Run pytest under Python devmode. #5715

    New and removed platforms:

•   Support Python 3.14 (beta 1+). #5646

•   Added support for GraalPy Python implementation (https://github.com/oracle/graalpython). #5380

•   Support and test iOS in CI. #5705

•   Support for PyPy 3.11 added. #5508 And test in ci. #5534

•   Support for PyPy 3.8 and 3.9 was dropped. #5578

•   Support for Python 3.7 was removed. (Official end-of-life: 2023-06-27). #5191

•   Support for CMake older than 3.15 removed. CMake 3.15-4.0 supported. #5304 and fix regression #5691.

•   Use scikit-build-core for the build backend for the PyPI pybind11. The CMake generation has been moved to the sdist->wheel step. PYBIND11_GLOBAL_SDIST has been removed. #5598 and updated docs/ci. #5676

•   clang 20 tested and used for clang-tidy. #5692

•   Drop testing on MSVC 2019 (as it is being removed from GitHub). #5712

•   Support Windows C++20 and Linux C++23 in tests. #5707

  Version 2.13.6 (September 13, 2024)

New Features:

•   A new self._pybind11_conduit_v1_() method is automatically added to all py::class_-wrapped types, to enable type-safe interoperability between different independent Python/C++ bindings systems, including pybind11 versions with different PYBIND11_INTERNALS_VERSION's. Supported on pybind11 2.11.2, 2.12.1, and 2.13.6+. #5296

    Bug fixes:

•   Using __cpp_nontype_template_args instead of __cpp_nontype_template_parameter_class. #5330

•   Properly translate C++ exception to Python exception when creating Python buffer from wrapped object. #5324

    Documentation:

•   Adds an answer (FAQ) for "What is a highly conclusive and simple way to find memory leaks?". #5340

  Version 2.13.5 (August 22, 2024)

Bug fixes:

•   Fix includes when using Windows long paths (\\?\ prefix). #5321

•   Support -Wpedantic in C++20 mode. #5322

•   Fix and test <ranges> support for py::tuple and py::list. #5314

  Version 2.13.4 (August 14, 2024)

Bug fixes:

•   Fix paths with spaces, including on Windows. (Replaces regression from #5302) #4874

    Documentation:

•   Remove repetitive words. #5308

  Version 2.13.3 (August 13, 2024)

Bug fixes:

•   Quote paths from pybind11-config #5302

•   Fix typo in Emscripten support when in config mode (CMake) #5301

  Version 2.13.2 (August 13, 2024)

New Features:

•   A pybind11::detail::type_caster_std_function_specializations feature was added, to support specializations for std::function's with return types that require custom to-Python conversion behavior (to primary use case is to catch and convert exceptions). #4597

    Changes:

•   Use PyMutex instead of std::mutex for internal locking in the free-threaded build. #5219

•   Add a special type annotation for C++ empty tuple. #5214

•   When compiling for WebAssembly, add the required exception flags (CMake 3.13+). #5298

    Bug fixes:

•   Make gil_safe_call_once_and_store thread-safe in free-threaded CPython. #5246

•   A missing #include <algorithm> in pybind11/typing.h was added to fix build errors (in case user code does not already depend on that include). #5208

•   Fix regression introduced in #5201 for GCC<10.3 in C++20 mode. #5205

•   Remove extra = when assigning flto value in the case for Clang in CMake. #5207

    Tests:

•   Adding WASM testing to our CI (Pyodide / Emscripten via scikit-build-core). #4745

•   clang-tidy (in GitHub Actions) was updated from clang 15 to clang 18. #5272

  Version 2.13.1 (June 26, 2024)

New Features:

•   Add support for Typing.Callable[..., T]. #5202

    Bug fixes:

•   Avoid aligned allocation in free-threaded build in order to support macOS versions before 10.14. #5200

  Version 2.13.0 (June 25, 2024)

New Features:

•   Support free-threaded CPython (3.13t). Add py::mod_gil_not_used() tag to indicate if a module supports running with the GIL disabled. #5148

•   Support for Python 3.6 was removed. (Official end-of-life: 2021-12-23). #5177

•   py::list gained a .clear() method. #5153

•   Support for Union, Optional, type[T], typing.TypeGuard, typing.TypeIs, typing.Never, typing.NoReturn and typing.Literal was added to pybind11/typing.h. #5166 #5165 #5194 #5193 #5192

•   In CMake, if PYBIND11_USE_CROSSCOMPILING is enabled, then CMAKE_CROSSCOMPILING will be respected and will keep pybind11 from accessing the interpreter during configuration. Several CMake variables will be required in this case, but can be deduced from the environment variable SETUPTOOLS_EXT_SUFFIX. The default (currently OFF) may be changed in the future. #5083

    Bug fixes:

•   A refcount bug (leading to heap-use-after-free) involving trampoline functions with PyObject * return type was fixed. #5156

•   Return py::ssize_t from .ref_count() instead of int. #5139

•   A subtle bug involving C++ types with unusual operator& overrides was fixed. #5189

•   Support Python 3.13 with minor fix, add to CI. #5127

•   Fix mistake affecting old cmake and old boost. #5149

    Documentation:

•   Build docs updated to feature scikit-build-core and meson-python, and updated setuptools instructions. #5168

    Tests:

•   Avoid immortal objects in tests. #5150

    CI:

•   Compile against Python 3.13t in CI.

•   Use macos-13 (Intel) for CI jobs for now (will drop Python 3.7 soon). #5109

•   Releases now have artifact attestations, visible at https://github.com/pybind/pybind11/attestations. #5196

    Other:

•   Some cleanup in preparation for 3.13 support. #5137

•   Avoid a warning by ensuring an iterator end check is included in release mode. #5129

•   Bump max cmake to 3.29. #5075

•   Update docs and noxfile. #5071

  Version 2.12.1 (September 13, 2024)

New Features:

•   A new self._pybind11_conduit_v1_() method is automatically added to all py::class_-wrapped types, to enable type-safe interoperability between different independent Python/C++ bindings systems, including pybind11 versions with different PYBIND11_INTERNALS_VERSION's. Supported on pybind11 2.11.2, 2.12.1, and 2.13.6+. #5296

  Version 2.12.0 (March 27, 2024)

New Features:

•   pybind11 now supports compiling for NumPy 2. Most code shouldn't change (see upgrade-guide-2.12 for details). However, if you experience issues you can define PYBIND11_NUMPY_1_ONLY to disable the new support for now, but this will be removed in the future. #5050

•   pybind11/gil_safe_call_once.h was added (it needs to be included explicitly). The primary use case is GIL-safe initialization of C++ static variables. #4877

•   Support move-only iterators in py::make_iterator, py::make_key_iterator, py::make_value_iterator. #4834

•   Two simple py::set_error() functions were added and the documentation was updated accordingly. In particular, py::exception<>::operator() was deprecated (use one of the new functions instead). The documentation for py::exception<> was further updated to not suggest code that may result in undefined behavior. #4772

    Bug fixes:

•   Removes potential for Undefined Behavior during process teardown. #4897

•   Improve compatibility with the nvcc compiler (especially CUDA 12.1/12.2). #4893

•   pybind11/numpy.h now imports NumPy's multiarray and _internal submodules with paths depending on the installed version of NumPy (for compatibility with NumPy 2). #4857

•   Builtins collections names in docstrings are now consistently rendered in lowercase (list, set, dict, tuple), in accordance with PEP 585. #4833

•   Added py::typing::Iterator<T>, py::typing::Iterable<T>. #4832

•   Render py::function as Callable in docstring. #4829

•   Also bump PYBIND11_INTERNALS_VERSION for MSVC, which unlocks two new features without creating additional incompatibilities. #4819

•   Guard against crashes/corruptions caused by modules built with different MSVC versions. #4779

•   A long-standing bug in the handling of Python multiple inheritance was fixed. See PR #4762 for the rather complex details. #4762

•   Fix bind_map with using declarations. #4952

•   Qualify py::detail::concat usage to avoid ADL selecting one from somewhere else, such as modernjson's concat. #4955

•   Use new PyCode API on Python 3.12+. #4916

•   Minor cleanup from warnings reported by Clazy. #4988

•   Remove typing and duplicate class_ for KeysView/ValuesView/ItemsView. #4985

•   Use PyObject_VisitManagedDict() and PyObject_ClearManagedDict() on Python 3.13 and newer. #4973

•   Update make_static_property_type() to make it compatible with Python 3.13. #4971

•   Render typed iterators for make_iterator, make_key_iterator, make_value_iterator. #4876

•   Add several missing type name specializations. #5073

•   Change docstring render for py::buffer, py::sequence and py::handle (to Buffer, Sequence, Any). #4831

•   Fixed base_enum.__str__ docstring. #4827

•   Enforce single line docstring signatures. #4735

•   Special 'typed' wrappers now available in typing.h to annotate tuple, dict, list, set, and function. #4259

•   Create handle_type_name specialization to type-hint variable length tuples. #5051

•   Setting PYBIND11_FINDPYTHON to OFF will force the old FindPythonLibs mechanism to be used. #5042

•   Skip empty PYBIND11_PYTHON_EXECUTABLE_LAST for the first cmake run. #4856

•   Fix FindPython mode exports & avoid pkg_resources if importlib.metadata available. #4941

•   Python_ADDITIONAL_VERSIONS (classic search) now includes 3.12. #4909

•   pybind11.pc is now relocatable by default as long as install destinations are not absolute paths. #4830

•   Correctly detect CMake FindPython removal when used as a subdirectory. #4806

•   Don't require the libs component on CMake 3.18+ when using PYBIND11_FINDPYTHON (fixes manylinux builds). #4805

•   pybind11_strip is no longer automatically applied when CMAKE_BUILD_TYPE is unset. #4780

•   Support DEBUG_POSFIX correctly for debug builds. #4761

•   Hardcode lto/thin lto for Emscripten cross-compiles. #4642

•   Upgrade maximum supported CMake version to 3.27 to fix CMP0148 warnings. #4786

    Documentation:

•   Small fix to grammar in functions.rst. #4791

•   Remove upper bound in example pyproject.toml for setuptools. #4774

    CI:

•   CI: Update NVHPC to 23.5 and Ubuntu 20.04. #4764

•   Test on PyPy 3.10. #4714

    Other:

•   Use Ruff formatter instead of Black. #4912

•   An assert() was added to help Coverty avoid generating a false positive. #4817

  Version 2.11.2 (September 13, 2024)

New Features:

•   A new self._pybind11_conduit_v1_() method is automatically added to all py::class_-wrapped types, to enable type-safe interoperability between different independent Python/C++ bindings systems, including pybind11 versions with different PYBIND11_INTERNALS_VERSION's. Supported on pybind11 2.11.2, 2.12.1, and 2.13.6+. #5296

  Version 2.11.1 (July 17, 2023)

Changes:

•   PYBIND11_NO_ASSERT_GIL_HELD_INCREF_DECREF is now provided as an option for disabling the default-on PyGILState_Check()'s in pybind11::handle's inc_ref() & dec_ref(). #4753

•   PYBIND11_ASSERT_GIL_HELD_INCREF_DECREF was disabled for PyPy in general (not just PyPy Windows). #4751

  Version 2.11.0 (July 14, 2023)

New features:

•   The newly added pybind11::detail::is_move_constructible trait can be specialized for cases in which std::is_move_constructible does not work as needed. This is very similar to the long-established pybind11::detail::is_copy_constructible. #4631

•   Introduce recursive_container_traits. #4623

•   pybind11/type_caster_pyobject_ptr.h was added to support automatic wrapping of APIs that make use of PyObject *. This header needs to included explicitly (i.e. it is not included implicitly with pybind/pybind11.h). #4601

•   format_descriptor<> & npy_format_descriptor<> PyObject * specializations were added. The latter enables py::array_t<PyObject *> to/from-python conversions. #4674

•   buffer_info gained an item_type_is_equivalent_to<T>() member function. #4674

•   The capsule API gained a user-friendly constructor (py::capsule(ptr, "name", dtor)). #4720

    Changes:

•   PyGILState_Check()'s in pybind11::handle's inc_ref() & dec_ref() are now enabled by default again. #4246

•   py::initialize_interpreter() using PyConfig_InitPythonConfig() instead of PyConfig_InitIsolatedConfig(), to obtain complete sys.path. #4473

•   Cast errors now always include Python type information, even if PYBIND11_DETAILED_ERROR_MESSAGES is not defined. This increases binary sizes slightly (~1.5%) but the error messages are much more informative. #4463

•   The docstring generation for the std::array-list caster was fixed. Previously, signatures included the size of the list in a non-standard, non-spec compliant way. The new format conforms to PEP 593. Tooling for processing the docstrings may need to be updated accordingly. #4679

•   Setter return values (which are inaccessible for all practical purposes) are no longer converted to Python (only to be discarded). #4621

•   Allow lambda specified to function definition to be noexcept(true) in C++17. #4593

•   Get rid of recursive template instantiations for concatenating type signatures on C++17 and higher. #4587

•   Compatibility with Python 3.12 (beta). Note that the minimum pybind11 ABI version for Python 3.12 is version 5. (The default ABI version for Python versions up to and including 3.11 is still version 4.). #4570

•   With PYBIND11_INTERNALS_VERSION 5 (default for Python 3.12+), MSVC builds use std::hash<std::type_index> and std::equal_to<std::type_index> instead of string-based type comparisons. This resolves issues when binding types defined in the unnamed namespace. #4319

•   Python exception __notes__ (introduced with Python 3.11) are now added to the error_already_set::what() output. #4678

    Build system improvements:

•   CMake 3.27 support was added, CMake 3.4 support was dropped. FindPython will be used if FindPythonInterp is not present. #4719

•   Update clang-tidy to 15 in CI. #4387

•   Moved the linting framework over to Ruff. #4483

•   Skip lto checks and target generation when CMAKE_INTERPROCEDURAL_OPTIMIZATION is defined. #4643

•   No longer inject -stdlib=libc++, not needed for modern Pythons (macOS 10.9+). #4639

•   PyPy 3.10 support was added, PyPy 3.7 support was dropped. #4728

•   Testing with Python 3.12 beta releases was added. #4713

  Version 2.10.4 (Mar 16, 2023)

Changes:

•   python3 -m pybind11 gained a --version option (prints the version and exits). #4526

    Bug Fixes:

•   Fix a warning when pydebug is enabled on Python 3.11. #4461

•   Ensure gil_scoped_release RAII is non-copyable. #4490

•   Ensure the tests dir does not show up with new versions of setuptools. #4510

•   Better stacklevel for a warning in setuptools helpers. #4516

  Version 2.10.3 (Jan 3, 2023)

Changes:

•   Temporarily made our GIL status assertions (added in 2.10.2) disabled by default (re-enable manually by defining PYBIND11_ASSERT_GIL_HELD_INCREF_DECREF, will be enabled in 2.11). #4432

•   Improved error messages when inc_ref/dec_ref are called with an invalid GIL state. #4427 #4436

    Bug Fixes:

•   Some minor touchups found by static analyzers. #4440

  Version 2.10.2 (Dec 20, 2022)

Changes:

•   scoped_interpreter constructor taking PyConfig. #4330

•   pybind11/eigen/tensor.h adds converters to and from Eigen::Tensor and Eigen::TensorMap. #4201

•   PyGILState_Check()'s were integrated to pybind11::handle inc_ref() & dec_ref(). The added GIL checks are guarded by PYBIND11_ASSERT_GIL_HELD_INCREF_DECREF, which is the default only if NDEBUG is not defined. (Made non-default in 2.10.3, will be active in 2.11) #4246

•   Add option for enable/disable enum members in docstring. #2768

•   Fixed typing of KeysView, ValuesView and ItemsView in bind_map. #4353

    Bug fixes:

•   Bug fix affecting only Python 3.6 under very specific, uncommon conditions: move PyEval_InitThreads() call to the correct location. #4350

•   Fix segfault bug when passing foreign native functions to functional.h. #4254

    Build system improvements:

•   Support setting PYTHON_LIBRARIES manually for Windows ARM cross-compilation (classic mode). #4406

•   Extend IPO/LTO detection for ICX (a.k.a IntelLLVM) compiler. #4402

•   Allow calling find_package(pybind11 CONFIG) multiple times from separate directories in the same CMake project and properly link Python (new mode). #4401

•   multiprocessing_set_spawn in pytest fixture for added safety. #4377

•   Fixed a bug in two pybind11/tools cmake scripts causing "Unknown arguments specified" errors. #4327

  Version 2.10.1 (Oct 31, 2022)

This is the first version to fully support embedding the newly released Python 3.11.

Changes:

•   Allow pybind11::capsule constructor to take null destructor pointers. #4221

•   embed.h was changed so that PYTHONPATH is used also with Python 3.11 (established behavior). #4119

•   A PYBIND11_SIMPLE_GIL_MANAGEMENT option was added (cmake, C++ define), along with many additional tests in test_gil_scoped.py. The option may be useful to try when debugging GIL-related issues, to determine if the more complex default implementation is or is not to blame. See #4216 for background. WARNING: Please be careful to not create ODR violations when using the option: everything that is linked together with mutual symbol visibility needs to be rebuilt. #4216

•   PYBIND11_EXPORT_EXCEPTION was made non-empty only under macOS. This makes Linux builds safer, and enables the removal of warning suppression pragmas for Windows. #4298

    Bug fixes:

•   Fixed a bug where UnicodeDecodeError was not propagated from various py::str ctors when decoding surrogate utf characters. #4294

•   Revert perfect forwarding for make_iterator. This broke at least one valid use case. May revisit later. #4234

•   Fix support for safe casts to void* (regression in 2.10.0). #4275

•   Fix char8_t support (regression in 2.9). #4278

•   Unicode surrogate character in Python exception message leads to process termination in error_already_set::what(). #4297

•   Fix MSVC 2019 v.1924 & C++14 mode error for overload_cast. #4188

•   Make augmented assignment operators non-const for the object-api. Behavior was previously broken for augmented assignment operators. #4065

•   Add proper error checking to C++ bindings for Python list append and insert. #4208

•   Work-around for Nvidia's CUDA nvcc compiler in versions 11.4.0 - 11.8.0. #4220

•   A workaround for PyPy was added in the py::error_already_set implementation, related to PR #1895 released with v2.10.0. #4079

•   Fixed compiler errors when C++23 std::forward_like is available. #4136

•   Properly raise exceptions in contains methods (like when an object in unhashable). #4209

•   Further improve another error in exception handling. #4232

•   get_local_internals() was made compatible with finalize_interpreter(), fixing potential freezes during interpreter finalization. #4192

    Performance and style:

•   Reserve space in set and STL map casters if possible. This will prevent unnecessary rehashing / resizing by knowing the number of keys ahead of time for Python to C++ casting. This improvement will greatly speed up the casting of large unordered maps and sets. #4194

•   GIL RAII scopes are non-copyable to avoid potential bugs. #4183

•   Explicitly default all relevant ctors for pytypes in the PYBIND11_OBJECT macros and enforce the clang-tidy checks modernize-use-equals-default in macros as well. #4017

•   Optimize iterator advancement in C++ bindings. #4237

•   Use the modern PyObject_GenericGetDict and PyObject_GenericSetDict for handling dynamic attribute dictionaries. #4106

•   Document that users should use PYBIND11_NAMESPACE instead of using pybind11 when opening namespaces. Using namespace declarations and namespace qualification remain the same as pybind11. This is done to ensure consistent symbol visibility. #4098

•   Mark detail::forward_like as constexpr. #4147

•   Optimize unpacking_collector when processing arg_v arguments. #4219

•   Optimize casting C++ object to None. #4269

    Build system improvements:

•   CMake: revert overwrite behavior, now opt-in with PYBIND11_PYTHONLIBS_OVERRWRITE OFF. #4195

•   Include a pkg-config file when installing pybind11, such as in the Python package. #4077

•   Avoid stripping debug symbols when CMAKE_BUILD_TYPE is set to DEBUG instead of Debug. #4078

•   Followup to #3948, fixing vcpkg again. #4123

  Version 2.10.0 (Jul 15, 2022)

Removed support for Python 2.7, Python 3.5, and MSVC 2015. Support for MSVC 2017 is limited due to availability of CI runners; we highly recommend MSVC 2019 or 2022 be used. Initial support added for Python 3.11.

New features:

•   py::anyset & py::frozenset were added, with copying (cast) to std::set (similar to set). #3901

•   Support bytearray casting to string. #3707

•   type_caster<std::monostate> was added. std::monostate is a tag type that allows std::variant to act as an optional, or allows default construction of a std::variant holding a non-default constructible type. #3818

•   pybind11::capsule::set_name added to mutate the name of the capsule instance. #3866

•   NumPy: dtype constructor from type number added, accessors corresponding to Python API dtype.num, dtype.byteorder, dtype.flags and dtype.alignment added. #3868

    Changes:

•   Python 3.6 is now the minimum supported version. #3688 #3719

•   The minimum version for MSVC is now 2017. #3722

•   Fix issues with CPython 3.11 betas and add to supported test matrix. #3923

•   error_already_set is now safer and more performant, especially for exceptions with long tracebacks, by delaying computation. #1895

•   Improve exception handling in python str bindings. #3826

•   The bindings for capsules now have more consistent exception handling. #3825

•   PYBIND11_OBJECT_CVT and PYBIND11_OBJECT_CVT_DEFAULT macro can now be used to define classes in namespaces other than pybind11. #3797

•   Error printing code now uses PYBIND11_DETAILED_ERROR_MESSAGES instead of requiring NDEBUG, allowing use with release builds if desired. #3913

•   Implicit conversion of the literal 0 to pybind11::handle is now disabled. #4008

    Bug fixes:

•   Fix exception handling when pybind11::weakref() fails. #3739

•   module_::def_submodule was missing proper error handling. This is fixed now. #3973

•   The behavior or error_already_set was made safer and the highly opaque "Unknown internal error occurred" message was replaced with a more helpful message. #3982

•   error_already_set::what() now handles non-normalized exceptions correctly. #3971

•   Support older C++ compilers where filesystem is not yet part of the standard library and is instead included in std::experimental::filesystem. #3840

•   Fix -Wfree-nonheap-object warnings produced by GCC by avoiding returning pointers to static objects with return_value_policy::take_ownership. #3946

•   Fix cast from pytype rvalue to another pytype. #3949

•   Ensure proper behavior when garbage collecting classes with dynamic attributes in Python >=3.9. #4051

•   A couple long-standing PYBIND11_NAMESPACE __attribute__((visibility("hidden"))) inconsistencies are now fixed (affects only unusual environments). #4043

•   pybind11::detail::get_internals() is now resilient to in-flight Python exceptions. #3981

•   Arrays with a dimension of size 0 are now properly converted to dynamic Eigen matrices (more common in NumPy 1.23). #4038

•   Avoid catching unrelated errors when importing NumPy. #3974

    Performance and style:

•   Added an accessor overload of (object &&key) to reference steal the object when using python types as keys. This prevents unnecessary reference count overhead for attr, dictionary, tuple, and sequence look ups. Added additional regression tests. Fixed a performance bug the caused accessor assignments to potentially perform unnecessary copies. #3970

•   Perfect forward all args of make_iterator. #3980

•   Avoid potential bug in pycapsule destructor by adding an error_guard to one of the dtors. #3958

•   Optimize dictionary access in strip_padding for numpy. #3994

•   stl_bind.h bindings now take slice args as a const-ref. #3852

•   Made slice constructor more consistent, and improve performance of some casters by allowing reference stealing. #3845

•   Change numpy dtype from_args method to use const ref. #3878

•   Follow rule of three to ensure PyErr_Restore is called only once. #3872

•   Added missing perfect forwarding for make_iterator functions. #3860

•   Optimize c++ to python function casting by using the rvalue caster. #3966

•   Optimize Eigen sparse matrix casting by removing unnecessary temporary. #4064

•   Avoid potential implicit copy/assignment constructors causing double free in strdup_guard. #3905

•   Enable clang-tidy checks misc-definitions-in-headers, modernize-loop-convert, and modernize-use-nullptr. #3881 #3988

    Build system improvements:

•   CMake: Fix file extension on Windows with cp36 and cp37 using FindPython. #3919

•   CMake: Support multiple Python targets (such as on vcpkg). #3948

•   CMake: Fix issue with NVCC on Windows. #3947

•   CMake: Drop the bitness check on cross compiles (like targeting WebAssembly via Emscripten). #3959

•   Add MSVC builds in debug mode to CI. #3784

•   MSVC 2022 C++20 coverage was added to GitHub Actions, including Eigen. #3732, #3741

    Backend and tidying up:

•   New theme for the documentation. #3109

•   Remove idioms in code comments. Use more inclusive language. #3809

•   #include <iostream> was removed from the pybind11/stl.h header. Your project may break if it has a transitive dependency on this include. The fix is to "Include What You Use". #3928

•   Avoid setup.py <command> usage in internal tests. #3734

  Version 2.9.2 (Mar 29, 2022)

Changes:

•   Enum now has an __index__ method on Python <3.8 too. #3700

•   Local internals are now cleared after finalizing the interpreter. #3744

    Bug fixes:

•   Better support for Python 3.11 alphas. #3694

•   PYBIND11_TYPE_CASTER now uses fully qualified symbols, so it can be used outside of pybind11::detail. #3758

•   Some fixes for PyPy 3.9. #3768

•   Fixed a potential memleak in PyPy in get_type_override. #3774

•   Fix usage of VISIBILITY_INLINES_HIDDEN. #3721

    Build system improvements:

•   Uses sysconfig module to determine installation locations on Python >= 3.10, instead of distutils which has been deprecated. #3764

•   Support Catch 2.13.5+ (supporting GLIBC 2.34+). #3679

•   Fix test failures with numpy 1.22 by ignoring whitespace when comparing str() of dtypes. #3682

    Backend and tidying up:

•   clang-tidy: added readability-qualified-auto, readability-braces-around-statements, cppcoreguidelines-prefer-member-initializer, clang-analyzer-optin.performance.Padding, cppcoreguidelines-pro-type-static-cast-downcast, and readability-inconsistent-declaration-parameter-name. #3702, #3699, #3716, #3709

•   clang-format was added to the pre-commit actions, and the entire code base automatically reformatted (after several iterations preparing for this leap). #3713

  Version 2.9.1 (Feb 2, 2022)

Changes:

•   If possible, attach Python exception with py::raise_from to TypeError when casting from C++ to Python. This will give additional info if Python exceptions occur in the caster. Adds a test case of trying to convert a set from C++ to Python when the hash function is not defined in Python. #3605

•   Add a mapping of C++11 nested exceptions to their Python exception equivalent using py::raise_from. This attaches the nested exceptions in Python using the __cause__ field. #3608

•   Propagate Python exception traceback using raise_from if a pybind11 function runs out of overloads. #3671

•   py::multiple_inheritance is now only needed when C++ bases are hidden from pybind11. #3650 and #3659

    Bug fixes:

•   Remove a boolean cast in numpy.h that causes MSVC C4800 warnings when compiling against Python 3.10 or newer. #3669

•   Render py::bool_ and py::float_ as bool and float respectively. #3622

    Build system improvements:

•   Fix CMake extension suffix computation on Python 3.10+. #3663

•   Allow CMAKE_ARGS to override CMake args in pybind11's own setup.py. #3577

•   Remove a few deprecated c-headers. #3610

•   More uniform handling of test targets. #3590

•   Add clang-tidy readability check to catch potentially swapped function args. #3611

  Version 2.9.0 (Dec 28, 2021)

This is the last version to support Python 2.7 and 3.5.

New Features:

•   Allow py::args to be followed by other arguments; the remaining arguments are implicitly keyword-only, as if a py::kw_only{} annotation had been used. #3402

    Changes:

•   Make str/bytes/memoryview more interoperable with std::string_view. #3521

•   Replace _ with const_name in internals, avoid defining pybind::_ if _ defined as macro (common gettext usage) #3423

    Bug fixes:

•   Fix a rare warning about extra copy in an Eigen constructor. #3486

•   Fix caching of the C++ overrides. #3465

•   Add missing std::forward calls to some cpp_function overloads. #3443

•   Support PyPy 7.3.7 and the PyPy3.8 beta. Test python-3.11 on PRs with the python dev label. #3419

•   Replace usage of deprecated Eigen::MappedSparseMatrix with Eigen::Map<Eigen::SparseMatrix<...>> for Eigen 3.3+. #3499

•   Tweaks to support Microsoft Visual Studio 2022. #3497

    Build system improvements:

•   Nicer CMake printout and IDE organisation for pybind11's own tests. #3479

•   CMake: report version type as part of the version string to avoid a spurious space in the package status message. #3472

•   Flags starting with -g in $CFLAGS and $CPPFLAGS are no longer overridden by .Pybind11Extension. #3436

•   Ensure ThreadPool is closed in setup_helpers. #3548

•   Avoid LTS on mips64 and ppc64le (reported broken). #3557

  v2.8.1 (Oct 27, 2021)

Changes and additions:

•   The simple namespace creation shortcut added in 2.8.0 was deprecated due to usage of CPython internal API, and will be removed soon. Use py::module_::import("types").attr("SimpleNamespace"). #3374

•   Add C++ Exception type to throw and catch AttributeError. Useful for defining custom __setattr__ and __getattr__ methods. #3387

    Fixes:

•   Fixed the potential for dangling references when using properties with std::optional types. #3376

•   Modernize usage of PyCodeObject on Python 3.9+ (moving toward support for Python 3.11a1) #3368

•   A long-standing bug in eigen.h was fixed (originally PR #3343). The bug was unmasked by newly added static_assert's in the Eigen 3.4.0 release. #3352

•   Support multiple raw inclusion of CMake helper files (Conan.io does this for multi-config generators). #3420

•   Fix harmless warning on upcoming CMake 3.22. #3368

•   Fix 2.8.0 regression with MSVC 2017 + C++17 mode + Python 3. #3407

•   Fix 2.8.0 regression that caused undefined behavior (typically segfaults) in make_key_iterator/make_value_iterator if dereferencing the iterator returned a temporary value instead of a reference. #3348

  v2.8.0 (Oct 4, 2021)

New features:

•   Added py::raise_from to enable chaining exceptions. #3215

•   Allow exception translators to be optionally registered local to a module instead of applying globally across all pybind11 modules. Use register_local_exception_translator(ExceptionTranslator&& translator) instead of register_exception_translator(ExceptionTranslator&& translator) to keep your exception remapping code local to the module. #2650

•   Add make_simple_namespace function for instantiating Python SimpleNamespace objects. Deprecated in 2.8.1. #2840

•   pybind11::scoped_interpreter and initialize_interpreter have new arguments to allow sys.argv initialization. #2341

•   Allow Python builtins to be used as callbacks in CPython. #1413

•   Added view to view arrays with a different datatype. #987

•   Implemented reshape on arrays. #984

•   Enable defining custom __new__ methods on classes by fixing bug preventing overriding methods if they have non-pybind11 siblings. #3265

•   Add make_value_iterator(), and fix make_key_iterator() to return references instead of copies. #3293

•   Improve the classes generated by bind_map: #3310

•   Change .items from an iterator to a dictionary view.

•   Add .keys and .values (both dictionary views).

•   Allow __contains__ to take any object.

•   pybind11::custom_type_setup was added, for customizing the PyHeapTypeObject corresponding to a class, which may be useful for enabling garbage collection support, among other things. #3287

    Changes:

•   Set __file__ constant when running eval_file in an embedded interpreter. #3233

•   Python objects and (C++17) std::optional now accepted in py::slice constructor. #1101

•   The pybind11 proxy types str, bytes, bytearray, tuple, list now consistently support passing ssize_t values for sizes and indexes. Previously, only size_t was accepted in several interfaces. #3219

•   Avoid evaluating PYBIND11_TLS_REPLACE_VALUE arguments more than once. #3290

    Fixes:

•   Bug fix: enum value's __int__ returning non-int when underlying type is bool or of char type. #1334

•   Fixes bug in setting error state in Capsule's pointer methods. #3261

•   A long-standing memory leak in py::cpp_function::initialize was fixed. #3229

•   Fixes thread safety for some pybind11::type_caster which require lifetime extension, such as for std::string_view. #3237

•   Restore compatibility with gcc 4.8.4 as distributed by ubuntu-trusty, linuxmint-17. #3270

    Build system improvements:

•   Fix regression in CMake Python package config: improper use of absolute path. #3144

•   Cached Python version information could become stale when CMake was re-run with a different Python version. The build system now detects this and updates this information. #3299

•   Specified UTF8-encoding in setup.py calls of open(). #3137

•   Fix a harmless warning from CMake 3.21 with the classic Python discovery. #3220

•   Eigen repo and version can now be specified as cmake options. #3324

    Backend and tidying up:

•   Reduced thread-local storage required for keeping alive temporary data for type conversion to one key per ABI version, rather than one key per extension module. This makes the total thread-local storage required by pybind11 2 keys per ABI version. #3275

•   Optimize NumPy array construction with additional moves. #3183

•   Conversion to std::string and std::string_view now avoids making an extra copy of the data on Python >= 3.3. #3257

•   Remove const modifier from certain C++ methods on Python collections (list, set, dict) such as (clear(), append(), insert(), etc...) and annotated them with py-non-const.

•   Enable readability clang-tidy-const-return and remove useless consts. #3254 #3194

•   The clang-tidy google-explicit-constructor option was enabled. #3250

•   Mark a pytype move constructor as noexcept (perf). #3236

•   Enable clang-tidy check to guard against inheritance slicing. #3210

•   Legacy warning suppression pragma were removed from eigen.h. On Unix platforms, please use -isystem for Eigen include directories, to suppress compiler warnings originating from Eigen headers. Note that CMake does this by default. No adjustments are needed for Windows. #3198

•   Format pybind11 with isort consistent ordering of imports #3195

•   The warnings-suppression "pragma clamp" at the top/bottom of pybind11 was removed, clearing the path to refactoring and IWYU cleanup. #3186

•   Enable most bugprone checks in clang-tidy and fix the found potential bugs and poor coding styles. #3166

•   Add clang-tidy-readability rules to make boolean casts explicit improving code readability. Also enabled other misc and readability clang-tidy checks. #3148

•   Move object in .pop() for list. #3116

  v2.7.1 (Aug 3, 2021)

Minor missing functionality added:

•   Allow Python builtins to be used as callbacks in CPython. #1413

    Bug fixes:

•   Fix regression in CMake Python package config: improper use of absolute path. #3144

•   Fix Mingw64 and add to the CI testing matrix. #3132

•   Specified UTF8-encoding in setup.py calls of open(). #3137

•   Add clang-tidy-readability rules to make boolean casts explicit improving code readability. Also enabled other misc and readability clang-tidy checks. #3148

•   Move object in .pop() for list. #3116

    Backend and tidying up:

•   Removed and fixed warning suppressions. #3127 #3129 #3135 #3141 #3142 #3150 #3152 #3160 #3161

  v2.7.0 (Jul 16, 2021)

New features:

•   Enable py::implicitly_convertible<py::none, ...> for py::class_-wrapped types. #3059

•   Allow function pointer extraction from overloaded functions. #2944

•   NumPy: added .char_() to type which gives the NumPy public char result, which also distinguishes types by bit length (unlike .kind()). #2864

•   Add pybind11::bytearray to manipulate bytearray similar to bytes. #2799

•   pybind11/stl/filesystem.h registers a type caster that, on C++17/Python 3.6+, converts std::filesystem::path to pathlib.Path and any os.PathLike to std::filesystem::path. #2730

•   A PYBIND11_VERSION_HEX define was added, similar to PY_VERSION_HEX. #3120

    Changes:

•   py::str changed to exclusively hold PyUnicodeObject. Previously py::str could also hold bytes, which is probably surprising, was never documented, and can mask bugs (e.g. accidental use of py::str instead of py::bytes). #2409

•   Add a safety guard to ensure that the Python GIL is held when C++ calls back into Python via object_api<>::operator() (e.g. py::function __call__). (This feature is available for Python 3.6+ only.) #2919

•   Catch a missing self argument in calls to __init__(). #2914

•   Use std::string_view if available to avoid a copy when passing an object to a std::ostream. #3042

•   An important warning about thread safety was added to the iostream.h documentation; attempts to make py::scoped_ostream_redirect thread safe have been removed, as it was only partially effective. #2995

    Fixes:

•   Performance: avoid unnecessary strlen calls. #3058

•   Fix auto-generated documentation string when using const T in pyarray_t. #3020

•   Unify error messages thrown by simple_collector/unpacking_collector. #3013

•   pybind11::builtin_exception is now explicitly exported, which means the types included/defined in different modules are identical, and exceptions raised in different modules can be caught correctly. The documentation was updated to explain that custom exceptions that are used across module boundaries need to be explicitly exported as well. #2999

•   Fixed exception when printing UTF-8 to a scoped_ostream_redirect. #2982

•   Pickle support enhancement: setstate implementation will attempt to setattr __dict__ only if the unpickled dict object is not empty, to not force use of py::dynamic_attr() unnecessarily. #2972

•   Allow negative timedelta values to roundtrip. #2870

•   Fix unchecked errors could potentially swallow signals/other exceptions. #2863

•   Add null pointer check with std::localtime. #2846

•   Fix the weakref constructor from py::object to create a new weakref on conversion. #2832

•   Avoid relying on exceptions in C++17 when getting a shared_ptr holder from a shared_from_this class. #2819

•   Allow the codec's exception to be raised instead of RuntimeError when casting from py::str to std::string. #2903

    Build system improvements:

•   In setup_helpers.py, test for platforms that have some multiprocessing features but lack semaphores, which ParallelCompile requires. #3043

•   Fix pybind11_INCLUDE_DIR in case CMAKE_INSTALL_INCLUDEDIR is absolute. #3005

•   Fix bug not respecting WITH_SOABI or WITHOUT_SOABI to CMake. #2938

•   Fix the default Pybind11Extension compilation flags with a Mingw64 python. #2921

•   Clang on Windows: do not pass /MP (ignored flag). #2824

•   pybind11.setup_helpers.intree_extensions can be used to generate Pybind11Extension instances from cpp files placed in the Python package source tree. #2831

    Backend and tidying up:

•   Enable clang-tidy performance, readability, and modernization checks throughout the codebase to enforce best coding practices. #3046, #3049, #3051, #3052, #3080, and #3094

•   Checks for common misspellings were added to the pre-commit hooks. #3076

•   Changed Werror to stricter Werror-all for Intel compiler and fixed minor issues. #2948

•   Fixed compilation with GCC < 5 when the user defines _GLIBCXX_USE_CXX11_ABI. #2956

•   Added nox support for easier local testing and linting of contributions. #3101 and #3121

•   Avoid RTD style issue with docutils 0.17+. #3119

•   Support pipx run, such as pipx run pybind11 --include for a quick compile. #3117

  v2.6.2 (Jan 26, 2021)

Minor missing functionality added:

•   enum: add missing Enum.value property. #2739

•   Allow thread termination to be avoided during shutdown for CPython 3.7+ via .disarm for gil_scoped_acquire/gil_scoped_release. #2657

    Fixed or improved behavior in a few special cases:

•   Fix bug where the constructor of object subclasses would not throw on being passed a Python object of the wrong type. #2701

•   The type_caster for integers does not convert Python objects with __int__ anymore with noconvert or during the first round of trying overloads. #2698

•   When casting to a C++ integer, __index__ is always called and not considered as conversion, consistent with Python 3.8+. #2801

    Build improvements:

•   Setup helpers: extra_compile_args and extra_link_args automatically set by Pybind11Extension are now prepended, which allows them to be overridden by user-set extra_compile_args and extra_link_args. #2808

•   Setup helpers: Don't trigger unused parameter warning. #2735

•   CMake: Support running with --warn-uninitialized active. #2806

•   CMake: Avoid error if included from two submodule directories. #2804

•   CMake: Fix STATIC / SHARED being ignored in FindPython mode. #2796

•   CMake: Respect the setting for CMAKE_CXX_VISIBILITY_PRESET if defined. #2793

•   CMake: Fix issue with FindPython2/FindPython3 not working with pybind11::embed. #2662

•   CMake: mixing local and installed pybind11's would prioritize the installed one over the local one (regression in 2.6.0). #2716

    Bug fixes:

•   Fixed segfault in multithreaded environments when using scoped_ostream_redirect. #2675

•   Leave docstring unset when all docstring-related options are disabled, rather than set an empty string. #2745

•   The module key in builtins that pybind11 uses to store its internals changed from std::string to a python str type (more natural on Python 2, no change on Python 3). #2814

•   Fixed assertion error related to unhandled (later overwritten) exception in CPython 3.8 and 3.9 debug builds. #2685

•   Fix py::gil_scoped_acquire assert with CPython 3.9 debug build. #2683

•   Fix issue with a test failing on pytest 6.2. #2741

    Warning fixes:

•   Fix warning modifying constructor parameter 'flag' that shadows a field of 'set_flag' [-Wshadow-field-in-constructor-modified]. #2780

•   Suppressed some deprecation warnings about old-style __init__/__setstate__ in the tests. #2759

    Valgrind work:

•   Fix invalid access when calling a pybind11 __init__ on a non-pybind11 class instance. #2755

•   Fixed various minor memory leaks in pybind11's test suite. #2758

•   Resolved memory leak in cpp_function initialization when exceptions occurred. #2756

•   Added a Valgrind build, checking for leaks and memory-related UB, to CI. #2746

    Compiler support:

•   Intel compiler was not activating C++14 support due to a broken define. #2679

•   Support ICC and NVIDIA HPC SDK in C++17 mode. #2729

•   Support Intel OneAPI compiler (ICC 20.2) and add to CI. #2573

  v2.6.1 (Nov 11, 2020)

•   py::exec, py::eval, and py::eval_file now add the builtins module as "__builtins__" to their globals argument, better matching exec and eval in pure Python. #2616

•   setup_helpers will no longer set a minimum macOS version higher than the current version. #2622

•   Allow deleting static properties. #2629

•   Seal a leak in def_buffer, cleaning up the capture object after the class_ object goes out of scope. #2634

•   pybind11_INCLUDE_DIRS was incorrect, potentially causing a regression if it was expected to include PYTHON_INCLUDE_DIRS (please use targets instead). #2636

•   Added parameter names to the py::enum_ constructor and methods, avoiding arg0 in the generated docstrings. #2637

•   Added needs_recompile optional function to the ParallelCompiler helper, to allow a recompile to be skipped based on a user-defined function. #2643

  v2.6.0 (Oct 21, 2020)

See upgrade-guide-2.6 for help upgrading to the new version.

New features:

•   Keyword-only arguments supported in Python 2 or 3 with py::kw_only(). #2100

•   Positional-only arguments supported in Python 2 or 3 with py::pos_only(). #2459

•   py::is_final() class modifier to block subclassing (CPython only). #2151

•   Added py::prepend(), allowing a function to be placed at the beginning of the overload chain. #1131

•   Access to the type object now provided with py::type::of<T>() and py::type::of(h). #2364

•   Perfect forwarding support for methods. #2048

•   Added py::error_already_set::discard_as_unraisable(). #2372

•   py::hash is now public. #2217

•   py::class_<union_type> is now supported. Note that writing to one data member of the union and reading another (type punning) is UB in C++. Thus pybind11-bound enums should never be used for such conversions. #2320.

•   Classes now check local scope when registering members, allowing a subclass to have a member with the same name as a parent (such as an enum). #2335

    Code correctness features:

•   Error now thrown when __init__ is forgotten on subclasses. #2152

•   Throw error if conversion to a pybind11 type if the Python object isn't a valid instance of that type, such as py::bytes(o) when py::object o isn't a bytes instance. #2349

•   Throw if conversion to str fails. #2477

    API changes:

•   py::module was renamed py::module_ to avoid issues with C++20 when used unqualified, but an alias py::module is provided for backward compatibility. #2489

•   Public constructors for py::module_ have been deprecated; please use pybind11::module_::create_extension_module if you were using the public constructor (fairly rare after PYBIND11_MODULE was introduced). #2552

•   PYBIND11_OVERLOAD* macros and get_overload function replaced by correctly-named PYBIND11_OVERRIDE* and get_override, fixing inconsistencies in the presence of a closing ; in these macros. get_type_overload is deprecated. #2325

    Packaging / building improvements:

•   The Python package was reworked to be more powerful and useful. #2433

•   build-setuptools is easier thanks to a new pybind11.setup_helpers module, which provides utilities to use setuptools with pybind11. It can be used via PEP 518, setup_requires, or by directly importing or copying setup_helpers.py into your project.

•   CMake configuration files are now included in the Python package. Use pybind11.get_cmake_dir() or python -m pybind11 --cmakedir to get the directory with the CMake configuration files, or include the site-packages location in your CMAKE_MODULE_PATH. Or you can use the new pybind11[global] extra when you install pybind11, which installs the CMake files and headers into your base environment in the standard location.

•   pybind11-config is another way to write python -m pybind11 if you have your PATH set up.

•   Added external typing support to the helper module, code from import pybind11 can now be type checked. #2588

•   Minimum CMake required increased to 3.4. #2338 and #2370

•   Full integration with CMake's C++ standard system and compile features replaces PYBIND11_CPP_STANDARD.

•   Generated config file is now portable to different Python/compiler/CMake versions.

•   Virtual environments prioritized if PYTHON_EXECUTABLE is not set (venv, virtualenv, and conda) (similar to the new FindPython mode).

•   Other CMake features now natively supported, like CMAKE_INTERPROCEDURAL_OPTIMIZATION, set(CMAKE_CXX_VISIBILITY_PRESET hidden).

•   CUDA as a language is now supported.

•   Helper functions pybind11_strip, pybind11_extension, pybind11_find_import added, see cmake/index.

•   Optional find-python-mode and nopython-mode with CMake. #2370

•   Uninstall target added. #2265 and #2346

•   pybind11_add_module() now accepts an optional OPT_SIZE flag that switches the binding target to size-based optimization if the global build type can not always be fixed to MinSizeRel (except in debug mode, where optimizations remain disabled). MinSizeRel or this flag reduces binary size quite substantially (~25% on some platforms). #2463

    Smaller or developer focused features and fixes:

•   Moved mkdoc.py to a new repo, pybind11-mkdoc. There are no longer submodules in the main repo.

•   py::memoryview segfault fix and update, with new py::memoryview::from_memory in Python 3, and documentation. #2223

•   Fix for buffer_info on Python 2. #2503

•   If __eq__ defined but not __hash__, __hash__ is now set to None. #2291

•   py::ellipsis now also works on Python 2. #2360

•   Pointer to std::tuple & std::pair supported in cast. #2334

•   Small fixes in NumPy support. py::array now uses py::ssize_t as first argument type. #2293

•   Added missing signature for py::array. #2363

•   unchecked_mutable_reference has access to operator () and [] when const. #2514

•   py::vectorize is now supported on functions that return void. #1969

•   py::capsule supports get_pointer and set_pointer. #1131

•   Fix crash when different instances share the same pointer of the same type. #2252

•   Fix for py::len not clearing Python's error state when it fails and throws. #2575

•   Bugfixes related to more extensive testing, new GitHub Actions CI. #2321

•   Bug in timezone issue in Eastern hemisphere midnight fixed. #2438

•   std::chrono::time_point now works when the resolution is not the same as the system. #2481

•   Bug fixed where py::array_t could accept arrays that did not match the requested ordering. #2484

•   Avoid a segfault on some compilers when types are removed in Python. #2564

•   py::arg::none() is now also respected when passing keyword arguments. #2611

•   PyPy fixes, PyPy 7.3.x now supported, including PyPy3. (Known issue with PyPy2 and Windows #2596). #2146

•   CPython 3.9.0 workaround for undefined behavior (macOS segfault). #2576

•   CPython 3.9 warning fixes. #2253

•   Improved C++20 support, now tested in CI. #2489 #2599

•   Improved but still incomplete debug Python interpreter support. #2025

•   NVCC (CUDA 11) now supported and tested in CI. #2461

•   NVIDIA PGI compilers now supported and tested in CI. #2475

•   At least Intel 18 now explicitly required when compiling with Intel. #2577

•   Extensive style checking in CI, with pre-commit support. Code modernization, checked by clang-tidy.

•   Expanded docs, including new main page, new installing section, and CMake helpers page, along with over a dozen new sections on existing pages.

•   In GitHub, new docs for contributing and new issue templates.

  v2.5.0 (Mar 31, 2020)

•   Use C++17 fold expressions in type casters, if available. This can improve performance during overload resolution when functions have multiple arguments. #2043.

•   Changed include directory resolution in pybind11/__init__.py and installation in setup.py. This fixes a number of open issues where pybind11 headers could not be found in certain environments. #1995.

•   C++20 char8_t and u8string support. #2026.

•   CMake: search for Python 3.9. bb9c91.

•   Fixes for MSYS-based build environments. #2087, #2053.

•   STL bindings for std::vector<...>::clear. #2074.

•   Read-only flag for py::buffer. #1466.

•   Exception handling during module initialization. bf2b031.

•   Support linking against a CPython debug build. #2025.

•   Fixed issues involving the availability and use of aligned new and delete. #1988, 759221.

•   Fixed a resource leak upon interpreter shutdown. #2020.

•   Fixed error handling in the boolean caster. #1976.

  v2.4.3 (Oct 15, 2019)

•   Adapt pybind11 to a C API convention change in Python 3.8. #1950.

  v2.4.2 (Sep 21, 2019)

•   Replaced usage of a C++14 only construct. #1929.

•   Made an ifdef future-proof for Python >= 4. f3109d.

  v2.4.1 (Sep 20, 2019)

•   Fixed a problem involving implicit conversion from enumerations to integers on Python 3.8. #1780.

  v2.4.0 (Sep 19, 2019)

•   Try harder to keep pybind11-internal data structures separate when there are potential ABI incompatibilities. Fixes crashes that occurred when loading multiple pybind11 extensions that were e.g. compiled by GCC (libstdc++) and Clang (libc++). #1588 and c9f5a.

•   Added support for __await__, __aiter__, and __anext__ protocols. #1842.

•   pybind11_add_module(): don't strip symbols when compiling in RelWithDebInfo mode. #1980.

•   enum_: Reproduce Python behavior when comparing against invalid values (e.g. None, strings, etc.). Add back support for __invert__(). #1912, #1907.

•   List insertion operation for py::list. Added .empty() to all collection types. Added py::set::contains() and py::dict::contains(). #1887, #1884, #1888.

•   py::details::overload_cast_impl is available in C++11 mode, can be used like overload_cast with an additional set of parentheses. #1581.

•   Fixed get_include() on Conda. #1877.

•   stl_bind.h: negative indexing support. #1882.

•   Minor CMake fix to add MinGW compatibility. #1851.

•   GIL-related fixes. #1836, 8b90b.

•   Other very minor/subtle fixes and improvements. #1329, #1910, #1863, #1847, #1890, #1860, #1848, #1821, #1837, #1833, #1748, #1852.

  v2.3.0 (June 11, 2019)

•   Significantly reduced module binary size (10-20%) when compiled in C++11 mode with GCC/Clang, or in any mode with MSVC. Function signatures are now always precomputed at compile time (this was previously only available in C++14 mode for non-MSVC compilers). #934.

•   Add basic support for tag-based static polymorphism, where classes provide a method to returns the desired type of an instance. #1326.

•   Python type wrappers (py::handle, py::object, etc.) now support map Python's number protocol onto C++ arithmetic operators such as operator+, operator/=, etc. #1511.

•   A number of improvements related to enumerations:

1.  The enum_ implementation was rewritten from scratch to reduce code bloat. Rather than instantiating a full implementation for each enumeration, most code is now contained in a generic base class. #1511.

2.  The value() method of py::enum_ now accepts an optional docstring that will be shown in the documentation of the associated enumeration. #1160.

3.  check for already existing enum value and throw an error if present. #1453.

•   Support for over-aligned type allocation via C++17's aligned new statement. #1582.

•   Added py::ellipsis() method for slicing of multidimensional NumPy arrays #1502.

•   Numerous Improvements to the mkdoc.py script for extracting documentation from C++ header files. #1788.

•   pybind11_add_module(): allow including Python as a SYSTEM include path. #1416.

•   pybind11/stl.h does not convert strings to vector<string> anymore. #1258.

•   Mark static methods as such to fix auto-generated Sphinx documentation. #1732.

•   Re-throw forced unwind exceptions (e.g. during pthread termination). #1208.

•   Added __contains__ method to the bindings of maps (std::map, std::unordered_map). #1767.

•   Improvements to gil_scoped_acquire. #1211.

•   Type caster support for std::deque<T>. #1609.

•   Support for std::unique_ptr holders, whose deleters differ between a base and derived class. #1353.

•   Construction of STL array/vector-like data structures from iterators. Added an extend() operation. #1709,

•   CMake build system improvements for projects that include non-C++ files (e.g. plain C, CUDA) in pybind11_add_module et al. #1678.

•   Fixed asynchronous invocation and deallocation of Python functions wrapped in std::function. #1595.

•   Fixes regarding return value policy propagation in STL type casters. #1603.

•   Fixed scoped enum comparisons. #1571.

•   Fixed iostream redirection for code that releases the GIL. #1368,

•   A number of CI-related fixes. #1757, #1744, #1670.

  v2.2.4 (September 11, 2018)

•   Use new Python 3.7 Thread Specific Storage (TSS) implementation if available. #1454, #1517.

•   Fixes for newer MSVC versions and C++17 mode. #1347, #1462.

•   Propagate return value policies to type-specific casters when casting STL containers. #1455.

•   Allow ostream-redirection of more than 1024 characters. #1479.

•   Set Py_DEBUG define when compiling against a debug Python build. #1438.

•   Untangle integer logic in number type caster to work for custom types that may only be castable to a restricted set of builtin types. #1442.

•   CMake build system: Remember Python version in cache file. #1434.

•   Fix for custom smart pointers: use std::addressof to obtain holder address instead of operator&. #1435.

•   Properly report exceptions thrown during module initialization. #1362.

•   Fixed a segmentation fault when creating empty-shaped NumPy array. #1371.

•   The version of Intel C++ compiler must be >= 2017, and this is now checked by the header files. #1363.

•   A few minor typo fixes and improvements to the test suite, and patches that silence compiler warnings.

•   Vectors now support construction from generators, as well as extend() from a list or generator. #1496.

  v2.2.3 (April 29, 2018)

•   The pybind11 header location detection was replaced by a new implementation that no longer depends on pip internals (the recently released pip 10 has restricted access to this API). #1190.

•   Small adjustment to an implementation detail to work around a compiler segmentation fault in Clang 3.3/3.4. #1350.

•   The minimal supported version of the Intel compiler was >= 17.0 since pybind11 v2.1. This check is now explicit, and a compile-time error is raised if the compiler meet the requirement. #1363.

•   Fixed an endianness-related fault in the test suite. #1287.

  v2.2.2 (February 7, 2018)

•   Fixed a segfault when combining embedded interpreter shutdown/reinitialization with external loaded pybind11 modules. #1092.

•   Eigen support: fixed a bug where Nx1/1xN numpy inputs couldn't be passed as arguments to Eigen vectors (which for Eigen are simply compile-time fixed Nx1/1xN matrices). #1106.

•   Clarified to license by moving the licensing of contributions from LICENSE into CONTRIBUTING.md: the licensing of contributions is not actually part of the software license as distributed. This isn't meant to be a substantial change in the licensing of the project, but addresses concerns that the clause made the license non-standard. #1109.

•   Fixed a regression introduced in 2.1 that broke binding functions with lvalue character literal arguments. #1128.

•   MSVC: fix for compilation failures under /permissive-, and added the flag to the appveyor test suite. #1155.

•   Fixed __qualname__ generation, and in turn, fixes how class names (especially nested class names) are shown in generated docstrings. #1171.

•   Updated the FAQ with a suggested project citation reference. #1189.

•   Added fixes for deprecation warnings when compiled under C++17 with -Wdeprecated turned on, and add -Wdeprecated to the test suite compilation flags. #1191.

•   Fixed outdated PyPI URLs in setup.py. #1213.

•   Fixed a refcount leak for arguments that end up in a py::args argument for functions with both fixed positional and py::args arguments. #1216.

•   Fixed a potential segfault resulting from possible premature destruction of py::args/py::kwargs arguments with overloaded functions. #1223.

•   Fixed del map[item] for a stl_bind.h bound stl map. #1229.

•   Fixed a regression from v2.1.x where the aggregate initialization could unintentionally end up at a constructor taking a templated std::initializer_list<T> argument. #1249.

•   Fixed an issue where calling a function with a keep_alive policy on the same nurse/patient pair would cause the internal patient storage to needlessly grow (unboundedly, if the nurse is long-lived). #1251.

•   Various other minor fixes.

  v2.2.1 (September 14, 2017)

•   Added py::module_::reload() member function for reloading a module. #1040.

•   Fixed a reference leak in the number converter. #1078.

•   Fixed compilation with Clang on host GCC < 5 (old libstdc++ which isn't fully C++11 compliant). #1062.

•   Fixed a regression where the automatic std::vector<bool> caster would fail to compile. The same fix also applies to any container which returns element proxies instead of references. #1053.

•   Fixed a regression where the py::keep_alive policy could not be applied to constructors. #1065.

•   Fixed a nullptr dereference when loading a py::module_local type that's only registered in an external module. #1058.

•   Fixed implicit conversion of accessors to types derived from py::object. #1076.

•   The name in PYBIND11_MODULE(name, variable) can now be a macro. #1082.

•   Relaxed overly strict py::pickle() check for matching get and set types. #1064.

•   Conversion errors now try to be more informative when it's likely that a missing header is the cause (e.g. forgetting <pybind11/stl.h>). #1077.

  v2.2.0 (August 31, 2017)

•   Support for embedding the Python interpreter. See the documentation page </advanced/embedding> for a full overview of the new features. #774, #889, #892, #920.

        #include <pybind11/embed.h>
        namespace py = pybind11;
        int main() {
            py::scoped_interpreter guard{}; // start the interpreter and keep it alive
            py::print("Hello, World!"); // use the Python API
        }

•   Support for inheriting from multiple C++ bases in Python. #693.

        from cpp_module import CppBase1, CppBase2
        class PyDerived(CppBase1, CppBase2):
            def __init__(self):
                CppBase1.__init__(self)  # C++ bases must be initialized explicitly
                CppBase2.__init__(self)

•   PYBIND11_MODULE is now the preferred way to create module entry points. PYBIND11_PLUGIN is deprecated. See macros for details. #879.

        // new
        PYBIND11_MODULE(example, m) {
            m.def("add", [](int a, int b) { return a + b; });
        }
        // old
        PYBIND11_PLUGIN(example) {
            py::module m("example");
            m.def("add", [](int a, int b) { return a + b; });
            return m.ptr();
        }

•   pybind11's headers and build system now more strictly enforce hidden symbol visibility for extension modules. This should be seamless for most users, but see the upgrade if you use a custom build system. #995.

•   Support for py::module_local types which allow multiple modules to export the same C++ types without conflicts. This is useful for opaque types like std::vector<int>. py::bind_vector and py::bind_map now default to py::module_local if their elements are builtins or local types. See module_local for details. #949, #981, #995, #997.

•   Custom constructors can now be added very easily using lambdas or factory functions which return a class instance by value, pointer or holder. This supersedes the old placement-new __init__ technique. See custom_constructors for details. #805, #1014.

        struct Example {
            Example(std::string);
        };
        py::class_<Example>(m, "Example")
            .def(py::init<std::string>()) // existing constructor
            .def(py::init([](int n) { // custom constructor
                return std::make_unique<Example>(std::to_string(n));
            }));

•   Similarly to custom constructors, pickling support functions are now bound using the py::pickle() adaptor which improves type safety. See the upgrade and pickling for details. #1038.

•   Builtin support for converting C++17 standard library types and general conversion improvements:

1.  C++17 std::variant is supported right out of the box. C++11/14 equivalents (e.g. boost::variant) can also be added with a simple user-defined specialization. See cpp17_container_casters for details. #811, #845, #989.

2.  Out-of-the-box support for C++17 std::string_view. #906.

3.  Improved compatibility of the builtin optional converter. #874.

4.  The bool converter now accepts numpy.bool_ and types which define __bool__ (Python 3.x) or __nonzero__ (Python 2.7). #925.

5.  C++-to-Python casters are now more efficient and move elements out of rvalue containers whenever possible. #851, #936, #938.

6.  Fixed bytes to std::string/char* conversion on Python 3. #817.

7.  Fixed lifetime of temporary C++ objects created in Python-to-C++ conversions. #924.

•   Scope guard call policy for RAII types, e.g. py::call_guard<py::gil_scoped_release>(), py::call_guard<py::scoped_ostream_redirect>(). See call_policies for details. #740.

•   Utility for redirecting C++ streams to Python (e.g. std::cout -> sys.stdout). Scope guard py::scoped_ostream_redirect in C++ and a context manager in Python. See ostream_redirect. #1009.

•   Improved handling of types and exceptions across module boundaries. #915, #951, #995.

•   Fixed destruction order of py::keep_alive nurse/patient objects in reference cycles. #856.

•   NumPy and buffer protocol related improvements:

1.  Support for negative strides in Python buffer objects/numpy arrays. This required changing integers from unsigned to signed for the related C++ APIs. Note: If you have compiler warnings enabled, you may notice some new conversion warnings after upgrading. These can be resolved with static_cast. #782.

2.  Support std::complex and arrays inside PYBIND11_NUMPY_DTYPE. #831, #832.

3.  Support for constructing py::buffer_info and py::arrays using arbitrary containers or iterators instead of requiring a std::vector. #788, #822, #860.

4.  Explicitly check numpy version and require >= 1.7.0. #819.

•   Support for allowing/prohibiting None for specific arguments and improved None overload resolution order. See none_arguments for details. #843. #859.

•   Added py::exec() as a shortcut for py::eval<py::eval_statements>() and support for C++11 raw string literals as input. See eval. #766, #827.

•   py::vectorize() ignores non-vectorizable arguments and supports member functions. #762.

•   Support for bound methods as callbacks (pybind11/functional.h). #815.

•   Allow aliasing pybind11 methods: cls.attr("foo") = cls.attr("bar"). #802.

•   Don't allow mixed static/non-static overloads. #804.

•   Fixed overriding static properties in derived classes. #784.

•   Added support for write only properties. #1144.

•   Improved deduction of member functions of a derived class when its bases aren't registered with pybind11. #855.

        struct Base {
            int foo() { return 42; }
        }
        struct Derived : Base {}
        // Now works, but previously required also binding `Base`
        py::class_<Derived>(m, "Derived")
            .def("foo", &Derived::foo); // function is actually from `Base`

•   The implementation of py::init<> now uses C++11 brace initialization syntax to construct instances, which permits binding implicit constructors of aggregate types. #1015.

        struct Aggregate {
            int a;
            std::string b;
        };
        py::class_<Aggregate>(m, "Aggregate")
            .def(py::init<int, const std::string &>());

•   Fixed issues with multiple inheritance with offset base/derived pointers. #812, #866, #960.

•   Fixed reference leak of type objects. #1030.

•   Improved support for the /std:c++14 and /std:c++latest modes on MSVC 2017. #841, #999.

•   Fixed detection of private operator new on MSVC. #893, #918.

•   Intel C++ compiler compatibility fixes. #937.

•   Fixed implicit conversion of py::enum_ to integer types on Python 2.7. #821.

•   Added py::hash to fetch the hash value of Python objects, and .def(hash(py::self)) to provide the C++ std::hash as the Python __hash__ method. #1034.

•   Fixed __truediv__ on Python 2 and __itruediv__ on Python 3. #867.

•   py::capsule objects now support the name attribute. This is useful for interfacing with scipy.LowLevelCallable. #902.

•   Fixed py::make_iterator's __next__() for past-the-end calls. #897.

•   Added error_already_set::matches() for checking Python exceptions. #772.

•   Deprecated py::error_already_set::clear(). It's no longer needed following a simplification of the py::error_already_set class. #954.

•   Deprecated py::handle::operator==() in favor of py::handle::is() #825.

•   Deprecated py::object::borrowed/py::object::stolen. Use py::object::borrowed_t{}/py::object::stolen_t{} instead. #771.

•   Changed internal data structure versioning to avoid conflicts between modules compiled with different revisions of pybind11. #1012.

•   Additional compile-time and run-time error checking and more informative messages. #786, #794, #803.

•   Various minor improvements and fixes. #764, #791, #795, #840, #844, #846, #849, #858, #862, #871, #872, #881, #888, #899, #928, #931, #944, #950, #952, #962, #965, #970, #978, #979, #986, #1020, #1027, #1037.

•   Testing improvements. #798, #882, #898, #900, #921, #923, #963.

  v2.1.1 (April 7, 2017)

•   Fixed minimum version requirement for MSVC 2015u3 #773.

  v2.1.0 (March 22, 2017)

•   pybind11 now performs function overload resolution in two phases. The first phase only considers exact type matches, while the second allows for implicit conversions to take place. A special noconvert() syntax can be used to completely disable implicit conversions for specific arguments. #643, #634, #650.

•   Fixed a regression where static properties no longer worked with classes using multiple inheritance. The py::metaclass attribute is no longer necessary (and deprecated as of this release) when binding classes with static properties. #679,

•   Classes bound using pybind11 can now use custom metaclasses. #679,

•   py::args and py::kwargs can now be mixed with other positional arguments when binding functions using pybind11. #611.

•   Improved support for C++11 unicode string and character types; added extensive documentation regarding pybind11's string conversion behavior. #624, #636, #715.

•   pybind11 can now avoid expensive copies when converting Eigen arrays to NumPy arrays (and vice versa). #610.

•   The "fast path" in py::vectorize now works for any full-size group of C or F-contiguous arrays. The non-fast path is also faster since it no longer performs copies of the input arguments (except when type conversions are necessary). #610.

•   Added fast, unchecked access to NumPy arrays via a proxy object. #746.

•   Transparent support for class-specific operator new and operator delete implementations. #755.

•   Slimmer and more efficient STL-compatible iterator interface for sequence types. #662.

•   Improved custom holder type support. #607.

•   nullptr to None conversion fixed in various builtin type casters. #732.

•   enum_ now exposes its members via a special __members__ attribute. #666.

•   std::vector bindings created using stl_bind.h can now optionally implement the buffer protocol. #488.

•   Automated C++ reference documentation using doxygen and breathe. #598.

•   Added minimum compiler version assertions. #727.

•   Improved compatibility with C++1z. #677.

•   Improved py::capsule API. Can be used to implement cleanup callbacks that are involved at module destruction time. #752.

•   Various minor improvements and fixes. #595, #588, #589, #603, #619, #648, #695, #720, #723, #729, #724, #742, #753.

  v2.0.1 (Jan 4, 2017)

•   Fix pointer to reference error in type_caster on MSVC #583.

•   Fixed a segmentation in the test suite due to a typo cd7eac.

  v2.0.0 (Jan 1, 2017)

•   Fixed a reference counting regression affecting types with custom metaclasses (introduced in v2.0.0-rc1). #571.

•   Quenched a CMake policy warning. #570.

  v2.0.0-rc1 (Dec 23, 2016)

The pybind11 developers are excited to issue a release candidate of pybind11 with a subsequent v2.0.0 release planned in early January next year.

An incredible amount of effort by went into pybind11 over the last ~5 months, leading to a release that is jam-packed with exciting new features and numerous usability improvements. The following list links PRs or individual commits whenever applicable.

Happy Christmas!

•   Support for binding C++ class hierarchies that make use of multiple inheritance. #410.

•   PyPy support: pybind11 now supports nightly builds of PyPy and will interoperate with the future 5.7 release. No code changes are necessary, everything "just" works as usual. Note that we only target the Python 2.7 branch for now; support for 3.x will be added once its cpyext extension support catches up. A few minor features remain unsupported for the time being (notably dynamic attributes in custom types). #527.

•   Significant work on the documentation -- in particular, the monolithic advanced.rst file was restructured into a easier to read hierarchical organization. #448.

•   Many NumPy-related improvements:

1.  Object-oriented API to access and modify NumPy ndarray instances, replicating much of the corresponding NumPy C API functionality. #402.

2.  NumPy array dtype array descriptors are now first-class citizens and are exposed via a new class py::dtype.

3.  Structured dtypes can be registered using the PYBIND11_NUMPY_DTYPE() macro. Special array constructors accepting dtype objects were also added.

    One potential caveat involving this change: format descriptor strings should now be accessed via format_descriptor::format() (however, for compatibility purposes, the old syntax format_descriptor::value will still work for non-structured data types). #308.

4.  Further improvements to support structured dtypes throughout the system. #472, #474, #459, #453, #452, and #505.

5.  Fast access operators. #497.

6.  Constructors for arrays whose storage is owned by another object. #440.

7.  Added constructors for array and array_t explicitly accepting shape and strides; if strides are not provided, they are deduced assuming C-contiguity. Also added simplified constructors for 1-dimensional case.

8.  Added buffer/NumPy support for char[N] and std::array<char, N> types.

9.  Added memoryview wrapper type which is constructible from buffer_info.

•   Eigen: many additional conversions and support for non-contiguous arrays/slices. #427, #315, #316, #312, and #267

•   Incompatible changes in class_<...>::class_():

1.  Declarations of types that provide access via the buffer protocol must now include the py::buffer_protocol() annotation as an argument to the class_ constructor.

2.  Declarations of types that require a custom metaclass (i.e. all classes which include static properties via commands such as def_readwrite_static()) must now include the py::metaclass() annotation as an argument to the class_ constructor.

    These two changes were necessary to make type definitions in pybind11 future-proof, and to support PyPy via its cpyext mechanism. #527.

3.  This version of pybind11 uses a redesigned mechanism for instantiating trampoline classes that are used to override virtual methods from within Python. This led to the following user-visible syntax change: instead of

        py::class_<TrampolineClass>("MyClass")
          .alias<MyClass>()
          ....

    write

        py::class_<MyClass, TrampolineClass>("MyClass")
          ....

    Importantly, both the original and the trampoline class are now specified as an arguments (in arbitrary order) to the py::class_ template, and the alias<..>() call is gone. The new scheme has zero overhead in cases when Python doesn't override any functions of the underlying C++ class. rev. 86d825.

•   Added eval and eval_file functions for evaluating expressions and statements from a string or file. rev. 0d3fc3.

•   pybind11 can now create types with a modifiable dictionary. #437 and #444.

•   Support for translation of arbitrary C++ exceptions to Python counterparts. #296 and #273.

•   Report full backtraces through mixed C++/Python code, better reporting for import errors, fixed GIL management in exception processing. #537, #494, rev. e72d95, and rev. 099d6e.

•   Support for bit-level operations, comparisons, and serialization of C++ enumerations. #503, #508, #380, #309. #311.

•   The class_ constructor now accepts its template arguments in any order. #385.

•   Attribute and item accessors now have a more complete interface which makes it possible to chain attributes as in obj.attr("a")[key].attr("b").attr("method")(1, 2, 3). #425.

•   Major redesign of the default and conversion constructors in pytypes.h. #464.

•   Added built-in support for std::shared_ptr holder type. It is no longer necessary to to include a declaration of the form PYBIND11_DECLARE_HOLDER_TYPE(T, std::shared_ptr<T>) (though continuing to do so won't cause an error). #454.

•   New py::overload_cast casting operator to select among multiple possible overloads of a function. An example:

        py::class_<Pet>(m, "Pet")
            .def("set", py::overload_cast<int>(&Pet::set), "Set the pet's age")
            .def("set", py::overload_cast<const std::string &>(&Pet::set), "Set the pet's name");

    This feature only works on C++14-capable compilers. #541.

•   C++ types are automatically cast to Python types, e.g. when assigning them as an attribute. For instance, the following is now legal:

        py::module m = /* ... */
        m.attr("constant") = 123;

    (Previously, a py::cast call was necessary to avoid a compilation error.) #551.

•   Redesigned pytest-based test suite. #321.

•   Instance tracking to detect reference leaks in test suite. #324

•   pybind11 can now distinguish between multiple different instances that are located at the same memory address, but which have different types. #329.

•   Improved logic in move return value policy. #510, #297.

•   Generalized unpacking API to permit calling Python functions from C++ using notation such as foo(a1, a2, *args, "ka"_a=1, "kb"_a=2, **kwargs). #372.

•   py::print() function whose behavior matches that of the native Python print() function. #372.

•   Added py::dict keyword constructor:auto d = dict("number"_a=42, "name"_a="World");. #372.

•   Added py::str::format() method and _s literal: py::str s = "1 + 2 = {}"_s.format(3);. #372.

•   Added py::repr() function which is equivalent to Python's builtin repr(). #333.

•   Improved construction and destruction logic for holder types. It is now possible to reference instances with smart pointer holder types without constructing the holder if desired. The PYBIND11_DECLARE_HOLDER_TYPE macro now accepts an optional second parameter to indicate whether the holder type uses intrusive reference counting. #533 and #561.

•   Mapping a stateless C++ function to Python and back is now "for free" (i.e. no extra indirections or argument conversion overheads). rev. 954b79.

•   Bindings for std::valarray<T>. #545.

•   Improved support for C++17 capable compilers. #562.

•   Bindings for std::optional<t>. #475, #476, #479, #499, and #501.

•   stl_bind.h: general improvements and support for std::map and std::unordered_map. #490, #282, #235.

•   The std::tuple, std::pair, std::list, and std::vector type casters now accept any Python sequence type as input. rev. 107285.

•   Improved CMake Python detection on multi-architecture Linux. #532.

•   Infrastructure to selectively disable or enable parts of the automatically generated docstrings. #486.

•   reference and reference_internal are now the default return value properties for static and non-static properties, respectively. #473. (the previous defaults were automatic). #473.

•   Support for std::unique_ptr with non-default deleters or no deleter at all (py::nodelete). #384.

•   Deprecated handle::call() method. The new syntax to call Python functions is simply handle(). It can also be invoked explicitly via handle::operator<X>(), where X is an optional return value policy.

•   Print more informative error messages when make_tuple() or cast() fail. #262.

•   Creation of holder types for classes deriving from std::enable_shared_from_this<> now also works for const values. #260.

•   make_iterator() improvements for better compatibility with various types (now uses prefix increment operator); it now also accepts iterators with different begin/end types as long as they are equality comparable. #247.

•   arg() now accepts a wider range of argument types for default values. #244.

•   Support keep_alive where the nurse object may be None. #341.

•   Added constructors for str and bytes from zero-terminated char pointers, and from char pointers and length. Added constructors for str from bytes and for bytes from str, which will perform UTF-8 decoding/encoding as required.

•   Many other improvements of library internals without user-visible changes

  1.8.1 (July 12, 2016)

•   Fixed a rare but potentially very severe issue when the garbage collector ran during pybind11 type creation.

  1.8.0 (June 14, 2016)

•   Redesigned CMake build system which exports a convenient pybind11_add_module function to parent projects.

•   std::vector<> type bindings analogous to Boost.Python's indexing_suite

•   Transparent conversion of sparse and dense Eigen matrices and vectors (eigen.h)

•   Added an ExtraFlags template argument to the NumPy array_t<> wrapper to disable an enforced cast that may lose precision, e.g. to create overloads for different precisions and complex vs real-valued matrices.

•   Prevent implicit conversion of floating point values to integral types in function arguments

•   Fixed incorrect default return value policy for functions returning a shared pointer

•   Don't allow registering a type via class_ twice

•   Don't allow casting a None value into a C++ lvalue reference

•   Fixed a crash in enum_::operator== that was triggered by the help() command

•   Improved detection of whether or not custom C++ types can be copy/move-constructed

•   Extended str type to also work with bytes instances

•   Added a "name"_a user defined string literal that is equivalent to py::arg("name").

•   When specifying function arguments via py::arg, the test that verifies the number of arguments now runs at compile time.

•   Added [[noreturn]] attribute to pybind11_fail() to quench some compiler warnings

•   List function arguments in exception text when the dispatch code cannot find a matching overload

•   Added PYBIND11_OVERLOAD_NAME and PYBIND11_OVERLOAD_PURE_NAME macros which can be used to override virtual methods whose name differs in C++ and Python (e.g. __call__ and operator())

•   Various minor iterator and make_iterator() improvements

•   Transparently support __bool__ on Python 2.x and Python 3.x

•   Fixed issue with destructor of unpickled object not being called

•   Minor CMake build system improvements on Windows

•   New pybind11::args and pybind11::kwargs types to create functions which take an arbitrary number of arguments and keyword arguments

•   New syntax to call a Python function from C++ using *args and *kwargs

•   The functions def_property_* now correctly process docstring arguments (these formerly caused a segmentation fault)

•   Many mkdoc.py improvements (enumerations, template arguments, DOC() macro accepts more arguments)

•   Cygwin support

•   Documentation improvements (pickling support, keep_alive, macro usage)

  1.7 (April 30, 2016)

•   Added a new move return value policy that triggers C++11 move semantics. The automatic return value policy falls back to this case whenever a rvalue reference is encountered

•   Significantly more general GIL state routines that are used instead of Python's troublesome PyGILState_Ensure and PyGILState_Release API

•   Redesign of opaque types that drastically simplifies their usage

•   Extended ability to pass values of type [const] void *

•   keep_alive fix: don't fail when there is no patient

•   functional.h: acquire the GIL before calling a Python function

•   Added Python RAII type wrappers none and iterable

•   Added *args and *kwargs pass-through parameters to pybind11.get_include() function

•   Iterator improvements and fixes

•   Documentation on return value policies and opaque types improved

  1.6 (April 30, 2016)

•   Skipped due to upload to PyPI gone wrong and inability to recover (https://github.com/pypa/packaging-problems/issues/74)

  1.5 (April 21, 2016)

•   For polymorphic types, use RTTI to try to return the closest type registered with pybind11

•   Pickling support for serializing and unserializing C++ instances to a byte stream in Python

•   Added a convenience routine make_iterator() which turns a range indicated by a pair of C++ iterators into a iterable Python object

•   Added len() and a variadic make_tuple() function

•   Addressed a rare issue that could confuse the current virtual function dispatcher and another that could lead to crashes in multi-threaded applications

•   Added a get_include() function to the Python module that returns the path of the directory containing the installed pybind11 header files

•   Documentation improvements: import issues, symbol visibility, pickling, limitations

•   Added casting support for std::reference_wrapper<>

  1.4 (April 7, 2016)

•   Transparent type conversion for std::wstring and wchar_t

•   Allow passing nullptr-valued strings

•   Transparent passing of void * pointers using capsules

•   Transparent support for returning values wrapped in std::unique_ptr<>

•   Improved docstring generation for compatibility with Sphinx

•   Nicer debug error message when default parameter construction fails

•   Support for "opaque" types that bypass the transparent conversion layer for STL containers

•   Redesigned type casting interface to avoid ambiguities that could occasionally cause compiler errors

•   Redesigned property implementation; fixes crashes due to an unfortunate default return value policy

•   Anaconda package generation support

  1.3 (March 8, 2016)

•   Added support for the Intel C++ compiler (v15+)

•   Added support for the STL unordered set/map data structures

•   Added support for the STL linked list data structure

•   NumPy-style broadcasting support in pybind11::vectorize

•   pybind11 now displays more verbose error messages when arg::operator=() fails

•   pybind11 internal data structures now live in a version-dependent namespace to avoid ABI issues

•   Many, many bugfixes involving corner cases and advanced usage

  1.2 (February 7, 2016)

•   Optional: efficient generation of function signatures at compile time using C++14

•   Switched to a simpler and more general way of dealing with function default arguments. Unused keyword arguments in function calls are now detected and cause errors as expected

•   New keep_alive call policy analogous to Boost.Python's with_custodian_and_ward

•   New pybind11::base<> attribute to indicate a subclass relationship

•   Improved interface for RAII type wrappers in pytypes.h

•   Use RAII type wrappers consistently within pybind11 itself. This fixes various potential refcount leaks when exceptions occur

•   Added new bytes RAII type wrapper (maps to string in Python 2.7)

•   Made handle and related RAII classes const correct, using them more consistently everywhere now

•   Got rid of the ugly __pybind11__ attributes on the Python side---they are now stored in a C++ hash table that is not visible in Python

•   Fixed refcount leaks involving NumPy arrays and bound functions

•   Vastly improved handling of shared/smart pointers

•   Removed an unnecessary copy operation in pybind11::vectorize

•   Fixed naming clashes when both pybind11 and NumPy headers are included

•   Added conversions for additional exception types

•   Documentation improvements (using multiple extension modules, smart pointers, other minor clarifications)

•   unified infrastructure for parsing variadic arguments in class_ and cpp_function

•   Fixed license text (was: ZLIB, should have been: 3-clause BSD)

•   Python 3.2 compatibility

•   Fixed remaining issues when accessing types in another plugin module

•   Added enum comparison and casting methods

•   Improved SFINAE-based detection of whether types are copy-constructible

•   Eliminated many warnings about unused variables and the use of offsetof()

•   Support for std::array<> conversions

  1.1 (December 7, 2015)

•   Documentation improvements (GIL, wrapping functions, casting, fixed many typos)

•   Generalized conversion of integer types

•   Improved support for casting function objects

•   Improved support for std::shared_ptr<> conversions

•   Initial support for std::set<> conversions

•   Fixed type resolution issue for types defined in a separate plugin module

•   CMake build system improvements

•   Factored out generic functionality to non-templated code (smaller code size)

•   Added a code size / compile time benchmark vs Boost.Python

•   Added an appveyor CI script

  1.0 (October 15, 2015)

•   Initial release

  Upgrade guide

This is a companion guide to the Changelog. While the changelog briefly lists all of the new features, improvements and bug fixes, this upgrade guide focuses only the subset which directly impacts your experience when upgrading to a new version. But it goes into more detail. This includes things like deprecated APIs and their replacements, build system changes, general code modernization and other useful information.

  v3.0

pybind11 v3.0 introduces major new features, but the vast majority of existing extensions are expected to build and run without modification. Minor adjustments may be needed in rare cases, and any such changes can be easily wrapped in preprocessor conditionals to maintain compatibility with the 2.x series.

However, due to new features and modernizations, extensions built with pybind11 v3.0 are not ABI-compatible with those built using v2.13. To ensure cross-extension-module compatibility, it is recommended to rebuild all pybind11-based extensions with v3.0.

CMake support now defaults to the modern FindPython module. If you haven't updated yet, we provide some backward compatibility for PYTHON_* variables, but you should switch to using Python_* variables instead. Note that setting PYTHON_* variables no longer affects the build.

A major new feature in this release is the integration of py::smart_holder, which improves support for std::unique_ptr and std::shared_ptr, resolving several long-standing issues. See py::smart_holder for details. Closely related is the addition of py::trampoline_self_life_support, documented under Overriding virtual functions in Python.

This release includes a major modernization of cross-extension-module ABI compatibility handling. The new implementation reflects actual ABI compatibility much more accurately than in previous versions. The details are subtle and complex; see #4953 and #5439.

Also new in v3.0 is py::native_enum, a modern API for exposing C++ enumerations as native Python types — typically standard-library enum.Enum or related subclasses. This provides improved integration with Python's enum system, compared to the older (now deprecated) py::enum_. See #5555 for details. Note that #include <pybind11/native_enum.h> is not included automatically and must be added explicitly.

Functions exposed with pybind11 are now pickleable. This removes a long-standing obstacle when using pybind11-bound functions with Python features that rely on pickling, such as multiprocessing and caching tools. See #5580 for details.

Anything producing a deprecation warning in the 2.x series may be removed in a future minor release of 3.x. Most of these are still present in 3.0 in order to ease transition. The new Deprecated page details deprecations.

  Migration Recommendations

We recommend migrating to pybind11 v3.0 promptly, while keeping initial changes to a minimum. Most projects can upgrade simply by updating the pybind11 version, without altering existing binding code.

After a short stabilization period — enough to surface any subtle issues — you may incrementally adopt new features where appropriate:

•   Use py::smart_holder and py::trampoline_self_life_support as needed, or to improve code health. Note that py::classh is available as a shortcut — for example, py::classh<Pet> is shorthand for py::class_<Pet, py::smart_holder>. This is designed to enable easy experimentation with py::smart_holder without introducing distracting whitespace changes. In many cases, a global replacement of py::class_ with py::classh can be an effective first step. Build failures will quickly identify places where std::shared_ptr<...> holders need to be removed. Runtime failures (assuming good unit test coverage) will highlight base-and-derived class situations that require coordinated changes.

    Note that py::bind_vector and py::bind_map (in pybind11/stl_bind.h) have a holder_type template parameter that defaults to std::unique_ptr. If py::smart_holder functionality is desired or required, use e.g. py::bind_vector<VecType, py::smart_holder>.

•   Gradually migrate from py::enum_ to py::native_enum to improve integration with Python's standard enum types.

    There is no urgency to refactor existing, working bindings — adopt new features as the need arises or as part of ongoing maintenance efforts.

    If you are using CMake, update to FindPython variables (mostly changing variables from PYTHON_* -> Python_*). You should see if you can use set(PYBIND11_FINDPYTHON ON), which has been supported for years and will avoid setting the compatibility mode variables (and will avoid a warning).

  Potential stumbling blocks when migrating to v3.0

The following issues are very unlikely to arise, and easy to work around:

•   In rare cases, a C++ enum may be bound to Python via a custom type caster. In such cases, a template specialization like this may be required:

        #if defined(PYBIND11_HAS_NATIVE_ENUM)
        namespace pybind11::detail {
        template <typename FancyEnum>
        struct type_caster_enum_type_enabled<
            FancyEnum,
            enable_if_t<is_fancy_enum<FancyEnum>::value>> : std::false_type {};
        }
        #endif

    This specialization is needed only if the custom type caster is templated.

    The PYBIND11_HAS_NATIVE_ENUM guard is needed only if backward compatibility with pybind11v2 is required.

•   Similarly, template specializations like the following may be required if there are custom

•   pybind11::detail::copyable_holder_caster or

•   pybind11::detail::move_only_holder_caster

    implementations that are used for std::shared_ptr or std::unique_ptr conversions:

        #if defined(PYBIND11_HAS_INTERNALS_WITH_SMART_HOLDER_SUPPORT)
        namespace pybind11::detail {
        template <typename ExampleType>
        struct copyable_holder_caster_shared_ptr_with_smart_holder_support_enabled<
            ExampleType,
            enable_if_t<is_example_type<ExampleType>::value>> : std::false_type {};
        }
        #endif

        #if defined(PYBIND11_HAS_INTERNALS_WITH_SMART_HOLDER_SUPPORT)
        namespace pybind11::detail {
        template <typename ExampleType>
        struct move_only_holder_caster_unique_ptr_with_smart_holder_support_enabled<
            ExampleType,
            enable_if_t<is_example_type<ExampleType>::value>> : std::false_type {};
        }
        #endif

    The PYBIND11_HAS_INTERNALS_WITH_SMART_HOLDER_SUPPORT guard is needed only if backward compatibility with pybind11v2 is required.

    (Note that copyable_holder_caster and move_only_holder_caster are not documented, although they existed since 2017.)

  v2.12

NumPy support has been upgraded to support the 2.x series too. The two relevant changes are that:

•   dtype.flags() is now a uint64 and dtype.alignment() an ssize_t (and NumPy may return an larger than integer value for itemsize() in NumPy 2.x).

•   The long deprecated NumPy function PyArray_GetArrayParamsFromObject function is not available anymore.

    Due to NumPy changes, you may experience difficulties updating to NumPy 2. Please see the NumPy 2 migration guide for details. For example, a more direct change could be that the default integer "int_" (and "uint") is now ssize_t and not long (affects 64bit windows).

    If you want to only support NumPy 1.x for now and are having problems due to the two internal changes listed above, you can define PYBIND11_NUMPY_1_ONLY to disable the new support for now. Make sure you define this on all pybind11 compile units, since it could be a source of ODR violations if used inconsistently. This option will be removed in the future, so adapting your code is highly recommended.

  v2.11

•   The minimum version of CMake is now 3.5. A future version will likely move to requiring something like CMake 3.15. Note that CMake 3.27 is removing the long-deprecated support for FindPythonInterp if you set 3.27 as the minimum or maximum supported version. To prepare for that future, CMake 3.15+ using FindPython or setting PYBIND11_FINDPYTHON is highly recommended, otherwise pybind11 will automatically switch to using FindPython if FindPythonInterp is not available.

  v2.9

•   Any usage of the recently added py::make_simple_namespace should be converted to using py::module_::import("types").attr("SimpleNamespace") instead.

•   The use of _ in custom type casters can now be replaced with the more readable const_name instead. The old _ shortcut has been retained unless it is being used as a macro (like for gettext).

  v2.7

Before v2.7, py::str can hold PyUnicodeObject or PyBytesObject, and py::isinstance<str>() is true for both py::str and py::bytes. Starting with v2.7, py::str exclusively holds PyUnicodeObject (#2409), and py::isinstance<str>() is true only for py::str. To help in the transition of user code, the PYBIND11_STR_LEGACY_PERMISSIVE macro is provided as an escape hatch to go back to the legacy behavior. This macro will be removed in future releases. Two types of required fixes are expected to be common:

•   Accidental use of py::str instead of py::bytes, masked by the legacy behavior. These are probably very easy to fix, by changing from py::str to py::bytes.

•   Reliance on py::isinstance<str>(obj) being true for py::bytes. This is likely to be easy to fix in most cases by adding || py::isinstance<bytes>(obj), but a fix may be more involved, e.g. if py::isinstance<T> appears in a template. Such situations will require careful review and custom fixes.

  v2.6

Usage of the PYBIND11_OVERLOAD* macros and get_overload function should be replaced by PYBIND11_OVERRIDE* and get_override. In the future, the old macros may be deprecated and removed.

py::module has been renamed py::module_, but a backward compatible typedef has been included. This change was to avoid a language change in C++20 that requires unqualified module not be placed at the start of a logical line. Qualified usage is unaffected and the typedef will remain unless the C++ language rules change again.

The public constructors of py::module_ have been deprecated. Use PYBIND11_MODULE or module_::create_extension_module instead.

An error is now thrown when __init__ is forgotten on subclasses. This was incorrect before, but was not checked. Add a call to __init__ if it is missing.

A py::type_error is now thrown when casting to a subclass (like py::bytes from py::object) if the conversion is not valid. Make a valid conversion instead.

The undocumented h.get_type() method has been deprecated and replaced by py::type::of(h).

Enums now have a __str__ method pre-defined; if you want to override it, the simplest fix is to add the new py::prepend() tag when defining "__str__".

If __eq__ defined but not __hash__, __hash__ is now set to None, as in normal CPython. You should add __hash__ if you intended the class to be hashable, possibly using the new py::hash shortcut.

The constructors for py::array now always take signed integers for size, for consistency. This may lead to compiler warnings on some systems. Cast to py::ssize_t instead of std::size_t.

The tools/clang submodule and tools/mkdoc.py have been moved to a standalone package, pybind11-mkdoc. If you were using those tools, please use them via a pip install from the new location.

The pybind11 package on PyPI no longer fills the wheel "headers" slot - if you were using the headers from this slot, they are available by requesting the global extra, that is, pip install "pybind11[global]". (Most users will be unaffected, as the pybind11/include location is reported by python -m pybind11 --includes and pybind11.get_include() is still correct and has not changed since 2.5).

  CMake support:

The minimum required version of CMake is now 3.4. Several details of the CMake support have been deprecated; warnings will be shown if you need to change something. The changes are:

•   PYBIND11_CPP_STANDARD=<platform-flag> is deprecated, please use CMAKE_CXX_STANDARD=<number> instead, or any other valid CMake CXX or CUDA standard selection method, like target_compile_features.

•   If you do not request a standard, pybind11 targets will compile with the compiler default, but not less than C++11, instead of forcing C++14 always. If you depend on the old behavior, please use set(CMAKE_CXX_STANDARD 14 CACHE STRING "") instead.

•   Direct pybind11::module usage should always be accompanied by at least set(CMAKE_CXX_VISIBILITY_PRESET hidden) or similar - it used to try to manually force this compiler flag (but not correctly on all compilers or with CUDA).

•   pybind11_add_module's SYSTEM argument is deprecated and does nothing; linking now behaves like other imported libraries consistently in both config and submodule mode, and behaves like a SYSTEM library by default.

•   If PYTHON_EXECUTABLE is not set, virtual environments (venv, virtualenv, and conda) are prioritized over the standard search (similar to the new FindPython mode).

    In addition, the following changes may be of interest:

•   CMAKE_INTERPROCEDURAL_OPTIMIZATION will be respected by pybind11_add_module if set instead of linking to pybind11::lto or pybind11::thin_lto.

•   Using find_package(Python COMPONENTS Interpreter Development) before pybind11 will cause pybind11 to use the new Python mechanisms instead of its own custom search, based on a patched version of classic FindPythonInterp / FindPythonLibs. In the future, this may become the default. A recent (3.15+ or 3.18.2+) version of CMake is recommended.

  v2.5

The Python package now includes the headers as data in the package itself, as well as in the "headers" wheel slot. pybind11 --includes and pybind11.get_include() report the new location, which is always correct regardless of how pybind11 was installed, making the old user= argument meaningless. If you are not using the function to get the location already, you are encouraged to switch to the package location.

  v2.2

  Deprecation of the PYBIND11_PLUGIN macro

PYBIND11_MODULE is now the preferred way to create module entry points. The old macro emits a compile-time deprecation warning.

    // old
    PYBIND11_PLUGIN(example) {
        py::module m("example", "documentation string");
        m.def("add", [](int a, int b) { return a + b; });
        return m.ptr();
    }
    // new
    PYBIND11_MODULE(example, m) {
        m.doc() = "documentation string"; // optional
        m.def("add", [](int a, int b) { return a + b; });
    }

  New API for defining custom constructors and pickling functions

The old placement-new custom constructors have been deprecated. The new approach uses py::init() and factory functions to greatly improve type safety.

Placement-new can be called accidentally with an incompatible type (without any compiler errors or warnings), or it can initialize the same object multiple times if not careful with the Python-side __init__ calls. The new-style custom constructors prevent such mistakes. See Custom constructors for details.

    // old -- deprecated (runtime warning shown only in debug mode)
    py::class<Foo>(m, "Foo")
        .def("__init__", [](Foo &self, ...) {
            new (&self) Foo(...); // uses placement-new
        });
    // new
    py::class<Foo>(m, "Foo")
        .def(py::init([](...) { // Note: no `self` argument
            return new Foo(...); // return by raw pointer
            // or: return std::make_unique<Foo>(...); // return by holder
            // or: return Foo(...); // return by value (move constructor)
        }));

Mirroring the custom constructor changes, py::pickle() is now the preferred way to get and set object state. See Pickling support for details.

    // old -- deprecated (runtime warning shown only in debug mode)
    py::class<Foo>(m, "Foo")
        ...
        .def("__getstate__", [](const Foo &self) {
            return py::make_tuple(self.value1(), self.value2(), ...);
        })
        .def("__setstate__", [](Foo &self, py::tuple t) {
            new (&self) Foo(t[0].cast<std::string>(), ...);
        });
    // new
    py::class<Foo>(m, "Foo")
        ...
        .def(py::pickle(
            [](const Foo &self) { // __getstate__
                return py::make_tuple(self.value1(), self.value2(), ...); // unchanged
            },
            [](py::tuple t) { // __setstate__, note: no `self` argument
                return new Foo(t[0].cast<std::string>(), ...);
                // or: return std::make_unique<Foo>(...); // return by holder
                // or: return Foo(...); // return by value (move constructor)
            }
        ));

For both the constructors and pickling, warnings are shown at module initialization time (on import, not when the functions are called). They're only visible when compiled in debug mode. Sample warning:

    pybind11-bound class 'mymodule.Foo' is using an old-style placement-new '__init__'
    which has been deprecated. See the upgrade guide in pybind11's docs.

  Stricter enforcement of hidden symbol visibility for pybind11 modules

pybind11 now tries to actively enforce hidden symbol visibility for modules. If you're using either one of pybind11's CMake or Python build systems (the two example repositories) and you haven't been exporting any symbols, there's nothing to be concerned about. All the changes have been done transparently in the background. If you were building manually or relied on specific default visibility, read on.

Setting default symbol visibility to hidden has always been recommended for pybind11 (see How can I create smaller binaries?). On Linux and macOS, hidden symbol visibility (in conjunction with the strip utility) yields much smaller module binaries. CPython's extension docs also recommend hiding symbols by default, with the goal of avoiding symbol name clashes between modules. Starting with v2.2, pybind11 enforces this more strictly: (1) by declaring all symbols inside the pybind11 namespace as hidden and (2) by including the -fvisibility=hidden flag on Linux and macOS (only for extension modules, not for embedding the interpreter).

The namespace-scope hidden visibility is done automatically in pybind11's headers and it's generally transparent to users. It ensures that:

•   Modules compiled with different pybind11 versions don't clash with each other.

•   Some new features, like py::module_local bindings, can work as intended.

    The -fvisibility=hidden flag applies the same visibility to user bindings outside of the pybind11 namespace. It's now set automatic by pybind11's CMake and Python build systems, but this needs to be done manually by users of other build systems. Adding this flag:

•   Minimizes the chances of symbol conflicts between modules. E.g. if two unrelated modules were statically linked to different (ABI-incompatible) versions of the same third-party library, a symbol clash would be likely (and would end with unpredictable results).

•   Produces smaller binaries on Linux and macOS, as pointed out previously.

    Within pybind11's CMake build system, pybind11_add_module has always been setting the -fvisibility=hidden flag in release mode. From now on, it's being applied unconditionally, even in debug mode and it can no longer be opted out of with the NO_EXTRAS option. The pybind11::module target now also adds this flag to its interface. The pybind11::embed target is unchanged.

    The most significant change here is for the pybind11::module target. If you were previously relying on default visibility, i.e. if your Python module was doubling as a shared library with dependents, you'll need to either export symbols manually (recommended for cross-platform libraries) or factor out the shared library (and have the Python module link to it like the other dependents). As a temporary workaround, you can also restore default visibility using the CMake code below, but this is not recommended in the long run:

        target_link_libraries(mymodule PRIVATE pybind11::module)
        add_library(restore_default_visibility INTERFACE)
        target_compile_options(restore_default_visibility INTERFACE -fvisibility=default)
        target_link_libraries(mymodule PRIVATE restore_default_visibility)

  Local STL container bindings

Previous pybind11 versions could only bind types globally -- all pybind11 modules, even unrelated ones, would have access to the same exported types. However, this would also result in a conflict if two modules exported the same C++ type, which is especially problematic for very common types, e.g. std::vector<int>. Module-local class bindings were added to resolve this (see that section for a complete usage guide).

py::class_ still defaults to global bindings (because these types are usually unique across modules), however in order to avoid clashes of opaque types, py::bind_vector and py::bind_map will now bind STL containers as py::module_local if their elements are: builtins (int, float, etc.), not bound using py::class_, or bound as py::module_local. For example, this change allows multiple modules to bind std::vector<int> without causing conflicts. See Binding STL containers for more details.

When upgrading to this version, if you have multiple modules which depend on a single global binding of an STL container, note that all modules can still accept foreign py::module_local types in the direction of Python-to-C++. The locality only affects the C++-to-Python direction. If this is needed in multiple modules, you'll need to either:

•   Add a copy of the same STL binding to all of the modules which need it.

•   Restore the global status of that single binding by marking it py::module_local(false).

    The latter is an easy workaround, but in the long run it would be best to localize all common type bindings in order to avoid conflicts with third-party modules.

  Negative strides for Python buffer objects and numpy arrays

Support for negative strides required changing the integer type from unsigned to signed in the interfaces of py::buffer_info and py::array. If you have compiler warnings enabled, you may notice some new conversion warnings after upgrading. These can be resolved using static_cast.

  Deprecation of some py::object APIs

To compare py::object instances by pointer, you should now use obj1.is(obj2) which is equivalent to obj1 is obj2 in Python. Previously, pybind11 used operator== for this (obj1 == obj2), but that could be confusing and is now deprecated (so that it can eventually be replaced with proper rich object comparison in a future release).

For classes which inherit from py::object, borrowed and stolen were previously available as protected constructor tags. Now the types should be used directly instead: borrowed_t{} and stolen_t{} (#771).

  Stricter compile-time error checking

Some error checks have been moved from run time to compile time. Notably, automatic conversion of std::shared_ptr<T> is not possible when T is not directly registered with py::class_<T> (e.g. std::shared_ptr<int> or std::shared_ptr<std::vector<T>> are not automatically convertible). Attempting to bind a function with such arguments now results in a compile-time error instead of waiting to fail at run time.

py::init<...>() constructor definitions are also stricter and now prevent bindings which could cause unexpected behavior:

    struct Example {
        Example(int &);
    };
    py::class_<Example>(m, "Example")
        .def(py::init<int &>()); // OK, exact match
        // .def(py::init<int>()); // compile-time error, mismatch

A non-const lvalue reference is not allowed to bind to an rvalue. However, note that a constructor taking const T & can still be registered using py::init<T>() because a const lvalue reference can bind to an rvalue.

  v2.1

  Minimum compiler versions are enforced at compile time

The minimums also apply to v2.0 but the check is now explicit and a compile-time error is raised if the compiler does not meet the requirements:

•   GCC >= 4.8

•   clang >= 3.3 (appleclang >= 5.0)

•   MSVC >= 2015u3

•   Intel C++ >= 15.0

  The py::metaclass attribute is not required for static properties

Binding classes with static properties is now possible by default. The zero-parameter version of py::metaclass() is deprecated. However, a new one-parameter py::metaclass(python_type) version was added for rare cases when a custom metaclass is needed to override pybind11's default.

    // old -- emits a deprecation warning
    py::class_<Foo>(m, "Foo", py::metaclass())
        .def_property_readonly_static("foo", ...);
    // new -- static properties work without the attribute
    py::class_<Foo>(m, "Foo")
        .def_property_readonly_static("foo", ...);
    // new -- advanced feature, override pybind11's default metaclass
    py::class_<Bar>(m, "Bar", py::metaclass(custom_python_type))
        ...

  v2.0

  Breaking changes in py::class_

These changes were necessary to make type definitions in pybind11 future-proof, to support PyPy via its cpyext mechanism (#527), and to improve efficiency (rev. 86d825).

1.  Declarations of types that provide access via the buffer protocol must now include the py::buffer_protocol() annotation as an argument to the py::class_ constructor.

        py::class_<Matrix>("Matrix", py::buffer_protocol())
            .def(py::init<...>())
            .def_buffer(...);

2.  Classes which include static properties (e.g. def_readwrite_static()) must now include the py::metaclass() attribute. Note: this requirement has since been removed in v2.1. If you're upgrading from 1.x, it's recommended to skip directly to v2.1 or newer.

3.  This version of pybind11 uses a redesigned mechanism for instantiating trampoline classes that are used to override virtual methods from within Python. This led to the following user-visible syntax change:

        // old v1.x syntax
        py::class_<TrampolineClass>("MyClass")
            .alias<MyClass>()
            ...
        // new v2.x syntax
        py::class_<MyClass, TrampolineClass>("MyClass")
            ...

    Importantly, both the original and the trampoline class are now specified as arguments to the py::class_ template, and the alias<..>() call is gone. The new scheme has zero overhead in cases when Python doesn't override any functions of the underlying C++ class. rev. 86d825.

    The class type must be the first template argument given to py::class_ while the trampoline can be mixed in arbitrary order with other arguments (see the following section).

  Deprecation of the py::base<T>() attribute

py::base<T>() was deprecated in favor of specifying T as a template argument to py::class_. This new syntax also supports multiple inheritance. Note that, while the type being exported must be the first argument in the py::class_<Class, ...> template, the order of the following types (bases, holder and/or trampoline) is not important.

    // old v1.x
    py::class_<Derived>("Derived", py::base<Base>());
    // new v2.x
    py::class_<Derived, Base>("Derived");
    // new -- multiple inheritance
    py::class_<Derived, Base1, Base2>("Derived");
    // new -- apart from `Derived` the argument order can be arbitrary
    py::class_<Derived, Base1, Holder, Base2, Trampoline>("Derived");

  Out-of-the-box support for std::shared_ptr

The relevant type caster is now built in, so it's no longer necessary to include a declaration of the form:

    PYBIND11_DECLARE_HOLDER_TYPE(T, std::shared_ptr<T>)

Continuing to do so won't cause an error or even a deprecation warning, but it's completely redundant.

  Deprecation of a few py::object APIs

All of the old-style calls emit deprecation warnings.
Old syntax | New syntax
obj.call(args...) | obj(args...)
obj.str() | py::str(obj)
auto l = py::list(obj); l.check() | py::isinstance<py::list>(obj)
py::object(ptr, true) | py::reinterpret_borrow<py::object>(ptr)
py::object(ptr, false) | py::reinterpret_steal<py::object>(ptr)
if (obj.attr("foo")) | if (py::hasattr(obj, "foo"))
if (obj["bar"]) | if (obj.contains("bar"))

  Installing the library

There are several ways to get the pybind11 source, which lives at pybind/pybind11 on GitHub. The pybind11 developers recommend one of the first three ways listed here, submodule, PyPI, or conda-forge, for obtaining pybind11.

  Include as a submodule

When you are working on a project in Git, you can use the pybind11 repository as a submodule. From your git repository, use:

    git submodule add -b stable ../../pybind/pybind11 extern/pybind11
    git submodule update --init

This assumes you are placing your dependencies in extern/, and that you are using GitHub; if you are not using GitHub, use the full https or ssh URL instead of the relative URL ../../pybind/pybind11 above. Some other servers also require the .git extension (GitHub does not).

From here, you can now include extern/pybind11/include, or you can use the various integration tools (see Build systems) pybind11 provides directly from the local folder.

  Include with PyPI

You can download the sources and CMake files as a Python package from PyPI using Pip. Just use:

    pip install pybind11

This will provide pybind11 in a standard Python package format. If you want pybind11 available directly in your environment root, you can use:

    pip install "pybind11[global]"

This is not recommended if you are installing with your system Python, as it will add files to /usr/local/include/pybind11 and /usr/local/share/cmake/pybind11, so unless that is what you want, it is recommended only for use in virtual environments or your pyproject.toml file (see Build systems).

  Include with conda-forge

You can use pybind11 with conda packaging via conda-forge:

    conda install -c conda-forge pybind11

  Include with vcpkg

You can download and install pybind11 using the Microsoft vcpkg dependency manager:

    git clone https://github.com/Microsoft/vcpkg.git
    cd vcpkg
    ./bootstrap-vcpkg.sh
    ./vcpkg integrate install
    vcpkg install pybind11

The pybind11 port in vcpkg is kept up to date by Microsoft team members and community contributors. If the version is out of date, please create an issue or pull request on the vcpkg repository.

  Global install with brew

The brew package manager (Homebrew on macOS, or Linuxbrew on Linux) has a pybind11 package. To install:

    brew install pybind11

  Other options

Other locations you can find pybind11 are listed here; these are maintained by various packagers and the community.

  First steps

This sections demonstrates the basic features of pybind11. Before getting started, make sure that development environment is set up to compile the included set of test cases.

  Compiling the test cases

  Linux/macOS

On Linux you'll need to install the python-dev or python3-dev packages as well as cmake. On macOS, the included python version works out of the box, but cmake must still be installed.

After installing the prerequisites, run

    mkdir build
    cd build
    cmake ..
    make check -j 4

The last line will both compile and run the tests.

  Windows

On Windows, only Visual Studio 2017 and newer are supported.

NOTE: To use the C++17 in Visual Studio 2017 (MSVC 14.1), pybind11 requires the flag /permissive- to be passed to the compiler to enforce standard conformance. When building with Visual Studio 2019, this is not strictly necessary, but still advised.

To compile and run the tests:

    mkdir build
    cd build
    cmake ..
    cmake --build . --config Release --target check

This will create a Visual Studio project, compile and run the target, all from the command line.

NOTE: If all tests fail, make sure that the Python binary and the testcases are compiled for the same processor type and bitness (i.e. either i386 or x86_64). You can specify x86_64 as the target architecture for the generated Visual Studio project using cmake -A x64 ...

SEE ALSO: Advanced users who are already familiar with Boost.Python may want to skip the tutorial and look at the test cases in the tests directory, which exercise all features of pybind11.

  Header and namespace conventions

For brevity, all code examples assume that the following two lines are present:

    #include <pybind11/pybind11.h>
    namespace py = pybind11;

NOTE: pybind11/pybind11.h includes Python.h, as such it must be the first file included in any source file or header for the same reasons as Python.h.

Some features may require additional headers, but those will be specified as needed.

  Creating bindings for a simple function

Let's start by creating Python bindings for an extremely simple function, which adds two numbers and returns their result:

    int add(int i, int j) {
        return i + j;
    }

For simplicity [1], we'll put both this function and the binding code into a file named example.cpp with the following contents:

    #include <pybind11/pybind11.h>
    namespace py = pybind11;
    int add(int i, int j) {
        return i + j;
    }
    PYBIND11_MODULE(example, m, py::mod_gil_not_used()) {
        m.doc() = "pybind11 example plugin"; // optional module docstring
        m.def("add", &add, "A function that adds two numbers");
    }

[1] In practice, implementation and binding code will generally be located in separate files.

    The PYBIND11_MODULE() macro creates a function that will be called when an import statement is issued from within Python. The module name (example) is given as the first macro argument (it should not be in quotes). The second argument (m) defines a variable of type py::module_ which is the main interface for creating bindings. The method module_::def() generates binding code that exposes the add() function to Python.

    NOTE: Notice how little code was needed to expose our function to Python: all details regarding the function's parameters and return value were automatically inferred using template metaprogramming. This overall approach and the used syntax are borrowed from Boost.Python, though the underlying implementation is very different.

    pybind11 is a header-only library, hence it is not necessary to link against any special libraries and there are no intermediate (magic) translation steps. On Linux, the above example can be compiled using the following command:

        $ c++ -O3 -Wall -shared -std=c++11 -fPIC $(python3 -m pybind11 --includes) example.cpp -o example$(python3 -m pybind11 --extension-suffix)

    NOTE: If you used Include as a submodule to get the pybind11 source, then use $(python3-config --includes) -Iextern/pybind11/include instead of $(python3 -m pybind11 --includes) in the above compilation, as explained in Building manually.

    For more details on the required compiler flags on Linux and macOS, see Building manually. For complete cross-platform compilation instructions, refer to the Build systems page.

    The python_example and cmake_example repositories are also a good place to start. They are both complete project examples with cross-platform build systems. The only difference between the two is that python_example uses Python's setuptools to build the module, while cmake_example uses CMake (which may be preferable for existing C++ projects).

    Building the above C++ code will produce a binary module file that can be imported to Python. Assuming that the compiled module is located in the current directory, the following interactive Python session shows how to load and execute the example:

        $ python
        Python 3.9.10 (main, Jan 15 2022, 11:48:04)
        [Clang 13.0.0 (clang-1300.0.29.3)] on darwin
        Type "help", "copyright", "credits" or "license" for more information.
        >>> import example
        >>> example.add(1, 2)
        3
        >>>

  Keyword arguments

With a simple code modification, it is possible to inform Python about the names of the arguments ("i" and "j" in this case).

    m.def("add", &add, "A function which adds two numbers",
          py::arg("i"), py::arg("j"));

arg is one of several special tag classes which can be used to pass metadata into module_::def(). With this modified binding code, we can now call the function using keyword arguments, which is a more readable alternative particularly for functions taking many parameters:

    >>> import example
    >>> example.add(i=1, j=2)
    3L

The keyword names also appear in the function signatures within the documentation.

    >>> help(example)
    ....
    FUNCTIONS
        add(...)
            Signature : (i: int, j: int) -> int
            A function which adds two numbers

A shorter notation for named arguments is also available:

    // regular notation
    m.def("add1", &add, py::arg("i"), py::arg("j"));
    // shorthand
    using namespace pybind11::literals;
    m.def("add2", &add, "i"_a, "j"_a);

The _a suffix forms a C++11 literal which is equivalent to arg. Note that the literal operator must first be made visible with the directive using namespace pybind11::literals. This does not bring in anything else from the pybind11 namespace except for literals.

  Default arguments

Suppose now that the function to be bound has default arguments, e.g.:

    int add(int i = 1, int j = 2) {
        return i + j;
    }

Unfortunately, pybind11 cannot automatically extract these parameters, since they are not part of the function's type information. However, they are simple to specify using an extension of arg:

    m.def("add", &add, "A function which adds two numbers",
          py::arg("i") = 1, py::arg("j") = 2);

The default values also appear within the documentation.

    >>> help(example)
    ....
    FUNCTIONS
        add(...)
            Signature : (i: int = 1, j: int = 2) -> int
            A function which adds two numbers

The shorthand notation is also available for default arguments:

    // regular notation
    m.def("add1", &add, py::arg("i") = 1, py::arg("j") = 2);
    // shorthand
    m.def("add2", &add, "i"_a=1, "j"_a=2);

  Exporting variables

To expose a value from C++, use the attr function to register it in a module as shown below. Built-in types and general objects (more on that later) are automatically converted when assigned as attributes, and can be explicitly converted using the function py::cast.

    PYBIND11_MODULE(example, m, py::mod_gil_not_used()) {
        m.attr("the_answer") = 42;
        py::object world = py::cast("World");
        m.attr("what") = world;
    }

These are then accessible from Python:

    >>> import example
    >>> example.the_answer
    42
    >>> example.what
    'World'

  Supported data types

A large number of data types are supported out of the box and can be used seamlessly as functions arguments, return values or with py::cast in general. For a full overview, see the Type conversions section.

  Object-oriented code

  Creating bindings for a custom type

Let's now look at a more complex example where we'll create bindings for a custom C++ data structure named Pet. Its definition is given below:

    struct Pet {
        Pet(const std::string &name) : name(name) { }
        void setName(const std::string &name_) { name = name_; }
        const std::string &getName() const { return name; }
        std::string name;
    };

The binding code for Pet looks as follows:

    #include <pybind11/pybind11.h>
    namespace py = pybind11;
    PYBIND11_MODULE(example, m, py::mod_gil_not_used()) {
        py::class_<Pet>(m, "Pet")
            .def(py::init<const std::string &>())
            .def("setName", &Pet::setName)
            .def("getName", &Pet::getName);
    }

py::class_ creates bindings for a C++ class or struct-style data structure. init() is a convenience function that takes the types of a constructor's parameters as template arguments and wraps the corresponding constructor (see the Custom constructors section for details).

NOTE: Starting with pybind11v3, it is recommended to include py::smart_holder in most situations for safety, especially if you plan to support conversions to C++ smart pointers. See py::smart_holder for more information.

An interactive Python session demonstrating this example is shown below:

    % python
    >>> import example
    >>> p = example.Pet("Molly")
    >>> print(p)
    <example.Pet object at 0x10cd98060>
    >>> p.getName()
    'Molly'
    >>> p.setName("Charly")
    >>> p.getName()
    'Charly'

SEE ALSO: Static member functions can be bound in the same way using class_::def_static().

NOTE: Binding C++ types in unnamed namespaces (also known as anonymous namespaces) works reliably on many platforms, but not all. The XFAIL_CONDITION in tests/test_unnamed_namespace_a.py encodes the currently known conditions. For background see #4319. If portability is a concern, it is therefore not recommended to bind C++ types in unnamed namespaces. It will be safest to manually pick unique namespace names.

  Keyword and default arguments

It is possible to specify keyword and default arguments using the syntax discussed in the previous chapter. Refer to the sections Keyword arguments and Default arguments for details.

  Binding lambda functions

Note how print(p) produced a rather useless summary of our data structure in the example above:

    >>> print(p)
    <example.Pet object at 0x10cd98060>

To address this, we could bind a utility function that returns a human-readable summary to the special method slot named __repr__. Unfortunately, there is no suitable functionality in the Pet data structure, and it would be nice if we did not have to change it. This can easily be accomplished by binding a Lambda function instead:

    py::class_<Pet>(m, "Pet")
        .def(py::init<const std::string &>())
        .def("setName", &Pet::setName)
        .def("getName", &Pet::getName)
        .def("__repr__",
            [](const Pet &a) {
                return "<example.Pet named '" + a.name + "'>";
            }
        );

Both stateless [1] and stateful lambda closures are supported by pybind11. With the above change, the same Python code now produces the following output:

    >>> print(p)
    <example.Pet named 'Molly'>

[1] Stateless closures are those with an empty pair of brackets [] as the capture object.

  Instance and static fields

We can also directly expose the name field using the class_::def_readwrite() method. A similar class_::def_readonly() method also exists for const fields.

    py::class_<Pet>(m, "Pet")
        .def(py::init<const std::string &>())
        .def_readwrite("name", &Pet::name)
        // ... remainder ...

This makes it possible to write

    >>> p = example.Pet("Molly")
    >>> p.name
    'Molly'
    >>> p.name = "Charly"
    >>> p.name
    'Charly'

Now suppose that Pet::name was a private internal variable that can only be accessed via setters and getters.

    class Pet {
    public:
        Pet(const std::string &name) : name(name) { }
        void setName(const std::string &name_) { name = name_; }
        const std::string &getName() const { return name; }
    private:
        std::string name;
    };

In this case, the method class_::def_property() (class_::def_property_readonly() for read-only data) can be used to provide a field-like interface within Python that will transparently call the setter and getter functions:

    py::class_<Pet>(m, "Pet")
        .def(py::init<const std::string &>())
        .def_property("name", &Pet::getName, &Pet::setName)
        // ... remainder ...

Write only properties can be defined by passing nullptr as the input for the read function.

SEE ALSO: Similar functions class_::def_readwrite_static(), class_::def_readonly_static() class_::def_property_static(), and class_::def_property_readonly_static() are provided for binding static variables and properties. Please also see the section on Static properties in the advanced part of the documentation.

  Dynamic attributes

Native Python classes can pick up new attributes dynamically:

    >>> class Pet:
    ...     name = "Molly"
    ...
    >>> p = Pet()
    >>> p.name = "Charly"  # overwrite existing
    >>> p.age = 2  # dynamically add a new attribute

By default, classes exported from C++ do not support this and the only writable attributes are the ones explicitly defined using class_::def_readwrite() or class_::def_property().

    py::class_<Pet>(m, "Pet")
        .def(py::init<>())
        .def_readwrite("name", &Pet::name);

Trying to set any other attribute results in an error:

    >>> p = example.Pet()
    >>> p.name = "Charly"  # OK, attribute defined in C++
    >>> p.age = 2  # fail
    AttributeError: 'Pet' object has no attribute 'age'

To enable dynamic attributes for C++ classes, the py::dynamic_attr tag must be added to the py::class_ constructor:

    py::class_<Pet>(m, "Pet", py::dynamic_attr())
        .def(py::init<>())
        .def_readwrite("name", &Pet::name);

Now everything works as expected:

    >>> p = example.Pet()
    >>> p.name = "Charly"  # OK, overwrite value in C++
    >>> p.age = 2  # OK, dynamically add a new attribute
    >>> p.__dict__  # just like a native Python class
    {'age': 2}

Note that there is a small runtime cost for a class with dynamic attributes. Not only because of the addition of a __dict__, but also because of more expensive garbage collection tracking which must be activated to resolve possible circular references. Native Python classes incur this same cost by default, so this is not anything to worry about. By default, pybind11 classes are more efficient than native Python classes. Enabling dynamic attributes just brings them on par.

  Inheritance and automatic downcasting

Suppose now that the example consists of two data structures with an inheritance relationship:

    struct Pet {
        Pet(const std::string &name) : name(name) { }
        std::string name;
    };
    struct Dog : Pet {
        Dog(const std::string &name) : Pet(name) { }
        std::string bark() const { return "woof!"; }
    };

There are two different ways of indicating a hierarchical relationship to pybind11: the first specifies the C++ base class as an extra template parameter of the py::class_:

    py::class_<Pet>(m, "Pet")
       .def(py::init<const std::string &>())
       .def_readwrite("name", &Pet::name);
    // Method 1: template parameter:
    py::class_<Dog, Pet /* <- specify C++ parent type */>(m, "Dog")
        .def(py::init<const std::string &>())
        .def("bark", &Dog::bark);

Alternatively, we can also assign a name to the previously bound Pet py::class_ object and reference it when binding the Dog class:

    py::class_<Pet> pet(m, "Pet");
    pet.def(py::init<const std::string &>())
       .def_readwrite("name", &Pet::name);
    // Method 2: pass parent class_ object:
    py::class_<Dog>(m, "Dog", pet /* <- specify Python parent type */)
        .def(py::init<const std::string &>())
        .def("bark", &Dog::bark);

Functionality-wise, both approaches are equivalent. Afterwards, instances will expose fields and methods of both types:

    >>> p = example.Dog("Molly")
    >>> p.name
    'Molly'
    >>> p.bark()
    'woof!'

The C++ classes defined above are regular non-polymorphic types with an inheritance relationship. This is reflected in Python:

    // Return a base pointer to a derived instance
    m.def("pet_store", []() { return std::unique_ptr<Pet>(new Dog("Molly")); });

    >>> p = example.pet_store()
    >>> type(p)  # `Dog` instance behind `Pet` pointer
    Pet          # no pointer downcasting for regular non-polymorphic types
    >>> p.bark()
    AttributeError: 'Pet' object has no attribute 'bark'

The function returned a Dog instance, but because it's a non-polymorphic type behind a base pointer, Python only sees a Pet. In C++, a type is only considered polymorphic if it has at least one virtual function and pybind11 will automatically recognize this:

    struct PolymorphicPet {
        virtual ~PolymorphicPet() = default;
    };
    struct PolymorphicDog : PolymorphicPet {
        std::string bark() const { return "woof!"; }
    };
    // Same binding code
    py::class_<PolymorphicPet>(m, "PolymorphicPet");
    py::class_<PolymorphicDog, PolymorphicPet>(m, "PolymorphicDog")
        .def(py::init<>())
        .def("bark", &PolymorphicDog::bark);
    // Again, return a base pointer to a derived instance
    m.def("pet_store2", []() { return std::unique_ptr<PolymorphicPet>(new PolymorphicDog); });

    >>> p = example.pet_store2()
    >>> type(p)
    PolymorphicDog  # automatically downcast
    >>> p.bark()
    'woof!'

Given a pointer to a polymorphic base, pybind11 performs automatic downcasting to the actual derived type. Note that this goes beyond the usual situation in C++: we don't just get access to the virtual functions of the base, we get the concrete derived type including functions and attributes that the base type may not even be aware of.

SEE ALSO: For more information about polymorphic behavior see Overriding virtual functions in Python.

  Overloaded methods

Sometimes there are several overloaded C++ methods with the same name taking different kinds of input arguments:

    struct Pet {
        Pet(const std::string &name, int age) : name(name), age(age) { }
        void set(int age_) { age = age_; }
        void set(const std::string &name_) { name = name_; }
        std::string name;
        int age;
    };

Attempting to bind Pet::set will cause an error since the compiler does not know which method the user intended to select. We can disambiguate by casting them to function pointers. Binding multiple functions to the same Python name automatically creates a chain of function overloads that will be tried in sequence.

    py::class_<Pet>(m, "Pet")
       .def(py::init<const std::string &, int>())
       .def("set", static_cast<void (Pet::*)(int)>(&Pet::set), "Set the pet's age")
       .def("set", static_cast<void (Pet::*)(const std::string &)>(&Pet::set), "Set the pet's name");

The overload signatures are also visible in the method's docstring:

    >>> help(example.Pet)
    class Pet(__builtin__.object)
     |  Methods defined here:
     |
     |  __init__(...)
     |      Signature : (Pet, str, int) -> NoneType
     |
     |  set(...)
     |      1. Signature : (Pet, int) -> NoneType
     |
     |      Set the pet's age
     |
     |      2. Signature : (Pet, str) -> NoneType
     |
     |      Set the pet's name

If you have a C++14 compatible compiler [2], you can use an alternative syntax to cast the overloaded function:

    py::class_<Pet>(m, "Pet")
        .def("set", py::overload_cast<int>(&Pet::set), "Set the pet's age")
        .def("set", py::overload_cast<const std::string &>(&Pet::set), "Set the pet's name");

Here, py::overload_cast only requires the parameter types to be specified. The return type and class are deduced. This avoids the additional noise of void (Pet::*)() as seen in the raw cast. If a function is overloaded based on constness, the py::const_ tag should be used:

    struct Widget {
        int foo(int x, float y);
        int foo(int x, float y) const;
    };
    py::class_<Widget>(m, "Widget")
       .def("foo_mutable", py::overload_cast<int, float>(&Widget::foo))
       .def("foo_const",   py::overload_cast<int, float>(&Widget::foo, py::const_));

If you prefer the py::overload_cast syntax but have a C++11 compatible compiler only, you can use py::detail::overload_cast_impl with an additional set of parentheses:

    template <typename... Args>
    using overload_cast_ = pybind11::detail::overload_cast_impl<Args...>;
    py::class_<Pet>(m, "Pet")
        .def("set", overload_cast_<int>()(&Pet::set), "Set the pet's age")
        .def("set", overload_cast_<const std::string &>()(&Pet::set), "Set the pet's name");

[2] A compiler which supports the -std=c++14 flag.

    NOTE: To define multiple overloaded constructors, simply declare one after the other using the .def(py::init<...>()) syntax. The existing machinery for specifying keyword and default arguments also works.

  ☝️ Pitfalls with raw pointers and shared ownership

py::class_-wrapped objects automatically manage the lifetime of the wrapped C++ object, in collaboration with the chosen holder type (see Smart pointers & py::class_). When wrapping C++ functions involving raw pointers, care needs to be taken to not accidentally undermine automatic lifetime management. For example, ownership is inadvertently transferred here:

    class Child { };
    class Parent {
    public:
       Parent() : child(std::make_shared<Child>()) { }
       Child *get_child() { return child.get(); }  /* DANGER */
    private:
        std::shared_ptr<Child> child;
    };
    PYBIND11_MODULE(example, m, py::mod_gil_not_used()) {
        py::class_<Child, std::shared_ptr<Child>>(m, "Child");
        py::class_<Parent, std::shared_ptr<Parent>>(m, "Parent")
           .def(py::init<>())
           .def("get_child", &Parent::get_child);  /* PROBLEM */
    }

The following Python code leads to undefined behavior, likely resulting in a segmentation fault.

    from example import Parent
    print(Parent().get_child())

Part of the /* PROBLEM */ here is that pybind11 falls back to using return_value_policy::take_ownership as the default (see Return value policies). The fact that the Child instance is already managed by std::shared_ptr<Child> is lost. Therefore pybind11 will create a second independent std::shared_ptr<Child> that also claims ownership of the pointer, eventually leading to heap-use-after-free or double-free errors.

There are various ways to resolve this issue, either by changing the Child or Parent C++ implementations (e.g. using std::enable_shared_from_this<Child> as a base class for Child, or adding a member function to Parent that returns std::shared_ptr<Child>), or if that is not feasible, by using return_value_policy::reference_internal. What is the best approach depends on the exact situation.

A highly effective way to stay in the clear — even in pure C++, but especially when binding C++ code to Python — is to consistently prefer std::shared_ptr or std::unique_ptr over passing raw pointers.

  Enumerations and internal types

Let's now suppose that the example class contains internal types like enumerations, e.g.:

    struct Pet {
        enum Kind {
            Dog = 0,
            Cat
        };
        struct Attributes {
            float age = 0;
        };
        Pet(const std::string &name, Kind type) : name(name), type(type) { }
        std::string name;
        Kind type;
        Attributes attr;
    };

The binding code for this example looks as follows:

    #include <pybind11/native_enum.h> // Not already included with pybind11.h
    py::class_<Pet> pet(m, "Pet");
    pet.def(py::init<const std::string &, Pet::Kind>())
        .def_readwrite("name", &Pet::name)
        .def_readwrite("type", &Pet::type)
        .def_readwrite("attr", &Pet::attr);
    py::native_enum<Pet::Kind>(pet, "Kind", "enum.Enum")
        .value("Dog", Pet::Kind::Dog)
        .value("Cat", Pet::Kind::Cat)
        .export_values()
        .finalize();
    py::class_<Pet::Attributes>(pet, "Attributes")
        .def(py::init<>())
        .def_readwrite("age", &Pet::Attributes::age);

To ensure that the nested types Kind and Attributes are created within the scope of Pet, the pet py::class_ instance must be supplied to the py::native_enum and py::class_ constructors. The .export_values() function is available for exporting the enum entries into the parent scope, if desired.

py::native_enum was introduced with pybind11v3. It binds C++ enum types to native Python enum types, typically types in Python's stdlib enum module, which are PEP 435 compatible. This is the recommended way to bind C++ enums. The older py::enum_ is not PEP 435 compatible (see issue #2332) but remains supported indefinitely for backward compatibility. New bindings should prefer py::native_enum.

NOTE: The deprecated py::enum_ is documented here.

The .finalize() call above is needed because Python's native enums cannot be built incrementally — all name/value pairs need to be passed at once. To achieve this, py::native_enum acts as a buffer to collect the name/value pairs. The .finalize() call uses the accumulated name/value pairs to build the arguments for constructing a native Python enum type.

The py::native_enum constructor takes a third argument, native_type_name, which specifies the fully qualified name of the Python base class to use — e.g., "enum.Enum" or "enum.IntEnum". A fourth optional argument, class_doc, provides the docstring for the generated class.

For example:

    py::native_enum<Pet::Kind>(pet, "Kind", "enum.IntEnum", "Constant specifying the kind of pet")

You may use any fully qualified Python name for native_type_name. The only requirement is that the named type is similar to enum.Enum in these ways:

•   Has a constructor similar to that of enum.Enum:

        Colors = enum.Enum("Colors", (("Red", 0), ("Green", 1)))

•   A C++ underlying enum value can be passed to the constructor for the Python enum value:

        red = Colors(0)

•   The enum values have a .value property yielding a value that can be cast to the C++ underlying type:

        underlying = red.value

    As of Python 3.13, the compatible types in the stdlib enum module are: Enum, IntEnum, Flag, IntFlag.

    NOTE: In rare cases, a C++ enum may be bound to Python via a custom type caster. In such cases, a template specialization like this may be required:

        #if defined(PYBIND11_HAS_NATIVE_ENUM)
        namespace pybind11::detail {
        template <typename FancyEnum>
        struct type_caster_enum_type_enabled<
            FancyEnum,
            enable_if_t<is_fancy_enum<FancyEnum>::value>> : std::false_type {};
        }
        #endif

    This specialization is needed only if the custom type caster is templated.

    The PYBIND11_HAS_NATIVE_ENUM guard is needed only if backward compatibility with pybind11v2 is required.

  Build systems

For an overview of Python packaging including compiled packaging with a pybind11 example, along with a cookiecutter that includes several pybind11 options, see the Scientific Python Development Guide.

  Modules with CMake

A Python extension module can be created with just a few lines of code:

    cmake_minimum_required(VERSION 3.15...4.2)
    project(example LANGUAGES CXX)
    set(PYBIND11_FINDPYTHON ON)
    find_package(pybind11 CONFIG REQUIRED)
    pybind11_add_module(example example.cpp)
    install(TARGETS example DESTINATION .)

(You use the add_subdirectory instead, see the example in Building with CMake.) In this example, the code is located in a file named example.cpp. Either method will import the pybind11 project which provides the pybind11_add_module function. It will take care of all the details needed to build a Python extension module on any platform.

To build with pip, build, cibuildwheel, uv, or other Python tools, you can add a pyproject.toml file like this:

    [build-system]
    requires = ["scikit-build-core", "pybind11"]
    build-backend = "scikit_build_core.build"
    [project]
    name = "example"
    version = "0.1.0"

You don't need setuptools files like MANIFEST.in, setup.py, or setup.cfg, as this is not setuptools. See scikit-build-core for details. For projects you plan to upload to PyPI, be sure to fill out the [project] table with other important metadata as well (see Writing pyproject.toml).

A working sample project can be found in the [scikit_build_example] repository. An older and harder-to-maintain method is in [cmake_example]. More details about our cmake support can be found below in Building with CMake.

[scikit_build_example]
    https://github.com/pybind/scikit_build_example

[cmake_example]
    https://github.com/pybind/cmake_example

  Modules with meson-python

You can also build a package with Meson using meson-python, if you prefer that. Your meson.build file would look something like this:

    project(
        'example',
        'cpp',
        version: '0.1.0',
        default_options: [
            'cpp_std=c++11',
        ],
    )
    py = import('python').find_installation(pure: false)
    pybind11_dep = dependency('pybind11')
    py.extension_module('example',
        'example.cpp',
        install: true,
        dependencies : [pybind11_dep],
    )

And you would need a pyproject.toml file like this:

    [build-system]
    requires = ["meson-python", "pybind11"]
    build-backend = "mesonpy"

Meson-python requires your project to be in git (or mercurial) as it uses it for the SDist creation. For projects you plan to upload to PyPI, be sure to fill out the [project] table as well (see Writing pyproject.toml).

  Modules with setuptools

For projects on PyPI, a historically popular option is setuptools. Sylvain Corlay has kindly provided an example project which shows how to set up everything, including automatic generation of documentation using Sphinx. Please refer to the [python_example] repository.

[python_example]
    https://github.com/pybind/python_example

    A helper file is provided with pybind11 that can simplify usage with setuptools.

    To use pybind11 inside your setup.py, you have to have some system to ensure that pybind11 is installed when you build your package. There are four possible ways to do this, and pybind11 supports all four: You can ask all users to install pybind11 beforehand (bad), you can use Build requirements (good), setup_requires= (discouraged), or you can Copy manually (works but you have to manually sync your copy to get updates). Third party packagers like conda-forge generally strongly prefer the pyproject.toml method, as it gives them control over the pybind11 version, and they may apply patches, etc.

    An example of a setup.py using pybind11's helpers:

        from glob import glob
        from setuptools import setup
        from pybind11.setup_helpers import Pybind11Extension
        ext_modules = [
            Pybind11Extension(
                "python_example",
                sorted(glob("src/*.cpp")),  # Sort source files for reproducibility
            ),
        ]
        setup(..., ext_modules=ext_modules)

    If you want to do an automatic search for the highest supported C++ standard, that is supported via a build_ext command override; it will only affect Pybind11Extensions:

        from glob import glob
        from setuptools import setup
        from pybind11.setup_helpers import Pybind11Extension, build_ext
        ext_modules = [
            Pybind11Extension(
                "python_example",
                sorted(glob("src/*.cpp")),
            ),
        ]
        setup(..., cmdclass={"build_ext": build_ext}, ext_modules=ext_modules)

    If you have single-file extension modules that are directly stored in the Python source tree (foo.cpp in the same directory as where a foo.py would be located), you can also generate Pybind11Extensions using setup_helpers.intree_extensions: intree_extensions(["path/to/foo.cpp", ...]) returns a list of Pybind11Extensions which can be passed to ext_modules, possibly after further customizing their attributes (libraries, include_dirs, etc.). By doing so, a foo.*.so extension module will be generated and made available upon installation.

    intree_extension will automatically detect if you are using a src-style layout (as long as no namespace packages are involved), but you can also explicitly pass package_dir to it (as in setuptools.setup).

    Since pybind11 does not require NumPy when building, a light-weight replacement for NumPy's parallel compilation distutils tool is included. Use it like this:

        from pybind11.setup_helpers import ParallelCompile
        # Optional multithreaded build
        ParallelCompile("NPY_NUM_BUILD_JOBS").install()
        setup(...)

    The argument is the name of an environment variable to control the number of threads, such as NPY_NUM_BUILD_JOBS (as used by NumPy), though you can set something different if you want; CMAKE_BUILD_PARALLEL_LEVEL is another choice a user might expect. You can also pass default=N to set the default number of threads (0 will take the number of threads available) and max=N, the maximum number of threads; if you have a large extension you may want set this to a memory dependent number.

    If you are developing rapidly and have a lot of C++ files, you may want to avoid rebuilding files that have not changed. For simple cases were you are using pip install -e . and do not have local headers, you can skip the rebuild if an object file is newer than its source (headers are not checked!) with the following:

        from pybind11.setup_helpers import ParallelCompile, naive_recompile
        ParallelCompile("NPY_NUM_BUILD_JOBS", needs_recompile=naive_recompile).install()

    If you have a more complex build, you can implement a smarter function and pass it to needs_recompile, or you can use [Ccache] instead. CXX="cache g++" pip install -e . would be the way to use it with GCC, for example. Unlike the simple solution, this even works even when not compiling in editable mode, but it does require Ccache to be installed.

    Keep in mind that Pip will not even attempt to rebuild if it thinks it has already built a copy of your code, which it deduces from the version number. One way to avoid this is to use [setuptools_scm], which will generate a version number that includes the number of commits since your last tag and a hash for a dirty directory. Another way to force a rebuild is purge your cache or use Pip's --no-cache-dir option.

    You also need a MANIFEST.in file to include all relevant files so that you can make an SDist. If you use pypa-build, that will build an SDist then a wheel from that SDist by default, so you can look inside those files (wheels are just zip files with a .whl extension) to make sure you aren't missing files. check-manifest (setuptools specific) or check-sdist (general) are CLI tools that can compare the SDist contents with your source control.

[Ccache]
    https://ccache.dev

[setuptools_scm]
    https://github.com/pypa/setuptools_scm

  Build requirements

With a pyproject.toml file, you can ensure that pybind11 is available during the compilation of your project. When this file exists, Pip will make a new virtual environment, download just the packages listed here in requires=, and build a wheel (binary Python package). It will then throw away the environment, and install your wheel.

Your pyproject.toml file will likely look something like this:

    [build-system]
    requires = ["setuptools", "pybind11"]
    build-backend = "setuptools.build_meta"

  Copy manually

You can also copy setup_helpers.py directly to your project; it was designed to be usable standalone, like the old example setup.py. You can set include_pybind11=False to skip including the pybind11 package headers, so you can use it with git submodules and a specific git version. If you use this, you will need to import from a local file in setup.py and ensure the helper file is part of your MANIFEST.

Closely related, if you include pybind11 as a subproject, you can run the setup_helpers.py inplace. If loaded correctly, this should even pick up the correct include for pybind11, though you can turn it off as shown above if you want to input it manually.

Suggested usage if you have pybind11 as a submodule in extern/pybind11:

    DIR = os.path.abspath(os.path.dirname(__file__))
    sys.path.append(os.path.join(DIR, "extern", "pybind11"))
    from pybind11.setup_helpers import Pybind11Extension  # noqa: E402
    del sys.path[-1]

Changed in version 2.6: Added setup_helpers file.

  Building with cppimport

[cppimport] is a small Python import hook that determines whether there is a C++ source file whose name matches the requested module. If there is, the file is compiled as a Python extension using pybind11 and placed in the same folder as the C++ source file. Python is then able to find the module and load it.

[cppimport]
    https://github.com/tbenthompson/cppimport

  Building with CMake

For C++ codebases that have an existing CMake-based build system, a Python extension module can be created with just a few lines of code, as seen above in the module section. Pybind11 currently defaults to the old mechanism, though be aware that CMake 3.27 removed the old mechanism, so pybind11 will automatically switch if the old mechanism is not available. Please opt into the new mechanism if at all possible. Our default may change in future versions. This is the minimum required:

Changed in version 2.6: CMake 3.4+ is required.

Changed in version 2.11: CMake 3.5+ is required.

Changed in version 2.14: CMake 3.15+ is required.

Further information can be found at CMake helpers.

  pybind11_add_module

To ease the creation of Python extension modules, pybind11 provides a CMake function with the following signature:

    pybind11_add_module(<name> [MODULE | SHARED] [EXCLUDE_FROM_ALL]
                        [NO_EXTRAS] [THIN_LTO] [OPT_SIZE] source1 [source2 ...])

This function behaves very much like CMake's builtin add_library (in fact, it's a wrapper function around that command). It will add a library target called <name> to be built from the listed source files. In addition, it will take care of all the Python-specific compiler and linker flags as well as the OS- and Python-version-specific file extension. The produced target <name> can be further manipulated with regular CMake commands.

MODULE or SHARED may be given to specify the type of library. If no type is given, MODULE is used by default which ensures the creation of a Python-exclusive module. Specifying SHARED will create a more traditional dynamic library which can also be linked from elsewhere. EXCLUDE_FROM_ALL removes this target from the default build (see CMake docs for details).

Since pybind11 is a template library, pybind11_add_module adds compiler flags to ensure high quality code generation without bloat arising from long symbol names and duplication of code in different translation units. It sets default visibility to hidden, which is required for some pybind11 features and functionality when attempting to load multiple pybind11 modules compiled under different pybind11 versions. It also adds additional flags enabling LTO (Link Time Optimization) and strip unneeded symbols. See the FAQ entry for a more detailed explanation. These latter optimizations are never applied in Debug mode. If NO_EXTRAS is given, they will always be disabled, even in Release mode. However, this will result in code bloat and is generally not recommended.

As stated above, LTO is enabled by default. Some newer compilers also support different flavors of LTO such as ThinLTO. Setting THIN_LTO will cause the function to prefer this flavor if available. The function falls back to regular LTO if -flto=thin is not available. If CMAKE_INTERPROCEDURAL_OPTIMIZATION is set (either ON or OFF), then that will be respected instead of the built-in flag search.

NOTE: If you want to set the property form on targets or the CMAKE_INTERPROCEDURAL_OPTIMIZATION_<CONFIG> versions of this, you should still use set(CMAKE_INTERPROCEDURAL_OPTIMIZATION OFF) (otherwise a no-op) to disable pybind11's ipo flags.

The OPT_SIZE flag enables size-based optimization equivalent to the standard /Os or -Os compiler flags and the MinSizeRel build type, which avoid optimizations that can substantially increase the size of the resulting binary. This flag is particularly useful in projects that are split into performance-critical parts and associated bindings. In this case, we can compile the project in release mode (and hence, optimize performance globally), and specify OPT_SIZE for the binding target, where size might be the main concern as performance is often less critical here. A ~25% size reduction has been observed in practice. This flag only changes the optimization behavior at a per-target level and takes precedence over the global CMake build type (Release, RelWithDebInfo) except for Debug builds, where optimizations remain disabled.

  Configuration variables

By default, pybind11 will compile modules with the compiler default or the minimum standard required by pybind11, whichever is higher. You can set the standard explicitly with CMAKE_CXX_STANDARD:

    set(CMAKE_CXX_STANDARD 14 CACHE STRING "C++ version selection")  # or 11, 14, 17, 20
    set(CMAKE_CXX_STANDARD_REQUIRED ON)  # optional, ensure standard is supported
    set(CMAKE_CXX_EXTENSIONS OFF)  # optional, keep compiler extensions off

The variables can also be set when calling CMake from the command line using the -D<variable>=<value> flag. You can also manually set CXX_STANDARD on a target or use target_compile_features on your targets - anything that CMake supports.

Classic Python support: The target Python version can be selected by setting PYBIND11_PYTHON_VERSION or an exact Python installation can be specified with PYTHON_EXECUTABLE. For example:

    cmake -DPYBIND11_PYTHON_VERSION=3.8 ..
    # Another method:
    cmake -DPYTHON_EXECUTABLE=/path/to/python ..
    # This often is a good way to get the current Python, works in environments:
    cmake -DPYTHON_EXECUTABLE=$(python3 -c "import sys; print(sys.executable)") ..

  find_package vs. add_subdirectory

For CMake-based projects that don't include the pybind11 repository internally, an external installation can be detected through find_package(pybind11). See the Config file docstring for details of relevant CMake variables.

    cmake_minimum_required(VERSION 3.15...4.2)
    project(example LANGUAGES CXX)
    find_package(pybind11 REQUIRED)
    pybind11_add_module(example example.cpp)

Note that find_package(pybind11) will only work correctly if pybind11 has been correctly installed on the system, e. g. after downloading or cloning the pybind11 repository :

    # Classic CMake
    cd pybind11
    mkdir build
    cd build
    cmake ..
    make install
    # CMake 3.15+
    cd pybind11
    cmake -S . -B build
    cmake --build build -j 2  # Build on 2 cores
    cmake --install build

Once detected, the aforementioned pybind11_add_module can be employed as before. The function usage and configuration variables are identical no matter if pybind11 is added as a subdirectory or found as an installed package. You can refer to the same [cmake_example] repository for a full sample project -- just swap out add_subdirectory for find_package.

  FindPython mode

Modern CMake (3.18.2+ ideal) added a new module called FindPython that had a highly improved search algorithm and modern targets and tools. If you use FindPython, pybind11 will detect this and use the existing targets instead:

    cmake_minimum_required(VERSION 3.15...4.2)
    project(example LANGUAGES CXX)
    find_package(Python 3.8 COMPONENTS Interpreter Development REQUIRED)
    find_package(pybind11 CONFIG REQUIRED)
    # or add_subdirectory(pybind11)
    pybind11_add_module(example example.cpp)

You can also use the targets (as listed below) with FindPython. If you define PYBIND11_FINDPYTHON, pybind11 will perform the FindPython step for you (mostly useful when building pybind11's own tests, or as a way to change search algorithms from the CMake invocation, with -DPYBIND11_FINDPYTHON=ON.

WARNING: If you use FindPython to multi-target Python versions, use the individual targets listed below, and avoid targets that directly include Python parts.

There are many ways to hint or force a discovery of a specific Python installation), setting Python_ROOT_DIR may be the most common one (though with virtualenv/venv support, and Conda support, this tends to find the correct Python version more often than the old system did).

WARNING: When the Python libraries (i.e. libpythonXX.a and libpythonXX.so on Unix) are not available, as is the case on a manylinux image, the Development component will not be resolved by FindPython. When not using the embedding functionality, CMake 3.18+ allows you to specify Development.Module instead of Development to resolve this issue.

Added in version 2.6.

  Advanced: interface library targets

Pybind11 supports modern CMake usage patterns with a set of interface targets, available in all modes. The targets provided are:

pybind11::headers
    Just the pybind11 headers and minimum compile requirements

pybind11::pybind11
    Python headers + pybind11::headers

pybind11::python_link_helper
    Just the "linking" part of pybind11:module

pybind11::module
    Everything for extension modules - pybind11::pybind11 + Python::Module (FindPython) or pybind11::python_link_helper

pybind11::embed
    Everything for embedding the Python interpreter - pybind11::pybind11 + Python::Python (FindPython) or Python libs

pybind11::lto / pybind11::thin_lto
    An alternative to INTERPROCEDURAL_OPTIMIZATION for adding link-time optimization.

pybind11::windows_extras
    /bigobj and /mp for MSVC.

pybind11::opt_size
    /Os for MSVC, -Os for other compilers. Does nothing for debug builds.

    Two helper functions are also provided:

pybind11_strip(target)
    Strips a target (uses CMAKE_STRIP after the target is built)

pybind11_extension(target)
    Sets the correct extension (with SOABI) for a target.

    You can use these targets to build complex applications. For example, the add_python_module function is identical to:

        cmake_minimum_required(VERSION 3.15...4.2)
        project(example LANGUAGES CXX)
        find_package(pybind11 REQUIRED)  # or add_subdirectory(pybind11)
        add_library(example MODULE main.cpp)
        target_link_libraries(example PRIVATE pybind11::module pybind11::lto pybind11::windows_extras)
        pybind11_extension(example)
        if(NOT MSVC AND NOT ${CMAKE_BUILD_TYPE} MATCHES Debug|RelWithDebInfo)
            # Strip unnecessary sections of the binary on Linux/macOS
            pybind11_strip(example)
        endif()
        set_target_properties(example PROPERTIES CXX_VISIBILITY_PRESET "hidden"
                                                 CUDA_VISIBILITY_PRESET "hidden")

    Instead of setting properties, you can set CMAKE_* variables to initialize these correctly.

    WARNING: Since pybind11 is a metatemplate library, it is crucial that certain compiler flags are provided to ensure high quality code generation. In contrast to the pybind11_add_module() command, the CMake interface provides a composable set of targets to ensure that you retain flexibility. It can be especially important to provide or set these properties; the FAQ contains an explanation on why these are needed.

    Added in version 2.6.

  Advanced: NOPYTHON mode

If you want complete control, you can set PYBIND11_NOPYTHON to completely disable Python integration (this also happens if you run FindPython2 and FindPython3 without running FindPython). This gives you complete freedom to integrate into an existing system (like Scikit-Build's PythonExtensions). pybind11_add_module and pybind11_extension will be unavailable, and the targets will be missing any Python specific behavior.

Added in version 2.6.

  Embedding the Python interpreter

In addition to extension modules, pybind11 also supports embedding Python into a C++ executable or library. In CMake, simply link with the pybind11::embed target. It provides everything needed to get the interpreter running. The Python headers and libraries are attached to the target. Unlike pybind11::module, there is no need to manually set any additional properties here. For more information about usage in C++, see Embedding the interpreter.

    cmake_minimum_required(VERSION 3.15...4.2)
    project(example LANGUAGES CXX)
    find_package(pybind11 REQUIRED)  # or add_subdirectory(pybind11)
    add_executable(example main.cpp)
    target_link_libraries(example PRIVATE pybind11::embed)

  Building manually

pybind11 is a header-only library, hence it is not necessary to link against any special libraries and there are no intermediate (magic) translation steps.

On Linux, you can compile an example such as the one given in Creating bindings for a simple function using the following command:

    $ c++ -O3 -Wall -shared -std=c++11 -fPIC $(python3 -m pybind11 --includes) example.cpp -o example$(python3-config --extension-suffix)

The python3 -m pybind11 --includes command fetches the include paths for both pybind11 and Python headers. This assumes that pybind11 has been installed using pip or conda. If it hasn't, you can also manually specify -I <path-to-pybind11>/include together with the Python includes path python3-config --includes.

On macOS: the build command is almost the same but it also requires passing the -undefined dynamic_lookup flag so as to ignore missing symbols when building the module:

    $ c++ -O3 -Wall -shared -std=c++11 -undefined dynamic_lookup $(python3 -m pybind11 --includes) example.cpp -o example$(python3-config --extension-suffix)

In general, it is advisable to include several additional build parameters that can considerably reduce the size of the created binary. Refer to section Building with CMake for a detailed example of a suitable cross-platform CMake-based build system that works on all platforms including Windows.

NOTE: On Linux and macOS, it's better to (intentionally) not link against libpython. The symbols will be resolved when the extension library is loaded into a Python binary. This is preferable because you might have several different installations of a given Python version (e.g. the system-provided Python, and one that ships with a piece of commercial software). In this way, the plugin will work with both versions, instead of possibly importing a second Python library into a process that already contains one (which will lead to a segfault).

  Building with Bazel

You can build with the Bazel build system using the pybind11_bazel repository.

  Building with Meson

You can use Meson, which has support for pybind11 as a dependency (internally relying on our pkg-config support). See the module example above.

  Generating binding code automatically

The Binder project is a tool for automatic generation of pybind11 binding code by introspecting existing C++ codebases using LLVM/Clang. See the [binder] documentation for details.

[binder]
    http://cppbinder.readthedocs.io/en/latest/about.html

    [AutoWIG] is a Python library that wraps automatically compiled libraries into high-level languages. It parses C++ code using LLVM/Clang technologies and generates the wrappers using the Mako templating engine. The approach is automatic, extensible, and applies to very complex C++ libraries, composed of thousands of classes or incorporating modern meta-programming constructs.

[AutoWIG]
    https://github.com/StatisKit/AutoWIG

    [semiwrap] is a build tool that makes it simpler to wrap C/C++ libraries with pybind11 by automating large portions of the wrapping process and handling some of the more complex aspects of creating pybind11 based wrappers (especially with trampolines to allow inheriting from C++ classes from Python). It includes a hatchling plugin that autogenerates meson.build files that can be built using meson, and those build files parse your wrapped headers and generate/compile pybind11 based wrappers into python extension modules.

[semiwrap]
    https://semiwrap.readthedocs.io

    [litgen] is an automatic python bindings generator with a focus on generating documented and discoverable bindings: bindings will nicely reproduce the documentation found in headers. It is based on srcML (srcml.org), a highly scalable, multi-language parsing tool with a developer centric approach. The API that you want to expose to python must be C++14 compatible (but your implementation can use more modern constructs).

[litgen]
    https://pthom.github.io/litgen

  Functions

Before proceeding with this section, make sure that you are already familiar with the basics of binding functions and classes, as explained in First steps and Object-oriented code. The following guide is applicable to both free and member functions, i.e. methods in Python.

  Return value policies

Python and C++ use fundamentally different ways of managing the memory and lifetime of objects managed by them. This can lead to issues when creating bindings for functions that return a non-trivial type. Just by looking at the type information, it is not clear whether Python should take charge of the returned value and eventually free its resources, or if this is handled on the C++ side. For this reason, pybind11 provides several return value policy annotations that can be passed to the module_::def() and class_::def() functions. The default policy is return_value_policy::automatic.

Return value policies are tricky, and it's very important to get them right. Just to illustrate what can go wrong, consider the following simple example:

    /* Function declaration */
    Data *get_data() { return _data; /* (pointer to a static data structure) */ }
    ...
    /* Binding code */
    m.def("get_data", &get_data); // <-- KABOOM, will cause crash when called from Python

What's going on here? When get_data() is called from Python, the return value (a native C++ type) must be wrapped to turn it into a usable Python type. In this case, the default return value policy (return_value_policy::automatic) causes pybind11 to assume ownership of the static _data instance.

When Python's garbage collector eventually deletes the Python wrapper, pybind11 will also attempt to delete the C++ instance (via operator delete()) due to the implied ownership. At this point, the entire application will come crashing down, though errors could also be more subtle and involve silent data corruption.

In the above example, the policy return_value_policy::reference should have been specified so that the global data instance is only referenced without any implied transfer of ownership, i.e.:

    m.def("get_data", &get_data, py::return_value_policy::reference);

On the other hand, this is not the right policy for many other situations, where ignoring ownership could lead to resource leaks. As a developer using pybind11, it's important to be familiar with the different return value policies, including which situation calls for which one of them. The following table provides an overview of available policies:
Return value policy | Description
return_value_policy::take_ownership | Reference an existing object (i.e. do not create a new copy) and take ownership. Python will call the destructor and delete operator when the object's reference count reaches zero. Undefined behavior ensues when the C++ side does the same, or when the data was not dynamically allocated.
return_value_policy::copy | Create a new copy of the returned object, which will be owned by Python. This policy is comparably safe because the lifetimes of the two instances are decoupled.
return_value_policy::move | Use std::move to move the return value contents into a new instance that will be owned by Python. This policy is comparably safe because the lifetimes of the two instances (move source and destination) are decoupled.
return_value_policy::reference | Reference an existing object, but do not take ownership. The C++ side is responsible for managing the object's lifetime and deallocating it when it is no longer used. Warning: undefined behavior will ensue when the C++ side deletes an object that is still referenced and used by Python.
return_value_policy::reference_internal | If the return value is an lvalue reference or a pointer, the parent object (the implicit this, or self argument of the called method or property) is kept alive for at least the lifespan of the return value. Otherwise this policy falls back to return_value_policy::move (see #5528). Internally, this policy works just like return_value_policy::reference but additionally applies a keep_alive<0, 1> call policy (described in the next section) that prevents the parent object from being garbage collected as long as the return value is referenced by Python. This is the default policy for property getters created via def_property, def_readwrite, etc.
return_value_policy::automatic | This policy falls back to the policy return_value_policy::take_ownership when the return value is a pointer. Otherwise, it uses return_value_policy::move or return_value_policy::copy for rvalue and lvalue references, respectively. See above for a description of what all of these different policies do. This is the default policy for py::class_-wrapped types.
return_value_policy::automatic_reference | As above, but use policy return_value_policy::reference when the return value is a pointer. This is the default conversion policy for function arguments when calling Python functions manually from C++ code (i.e. via handle::operator()) and the casters in pybind11/stl.h. You probably won't need to use this explicitly.

Return value policies can also be applied to properties:

    class_<MyClass>(m, "MyClass")
        .def_property("data", &MyClass::getData, &MyClass::setData,
                      py::return_value_policy::copy);

Technically, the code above applies the policy to both the getter and the setter function, however, the setter doesn't really care about return value policies which makes this a convenient terse syntax. Alternatively, targeted arguments can be passed through the cpp_function constructor:

    class_<MyClass>(m, "MyClass")
        .def_property("data",
            py::cpp_function(&MyClass::getData, py::return_value_policy::copy),
            py::cpp_function(&MyClass::setData)
        );

WARNING: Code with invalid return value policies might access uninitialized memory or free data structures multiple times, which can lead to hard-to-debug non-determinism and segmentation faults, hence it is worth spending the time to understand all the different options in the table above.

NOTE: One important aspect of the above policies is that they only apply to instances which pybind11 has not seen before, in which case the policy clarifies essential questions about the return value's lifetime and ownership. When pybind11 knows the instance already (as identified by its type and address in memory), it will return the existing Python object wrapper rather than creating a new copy.

NOTE: The next section on Additional call policies discusses call policies that can be specified in addition to a return value policy from the list above. Call policies indicate reference relationships that can involve both return values and parameters of functions.

NOTE: As an alternative to elaborate call policies and lifetime management logic, consider using smart pointers (see the section on Custom smart pointers for details). Smart pointers can tell whether an object is still referenced from C++ or Python, which generally eliminates the kinds of inconsistencies that can lead to crashes or undefined behavior. For functions returning smart pointers, it is not necessary to specify a return value policy.

  Additional call policies

In addition to the above return value policies, further call policies can be specified to indicate dependencies between parameters or ensure a certain state for the function call.

  Keep alive

In general, this policy is required when the C++ object is any kind of container and another object is being added to the container. keep_alive<Nurse, Patient> indicates that the argument with index Patient should be kept alive at least until the argument with index Nurse is freed by the garbage collector. Argument indices start at one, while zero refers to the return value. For methods, index 1 refers to the implicit this pointer, while regular arguments begin at index 2. Arbitrarily many call policies can be specified. When a Nurse with value None is detected at runtime, the call policy does nothing.

When the nurse is not a pybind11-registered type, the implementation internally relies on the ability to create a weak reference to the nurse object. When the nurse object is not a pybind11-registered type and does not support weak references, an exception will be thrown.

If you use an incorrect argument index, you will get a RuntimeError saying Could not activate keep_alive!. You should review the indices you're using.

Consider the following example: here, the binding code for a list append operation ties the lifetime of the newly added element to the underlying container:

    py::class_<List>(m, "List")
        .def("append", &List::append, py::keep_alive<1, 2>());

For consistency, the argument indexing is identical for constructors. Index 1 still refers to the implicit this pointer, i.e. the object which is being constructed. Index 0 refers to the return type which is presumed to be void when a constructor is viewed like a function. The following example ties the lifetime of the constructor element to the constructed object:

    py::class_<Nurse>(m, "Nurse")
        .def(py::init<Patient &>(), py::keep_alive<1, 2>());

NOTE: keep_alive is analogous to the with_custodian_and_ward (if Nurse, Patient != 0) and with_custodian_and_ward_postcall (if Nurse/Patient == 0) policies from Boost.Python.

  Call guard

The call_guard<T> policy allows any scope guard type T to be placed around the function call. For example, this definition:

    m.def("foo", foo, py::call_guard<T>());

is equivalent to the following pseudocode:

    m.def("foo", [](args...) {
        T scope_guard;
        return foo(args...); // forwarded arguments
    });

The only requirement is that T is default-constructible, but otherwise any scope guard will work. This is very useful in combination with gil_scoped_release. See Global Interpreter Lock (GIL).

Multiple guards can also be specified as py::call_guard<T1, T2, T3...>. The constructor order is left to right and destruction happens in reverse.

SEE ALSO: The file tests/test_call_policies.cpp contains a complete example that demonstrates using keep_alive and call_guard in more detail.

  Python objects as arguments

pybind11 exposes all major Python types using thin C++ wrapper classes. These wrapper classes can also be used as parameters of functions in bindings, which makes it possible to directly work with native Python types on the C++ side. For instance, the following statement iterates over a Python dict:

    void print_dict(const py::dict& dict) {
        /* Easily interact with Python types */
        for (auto item : dict)
            std::cout << "key=" << std::string(py::str(item.first)) << ", "
                      << "value=" << std::string(py::str(item.second)) << std::endl;
    }

It can be exported:

    m.def("print_dict", &print_dict);

And used in Python as usual:

    >>> print_dict({"foo": 123, "bar": "hello"})
    key=foo, value=123
    key=bar, value=hello

For more information on using Python objects in C++, see Python C++ interface.

  Accepting *args and **kwargs

Python provides a useful mechanism to define functions that accept arbitrary numbers of arguments and keyword arguments:

    def generic(*args, **kwargs):
        ...  # do something with args and kwargs

Such functions can also be created using pybind11:

    void generic(py::args args, const py::kwargs& kwargs) {
        /// .. do something with args
        if (kwargs)
            /// .. do something with kwargs
    }
    /// Binding code
    m.def("generic", &generic);

The class py::args derives from py::tuple and py::kwargs derives from py::dict.

You may also use just one or the other, and may combine these with other arguments. Note, however, that py::kwargs must always be the last argument of the function, and py::args implies that any further arguments are keyword-only (see Keyword-only arguments).

Please refer to the other examples for details on how to iterate over these, and on how to cast their entries into C++ objects. A demonstration is also available in tests/test_kwargs_and_defaults.cpp.

NOTE: When combining *args or **kwargs with Keyword arguments you should not include py::arg tags for the py::args and py::kwargs arguments.

  Default arguments revisited

The section on Default arguments previously discussed basic usage of default arguments using pybind11. One noteworthy aspect of their implementation is that default arguments are converted to Python objects right at declaration time. Consider the following example:

    py::class_<MyClass>("MyClass")
        .def("myFunction", py::arg("arg") = SomeType(123));

In this case, pybind11 must already be set up to deal with values of the type SomeType (via a prior instantiation of py::class_<SomeType>), or an exception will be thrown.

Another aspect worth highlighting is that the "preview" of the default argument in the function signature is generated using the object's __repr__ method. If not available, the signature may not be very helpful, e.g.:

    FUNCTIONS
    ...
    |  myFunction(...)
    |      Signature : (MyClass, arg : SomeType = <SomeType object at 0x101b7b080>) -> NoneType
    ...

The first way of addressing this is by defining SomeType.__repr__. Alternatively, it is possible to specify the human-readable preview of the default argument manually using the arg_v notation:

    py::class_<MyClass>("MyClass")
        .def("myFunction", py::arg_v("arg", SomeType(123), "SomeType(123)"));

Sometimes it may be necessary to pass a null pointer value as a default argument. In this case, remember to cast it to the underlying type in question, like so:

    py::class_<MyClass>("MyClass")
        .def("myFunction", py::arg("arg") = static_cast<SomeType *>(nullptr));

  Keyword-only arguments

Python implements keyword-only arguments by specifying an unnamed * argument in a function definition:

    def f(a, *, b):  # a can be positional or via keyword; b must be via keyword
        pass
    f(a=1, b=2)  # good
    f(b=2, a=1)  # good
    f(1, b=2)  # good
    f(1, 2)  # TypeError: f() takes 1 positional argument but 2 were given

Pybind11 provides a py::kw_only object that allows you to implement the same behaviour by specifying the object between positional and keyword-only argument annotations when registering the function:

    m.def("f", [](int a, int b) { /* ... */ },
          py::arg("a"), py::kw_only(), py::arg("b"));

Added in version 2.6.

A py::args argument implies that any following arguments are keyword-only, as if py::kw_only() had been specified in the same relative location of the argument list as the py::args argument. The py::kw_only() may be included to be explicit about this, but is not required.

Changed in version 2.9: This can now be combined with py::args. Before, py::args could only occur at the end of the argument list, or immediately before a py::kwargs argument at the end.

  Positional-only arguments

Python 3.8 introduced a new positional-only argument syntax, using / in the function definition (note that this has been a convention for CPython positional arguments, such as in pow(), since Python 2). You can do the same thing in any version of Python using py::pos_only():

    m.def("f", [](int a, int b) { /* ... */ },
           py::arg("a"), py::pos_only(), py::arg("b"));

You now cannot give argument a by keyword. This can be combined with keyword-only arguments, as well.

Added in version 2.6.

  Non-converting arguments

Certain argument types may support conversion from one type to another. Some examples of conversions are:

•   Implicit conversions declared using py::implicitly_convertible<A,B>()

•   Calling a method accepting a double with an integer argument

•   Calling a std::complex<float> argument with a non-complex python type (for example, with a float). (Requires the optional pybind11/complex.h header).

•   Calling a function taking an Eigen matrix reference with a numpy array of the wrong type or of an incompatible data layout. (Requires the optional pybind11/eigen.h header).

    This behaviour is sometimes undesirable: the binding code may prefer to raise an error rather than convert the argument. This behaviour can be obtained through py::arg by calling the .noconvert() method of the py::arg object, such as:

        m.def("floats_only", [](double f) { return 0.5 * f; }, py::arg("f").noconvert());
        m.def("floats_preferred", [](double f) { return 0.5 * f; }, py::arg("f"));

    Attempting the call the second function (the one without .noconvert()) with an integer will succeed, but attempting to call the .noconvert() version will fail with a TypeError:

        >>> floats_preferred(4)
        2.0
        >>> floats_only(4)
        Traceback (most recent call last):
          File "<stdin>", line 1, in <module>
        TypeError: floats_only(): incompatible function arguments. The following argument types are supported:
            1. (f: float) -> float
        Invoked with: 4

    You may, of course, combine this with the _a shorthand notation (see Keyword arguments) and/or Default arguments. It is also permitted to omit the argument name by using the py::arg() constructor without an argument name, i.e. by specifying py::arg().noconvert().

    NOTE: When specifying py::arg options it is necessary to provide the same number of options as the bound function has arguments. Thus if you want to enable no-convert behaviour for just one of several arguments, you will need to specify a py::arg() annotation for each argument with the no-convert argument modified to py::arg().noconvert().

  Allow/Prohibiting None arguments

When a C++ type registered with py::class_ is passed as an argument to a function taking the instance as pointer or shared holder (e.g. shared_ptr or a custom, copyable holder as described in Custom smart pointers), pybind allows None to be passed from Python which results in calling the C++ function with nullptr (or an empty holder) for the argument.

To explicitly enable or disable this behaviour, using the .none method of the py::arg object:

    py::class_<Dog>(m, "Dog").def(py::init<>());
    py::class_<Cat>(m, "Cat").def(py::init<>());
    m.def("bark", [](Dog *dog) -> std::string {
        if (dog) return "woof!"; /* Called with a Dog instance */
        else return "(no dog)"; /* Called with None, dog == nullptr */
    }, py::arg("dog").none(true));
    m.def("meow", [](Cat *cat) -> std::string {
        // Can't be called with None argument
        return "meow";
    }, py::arg("cat").none(false));

With the above, the Python call bark(None) will return the string "(no dog)", while attempting to call meow(None) will raise a TypeError:

    >>> from animals import Dog, Cat, bark, meow
    >>> bark(Dog())
    'woof!'
    >>> meow(Cat())
    'meow'
    >>> bark(None)
    '(no dog)'
    >>> meow(None)
    Traceback (most recent call last):
      File "<stdin>", line 1, in <module>
    TypeError: meow(): incompatible function arguments. The following argument types are supported:
        1. (cat: animals.Cat) -> str
    Invoked with: None

The default behaviour when the tag is unspecified is to allow None.

NOTE: Even when .none(true) is specified for an argument, None will be converted to a nullptr only for custom and opaque types. Pointers to built-in types (double *, int *, ...) and STL types (std::vector<T> *, ...; if pybind11/stl.h is included) are copied when converted to C++ (see Overview) and will not allow None as argument. To pass optional argument of these copied types consider using std::optional<T>

  Overload resolution order

When a function or method with multiple overloads is called from Python, pybind11 determines which overload to call in two passes. The first pass attempts to call each overload without allowing argument conversion (as if every argument had been specified as py::arg().noconvert() as described above).

If no overload succeeds in the no-conversion first pass, a second pass is attempted in which argument conversion is allowed (except where prohibited via an explicit py::arg().noconvert() attribute in the function definition).

If the second pass also fails a TypeError is raised.

Within each pass, overloads are tried in the order they were registered with pybind11. If the py::prepend() tag is added to the definition, a function can be placed at the beginning of the overload sequence instead, allowing user overloads to proceed built in functions.

What this means in practice is that pybind11 will prefer any overload that does not require conversion of arguments to an overload that does, but otherwise prefers earlier-defined overloads to later-defined ones.

NOTE: pybind11 does not further prioritize based on the number/pattern of overloaded arguments. That is, pybind11 does not prioritize a function requiring one conversion over one requiring three, but only prioritizes overloads requiring no conversion at all to overloads that require conversion of at least one argument.

Added in version 2.6: The py::prepend() tag.

  Binding functions with template parameters

You can bind functions that have template parameters. Here's a function:

    template <typename T>
    void set(T t);

C++ templates cannot be instantiated at runtime, so you cannot bind the non-instantiated function:

    // BROKEN (this will not compile)
    m.def("set", &set);

You must bind each instantiated function template separately. You may bind each instantiation with the same name, which will be treated the same as an overloaded function:

    m.def("set", &set<int>);
    m.def("set", &set<std::string>);

Sometimes it's more clear to bind them with separate names, which is also an option:

    m.def("setInt", &set<int>);
    m.def("setString", &set<std::string>);

  Classes

This section presents advanced binding code for classes and it is assumed that you are already familiar with the basics from Object-oriented code.

  Overriding virtual functions in Python

Suppose that a C++ class or interface has a virtual function that we'd like to override from within Python (we'll focus on the class Animal; Dog is given as a specific example of how one would do this with traditional C++ code).

    class Animal {
    public:
        virtual ~Animal() { }
        virtual std::string go(int n_times) = 0;
    };
    class Dog : public Animal {
    public:
        std::string go(int n_times) override {
            std::string result;
            for (int i=0; i<n_times; ++i)
                result += "woof! ";
            return result;
        }
    };

Let's also suppose that we are given a plain function which calls the function go() on an arbitrary Animal instance.

    std::string call_go(Animal *animal) {
        return animal->go(3);
    }

Normally, the binding code for these classes would look as follows:

    PYBIND11_MODULE(example, m, py::mod_gil_not_used()) {
        py::class_<Animal>(m, "Animal")
            .def("go", &Animal::go);
        py::class_<Dog, Animal>(m, "Dog")
            .def(py::init<>());
        m.def("call_go", &call_go);
    }

However, these bindings are impossible to extend: Animal is not constructible, and we clearly require some kind of "trampoline" that redirects virtual calls back to Python.

Defining a new type of Animal from within Python is possible but requires a helper class that is defined as follows:

    class PyAnimal : public Animal, public py::trampoline_self_life_support {
    public:
        /* Inherit the constructors */
        using Animal::Animal;
        /* Trampoline (need one for each virtual function) */
        std::string go(int n_times) override {
            PYBIND11_OVERRIDE_PURE(
                std::string, /* Return type */
                Animal,      /* Parent class */
                go,          /* Name of function in C++ (must match Python name) */
                n_times      /* Argument(s) */
            );
        }
    };

The py::trampoline_self_life_support base class is needed to ensure that a std::unique_ptr can safely be passed between Python and C++. To help you steer clear of notorious pitfalls (e.g. inheritance slicing), pybind11 enforces that trampoline classes inherit from py::trampoline_self_life_support if used in in combination with py::smart_holder.

NOTE: For completeness, the base class has no effect if a holder other than py::smart_holder used, including the default std::unique_ptr<T>. To avoid confusion, pybind11 will fail to compile bindings that combine py::trampoline_self_life_support with a holder other than py::smart_holder.

Please think twice, though, before deciding to not use the safer py::smart_holder. The pitfalls associated with avoiding it are very real, and the overhead for using it is very likely in the noise.

The macro PYBIND11_OVERRIDE_PURE should be used for pure virtual functions, and PYBIND11_OVERRIDE should be used for functions which have a default implementation. There are also two alternate macros PYBIND11_OVERRIDE_PURE_NAME and PYBIND11_OVERRIDE_NAME which take a string-valued name argument between the Parent class and Name of the function slots, which defines the name of function in Python. This is required when the C++ and Python versions of the function have different names, e.g. operator() vs __call__.

The binding code also needs a few minor adaptations (highlighted):

    PYBIND11_MODULE(example, m, py::mod_gil_not_used()) {
        py::class_<Animal, PyAnimal /* <--- trampoline */, py::smart_holder>(m, "Animal")
            .def(py::init<>())
            .def("go", &Animal::go);
        py::class_<Dog, Animal, py::smart_holder>(m, "Dog")
            .def(py::init<>());
        m.def("call_go", &call_go);
    }

Importantly, pybind11 is made aware of the trampoline helper class by specifying it as an extra template argument to py::class_. (This can also be combined with other template arguments such as a custom holder type; the order of template types does not matter). Following this, we are able to define a constructor as usual.

Bindings should be made against the actual class, not the trampoline helper class.

    py::class_<Animal, PyAnimal /* <--- trampoline */, py::smart_holder>(m, "Animal");
        .def(py::init<>())
        .def("go", &Animal::go); /* <--- DO NOT USE &PyAnimal::go HERE */

Note, however, that the above is sufficient for allowing python classes to extend Animal, but not Dog: see Combining virtual functions and inheritance for the necessary steps required to providing proper overriding support for inherited classes.

The Python session below shows how to override Animal::go and invoke it via a virtual method call.

    >>> from example import *
    >>> d = Dog()
    >>> call_go(d)
    'woof! woof! woof! '
    >>> class Cat(Animal):
    ...     def go(self, n_times):
    ...         return "meow! " * n_times
    ...
    >>> c = Cat()
    >>> call_go(c)
    'meow! meow! meow! '

If you are defining a custom constructor in a derived Python class, you must ensure that you explicitly call the bound C++ constructor using __init__, regardless of whether it is a default constructor or not. Otherwise, the memory for the C++ portion of the instance will be left uninitialized, which will generally leave the C++ instance in an invalid state and cause undefined behavior if the C++ instance is subsequently used.

Changed in version 2.6: The default pybind11 metaclass will throw a TypeError when it detects that __init__ was not called by a derived class.

Here is an example:

    class Dachshund(Dog):
        def __init__(self, name):
            Dog.__init__(self)  # Without this, a TypeError is raised.
            self.name = name
        def bark(self):
            return "yap!"

Note that a direct __init__ constructor should be called, and super() should not be used. For simple cases of linear inheritance, super() may work, but once you begin mixing Python and C++ multiple inheritance, things will fall apart due to differences between Python's MRO and C++'s mechanisms.

Please take a look at the General notes regarding convenience macros before using this feature.

NOTE: When the overridden type returns a reference or pointer to a type that pybind11 converts from Python (for example, numeric values, std::string, and other built-in value-converting types), there are some limitations to be aware of:

•   because in these cases there is no C++ variable to reference (the value is stored in the referenced Python variable), pybind11 provides one in the PYBIND11_OVERRIDE macros (when needed) with static storage duration. Note that this means that invoking the overridden method on any instance will change the referenced value stored in all instances of that type.

•   Attempts to modify a non-const reference will not have the desired effect: it will change only the static cache variable, but this change will not propagate to underlying Python instance, and the change will be replaced the next time the override is invoked.

    WARNING: The PYBIND11_OVERRIDE and accompanying macros used to be called PYBIND11_OVERLOAD up until pybind11 v2.5.0, and get_override() used to be called get_overload. This naming was corrected and the older macro and function names may soon be deprecated, in order to reduce confusion with overloaded functions and methods and py::overload_cast (see Object-oriented code).

    SEE ALSO: The file tests/test_virtual_functions.cpp contains a complete example that demonstrates how to override virtual functions using pybind11 in more detail.

  Combining virtual functions and inheritance

When combining virtual methods with inheritance, you need to be sure to provide an override for each method for which you want to allow overrides from derived python classes. For example, suppose we extend the above Animal/Dog example as follows:

    class Animal {
    public:
        virtual std::string go(int n_times) = 0;
        virtual std::string name() { return "unknown"; }
    };
    class Dog : public Animal {
    public:
        std::string go(int n_times) override {
            std::string result;
            for (int i=0; i<n_times; ++i)
                result += bark() + " ";
            return result;
        }
        virtual std::string bark() { return "woof!"; }
    };

then the trampoline class for Animal must, as described in the previous section, override go() and name(), but in order to allow python code to inherit properly from Dog, we also need a trampoline class for Dog that overrides both the added bark() method and the go() and name() methods inherited from Animal (even though Dog doesn't directly override the name() method):

    class PyAnimal : public Animal, public py::trampoline_self_life_support {
    public:
        using Animal::Animal; // Inherit constructors
        std::string go(int n_times) override { PYBIND11_OVERRIDE_PURE(std::string, Animal, go, n_times); }
        std::string name() override { PYBIND11_OVERRIDE(std::string, Animal, name, ); }
    };
    class PyDog : public Dog, public py::trampoline_self_life_support {
    public:
        using Dog::Dog; // Inherit constructors
        std::string go(int n_times) override { PYBIND11_OVERRIDE(std::string, Dog, go, n_times); }
        std::string name() override { PYBIND11_OVERRIDE(std::string, Dog, name, ); }
        std::string bark() override { PYBIND11_OVERRIDE(std::string, Dog, bark, ); }
    };

NOTE: Note the trailing commas in the PYBIND11_OVERRIDE calls to name() and bark(). These are needed to portably implement a trampoline for a function that does not take any arguments. For functions that take a nonzero number of arguments, the trailing comma must be omitted.

A registered class derived from a pybind11-registered class with virtual methods requires a similar trampoline class, even if it doesn't explicitly declare or override any virtual methods itself:

    class Husky : public Dog {};
    class PyHusky : public Husky, public py::trampoline_self_life_support {
    public:
        using Husky::Husky; // Inherit constructors
        std::string go(int n_times) override { PYBIND11_OVERRIDE_PURE(std::string, Husky, go, n_times); }
        std::string name() override { PYBIND11_OVERRIDE(std::string, Husky, name, ); }
        std::string bark() override { PYBIND11_OVERRIDE(std::string, Husky, bark, ); }
    };

There is, however, a technique that can be used to avoid this duplication (which can be especially helpful for a base class with several virtual methods). The technique involves using template trampoline classes, as follows:

    template <class AnimalBase = Animal>
    class PyAnimal : public AnimalBase, public py::trampoline_self_life_support {
    public:
        using AnimalBase::AnimalBase; // Inherit constructors
        std::string go(int n_times) override { PYBIND11_OVERRIDE_PURE(std::string, AnimalBase, go, n_times); }
        std::string name() override { PYBIND11_OVERRIDE(std::string, AnimalBase, name, ); }
    };
    template <class DogBase = Dog>
    class PyDog : public PyAnimal<DogBase>, public py::trampoline_self_life_support {
    public:
        using PyAnimal<DogBase>::PyAnimal; // Inherit constructors
        // Override PyAnimal's pure virtual go() with a non-pure one:
        std::string go(int n_times) override { PYBIND11_OVERRIDE(std::string, DogBase, go, n_times); }
        std::string bark() override { PYBIND11_OVERRIDE(std::string, DogBase, bark, ); }
    };

This technique has the advantage of requiring just one trampoline method to be declared per virtual method and pure virtual method override. It does, however, require the compiler to generate at least as many methods (and possibly more, if both pure virtual and overridden pure virtual methods are exposed, as above).

The classes are then registered with pybind11 using:

    py::class_<Animal, PyAnimal<>, py::smart_holder> animal(m, "Animal");
    py::class_<Dog, Animal, PyDog<>, py::smart_holder> dog(m, "Dog");
    py::class_<Husky, Dog, PyDog<Husky>, py::smart_holder> husky(m, "Husky");
    // ... add animal, dog, husky definitions

Note that Husky did not require a dedicated trampoline template class at all, since it neither declares any new virtual methods nor provides any pure virtual method implementations.

With either the repeated-virtuals or templated trampoline methods in place, you can now create a python class that inherits from Dog:

    class ShihTzu(Dog):
        def bark(self):
            return "yip!"

SEE ALSO: See the file tests/test_virtual_functions.cpp for complete examples using both the duplication and templated trampoline approaches.

  Extended trampoline class functionality

  Forced trampoline class initialisation

The trampoline classes described in the previous sections are, by default, only initialized when needed. More specifically, they are initialized when a python class actually inherits from a registered type (instead of merely creating an instance of the registered type), or when a registered constructor is only valid for the trampoline class but not the registered class. This is primarily for performance reasons: when the trampoline class is not needed for anything except virtual method dispatching, not initializing the trampoline class improves performance by avoiding needing to do a run-time check to see if the inheriting python instance has an overridden method.

Sometimes, however, it is useful to always initialize a trampoline class as an intermediate class that does more than just handle virtual method dispatching. For example, such a class might perform extra class initialization, extra destruction operations, and might define new members and methods to enable a more python-like interface to a class.

In order to tell pybind11 that it should always initialize the trampoline class when creating new instances of a type, the class constructors should be declared using py::init_alias<Args, ...>() instead of the usual py::init<Args, ...>(). This forces construction via the trampoline class, ensuring member initialization and (eventual) destruction.

SEE ALSO: See the file tests/test_virtual_functions.cpp for complete examples showing both normal and forced trampoline instantiation.

  Different method signatures

The macro's introduced in Overriding virtual functions in Python cover most of the standard use cases when exposing C++ classes to Python. Sometimes it is hard or unwieldy to create a direct one-on-one mapping between the arguments and method return type.

An example would be when the C++ signature contains output arguments using references (See also Limitations involving reference arguments). Another way of solving this is to use the method body of the trampoline class to do conversions to the input and return of the Python method.

The main building block to do so is the get_override(), this function allows retrieving a method implemented in Python from within the trampoline's methods. Consider for example a C++ method which has the signature bool myMethod(int32_t& value), where the return indicates whether something should be done with the value. This can be made convenient on the Python side by allowing the Python function to return None or an int:

    bool MyClass::myMethod(int32_t& value)
    {
        pybind11::gil_scoped_acquire gil;  // Acquire the GIL while in this scope.
        // Try to look up the overridden method on the Python side.
        pybind11::function override = pybind11::get_override(this, "myMethod");
        if (override) {  // method is found
            auto obj = override(value);  // Call the Python function.
            if (py::isinstance<py::int_>(obj)) {  // check if it returned a Python integer type
                value = obj.cast<int32_t>();  // Cast it and assign it to the value.
                return true;  // Return true; value should be used.
            } else {
                return false;  // Python returned none, return false.
            }
        }
        return false;  // Alternatively return MyClass::myMethod(value);
    }

  Avoiding Inheritance Slicing and std::weak_ptr surprises

When working with classes that use virtual functions and are subclassed in Python, special care must be taken when converting Python objects to std::shared_ptr<T>. Depending on whether the class uses a plain std::shared_ptr holder or py::smart_holder, the resulting shared_ptr may either allow inheritance slicing or lead to potentially surprising behavior when constructing std::weak_ptr instances.

This section explains how std::shared_ptr and py::smart_holder manage object lifetimes differently, how these differences affect trampoline-derived objects, and what options are available to achieve the situation-specific desired behavior.

When using std::shared_ptr as the holder type, converting a Python object to a std::shared_ptr<T> (e.g., obj.cast<std::shared_ptr<T>>(), or simply passing the Python object as an argument to a .def()-ed function) returns a shared_ptr that shares ownership with the original class_ holder, usually preserving object lifetime. However, for Python classes that derive from a trampoline, if the Python object is destroyed, only the base C++ object may remain alive, leading to inheritance slicing (see #1333).

In contrast, with py::smart_holder, converting a Python object to a std::shared_ptr<T> returns a new shared_ptr with an independent control block that keeps the derived Python object alive. This avoids inheritance slicing but can lead to unintended behavior when creating std::weak_ptr instances (see #5623).

If it is necessary to obtain a std::weak_ptr that shares the control block with the smart_holder—at the cost of reintroducing potential inheritance slicing—you can use py::potentially_slicing_weak_ptr<T>(obj).

When precise lifetime management of derived Python objects is important, using a Python-side weakref is the most reliable approach, as it avoids both inheritance slicing and unintended interactions with std::weak_ptr semantics in C++.

SEE ALSO:

•   potentially_slicing_weak_ptr() C++ documentation

•   tests/test_potentially_slicing_weak_ptr.cpp

  Custom constructors

The syntax for binding constructors was previously introduced, but it only works when a constructor of the appropriate arguments actually exists on the C++ side. To extend this to more general cases, pybind11 makes it possible to bind factory functions as constructors. For example, suppose you have a class like this:

    class Example {
    private:
        Example(int); // private constructor
    public:
        // Factory function:
        static Example create(int a) { return Example(a); }
    };
    py::class_<Example>(m, "Example")
        .def(py::init(&Example::create));

While it is possible to create a straightforward binding of the static create method, it may sometimes be preferable to expose it as a constructor on the Python side. This can be accomplished by calling .def(py::init(...)) with the function reference returning the new instance passed as an argument. It is also possible to use this approach to bind a function returning a new instance by raw pointer or by the holder (e.g. std::unique_ptr).

The following example shows the different approaches:

    class Example {
    private:
        Example(int); // private constructor
    public:
        // Factory function - returned by value:
        static Example create(int a) { return Example(a); }
        // These constructors are publicly callable:
        Example(double);
        Example(int, int);
        Example(std::string);
    };
    py::class_<Example>(m, "Example")
        // Bind the factory function as a constructor:
        .def(py::init(&Example::create))
        // Bind a lambda function returning a pointer wrapped in a holder:
        .def(py::init([](std::string arg) {
            return std::unique_ptr<Example>(new Example(arg));
        }))
        // Return a raw pointer:
        .def(py::init([](int a, int b) { return new Example(a, b); }))
        // You can mix the above with regular C++ constructor bindings as well:
        .def(py::init<double>())
        ;

When the constructor is invoked from Python, pybind11 will call the factory function and store the resulting C++ instance in the Python instance.

When combining factory functions constructors with virtual function trampolines there are two approaches. The first is to add a constructor to the alias class that takes a base value by rvalue-reference. If such a constructor is available, it will be used to construct an alias instance from the value returned by the factory function. The second option is to provide two factory functions to py::init(): the first will be invoked when no alias class is required (i.e. when the class is being used but not inherited from in Python), and the second will be invoked when an alias is required.

You can also specify a single factory function that always returns an alias instance: this will result in behaviour similar to py::init_alias<...>(), as described in the extended trampoline class documentation.

The following example shows the different factory approaches for a class with an alias:

    #include <pybind11/factory.h>
    class Example {
    public:
        // ...
        virtual ~Example() = default;
    };
    class PyExample : public Example, public py::trampoline_self_life_support {
    public:
        using Example::Example;
        PyExample(Example &&base) : Example(std::move(base)) {}
    };
    py::class_<Example, PyExample, py::smart_holder>(m, "Example")
        // Returns an Example pointer.  If a PyExample is needed, the Example
        // instance will be moved via the extra constructor in PyExample, above.
        .def(py::init([]() { return new Example(); }))
        // Two callbacks:
        .def(py::init([]() { return new Example(); } /* no alias needed */,
                      []() { return new PyExample(); } /* alias needed */))
        // *Always* returns an alias instance (like py::init_alias<>())
        .def(py::init([]() { return new PyExample(); }))
        ;

  Brace initialization

pybind11::init<> internally uses C++11 brace initialization to call the constructor of the target class. This means that it can be used to bind implicit constructors as well:

    struct Aggregate {
        int a;
        std::string b;
    };
    py::class_<Aggregate>(m, "Aggregate")
        .def(py::init<int, const std::string &>());

NOTE: Note that brace initialization preferentially invokes constructor overloads taking a std::initializer_list. In the rare event that this causes an issue, you can work around it by using py::init(...) with a lambda function that constructs the new object as desired.

  Non-public destructors

If a class has a private or protected destructor (as might e.g. be the case in a singleton pattern), a compile error will occur when creating bindings via pybind11. The underlying issue is that the std::unique_ptr holder type that is responsible for managing the lifetime of instances will reference the destructor even if no deallocations ever take place. In order to expose classes with private or protected destructors, it is possible to override the holder type via a holder type argument to py::class_. Pybind11 provides a helper class py::nodelete that disables any destructor invocations. In this case, it is crucial that instances are deallocated on the C++ side to avoid memory leaks.

    /* ... definition ... */
    class MyClass {
    private:
        ~MyClass() { }
    };
    /* ... binding code ... */
    py::class_<MyClass, std::unique_ptr<MyClass, py::nodelete>>(m, "MyClass")
        .def(py::init<>())

  Destructors that call Python

If a Python function is invoked from a C++ destructor, an exception may be thrown of type error_already_set. If this error is thrown out of a class destructor, std::terminate() will be called, terminating the process. Class destructors must catch all exceptions of type error_already_set to discard the Python exception using error_already_set::discard_as_unraisable().

Every Python function should be treated as possibly throwing. When a Python generator stops yielding items, Python will throw a StopIteration exception, which can pass though C++ destructors if the generator's stack frame holds the last reference to C++ objects.

For more information, see the documentation on exceptions.

    class MyClass {
    public:
        ~MyClass() {
            try {
                py::print("Even printing is dangerous in a destructor");
                py::exec("raise ValueError('This is an unraisable exception')");
            } catch (py::error_already_set &e) {
                // error_context should be information about where/why the occurred,
                // e.g. use __func__ to get the name of the current function
                e.discard_as_unraisable(__func__);
            }
        }
    };

NOTE: pybind11 does not support C++ destructors marked noexcept(false).

Added in version 2.6.

  Implicit conversions

Suppose that instances of two types A and B are used in a project, and that an A can easily be converted into an instance of type B (examples of this could be a fixed and an arbitrary precision number type).

    py::class_<A>(m, "A")
        /// ... members ...
    py::class_<B>(m, "B")
        .def(py::init<A>())
        /// ... members ...
    m.def("func",
        [](const B &) { /* .... */ }
    );

To invoke the function func using a variable a containing an A instance, we'd have to write func(B(a)) in Python. On the other hand, C++ will automatically apply an implicit type conversion, which makes it possible to directly write func(a).

In this situation (i.e. where B has a constructor that converts from A), the following statement enables similar implicit conversions on the Python side:

    py::implicitly_convertible<A, B>();

NOTE: Implicit conversions from A to B only work when B is a custom data type that is exposed to Python via pybind11.

To prevent runaway recursion, implicit conversions are non-reentrant: an implicit conversion invoked as part of another implicit conversion of the same type (i.e. from A to B) will fail.

  Static properties

The section on Instance and static fields discussed the creation of instance properties that are implemented in terms of C++ getters and setters.

Static properties can also be created in a similar way to expose getters and setters of static class attributes. Note that the implicit self argument also exists in this case and is used to pass the Python type subclass instance. This parameter will often not be needed by the C++ side, and the following example illustrates how to instantiate a lambda getter function that ignores it:

    py::class_<Foo>(m, "Foo")
        .def_property_readonly_static("foo", [](py::object /* self */) { return Foo(); });

  Operator overloading

Suppose that we're given the following Vector2 class with a vector addition and scalar multiplication operation, all implemented using overloaded operators in C++.

    class Vector2 {
    public:
        Vector2(float x, float y) : x(x), y(y) { }
        Vector2 operator+(const Vector2 &v) const { return Vector2(x + v.x, y + v.y); }
        Vector2 operator*(float value) const { return Vector2(x * value, y * value); }
        Vector2& operator+=(const Vector2 &v) { x += v.x; y += v.y; return *this; }
        Vector2& operator*=(float v) { x *= v; y *= v; return *this; }
        friend Vector2 operator*(float f, const Vector2 &v) {
            return Vector2(f * v.x, f * v.y);
        }
        std::string toString() const {
            return "[" + std::to_string(x) + ", " + std::to_string(y) + "]";
        }
    private:
        float x, y;
    };

The following snippet shows how the above operators can be conveniently exposed to Python.

    #include <pybind11/operators.h>
    PYBIND11_MODULE(example, m, py::mod_gil_not_used()) {
        py::class_<Vector2>(m, "Vector2")
            .def(py::init<float, float>())
            .def(py::self + py::self)
            .def(py::self += py::self)
            .def(py::self *= float())
            .def(float() * py::self)
            .def(py::self * float())
            .def(-py::self)
            .def("__repr__", &Vector2::toString);
    }

Note that a line like

    .def(py::self * float())

is really just short hand notation for

    .def("__mul__", [](const Vector2 &a, float b) {
        return a * b;
    }, py::is_operator())

This can be useful for exposing additional operators that don't exist on the C++ side, or to perform other types of customization. The py::is_operator flag marker is needed to inform pybind11 that this is an operator, which returns NotImplemented when invoked with incompatible arguments rather than throwing a type error.

NOTE: To use the more convenient py::self notation, the additional header file pybind11/operators.h must be included.

SEE ALSO: The file tests/test_operator_overloading.cpp contains a complete example that demonstrates how to work with overloaded operators in more detail.

  Pickling support

Python's pickle module provides a powerful facility to serialize and de-serialize a Python object graph into a binary data stream. To pickle and unpickle C++ classes using pybind11, a py::pickle() definition must be provided. Suppose the class in question has the following signature:

    class Pickleable {
    public:
        Pickleable(const std::string &value) : m_value(value) { }
        const std::string &value() const { return m_value; }
        void setExtra(int extra) { m_extra = extra; }
        int extra() const { return m_extra; }
    private:
        std::string m_value;
        int m_extra = 0;
    };

Pickling support in Python is enabled by defining the __setstate__ and __getstate__ methods [1]. For pybind11 classes, use py::pickle() to bind these two functions:

    py::class_<Pickleable>(m, "Pickleable")
        .def(py::init<std::string>())
        .def("value", &Pickleable::value)
        .def("extra", &Pickleable::extra)
        .def("setExtra", &Pickleable::setExtra)
        .def(py::pickle(
            [](const Pickleable &p) { // __getstate__
                /* Return a tuple that fully encodes the state of the object */
                return py::make_tuple(p.value(), p.extra());
            },
            [](py::tuple t) { // __setstate__
                if (t.size() != 2)
                    throw std::runtime_error("Invalid state!");
                /* Create a new C++ instance */
                Pickleable p(t[0].cast<std::string>());
                /* Assign any additional state */
                p.setExtra(t[1].cast<int>());
                return p;
            }
        ));

The __setstate__ part of the py::pickle() definition follows the same rules as the single-argument version of py::init(). The return type can be a value, pointer or holder type. See Custom constructors for details.

An instance can now be pickled as follows:

    import pickle
    p = Pickleable("test_value")
    p.setExtra(15)
    data = pickle.dumps(p)

NOTE: If given, the second argument to dumps must be 2 or larger - 0 and 1 are not supported. Newer versions are also fine; for instance, specify -1 to always use the latest available version. Beware: failure to follow these instructions will cause important pybind11 memory allocation routines to be skipped during unpickling, which will likely lead to memory corruption and/or segmentation faults.

SEE ALSO: The file tests/test_pickling.cpp contains a complete example that demonstrates how to pickle and unpickle types using pybind11 in more detail.

[1] http://docs.python.org/3/library/pickle.html#pickling-class-instances

  Deepcopy support

Python normally uses references in assignments. Sometimes a real copy is needed to prevent changing all copies. The copy module [2] provides these capabilities.

A class with pickle support is automatically also (deep)copy compatible. However, performance can be improved by adding custom __copy__ and __deepcopy__ methods.

For simple classes (deep)copy can be enabled by using the copy constructor, which should look as follows:

    py::class_<Copyable>(m, "Copyable")
        .def("__copy__",  [](const Copyable &self) {
            return Copyable(self);
        })
        .def("__deepcopy__", [](const Copyable &self, py::dict) {
            return Copyable(self);
        }, "memo"_a);

NOTE: Dynamic attributes will not be copied in this example.

[2] https://docs.python.org/3/library/copy.html

  Multiple Inheritance

pybind11 can create bindings for types that derive from multiple base types (aka. multiple inheritance). To do so, specify all bases in the template arguments of the py::class_ declaration:

    py::class_<MyType, BaseType1, BaseType2, BaseType3>(m, "MyType")
       ...

The base types can be specified in arbitrary order, and they can even be interspersed with alias types and holder types (discussed earlier in this document)---pybind11 will automatically find out which is which. The only requirement is that the first template argument is the type to be declared.

It is also permitted to inherit multiply from exported C++ classes in Python, as well as inheriting from multiple Python and/or pybind11-exported classes.

There is one caveat regarding the implementation of this feature:

When only one base type is specified for a C++ type that actually has multiple bases, pybind11 will assume that it does not participate in multiple inheritance, which can lead to undefined behavior. In such cases, add the tag multiple_inheritance to the class constructor:

    py::class_<MyType, BaseType2>(m, "MyType", py::multiple_inheritance());

The tag is redundant and does not need to be specified when multiple base types are listed.

  Module-local class bindings

When creating a binding for a class, pybind11 by default makes that binding "global" across modules. What this means is that instances whose type is defined with a py::class_ statement in one module can be passed to or returned from a function defined in any other module that is "ABI compatible" with the first, i.e., that was built with sufficiently similar versions of pybind11 and of the C++ compiler and C++ standard library. The internal data structures that pybind11 uses to keep track of its types and instances are shared just as they would be if everything were in the same module. For example, this allows the following:

    // In the module1.cpp binding code for module1:
    py::class_<Pet>(m, "Pet")
        .def(py::init<std::string>())
        .def_readonly("name", &Pet::name);

    // In the module2.cpp binding code for module2:
    m.def("create_pet", [](std::string name) { return new Pet(name); });

    >>> from module1 import Pet
    >>> from module2 import create_pet
    >>> pet1 = Pet("Kitty")
    >>> pet2 = create_pet("Doggy")
    >>> pet2.name()
    'Doggy'

When writing binding code for a library, this is usually desirable: this allows, for example, splitting up a complex library into multiple Python modules.

In some cases, however, this can cause conflicts. For example, suppose two unrelated modules make use of an external C++ library and each provide custom bindings for one of that library's classes. This will result in an error when a Python program attempts to import both modules (directly or indirectly) because of conflicting definitions on the external type:

    // dogs.cpp
    // Binding for external library class:
    py::class_<pets::Pet>(m, "Pet")
        .def("name", &pets::Pet::name);
    // Binding for local extension class:
    py::class_<Dog, pets::Pet>(m, "Dog")
        .def(py::init<std::string>());

    // cats.cpp, in a completely separate project from the above dogs.cpp.
    // Binding for external library class:
    py::class_<pets::Pet>(m, "Pet")
        .def("get_name", &pets::Pet::name);
    // Binding for local extending class:
    py::class_<Cat, pets::Pet>(m, "Cat")
        .def(py::init<std::string>());

    >>> import cats
    >>> import dogs
    Traceback (most recent call last):
      File "<stdin>", line 1, in <module>
    ImportError: generic_type: type "Pet" is already registered!

To get around this, you can tell pybind11 to keep the external class binding localized to the module by passing the py::module_local() attribute into the py::class_ constructor:

    // Pet binding in dogs.cpp:
    py::class_<pets::Pet>(m, "Pet", py::module_local())
        .def("name", &pets::Pet::name);

    // Pet binding in cats.cpp:
    py::class_<pets::Pet>(m, "Pet", py::module_local())
        .def("get_name", &pets::Pet::name);

This makes the Python-side dogs.Pet and cats.Pet into distinct classes, avoiding the conflict and allowing both modules to be loaded. C++ code in the dogs module that casts or returns a Pet instance will result in a dogs.Pet Python instance, while C++ code in the cats module will result in a cats.Pet Python instance.

This does come with two caveats, however: First, external modules cannot return or cast a Pet instance to Python (unless they also provide their own local bindings). Second, from the Python point of view they are two distinct classes.

Note that the locality only applies in the C++ -> Python direction. When passing such a py::module_local type into a C++ function, the module-local classes are still considered. This means that if the following function is added to any module (including but not limited to the cats and dogs modules above) it will be callable with either a dogs.Pet or cats.Pet argument:

    m.def("pet_name", [](const pets::Pet &pet) { return pet.name(); });

For example, suppose the above function is added to each of cats.cpp, dogs.cpp and frogs.cpp (where frogs.cpp is some other module that does not bind Pets at all).

    >>> import cats, dogs, frogs  # No error because of the added py::module_local()
    >>> mycat, mydog = cats.Cat("Fluffy"), dogs.Dog("Rover")
    >>> (cats.pet_name(mycat), dogs.pet_name(mydog))
    ('Fluffy', 'Rover')
    >>> (cats.pet_name(mydog), dogs.pet_name(mycat), frogs.pet_name(mycat))
    ('Rover', 'Fluffy', 'Fluffy')

It is possible to use py::module_local() registrations in one module even if another module registers the same type globally: within the module with the module-local definition, all C++ instances will be cast to the associated bound Python type. In other modules any such values are converted to the global Python type created elsewhere.

NOTE: STL bindings (as provided via the optional pybind11/stl_bind.h header) apply py::module_local by default when the bound type might conflict with other modules; see Binding STL containers for details.

NOTE: The localization of the bound types is actually tied to the shared object or binary generated by the compiler/linker. For typical modules created with PYBIND11_MODULE(), this distinction is not significant. It is possible, however, when Embedding the interpreter to embed multiple modules in the same binary (see Adding embedded modules). In such a case, the localization will apply across all embedded modules within the same binary.

SEE ALSO: The file tests/test_local_bindings.cpp contains additional examples that demonstrate how py::module_local() works.

  Binding protected member functions

It's normally not possible to expose protected member functions to Python:

    class A {
    protected:
        int foo() const { return 42; }
    };
    py::class_<A>(m, "A")
        .def("foo", &A::foo); // error: 'foo' is a protected member of 'A'

On one hand, this is good because non-public members aren't meant to be accessed from the outside. But we may want to make use of protected functions in derived Python classes.

The following pattern makes this possible:

    class A {
    protected:
        int foo() const { return 42; }
    };
    class Publicist : public A { // helper type for exposing protected functions
    public:
        using A::foo; // inherited with different access modifier
    };
    py::class_<A>(m, "A") // bind the primary class
        .def("foo", &Publicist::foo); // expose protected methods via the publicist

This works because &Publicist::foo is exactly the same function as &A::foo (same signature and address), just with a different access modifier. The only purpose of the Publicist helper class is to make the function name public.

If the intent is to expose protected virtual functions which can be overridden in Python, the publicist pattern can be combined with the previously described trampoline:

    class A {
    public:
        virtual ~A() = default;
    protected:
        virtual int foo() const { return 42; }
    };
    class Trampoline : public A, public py::trampoline_self_life_support {
    public:
        int foo() const override { PYBIND11_OVERRIDE(int, A, foo, ); }
    };
    class Publicist : public A {
    public:
        using A::foo;
    };
    py::class_<A, Trampoline, py::smart_holder>(m, "A") // <-- `Trampoline` here
        .def("foo", &Publicist::foo); // <-- `Publicist` here, not `Trampoline`!

  Binding final classes

Some classes may not be appropriate to inherit from. In C++11, classes can use the final specifier to ensure that a class cannot be inherited from. The py::is_final attribute can be used to ensure that Python classes cannot inherit from a specified type. The underlying C++ type does not need to be declared final.

    class IsFinal final {};
    py::class_<IsFinal>(m, "IsFinal", py::is_final());

When you try to inherit from such a class in Python, you will now get this error:

    >>> class PyFinalChild(IsFinal):
    ...     pass
    ...
    TypeError: type 'IsFinal' is not an acceptable base type

NOTE: This attribute is currently ignored on PyPy

Added in version 2.6.

  Binding classes with template parameters

pybind11 can also wrap classes that have template parameters. Consider these classes:

    struct Cat {};
    struct Dog {};
    template <typename PetType>
    struct Cage {
        Cage(PetType& pet);
        PetType& get();
    };

C++ templates may only be instantiated at compile time, so pybind11 can only wrap instantiated templated classes. You cannot wrap a non-instantiated template:

    // BROKEN (this will not compile)
    py::class_<Cage>(m, "Cage");
        .def("get", &Cage::get);

You must explicitly specify each template/type combination that you want to wrap separately.

    // ok
    py::class_<Cage<Cat>>(m, "CatCage")
        .def("get", &Cage<Cat>::get);
    // ok
    py::class_<Cage<Dog>>(m, "DogCage")
        .def("get", &Cage<Dog>::get);

If your class methods have template parameters you can wrap those as well, but once again each instantiation must be explicitly specified:

    typename <typename T>
    struct MyClass {
        template <typename V>
        T fn(V v);
    };
    py::class_<MyClass<int>>(m, "MyClassT")
        .def("fn", &MyClass<int>::fn<std::string>);

  Custom automatic downcasters

As explained in Inheritance and automatic downcasting, pybind11 comes with built-in understanding of the dynamic type of polymorphic objects in C++; that is, returning a Pet to Python produces a Python object that knows it's wrapping a Dog, if Pet has virtual methods and pybind11 knows about Dog and this Pet is in fact a Dog. Sometimes, you might want to provide this automatic downcasting behavior when creating bindings for a class hierarchy that does not use standard C++ polymorphism, such as LLVM [3]. As long as there's some way to determine at runtime whether a downcast is safe, you can proceed by specializing the pybind11::polymorphic_type_hook template:

    enum class PetKind { Cat, Dog, Zebra };
    struct Pet {   // Not polymorphic: has no virtual methods
        const PetKind kind;
        int age = 0;
      protected:
        Pet(PetKind _kind) : kind(_kind) {}
    };
    struct Dog : Pet {
        Dog() : Pet(PetKind::Dog) {}
        std::string sound = "woof!";
        std::string bark() const { return sound; }
    };
    namespace PYBIND11_NAMESPACE {
        template<> struct polymorphic_type_hook<Pet> {
            static const void *get(const Pet *src, const std::type_info*& type) {
                // note that src may be nullptr
                if (src && src->kind == PetKind::Dog) {
                    type = &typeid(Dog);
                    return static_cast<const Dog*>(src);
                }
                return src;
            }
        };
    } // namespace PYBIND11_NAMESPACE

When pybind11 wants to convert a C++ pointer of type Base* to a Python object, it calls polymorphic_type_hook<Base>::get() to determine if a downcast is possible. The get() function should use whatever runtime information is available to determine if its src parameter is in fact an instance of some class Derived that inherits from Base. If it finds such a Derived, it sets type = &typeid(Derived) and returns a pointer to the Derived object that contains src. Otherwise, it just returns src, leaving type at its default value of nullptr. If you set type to a type that pybind11 doesn't know about, no downcasting will occur, and the original src pointer will be used with its static type Base*.

It is critical that the returned pointer and type argument of get() agree with each other: if type is set to something non-null, the returned pointer must point to the start of an object whose type is type. If the hierarchy being exposed uses only single inheritance, a simple return src; will achieve this just fine, but in the general case, you must cast src to the appropriate derived-class pointer (e.g. using static_cast<Derived>(src)) before allowing it to be returned as a void*.

[3] https://llvm.org/docs/HowToSetUpLLVMStyleRTTI.html

    NOTE: pybind11's standard support for downcasting objects whose types have virtual methods is implemented using polymorphic_type_hook too, using the standard C++ ability to determine the most-derived type of a polymorphic object using typeid() and to cast a base pointer to that most-derived type (even if you don't know what it is) using dynamic_cast<void*>.

    SEE ALSO: The file tests/test_tagbased_polymorphic.cpp contains a more complete example, including a demonstration of how to provide automatic downcasting for an entire class hierarchy without writing one get() function for each class.

  Accessing the type object

You can get the type object from a C++ class that has already been registered using:

    py::type T_py = py::type::of<T>();

You can directly use py::type::of(ob) to get the type object from any python object, just like type(ob) in Python.

NOTE: Other types, like py::type::of<int>(), do not work, see Type conversions.

Added in version 2.6.

  Custom type setup

For advanced use cases, such as enabling garbage collection support, you may wish to directly manipulate the PyHeapTypeObject corresponding to a py::class_ definition.

You can do that using py::custom_type_setup:

    struct ContainerOwnsPythonObjects {
        std::vector<py::object> list;
        void append(const py::object &obj) { list.emplace_back(obj); }
        py::object at(py::ssize_t index) const {
            if (index >= size() || index < 0) {
                throw py::index_error("Index out of range");
            }
            return list.at(py::size_t(index));
        }
        py::ssize_t size() const { return py::ssize_t_cast(list.size()); }
        void clear() { list.clear(); }
    };
    py::class_<ContainerOwnsPythonObjects> cls(
        m, "ContainerOwnsPythonObjects", py::custom_type_setup([](PyHeapTypeObject *heap_type) {
            auto *type = &heap_type->ht_type;
            type->tp_flags |= Py_TPFLAGS_HAVE_GC;
            type->tp_traverse = [](PyObject *self_base, visitproc visit, void *arg) {
    // https://docs.python.org/3/c-api/typeobj.html#c.PyTypeObject.tp_traverse
    #if PY_VERSION_HEX >= 0x03090000
                Py_VISIT(Py_TYPE(self_base));
    #endif
                if (py::detail::is_holder_constructed(self_base)) {
                    auto &self = py::cast<ContainerOwnsPythonObjects &>(py::handle(self_base));
                    for (auto &item : self.list) {
                        Py_VISIT(item.ptr());
                    }
                }
                return 0;
            };
            type->tp_clear = [](PyObject *self_base) {
                if (py::detail::is_holder_constructed(self_base)) {
                    auto &self = py::cast<ContainerOwnsPythonObjects &>(py::handle(self_base));
                    for (auto &item : self.list) {
                        Py_CLEAR(item.ptr());
                    }
                    self.list.clear();
                }
                return 0;
            };
        }));
    cls.def(py::init<>());
    cls.def("append", &ContainerOwnsPythonObjects::append);
    cls.def("at", &ContainerOwnsPythonObjects::at);
    cls.def("size", &ContainerOwnsPythonObjects::size);
    cls.def("clear", &ContainerOwnsPythonObjects::clear);

Added in version 2.8.

  Exceptions

  Built-in C++ to Python exception translation

When Python calls C++ code through pybind11, pybind11 provides a C++ exception handler that will trap C++ exceptions, translate them to the corresponding Python exception, and raise them so that Python code can handle them.

pybind11 defines translations for std::exception and its standard subclasses, and several special exception classes that translate to specific Python exceptions. Note that these are not actually Python exceptions, so they cannot be examined using the Python C API. Instead, they are pure C++ objects that pybind11 will translate the corresponding Python exception when they arrive at its exception handler.
Exception thrown by C++ | Translated to Python exception type
std::exception | RuntimeError
std::bad_alloc | MemoryError
std::domain_error | ValueError
std::invalid_argument | ValueError
std::length_error | ValueError
std::out_of_range | IndexError
std::range_error | ValueError
std::overflow_error | OverflowError
pybind11::stop_iteration | StopIteration (used to implement custom iterators)
pybind11::index_error | IndexError (used to indicate out of bounds access in __getitem__, __setitem__, etc.)
pybind11::key_error | KeyError (used to indicate out of bounds access in __getitem__, __setitem__ in dict-like objects, etc.)
pybind11::value_error | ValueError (used to indicate wrong value passed in container.remove(...))
pybind11::type_error | TypeError
pybind11::buffer_error | BufferError
pybind11::import_error | ImportError
pybind11::attribute_error | AttributeError
Any other exception | RuntimeError

Exception translation is not bidirectional. That is, catching the C++ exceptions defined above will not trap exceptions that originate from Python. For that, catch pybind11::error_already_set. See below for further details.

There is also a special exception cast_error that is thrown by handle::call() when the input arguments cannot be converted to Python objects.

  Registering custom translators

If the default exception conversion policy described above is insufficient, pybind11 also provides support for registering custom exception translators. Similar to pybind11 classes, exception translators can be local to the module they are defined in or global to the entire python session. To register a simple exception conversion that translates a C++ exception into a new Python exception using the C++ exception's what() method, a helper function is available:

    py::register_exception<CppExp>(module, "PyExp");

This call creates a Python exception class with the name PyExp in the given module and automatically converts any encountered exceptions of type CppExp into Python exceptions of type PyExp.

A matching function is available for registering a local exception translator:

    py::register_local_exception<CppExp>(module, "PyExp");

It is possible to specify base class for the exception using the third parameter, a handle:

    py::register_exception<CppExp>(module, "PyExp", PyExc_RuntimeError);
    py::register_local_exception<CppExp>(module, "PyExp", PyExc_RuntimeError);

Then PyExp can be caught both as PyExp and RuntimeError.

The class objects of the built-in Python exceptions are listed in the Python documentation on Standard Exceptions. The default base class is PyExc_Exception.

When more advanced exception translation is needed, the functions py::register_exception_translator(translator) and py::register_local_exception_translator(translator) can be used to register functions that can translate arbitrary exception types (and which may include additional logic to do so). The functions takes a stateless callable (e.g. a function pointer or a lambda function without captured variables) with the call signature void(std::exception_ptr).

When a C++ exception is thrown, the registered exception translators are tried in reverse order of registration (i.e. the last registered translator gets the first shot at handling the exception). All local translators will be tried before a global translator is tried.

Inside the translator, std::rethrow_exception should be used within a try block to re-throw the exception. One or more catch clauses to catch the appropriate exceptions should then be used with each clause using py::set_error() (see below).

To declare a custom Python exception type, declare a py::exception variable and use this in the associated exception translator (note: it is often useful to make this a static declaration when using it inside a lambda expression without requiring capturing).

The following example demonstrates this for a hypothetical exception classes MyCustomException and OtherException: the first is translated to a custom python exception MyCustomError, while the second is translated to a standard python RuntimeError:

    PYBIND11_CONSTINIT static py::gil_safe_call_once_and_store<py::object> exc_storage;
    exc_storage.call_once_and_store_result(
        [&]() { return py::exception<MyCustomException>(m, "MyCustomError"); });
    py::register_exception_translator([](std::exception_ptr p) {
        try {
            if (p) std::rethrow_exception(p);
        } catch (const MyCustomException &e) {
            py::set_error(exc_storage.get_stored(), e.what());
        } catch (const OtherException &e) {
            py::set_error(PyExc_RuntimeError, e.what());
        }
    });

Multiple exceptions can be handled by a single translator, as shown in the example above. If the exception is not caught by the current translator, the previously registered one gets a chance.

If none of the registered exception translators is able to handle the exception, it is handled by the default converter as described in the previous section.

SEE ALSO: The file tests/test_exceptions.cpp contains examples of various custom exception translators and custom exception types.

NOTE: Call py::set_error() for every exception caught in a custom exception translator. Failure to do so will cause Python to crash with SystemError: error return without exception set.

Exceptions that you do not plan to handle should simply not be caught, or may be explicitly (re-)thrown to delegate it to the other, previously-declared existing exception translators.

Note that libc++ and libstdc++ behave differently under macOS with -fvisibility=hidden. Therefore exceptions that are used across ABI boundaries need to be explicitly exported, as exercised in tests/test_exceptions.h. See also: "Problems with C++ exceptions" under GCC Wiki.

  Local vs Global Exception Translators

When a global exception translator is registered, it will be applied across all modules in the reverse order of registration. This can create behavior where the order of module import influences how exceptions are translated.

If module1 has the following translator:

    py::register_exception_translator([](std::exception_ptr p) {
      try {
          if (p) std::rethrow_exception(p);
      } catch (const std::invalid_argument &e) {
          py::set_error(PyExc_ArgumentError, "module1 handled this");
      }
    }

and module2 has the following similar translator:

    py::register_exception_translator([](std::exception_ptr p) {
      try {
          if (p) std::rethrow_exception(p);
      } catch (const std::invalid_argument &e) {
          py::set_error(PyExc_ArgumentError, "module2 handled this");
      }
    }

then which translator handles the invalid_argument will be determined by the order that module1 and module2 are imported. Since exception translators are applied in the reverse order of registration, which ever module was imported last will "win" and that translator will be applied.

If there are multiple pybind11 modules that share exception types (either standard built-in or custom) loaded into a single python instance and consistent error handling behavior is needed, then local translators should be used.

Changing the previous example to use register_local_exception_translator would mean that when invalid_argument is thrown in the module2 code, the module2 translator will always handle it, while in module1, the module1 translator will do the same.

  Handling exceptions from Python in C++

When C++ calls Python functions, such as in a callback function or when manipulating Python objects, and Python raises an Exception, pybind11 converts the Python exception into a C++ exception of type pybind11::error_already_set whose payload contains a C++ string textual summary and the actual Python exception. error_already_set is used to propagate Python exception back to Python (or possibly, handle them in C++).
Exception raised in Python | Thrown as C++ exception type
Any Python Exception | pybind11::error_already_set

For example:

    try {
        // open("missing.txt", "r")
        auto file = py::module_::import("io").attr("open")("missing.txt", "r");
        auto text = file.attr("read")();
        file.attr("close")();
    } catch (py::error_already_set &e) {
        if (e.matches(PyExc_FileNotFoundError)) {
            py::print("missing.txt not found");
        } else if (e.matches(PyExc_PermissionError)) {
            py::print("missing.txt found but not accessible");
        } else {
            throw;
        }
    }

Note that C++ to Python exception translation does not apply here, since that is a method for translating C++ exceptions to Python, not vice versa. The error raised from Python is always error_already_set.

This example illustrates this behavior:

    try {
        py::eval("raise ValueError('The Ring')");
    } catch (py::value_error &boromir) {
        // Boromir never gets the ring
        assert(false);
    } catch (py::error_already_set &frodo) {
        // Frodo gets the ring
        py::print("I will take the ring");
    }
    try {
        // py::value_error is a request for pybind11 to raise a Python exception
        throw py::value_error("The ball");
    } catch (py::error_already_set &cat) {
        // cat won't catch the ball since
        // py::value_error is not a Python exception
        assert(false);
    } catch (py::value_error &dog) {
        // dog will catch the ball
        py::print("Run Spot run");
        throw;  // Throw it again (pybind11 will raise ValueError)
    }

  Handling errors from the Python C API

Where possible, use pybind11 wrappers instead of calling the Python C API directly. When calling the Python C API directly, in addition to manually managing reference counts, one must follow the pybind11 error protocol, which is outlined here.

After calling the Python C API, if Python returns an error, throw py::error_already_set();, which allows pybind11 to deal with the exception and pass it back to the Python interpreter. This includes calls to the error setting functions such as py::set_error().

    py::set_error(PyExc_TypeError, "C API type error demo");
    throw py::error_already_set();
    // But it would be easier to simply...
    throw py::type_error("pybind11 wrapper type error");

Alternately, to ignore the error, call PyErr_Clear.

Any Python error must be thrown or cleared, or Python/pybind11 will be left in an invalid state.

  Handling warnings from the Python C API

Wrappers for handling Python warnings are provided in pybind11/warnings.h. This header must be included explicitly; it is not transitively included via pybind11/pybind11.h.

Warnings can be raised with the warn function:

    py::warnings::warn("This is a warning!", PyExc_Warning);
    // Optionally, a `stack_level` can be specified.
    py::warnings::warn("Another one!", PyExc_DeprecationWarning, 3);

New warning types can be registered at the module level using new_warning_type:

    py::warnings::new_warning_type(m, "CustomWarning", PyExc_RuntimeWarning);

  Chaining exceptions ('raise from')

Python has a mechanism for indicating that exceptions were caused by other exceptions:

    try:
        print(1 / 0)
    except Exception as exc:
        raise RuntimeError("could not divide by zero") from exc

To do a similar thing in pybind11, you can use the py::raise_from function. It sets the current python error indicator, so to continue propagating the exception you should throw py::error_already_set().

    try {
        py::eval("print(1 / 0"));
    } catch (py::error_already_set &e) {
        py::raise_from(e, PyExc_RuntimeError, "could not divide by zero");
        throw py::error_already_set();
    }

Added in version 2.8.

  Handling unraisable exceptions

If a Python function invoked from a C++ destructor or any function marked noexcept(true) (collectively, "noexcept functions") throws an exception, there is no way to propagate the exception, as such functions may not throw. Should they throw or fail to catch any exceptions in their call graph, the C++ runtime calls std::terminate() to abort immediately.

Similarly, Python exceptions raised in a class's __del__ method do not propagate, but sys.unraisablehook() is triggered and an auditing event is logged.

Any noexcept function should have a try-catch block that traps class:error_already_set (or any other exception that can occur). Note that pybind11 wrappers around Python exceptions such as pybind11::value_error are not Python exceptions; they are C++ exceptions that pybind11 catches and converts to Python exceptions. Noexcept functions cannot propagate these exceptions either. A useful approach is to convert them to Python exceptions and then discard_as_unraisable as shown below.

    void nonthrowing_func() noexcept(true) {
        try {
            // ...
        } catch (py::error_already_set &eas) {
            // Discard the Python error using Python APIs, using the C++ magic
            // variable __func__. Python already knows the type and value and of the
            // exception object.
            eas.discard_as_unraisable(__func__);
        } catch (const std::exception &e) {
            // Log and discard C++ exceptions.
            third_party::log(e);
        }
    }

Added in version 2.6.

  Smart pointers & py::class_

The binding generator for classes, py::class_, can be passed a template type that denotes a special holder type that is used to manage references to the object. If no such holder type template argument is given, the default for a type T is std::unique_ptr<T>.

NOTE: A py::class_ for a given C++ type T — and all its derived types — can only use a single holder type.

  py::smart_holder

Starting with pybind11v3, py::smart_holder is built into pybind11. It is the recommended py::class_ holder for most situations. However, for backward compatibility it is not the default holder, and there are no plans to make it the default holder in the future.

It is extremely easy to use the safer and more versatile py::smart_holder: simply add py::smart_holder to py::class_:

•   py::class_<T> to

•   py::class_<T, py::smart_holder>.

    NOTE: A shorthand, py::classh<T>, is provided for py::class_<T, py::smart_holder>. The h in py::classh stands for smart_holder but is shortened for brevity, ensuring it has the same number of characters as py::class_. This design choice facilitates easy experimentation with py::smart_holder without introducing distracting whitespace noise in diffs.

    The py::smart_holder functionality includes the following:

•   Support for two-way Python/C++ conversions for both std::unique_ptr<T> and std::shared_ptr<T> simultaneously.

•   Passing a Python object back to C++ via std::unique_ptr<T>, safely disowning the Python object.

•   Safely passing "trampoline" objects (objects with C++ virtual function overrides implemented in Python, see Overriding virtual functions in Python) via std::unique_ptr<T> or std::shared_ptr<T> back to C++: associated Python objects are automatically kept alive for the lifetime of the smart-pointer.

•   Full support for std::enable_shared_from_this (cppreference).

  std::unique_ptr

This is the default py::class_ holder and works as expected in most situations. However, handling base-and-derived classes involves a reinterpret_cast, which is, strictly speaking, undefined behavior. Also note that the std::unique_ptr holder only supports passing a std::unique_ptr from C++ to Python, but not the other way around. For example, the following code works as expected with py::class_<Example>:

    std::unique_ptr<Example> create_example() { return std::unique_ptr<Example>(new Example()); }

    m.def("create_example", &create_example);

However, this will fail with py::class_<Example> (but works with py::class_<Example, py::smart_holder>):

    void do_something_with_example(std::unique_ptr<Example> ex) { ... }

NOTE: The reinterpret_cast mentioned above is here. For completeness: The same cast is also applied to py::smart_holder, but that is safe, because py::smart_holder is not templated.

  std::shared_ptr

It is possible to use std::shared_ptr as the holder, for example:

    py::class_<Example, std::shared_ptr<Example> /* <- holder type */>(m, "Example");

Compared to using py::class_<Example, py::smart_holder>, there are two noteworthy disadvantages:

•   Because a py::class_ for a given C++ type T can only use a single holder type, std::unique_ptr<T> cannot even be passed from C++ to Python. This will become apparent only at runtime, often through a segmentation fault.

•   Similar to the std::unique_ptr holder, the handling of base-and-derived classes involves a reinterpret_cast that has strictly speaking undefined behavior, although it works as expected in most situations.

  Custom smart pointers

For custom smart pointers (e.g. c10::intrusive_ptr in pytorch), transparent conversions can be enabled using a macro invocation similar to the following. It must be declared at the top namespace level before any binding code:

    PYBIND11_DECLARE_HOLDER_TYPE(T, SmartPtr<T>)

The first argument of PYBIND11_DECLARE_HOLDER_TYPE() should be a placeholder name that is used as a template parameter of the second argument. Thus, feel free to use any identifier, but use it consistently on both sides; also, don't use the name of a type that already exists in your codebase.

The macro also accepts a third optional boolean parameter that is set to false by default. Specify

    PYBIND11_DECLARE_HOLDER_TYPE(T, SmartPtr<T>, true)

if SmartPtr<T> can always be initialized from a T* pointer without the risk of inconsistencies (such as multiple independent SmartPtr instances believing that they are the sole owner of the T* pointer). A common situation where true should be passed is when the T instances use intrusive reference counting.

Please take a look at the General notes regarding convenience macros before using this feature.

By default, pybind11 assumes that your custom smart pointer has a standard interface, i.e. provides a .get() member function to access the underlying raw pointer. If this is not the case, pybind11's holder_helper must be specialized:

    // Always needed for custom holder types
    PYBIND11_DECLARE_HOLDER_TYPE(T, SmartPtr<T>)
    // Only needed if the type's `.get()` goes by another name
    namespace PYBIND11_NAMESPACE { namespace detail {
        template <typename T>
        struct holder_helper<SmartPtr<T>> { // <-- specialization
            static const T *get(const SmartPtr<T> &p) { return p.getPointer(); }
        };
    }}

The above specialization informs pybind11 that the custom SmartPtr class provides .get() functionality via .getPointer().

NOTE: The two noteworthy disadvantages mentioned under the std::shared_ptr section apply similarly to custom smart pointer holders, but there is no established safe alternative in this case.

SEE ALSO: The file tests/test_smart_ptr.cpp contains a complete example that demonstrates how to work with custom reference-counting holder types in more detail.

  Type conversions

Apart from enabling cross-language function calls, a fundamental problem that a binding tool like pybind11 must address is to provide access to native Python types in C++ and vice versa. There are three fundamentally different ways to do this—which approach is preferable for a particular type depends on the situation at hand.

1.  Use a native C++ type everywhere. In this case, the type must be wrapped using pybind11-generated bindings so that Python can interact with it.

2.  Use a native Python type everywhere. It will need to be wrapped so that C++ functions can interact with it.

3.  Use a native C++ type on the C++ side and a native Python type on the Python side. pybind11 refers to this as a type conversion.

    Type conversions are the most "natural" option in the sense that native (non-wrapped) types are used everywhere. The main downside is that a copy of the data must be made on every Python ↔ C++ transition: this is needed since the C++ and Python versions of the same type generally won't have the same memory layout.

    pybind11 can perform many kinds of conversions automatically. An overview is provided in the table "List of all builtin conversions".

    The following subsections discuss the differences between these options in more detail. The main focus in this section is on type conversions, which represent the last case of the above list.

  Overview

1. Native type in C++, wrapper in Python

Exposing a custom C++ type using py::class_ was covered in detail in the Object-oriented code section. There, the underlying data structure is always the original C++ class while the py::class_ wrapper provides a Python interface. Internally, when an object like this is sent from C++ to Python, pybind11 will just add the outer wrapper layer over the native C++ object. Getting it back from Python is just a matter of peeling off the wrapper.

2. Wrapper in C++, native type in Python

This is the exact opposite situation. Now, we have a type which is native to Python, like a tuple or a list. One way to get this data into C++ is with the py::object family of wrappers. These are explained in more detail in the Python types section. We'll just give a quick example here:

    void print_list(py::list my_list) {
        for (auto item : my_list)
            std::cout << item << " ";
    }

    >>> print_list([1, 2, 3])
    1 2 3

The Python list is not converted in any way -- it's just wrapped in a C++ py::list class. At its core it's still a Python object. Copying a py::list will do the usual reference-counting like in Python. Returning the object to Python will just remove the thin wrapper.

3. Converting between native C++ and Python types

In the previous two cases we had a native type in one language and a wrapper in the other. Now, we have native types on both sides and we convert between them.

    void print_vector(const std::vector<int> &v) {
        for (auto item : v)
            std::cout << item << "\n";
    }

    >>> print_vector([1, 2, 3])
    1 2 3

In this case, pybind11 will construct a new std::vector<int> and copy each element from the Python list. The newly constructed object will be passed to print_vector. The same thing happens in the other direction: a new list is made to match the value returned from C++.

Lots of these conversions are supported out of the box, as shown in the table below. They are very convenient, but keep in mind that these conversions are fundamentally based on copying data. This is perfectly fine for small immutable types but it may become quite expensive for large data structures. This can be avoided by overriding the automatic conversion with a custom wrapper (i.e. the above-mentioned approach 1). This requires some manual effort and more details are available in the Making opaque types section.

  List of all builtin conversions

The following basic data types are supported out of the box (some may require an additional extension header to be included). To pass other data structures as arguments and return values, refer to the section on binding Object-oriented code.
Data type | Description | Header file
int8_t, uint8_t | 8-bit integers | pybind11/pybind11.h
int16_t, uint16_t | 16-bit integers | pybind11/pybind11.h
int32_t, uint32_t | 32-bit integers | pybind11/pybind11.h
int64_t, uint64_t | 64-bit integers | pybind11/pybind11.h
ssize_t, size_t | Platform-dependent size | pybind11/pybind11.h
float, double | Floating point types | pybind11/pybind11.h
bool | Two-state Boolean type | pybind11/pybind11.h
char | Character literal | pybind11/pybind11.h
char16_t | UTF-16 character literal | pybind11/pybind11.h
char32_t | UTF-32 character literal | pybind11/pybind11.h
wchar_t | Wide character literal | pybind11/pybind11.h
const char * | UTF-8 string literal | pybind11/pybind11.h
const char16_t * | UTF-16 string literal | pybind11/pybind11.h
const char32_t * | UTF-32 string literal | pybind11/pybind11.h
const wchar_t * | Wide string literal | pybind11/pybind11.h
std::string | STL dynamic UTF-8 string | pybind11/pybind11.h
std::u16string | STL dynamic UTF-16 string | pybind11/pybind11.h
std::u32string | STL dynamic UTF-32 string | pybind11/pybind11.h
std::wstring | STL dynamic wide string | pybind11/pybind11.h
std::string_view, std::u16string_view, etc. | STL C++17 string views | pybind11/pybind11.h
std::pair<T1, T2> | Pair of two custom types | pybind11/pybind11.h
std::tuple<...> | Arbitrary tuple of types | pybind11/pybind11.h
std::reference_wrapper<...> | Reference type wrapper | pybind11/pybind11.h
std::complex<T> | Complex numbers | pybind11/complex.h
std::array<T, Size> | STL static array | pybind11/stl.h
std::vector<T> | STL dynamic array | pybind11/stl.h
std::deque<T> | STL double-ended queue | pybind11/stl.h
std::valarray<T> | STL value array | pybind11/stl.h
std::list<T> | STL linked list | pybind11/stl.h
std::map<T1, T2> | STL ordered map | pybind11/stl.h
std::unordered_map<T1, T2> | STL unordered map | pybind11/stl.h
std::set<T> | STL ordered set | pybind11/stl.h
std::unordered_set<T> | STL unordered set | pybind11/stl.h
std::optional<T> | STL optional type (C++17) | pybind11/stl.h
std::experimental::optional<T> | STL optional type (exp.) | pybind11/stl.h
std::variant<...> | Type-safe union (C++17) | pybind11/stl.h
std::filesystem::path | STL path (C++17) [1] | pybind11/stl/filesystem.h
std::function<...> | STL polymorphic function | pybind11/functional.h
std::chrono::duration<...> | STL time duration | pybind11/chrono.h
std::chrono::time_point<...> | STL date/time | pybind11/chrono.h
Eigen::Matrix<...> | Eigen: dense matrix | pybind11/eigen.h
Eigen::Map<...> | Eigen: mapped memory | pybind11/eigen.h
Eigen::SparseMatrix<...> | Eigen: sparse matrix | pybind11/eigen.h

[1] std::filesystem::path is converted to pathlib.Path and can be loaded from os.PathLike, str, and bytes.

  Strings, bytes and Unicode conversions

  Passing Python strings to C++

When a Python str is passed from Python to a C++ function that accepts std::string or char * as arguments, pybind11 will encode the Python string to UTF-8. All Python str can be encoded in UTF-8, so this operation does not fail.

The C++ language is encoding agnostic. It is the responsibility of the programmer to track encodings. It's often easiest to simply use UTF-8 everywhere.

    m.def("utf8_test",
        [](const std::string &s) {
            cout << "utf-8 is icing on the cake.\n";
            cout << s;
        }
    );
    m.def("utf8_charptr",
        [](const char *s) {
            cout << "My favorite food is\n";
            cout << s;
        }
    );

    >>> utf8_test("🎂")
    utf-8 is icing on the cake.
    🎂
    >>> utf8_charptr("🍕")
    My favorite food is
    🍕

NOTE: Some terminal emulators do not support UTF-8 or emoji fonts and may not display the example above correctly.

The results are the same whether the C++ function accepts arguments by value or reference, and whether or not const is used.

  Passing bytes to C++

A Python bytes object will be passed to C++ functions that accept std::string or char* without conversion. In order to make a function only accept bytes (and not str), declare it as taking a py::bytes argument.

  Returning C++ strings to Python

When a C++ function returns a std::string or char* to a Python caller, pybind11 will assume that the string is valid UTF-8 and will decode it to a native Python str, using the same API as Python uses to perform bytes.decode('utf-8'). If this implicit conversion fails, pybind11 will raise a UnicodeDecodeError.

    m.def("std_string_return",
        []() {
            return std::string("This string needs to be UTF-8 encoded");
        }
    );

    >>> isinstance(example.std_string_return(), str)
    True

Because UTF-8 is inclusive of pure ASCII, there is never any issue with returning a pure ASCII string to Python. If there is any possibility that the string is not pure ASCII, it is necessary to ensure the encoding is valid UTF-8.

WARNING: Implicit conversion assumes that a returned char * is null-terminated. If there is no null terminator a buffer overrun will occur.

  Explicit conversions

If some C++ code constructs a std::string that is not a UTF-8 string, one can perform a explicit conversion and return a py::str object. Explicit conversion has the same overhead as implicit conversion.

    // This uses the Python C API to convert Latin-1 to Unicode
    m.def("str_output",
        []() {
            std::string s = "Send your r\xe9sum\xe9 to Alice in HR"; // Latin-1
            py::handle py_s = PyUnicode_DecodeLatin1(s.data(), s.length(), nullptr);
            if (!py_s) {
                throw py::error_already_set();
            }
            return py::reinterpret_steal<py::str>(py_s);
        }
    );

    >>> str_output()
    'Send your résumé to Alice in HR'

The Python C API provides several built-in codecs. Note that these all return new references, so use reinterpret_steal() when converting them to a str.

One could also use a third party encoding library such as libiconv to transcode to UTF-8.

  Return C++ strings without conversion

If the data in a C++ std::string does not represent text and should be returned to Python as bytes, then one can return the data as a py::bytes object.

    m.def("return_bytes",
        []() {
            std::string s("\xba\xd0\xba\xd0");  // Not valid UTF-8
            return py::bytes(s);  // Return the data without transcoding
        }
    );

    >>> example.return_bytes()
    b'\xba\xd0\xba\xd0'

Note the asymmetry: pybind11 will convert bytes to std::string without encoding, but cannot convert std::string back to bytes implicitly.

    m.def("asymmetry",
        [](std::string s) {  // Accepts str or bytes from Python
            return s;  // Looks harmless, but implicitly converts to str
        }
    );

    >>> isinstance(example.asymmetry(b"have some bytes"), str)
    True
    >>> example.asymmetry(b"\xba\xd0\xba\xd0")  # invalid utf-8 as bytes
    UnicodeDecodeError: 'utf-8' codec can't decode byte 0xba in position 0: invalid start byte

  Wide character strings

When a Python str is passed to a C++ function expecting std::wstring, wchar_t*, std::u16string or std::u32string, the str will be encoded to UTF-16 or UTF-32 depending on how the C++ compiler implements each type, in the platform's native endianness. When strings of these types are returned, they are assumed to contain valid UTF-16 or UTF-32, and will be decoded to Python str.

    #define UNICODE
    #include <windows.h>
    m.def("set_window_text",
        [](HWND hwnd, std::wstring s) {
            // Call SetWindowText with null-terminated UTF-16 string
            ::SetWindowText(hwnd, s.c_str());
        }
    );
    m.def("get_window_text",
        [](HWND hwnd) {
            const int buffer_size = ::GetWindowTextLength(hwnd) + 1;
            auto buffer = std::make_unique< wchar_t[] >(buffer_size);
            ::GetWindowText(hwnd, buffer.data(), buffer_size);
            std::wstring text(buffer.get());
            // wstring will be converted to Python str
            return text;
        }
    );

Strings in multibyte encodings such as Shift-JIS must transcoded to a UTF-8/16/32 before being returned to Python.

  Character literals

C++ functions that accept character literals as input will receive the first character of a Python str as their input. If the string is longer than one Unicode character, trailing characters will be ignored.

When a character literal is returned from C++ (such as a char or a wchar_t), it will be converted to a str that represents the single character.

    m.def("pass_char", [](char c) { return c; });
    m.def("pass_wchar", [](wchar_t w) { return w; });

    >>> example.pass_char("A")
    'A'

While C++ will cast integers to character types (char c = 0x65;), pybind11 does not convert Python integers to characters implicitly. The Python function chr() can be used to convert integers to characters.

    >>> example.pass_char(0x65)
    TypeError
    >>> example.pass_char(chr(0x65))
    'A'

If the desire is to work with an 8-bit integer, use int8_t or uint8_t as the argument type.

  Grapheme clusters

A single grapheme may be represented by two or more Unicode characters. For example 'é' is usually represented as U+00E9 but can also be expressed as the combining character sequence U+0065 U+0301 (that is, the letter 'e' followed by a combining acute accent). The combining character will be lost if the two-character sequence is passed as an argument, even though it renders as a single grapheme.

    >>> example.pass_wchar("é")
    'é'
    >>> combining_e_acute = "e" + "\u0301"
    >>> combining_e_acute
    'é'
    >>> combining_e_acute == "é"
    False
    >>> example.pass_wchar(combining_e_acute)
    'e'

Normalizing combining characters before passing the character literal to C++ may resolve some of these issues:

    >>> example.pass_wchar(unicodedata.normalize("NFC", combining_e_acute))
    'é'

In some languages (Thai for example), there are graphemes that cannot be expressed as a single Unicode code point, so there is no way to capture them in a C++ character type.

  C++17 string views

C++17 string views are automatically supported when compiling in C++17 mode. They follow the same rules for encoding and decoding as the corresponding STL string type (for example, a std::u16string_view argument will be passed UTF-16-encoded data, and a returned std::string_view will be decoded as UTF-8).

  References

•   The Absolute Minimum Every Software Developer Absolutely, Positively Must Know About Unicode and Character Sets (No Excuses!)

•   C++ - Using STL Strings at Win32 API Boundaries

  STL containers

  Automatic conversion

When including the additional header file pybind11/stl.h, conversions between std::vector<>/std::deque<>/std::list<>/std::array<>/std::valarray<>, std::set<>/std::unordered_set<>, and std::map<>/std::unordered_map<> and the Python list, set and dict data structures are automatically enabled. The types std::pair<> and std::tuple<> are already supported out of the box with just the core pybind11/pybind11.h header.

The major downside of these implicit conversions is that containers must be converted (i.e. copied) on every Python->C++ and C++->Python transition, which can have implications on the program semantics and performance. Please read the next sections for more details and alternative approaches that avoid this.

NOTE: Arbitrary nesting of any of these types is possible.

SEE ALSO: The file tests/test_stl.cpp contains a complete example that demonstrates how to pass STL data types in more detail.

  C++17 library containers

The pybind11/stl.h header also includes support for std::optional<> and std::variant<>. These require a C++17 compiler and standard library. In C++14 mode, std::experimental::optional<> is supported if available.

Various versions of these containers also exist for C++11 (e.g. in Boost). pybind11 provides an easy way to specialize the type_caster for such types:

    // `boost::optional` as an example -- can be any `std::optional`-like container
    namespace PYBIND11_NAMESPACE { namespace detail {
        template <typename T>
        struct type_caster<boost::optional<T>> : optional_caster<boost::optional<T>> {};
    }}

The above should be placed in a header file and included in all translation units where automatic conversion is needed. Similarly, a specialization can be provided for custom variant types:

    // `boost::variant` as an example -- can be any `std::variant`-like container
    namespace PYBIND11_NAMESPACE { namespace detail {
        template <typename... Ts>
        struct type_caster<boost::variant<Ts...>> : variant_caster<boost::variant<Ts...>> {};
        // Specifies the function used to visit the variant -- `apply_visitor` instead of `visit`
        template <>
        struct visit_helper<boost::variant> {
            template <typename... Args>
            static auto call(Args &&...args) -> decltype(boost::apply_visitor(args...)) {
                return boost::apply_visitor(args...);
            }
        };
    }} // namespace PYBIND11_NAMESPACE::detail

The visit_helper specialization is not required if your name::variant provides a name::visit() function. For any other function name, the specialization must be included to tell pybind11 how to visit the variant.

WARNING: When converting a variant type, pybind11 follows the same rules as when determining which function overload to call (Overload resolution order), and so the same caveats hold. In particular, the order in which the variant's alternatives are listed is important, since pybind11 will try conversions in this order. This means that, for example, when converting variant<int, bool>, the bool variant will never be selected, as any Python bool is already an int and is convertible to a C++ int. Changing the order of alternatives (and using variant<bool, int>, in this example) provides a solution.

NOTE: pybind11 only supports the modern implementation of boost::variant which makes use of variadic templates. This requires Boost 1.56 or newer.

  Making opaque types

pybind11 heavily relies on a template matching mechanism to convert parameters and return values that are constructed from STL data types such as vectors, linked lists, hash tables, etc. This even works in a recursive manner, for instance to deal with lists of hash maps of pairs of elementary and custom types, etc.

However, a fundamental limitation of this approach is that internal conversions between Python and C++ types involve a copy operation that prevents pass-by-reference semantics. What does this mean?

Suppose we bind the following function

    void append_1(std::vector<int> &v) {
       v.push_back(1);
    }

and call it from Python, the following happens:

    >>> v = [5, 6]
    >>> append_1(v)
    >>> print(v)
    [5, 6]

As you can see, when passing STL data structures by reference, modifications are not propagated back the Python side. A similar situation arises when exposing STL data structures using the def_readwrite or def_readonly functions:

    /* ... definition ... */
    class MyClass {
        std::vector<int> contents;
    };
    /* ... binding code ... */
    py::class_<MyClass>(m, "MyClass")
        .def(py::init<>())
        .def_readwrite("contents", &MyClass::contents);

In this case, properties can be read and written in their entirety. However, an append operation involving such a list type has no effect:

    >>> m = MyClass()
    >>> m.contents = [5, 6]
    >>> print(m.contents)
    [5, 6]
    >>> m.contents.append(7)
    >>> print(m.contents)
    [5, 6]

Finally, the involved copy operations can be costly when dealing with very large lists. To deal with all of the above situations, pybind11 provides a macro named PYBIND11_MAKE_OPAQUE(T) that disables the template-based conversion machinery of types, thus rendering them opaque. The contents of opaque objects are never inspected or extracted, hence they can be passed by reference. For instance, to turn std::vector<int> into an opaque type, add the declaration

    PYBIND11_MAKE_OPAQUE(std::vector<int>)

before any binding code (e.g. invocations to class_::def(), etc.). This macro must be specified at the top level (and outside of any namespaces), since it adds a template instantiation of type_caster. If your binding code consists of multiple compilation units, it must be present in every file (typically via a common header) preceding any usage of std::vector<int>. Opaque types must also have a corresponding py::class_ declaration to associate them with a name in Python, and to define a set of available operations, e.g.:

    py::class_<std::vector<int>>(m, "IntVector")
        .def(py::init<>())
        .def("clear", &std::vector<int>::clear)
        .def("pop_back", &std::vector<int>::pop_back)
        .def("__len__", [](const std::vector<int> &v) { return v.size(); })
        .def("__iter__", [](std::vector<int> &v) {
           return py::make_iterator(v.begin(), v.end());
        }, py::keep_alive<0, 1>()) /* Keep vector alive while iterator is used */
        // ....

SEE ALSO: The file tests/test_opaque_types.cpp contains a complete example that demonstrates how to create and expose opaque types using pybind11 in more detail.

  Binding STL containers

The ability to expose STL containers as native Python objects is a fairly common request, hence pybind11 also provides an optional header file named pybind11/stl_bind.h that does exactly this. The mapped containers try to match the behavior of their native Python counterparts as much as possible.

The following example showcases usage of pybind11/stl_bind.h:

    // Don't forget this
    #include <pybind11/stl_bind.h>
    PYBIND11_MAKE_OPAQUE(std::vector<int>)
    PYBIND11_MAKE_OPAQUE(std::map<std::string, double>)
    // ...
    // later in binding code:
    py::bind_vector<std::vector<int>>(m, "VectorInt");
    py::bind_map<std::map<std::string, double>>(m, "MapStringDouble");

When binding STL containers pybind11 considers the types of the container's elements to decide whether the container should be confined to the local module (via the Module-local class bindings feature). If the container element types are anything other than already-bound custom types bound without py::module_local() the container binding will have py::module_local() applied. This includes converting types such as numeric types, strings, Eigen types; and types that have not yet been bound at the time of the stl container binding. This module-local binding is designed to avoid potential conflicts between module bindings (for example, from two separate modules each attempting to bind std::vector<int> as a python type).

It is possible to override this behavior to force a definition to be either module-local or global. To do so, you can pass the attributes py::module_local() (to make the binding module-local) or py::module_local(false) (to make the binding global) into the py::bind_vector or py::bind_map arguments:

    py::bind_vector<std::vector<int>>(m, "VectorInt", py::module_local(false));

Note, however, that such a global binding would make it impossible to load this module at the same time as any other pybind module that also attempts to bind the same container type (std::vector<int> in the above example).

See Module-local class bindings for more details on module-local bindings.

SEE ALSO: The file tests/test_stl_binders.cpp shows how to use the convenience STL container wrappers.

  Functional

The following features must be enabled by including pybind11/functional.h.

  Callbacks and passing anonymous functions

The C++11 standard brought lambda functions and the generic polymorphic function wrapper std::function<> to the C++ programming language, which enable powerful new ways of working with functions. Lambda functions come in two flavors: stateless lambda function resemble classic function pointers that link to an anonymous piece of code, while stateful lambda functions additionally depend on captured variables that are stored in an anonymous lambda closure object.

Here is a simple example of a C++ function that takes an arbitrary function (stateful or stateless) with signature int -> int as an argument and runs it with the value 10.

    int func_arg(const std::function<int(int)> &f) {
        return f(10);
    }

The example below is more involved: it takes a function of signature int -> int and returns another function of the same kind. The return value is a stateful lambda function, which stores the value f in the capture object and adds 1 to its return value upon execution.

    std::function<int(int)> func_ret(const std::function<int(int)> &f) {
        return [f](int i) {
            return f(i) + 1;
        };
    }

This example demonstrates using python named parameters in C++ callbacks which requires using py::cpp_function as a wrapper. Usage is similar to defining methods of classes:

    py::cpp_function func_cpp() {
        return py::cpp_function([](int i) { return i+1; },
           py::arg("number"));
    }

After including the extra header file pybind11/functional.h, it is almost trivial to generate binding code for all of these functions.

    #include <pybind11/functional.h>
    PYBIND11_MODULE(example, m, py::mod_gil_not_used()) {
        m.def("func_arg", &func_arg);
        m.def("func_ret", &func_ret);
        m.def("func_cpp", &func_cpp);
    }

The following interactive session shows how to call them from Python.

    $ python
    >>> import example
    >>> def square(i):
    ...     return i * i
    ...
    >>> example.func_arg(square)
    100L
    >>> square_plus_1 = example.func_ret(square)
    >>> square_plus_1(4)
    17L
    >>> plus_1 = func_cpp()
    >>> plus_1(number=43)
    44L

WARNING: Keep in mind that passing a function from C++ to Python (or vice versa) will instantiate a piece of wrapper code that translates function invocations between the two languages. Naturally, this translation increases the computational cost of each function call somewhat. A problematic situation can arise when a function is copied back and forth between Python and C++ many times in a row, in which case the underlying wrappers will accumulate correspondingly. The resulting long sequence of C++ -> Python -> C++ -> ... roundtrips can significantly decrease performance.

There is one exception: pybind11 detects case where a stateless function (i.e. a function pointer or a lambda function without captured variables) is passed as an argument to another C++ function exposed in Python. In this case, there is no overhead. Pybind11 will extract the underlying C++ function pointer from the wrapped function to sidestep a potential C++ -> Python -> C++ roundtrip. This is demonstrated in tests/test_callbacks.cpp.

NOTE: This functionality is very useful when generating bindings for callbacks in C++ libraries (e.g. GUI libraries, asynchronous networking libraries, etc.).

The file tests/test_callbacks.cpp contains a complete example that demonstrates how to work with callbacks and anonymous functions in more detail.

  Chrono

When including the additional header file pybind11/chrono.h conversions from C++11 chrono datatypes to python datetime objects are automatically enabled. This header also enables conversions of python floats (often from sources such as time.monotonic(), time.perf_counter() and time.process_time()) into durations.

  An overview of clocks in C++11

A point of confusion when using these conversions is the differences between clocks provided in C++11. There are three clock types defined by the C++11 standard and users can define their own if needed. Each of these clocks have different properties and when converting to and from python will give different results.

The first clock defined by the standard is std::chrono::system_clock. This clock measures the current date and time. However, this clock changes with to updates to the operating system time. For example, if your time is synchronised with a time server this clock will change. This makes this clock a poor choice for timing purposes but good for measuring the wall time.

The second clock defined in the standard is std::chrono::steady_clock. This clock ticks at a steady rate and is never adjusted. This makes it excellent for timing purposes, however the value in this clock does not correspond to the current date and time. Often this clock will be the amount of time your system has been on, although it does not have to be. This clock will never be the same clock as the system clock as the system clock can change but steady clocks cannot.

The third clock defined in the standard is std::chrono::high_resolution_clock. This clock is the clock that has the highest resolution out of the clocks in the system. It is normally a typedef to either the system clock or the steady clock but can be its own independent clock. This is important as when using these conversions as the types you get in python for this clock might be different depending on the system. If it is a typedef of the system clock, python will get datetime objects, but if it is a different clock they will be timedelta objects.

  Provided conversions

C++ to Python

•

std::chrono::system_clock::time_point → datetime.datetime
    System clock times are converted to python datetime instances. They are in the local timezone, but do not have any timezone information attached to them (they are naive datetime objects).

•

std::chrono::duration → datetime.timedelta
    Durations are converted to timedeltas, any precision in the duration greater than microseconds is lost by rounding towards zero.

•

std::chrono::[other_clocks]::time_point → datetime.timedelta
    Any clock time that is not the system clock is converted to a time delta. This timedelta measures the time from the clocks epoch to now.

    Python to C++

•

datetime.datetime or datetime.date or datetime.time → std::chrono::system_clock::time_point
    Date/time objects are converted into system clock timepoints. Any timezone information is ignored and the type is treated as a naive object.

•

datetime.timedelta → std::chrono::duration
    Time delta are converted into durations with microsecond precision.

•

datetime.timedelta → std::chrono::[other_clocks]::time_point
    Time deltas that are converted into clock timepoints are treated as the amount of time from the start of the clocks epoch.

•

float → std::chrono::duration
    Floats that are passed to C++ as durations be interpreted as a number of seconds. These will be converted to the duration using duration_cast from the float.

•

float → std::chrono::[other_clocks]::time_point
    Floats that are passed to C++ as time points will be interpreted as the number of seconds from the start of the clocks epoch.

  Eigen

Eigen is C++ header-based library for dense and sparse linear algebra. Due to its popularity and widespread adoption, pybind11 provides transparent conversion and limited mapping support between Eigen and Scientific Python linear algebra data types.

To enable the built-in Eigen support you must include the optional header file pybind11/eigen.h.

  Pass-by-value

When binding a function with ordinary Eigen dense object arguments (for example, Eigen::MatrixXd), pybind11 will accept any input value that is already (or convertible to) a numpy.ndarray with dimensions compatible with the Eigen type, copy its values into a temporary Eigen variable of the appropriate type, then call the function with this temporary variable.

Sparse matrices are similarly copied to or from scipy.sparse.csr_matrix/scipy.sparse.csc_matrix objects.

  Pass-by-reference

One major limitation of the above is that every data conversion implicitly involves a copy, which can be both expensive (for large matrices) and disallows binding functions that change their (Matrix) arguments. Pybind11 allows you to work around this by using Eigen's Eigen::Ref<MatrixType> class much as you would when writing a function taking a generic type in Eigen itself (subject to some limitations discussed below).

When calling a bound function accepting a Eigen::Ref<const MatrixType> type, pybind11 will attempt to avoid copying by using an Eigen::Map object that maps into the source numpy.ndarray data: this requires both that the data types are the same (e.g. dtype='float64' and MatrixType::Scalar is double); and that the storage is layout compatible. The latter limitation is discussed in detail in the section below, and requires careful consideration: by default, numpy matrices and Eigen matrices are not storage compatible.

If the numpy matrix cannot be used as is (either because its types differ, e.g. passing an array of integers to an Eigen parameter requiring doubles, or because the storage is incompatible), pybind11 makes a temporary copy and passes the copy instead.

When a bound function parameter is instead Eigen::Ref<MatrixType> (note the lack of const), pybind11 will only allow the function to be called if it can be mapped and if the numpy array is writeable (that is a.flags.writeable is true). Any access (including modification) made to the passed variable will be transparently carried out directly on the numpy.ndarray.

This means you can write code such as the following and have it work as expected:

    void scale_by_2(Eigen::Ref<Eigen::VectorXd> v) {
        v *= 2;
    }

Note, however, that you will likely run into limitations due to numpy and Eigen's difference default storage order for data; see the below section on Storage orders for details on how to bind code that won't run into such limitations.

NOTE: Passing by reference is not supported for sparse types.

  Returning values to Python

When returning an ordinary dense Eigen matrix type to numpy (e.g. Eigen::MatrixXd or Eigen::RowVectorXf) pybind11 keeps the matrix and returns a numpy array that directly references the Eigen matrix: no copy of the data is performed. The numpy array will have array.flags.owndata set to False to indicate that it does not own the data, and the lifetime of the stored Eigen matrix will be tied to the returned array.

If you bind a function with a non-reference, const return type (e.g. const Eigen::MatrixXd), the same thing happens except that pybind11 also sets the numpy array's writeable flag to false.

If you return an lvalue reference or pointer, the usual pybind11 rules apply, as dictated by the binding function's return value policy (see the documentation on Return value policies for full details). That means, without an explicit return value policy, lvalue references will be copied and pointers will be managed by pybind11. In order to avoid copying, you should explicitly specify an appropriate return value policy, as in the following example:

    class MyClass {
        Eigen::MatrixXd big_mat = Eigen::MatrixXd::Zero(10000, 10000);
    public:
        Eigen::MatrixXd &getMatrix() { return big_mat; }
        const Eigen::MatrixXd &viewMatrix() { return big_mat; }
    };
    // Later, in binding code:
    py::class_<MyClass>(m, "MyClass")
        .def(py::init<>())
        .def("copy_matrix", &MyClass::getMatrix) // Makes a copy!
        .def("get_matrix", &MyClass::getMatrix, py::return_value_policy::reference_internal)
        .def("view_matrix", &MyClass::viewMatrix, py::return_value_policy::reference_internal)
        ;

    a = MyClass()
    m = a.get_matrix()  # flags.writeable = True,  flags.owndata = False
    v = a.view_matrix()  # flags.writeable = False, flags.owndata = False
    c = a.copy_matrix()  # flags.writeable = True,  flags.owndata = True
    # m[5,6] and v[5,6] refer to the same element, c[5,6] does not.

Note in this example that py::return_value_policy::reference_internal is used to tie the life of the MyClass object to the life of the returned arrays.

You may also return an Eigen::Ref, Eigen::Map or other map-like Eigen object (for example, the return value of matrix.block() and related methods) that map into a dense Eigen type. When doing so, the default behaviour of pybind11 is to simply reference the returned data: you must take care to ensure that this data remains valid! You may ask pybind11 to explicitly copy such a return value by using the py::return_value_policy::copy policy when binding the function. You may also use py::return_value_policy::reference_internal or a py::keep_alive to ensure the data stays valid as long as the returned numpy array does.

When returning such a reference of map, pybind11 additionally respects the readonly-status of the returned value, marking the numpy array as non-writeable if the reference or map was itself read-only.

NOTE: Sparse types are always copied when returned.

  Storage orders

Passing arguments via Eigen::Ref has some limitations that you must be aware of in order to effectively pass matrices by reference. First and foremost is that the default Eigen::Ref<MatrixType> class requires contiguous storage along columns (for column-major types, the default in Eigen) or rows if MatrixType is specifically an Eigen::RowMajor storage type. The former, Eigen's default, is incompatible with numpy's default row-major storage, and so you will not be able to pass numpy arrays to Eigen by reference without making one of two changes.

(Note that this does not apply to vectors (or column or row matrices): for such types the "row-major" and "column-major" distinction is meaningless).

The first approach is to change the use of Eigen::Ref<MatrixType> to the more general Eigen::Ref<MatrixType, 0, Eigen::Stride<Eigen::Dynamic, Eigen::Dynamic>> (or similar type with a fully dynamic stride type in the third template argument). Since this is a rather cumbersome type, pybind11 provides a py::EigenDRef<MatrixType> type alias for your convenience (along with EigenDMap for the equivalent Map, and EigenDStride for just the stride type).

This type allows Eigen to map into any arbitrary storage order. This is not the default in Eigen for performance reasons: contiguous storage allows vectorization that cannot be done when storage is not known to be contiguous at compile time. The default Eigen::Ref stride type allows non-contiguous storage along the outer dimension (that is, the rows of a column-major matrix or columns of a row-major matrix), but not along the inner dimension.

This type, however, has the added benefit of also being able to map numpy array slices. For example, the following (contrived) example uses Eigen with a numpy slice to multiply by 2 all coefficients that are both on even rows (0, 2, 4, ...) and in columns 2, 5, or 8:

    m.def("scale", [](py::EigenDRef<Eigen::MatrixXd> m, double c) { m *= c; });

    # a = np.array(...)
    scale_by_2(myarray[0::2, 2:9:3])

The second approach to avoid copying is more intrusive: rearranging the underlying data types to not run into the non-contiguous storage problem in the first place. In particular, that means using matrices with Eigen::RowMajor storage, where appropriate, such as:

    using RowMatrixXd = Eigen::Matrix<double, Eigen::Dynamic, Eigen::Dynamic, Eigen::RowMajor>;
    // Use RowMatrixXd instead of MatrixXd

Now bound functions accepting Eigen::Ref<RowMatrixXd> arguments will be callable with numpy's (default) arrays without involving a copying.

You can, alternatively, change the storage order that numpy arrays use by adding the order='F' option when creating an array:

    myarray = np.array(source, order="F")

Such an object will be passable to a bound function accepting an Eigen::Ref<MatrixXd> (or similar column-major Eigen type).

One major caveat with this approach, however, is that it is not entirely as easy as simply flipping all Eigen or numpy usage from one to the other: some operations may alter the storage order of a numpy array. For example, a2 = array.transpose() results in a2 being a view of array that references the same data, but in the opposite storage order!

While this approach allows fully optimized vectorized calculations in Eigen, it cannot be used with array slices, unlike the first approach.

When returning a matrix to Python (either a regular matrix, a reference via Eigen::Ref<>, or a map/block into a matrix), no special storage consideration is required: the created numpy array will have the required stride that allows numpy to properly interpret the array, whatever its storage order.

  Failing rather than copying

The default behaviour when binding Eigen::Ref<const MatrixType> Eigen references is to copy matrix values when passed a numpy array that does not conform to the element type of MatrixType or does not have a compatible stride layout. If you want to explicitly avoid copying in such a case, you should bind arguments using the py::arg().noconvert() annotation (as described in the Non-converting arguments documentation).

The following example shows an example of arguments that don't allow data copying to take place:

    // The method and function to be bound:
    class MyClass {
        // ...
        double some_method(const Eigen::Ref<const MatrixXd> &matrix) { /* ... */ }
    };
    float some_function(const Eigen::Ref<const MatrixXf> &big,
                        const Eigen::Ref<const MatrixXf> &small) {
        // ...
    }
    // The associated binding code:
    using namespace pybind11::literals; // for "arg"_a
    py::class_<MyClass>(m, "MyClass")
        // ... other class definitions
        .def("some_method", &MyClass::some_method, py::arg().noconvert());
    m.def("some_function", &some_function,
        "big"_a.noconvert(), // <- Don't allow copying for this arg
        "small"_a            // <- This one can be copied if needed
    );

With the above binding code, attempting to call the some_method(m) method on a MyClass object, or attempting to call some_function(m, m2) will raise a RuntimeError rather than making a temporary copy of the array. It will, however, allow the m2 argument to be copied into a temporary if necessary.

Note that explicitly specifying .noconvert() is not required for mutable Eigen references (e.g. Eigen::Ref<MatrixXd> without const on the MatrixXd): mutable references will never be called with a temporary copy.

  Vectors versus column/row matrices

Eigen and numpy have fundamentally different notions of a vector. In Eigen, a vector is simply a matrix with the number of columns or rows set to 1 at compile time (for a column vector or row vector, respectively). NumPy, in contrast, has comparable 2-dimensional 1xN and Nx1 arrays, but also has 1-dimensional arrays of size N.

When passing a 2-dimensional 1xN or Nx1 array to Eigen, the Eigen type must have matching dimensions: That is, you cannot pass a 2-dimensional Nx1 numpy array to an Eigen value expecting a row vector, or a 1xN numpy array as a column vector argument.

On the other hand, pybind11 allows you to pass 1-dimensional arrays of length N as Eigen parameters. If the Eigen type can hold a column vector of length N it will be passed as such a column vector. If not, but the Eigen type constraints will accept a row vector, it will be passed as a row vector. (The column vector takes precedence when both are supported, for example, when passing a 1D numpy array to a MatrixXd argument). Note that the type need not be explicitly a vector: it is permitted to pass a 1D numpy array of size 5 to an Eigen Matrix<double, Dynamic, 5>: you would end up with a 1x5 Eigen matrix. Passing the same to an Eigen::MatrixXd would result in a 5x1 Eigen matrix.

When returning an Eigen vector to numpy, the conversion is ambiguous: a row vector of length 4 could be returned as either a 1D array of length 4, or as a 2D array of size 1x4. When encountering such a situation, pybind11 compromises by considering the returned Eigen type: if it is a compile-time vector--that is, the type has either the number of rows or columns set to 1 at compile time--pybind11 converts to a 1D numpy array when returning the value. For instances that are a vector only at run-time (e.g. MatrixXd, Matrix<float, Dynamic, 4>), pybind11 returns the vector as a 2D array to numpy. If this isn't want you want, you can use array.reshape(...) to get a view of the same data in the desired dimensions.

SEE ALSO: The file tests/test_eigen.cpp contains a complete example that shows how to pass Eigen sparse and dense data types in more detail.

  Custom type casters

Some applications may prefer custom type casters that convert between existing Python types and C++ types, similar to the list ↔ std::vector and dict ↔ std::map conversions which are built into pybind11. Implementing custom type casters is fairly advanced usage. While it is recommended to use the pybind11 API as much as possible, more complex examples may require familiarity with the intricacies of the Python C API. You can refer to the Python/C API Reference Manual for more information.

The following snippets demonstrate how this works for a very simple Point2D type. We want this type to be convertible to C++ from Python types implementing the Sequence protocol and having two elements of type float. When returned from C++ to Python, it should be converted to a Python tuple[float, float]. For this type we could provide Python bindings for different arithmetic functions implemented in C++ (here demonstrated by a simple negate function).

    namespace user_space {
    struct Point2D {
        double x;
        double y;
    };
    Point2D negate(const Point2D &point) { return Point2D{-point.x, -point.y}; }
    } // namespace user_space

The following Python snippet demonstrates the intended usage of negate from the Python side:

    from my_math_module import docs_advanced_cast_custom as m
    point1 = [1.0, -1.0]
    point2 = m.negate(point1)
    assert point2 == (-1.0, 1.0)

To register the necessary conversion routines, it is necessary to add an instantiation of the pybind11::detail::type_caster<T> template. Although this is an implementation detail, adding an instantiation of this type is explicitly allowed.

    namespace pybind11 {
    namespace detail {
    template <>
    struct type_caster<user_space::Point2D> {
        // This macro inserts a lot of boilerplate code and sets the type hint.
        // `io_name` is used to specify different type hints for arguments and return values.
        // The signature of our negate function would then look like:
        // `negate(Sequence[float]) -> tuple[float, float]`
        PYBIND11_TYPE_CASTER(user_space::Point2D, io_name("Sequence[float]", "tuple[float, float]"));
        // C++ -> Python: convert `Point2D` to `tuple[float, float]`. The second and third arguments
        // are used to indicate the return value policy and parent object (for
        // return_value_policy::reference_internal) and are often ignored by custom casters.
        // The return value should reflect the type hint specified by the second argument of `io_name`.
        static handle
        cast(const user_space::Point2D &number, return_value_policy /*policy*/, handle /*parent*/) {
            return py::make_tuple(number.x, number.y).release();
        }
        // Python -> C++: convert a `PyObject` into a `Point2D` and return false upon failure. The
        // second argument indicates whether implicit conversions should be allowed.
        // The accepted types should reflect the type hint specified by the first argument of
        // `io_name`.
        bool load(handle src, bool /*convert*/) {
            // Check if handle is a Sequence
            if (!py::isinstance<py::sequence>(src)) {
                return false;
            }
            auto seq = py::reinterpret_borrow<py::sequence>(src);
            // Check if exactly two values are in the Sequence
            if (seq.size() != 2) {
                return false;
            }
            // Check if each element is either a float or an int
            for (auto item : seq) {
                if (!py::isinstance<py::float_>(item) && !py::isinstance<py::int_>(item)) {
                    return false;
                }
            }
            value.x = seq[0].cast<double>();
            value.y = seq[1].cast<double>();
            return true;
        }
    };
    } // namespace detail
    } // namespace pybind11
    // Bind the negate function
    PYBIND11_MODULE(docs_advanced_cast_custom, m, py::mod_gil_not_used()) { m.def("negate", user_space::negate); }

NOTE: A type_caster<T> defined with PYBIND11_TYPE_CASTER(T, ...) requires that T is default-constructible (value is first default constructed and then load() assigns to it).

NOTE: For further information on the return_value_policy argument of cast refer to Return value policies. To learn about the convert argument of load see Non-converting arguments.

WARNING: When using custom type casters, it's important to declare them consistently in every compilation unit of the Python extension module to satisfy the C++ One Definition Rule (ODR). Otherwise, undefined behavior can ensue.

NOTE: Using the type hint Sequence[float] signals to static type checkers, that not only tuples may be passed, but any type implementing the Sequence protocol, e.g., list[float]. Unfortunately, that loses the length information tuple[float, float] provides. One way of still providing some length information in type hints is using typing.Annotated, e.g., Annotated[Sequence[float], 2], or further add libraries like annotated-types.

  Python C++ interface

pybind11 exposes Python types and functions using thin C++ wrappers, which makes it possible to conveniently call Python code from C++ without resorting to Python's C API.

  Python types

  Available wrappers

All major Python types are available as thin C++ wrapper classes. These can also be used as function parameters -- see Python objects as arguments.

Available types include handle, object, bool_, int_, float_, str, bytes, tuple, list, dict, slice, none, capsule, iterable, iterator, function, buffer, array, and array_t.

WARNING: Be sure to review the Gotchas before using this heavily in your C++ API.

  Instantiating compound Python types from C++

Dictionaries can be initialized in the dict constructor:

    using namespace pybind11::literals; // to bring in the `_a` literal
    py::dict d("spam"_a=py::none(), "eggs"_a=42);

A tuple of python objects can be instantiated using py::make_tuple():

    py::tuple tup = py::make_tuple(42, py::none(), "spam");

Each element is converted to a supported Python type.

A simple namespace can be instantiated using

    using namespace pybind11::literals;  // to bring in the `_a` literal
    py::object SimpleNamespace = py::module_::import("types").attr("SimpleNamespace");
    py::object ns = SimpleNamespace("spam"_a=py::none(), "eggs"_a=42);

Attributes on a namespace can be modified with the py::delattr(), py::getattr(), and py::setattr() functions. Simple namespaces can be useful as lightweight stand-ins for class instances.

  Casting back and forth

In this kind of mixed code, it is often necessary to convert arbitrary C++ types to Python, which can be done using py::cast():

    MyClass *cls = ...;
    py::object obj = py::cast(cls);

The reverse direction uses the following syntax:

    py::object obj = ...;
    MyClass *cls = obj.cast<MyClass *>();

When conversion fails, both directions throw the exception cast_error.

  Accessing Python libraries from C++

It is also possible to import objects defined in the Python standard library or available in the current Python environment (sys.path) and work with these in C++.

This example obtains a reference to the Python Decimal class.

    // Equivalent to "from decimal import Decimal"
    py::object Decimal = py::module_::import("decimal").attr("Decimal");

    // Try to import scipy
    py::object scipy = py::module_::import("scipy");
    return scipy.attr("__version__");

  Calling Python functions

It is also possible to call Python classes, functions and methods via operator().

    // Construct a Python object of class Decimal
    py::object pi = Decimal("3.14159");

    // Use Python to make our directories
    py::object os = py::module_::import("os");
    py::object makedirs = os.attr("makedirs");
    makedirs("/tmp/path/to/somewhere");

One can convert the result obtained from Python to a pure C++ version if a py::class_ or type conversion is defined.

    py::function f = <...>;
    py::object result_py = f(1234, "hello", some_instance);
    MyClass &result = result_py.cast<MyClass>();

  Calling Python methods

To call an object's method, one can again use .attr to obtain access to the Python method.

    // Calculate e^π in decimal
    py::object exp_pi = pi.attr("exp")();
    py::print(py::str(exp_pi));

In the example above pi.attr("exp") is a bound method: it will always call the method for that same instance of the class. Alternately one can create an unbound method via the Python class (instead of instance) and pass the self object explicitly, followed by other arguments.

    py::object decimal_exp = Decimal.attr("exp");
    // Compute the e^n for n=0..4
    for (int n = 0; n < 5; n++) {
        py::print(decimal_exp(Decimal(n));
    }

  Keyword arguments

Keyword arguments are also supported. In Python, there is the usual call syntax:

    def f(number, say, to):
        ...  # function code
    f(1234, say="hello", to=some_instance)  # keyword call in Python

In C++, the same call can be made using:

    using namespace pybind11::literals; // to bring in the `_a` literal
    f(1234, "say"_a="hello", "to"_a=some_instance); // keyword call in C++

  Unpacking arguments

Unpacking of *args and **kwargs is also possible and can be mixed with other arguments:

    // * unpacking
    py::tuple args = py::make_tuple(1234, "hello", some_instance);
    f(*args);
    // ** unpacking
    py::dict kwargs = py::dict("number"_a=1234, "say"_a="hello", "to"_a=some_instance);
    f(**kwargs);
    // mixed keywords, * and ** unpacking
    py::tuple args = py::make_tuple(1234);
    py::dict kwargs = py::dict("to"_a=some_instance);
    f(*args, "say"_a="hello", **kwargs);

Generalized unpacking according to PEP448 is also supported:

    py::dict kwargs1 = py::dict("number"_a=1234);
    py::dict kwargs2 = py::dict("to"_a=some_instance);
    f(**kwargs1, "say"_a="hello", **kwargs2);

SEE ALSO: The file tests/test_pytypes.cpp contains a complete example that demonstrates passing native Python types in more detail. The file tests/test_callbacks.cpp presents a few examples of calling Python functions from C++, including keywords arguments and unpacking.

  Implicit casting

When using the C++ interface for Python types, or calling Python functions, objects of type object are returned. It is possible to invoke implicit conversions to subclasses like dict. The same holds for the proxy objects returned by operator[] or obj.attr(). Casting to subtypes improves code readability and allows values to be passed to C++ functions that require a specific subtype rather than a generic object.

    #include <pybind11/numpy.h>
    using namespace pybind11::literals;
    py::module_ os = py::module_::import("os");
    py::module_ path = py::module_::import("os.path");  // like 'import os.path as path'
    py::module_ np = py::module_::import("numpy");  // like 'import numpy as np'
    py::str curdir_abs = path.attr("abspath")(path.attr("curdir"));
    py::print(py::str("Current directory: ") + curdir_abs);
    py::dict environ = os.attr("environ");
    py::print(environ["HOME"]);
    py::array_t<float> arr = np.attr("ones")(3, "dtype"_a="float32");
    py::print(py::repr(arr + py::int_(1)));

These implicit conversions are available for subclasses of object; there is no need to call obj.cast() explicitly as for custom classes, see Casting back and forth.

NOTE: If a trivial conversion via move constructor is not possible, both implicit and explicit casting (calling obj.cast()) will attempt a "rich" conversion. For instance, py::list env = os.attr("environ"); will succeed and is equivalent to the Python code env = list(os.environ) that produces a list of the dict keys.

  Handling exceptions

Python exceptions from wrapper classes will be thrown as a py::error_already_set. See Handling exceptions from Python in C++ for more information on handling exceptions raised when calling C++ wrapper classes.

  Gotchas

  Default-Constructed Wrappers

When a wrapper type is default-constructed, it is not a valid Python object (i.e. it is not py::none()). It is simply the same as PyObject* null pointer. To check for this, use static_cast<bool>(my_wrapper).

  Assigning py::none() to wrappers

You may be tempted to use types like py::str and py::dict in C++ signatures (either pure C++, or in bound signatures), and assign them default values of py::none(). However, in a best case scenario, it will fail fast because None is not convertible to that type (e.g. py::dict), or in a worse case scenario, it will silently work but corrupt the types you want to work with (e.g. py::str(py::none()) will yield "None" in Python).

  NumPy

  Buffer protocol

Python supports an extremely general and convenient approach for exchanging data between plugin libraries. Types can expose a buffer view [1], which provides fast direct access to the raw internal data representation. Suppose we want to bind the following simplistic Matrix class:

    class Matrix {
    public:
        Matrix(size_t rows, size_t cols) : m_rows(rows), m_cols(cols) {
            m_data = new float[rows*cols];
        }
        float *data() { return m_data; }
        size_t rows() const { return m_rows; }
        size_t cols() const { return m_cols; }
    private:
        size_t m_rows, m_cols;
        float *m_data;
    };

The following binding code exposes the Matrix contents as a buffer object, making it possible to cast Matrices into NumPy arrays. It is even possible to completely avoid copy operations with Python expressions like np.array(matrix_instance, copy = False).

    py::class_<Matrix>(m, "Matrix", py::buffer_protocol())
       .def_buffer([](Matrix &m) -> py::buffer_info {
            return py::buffer_info(
                m.data(),                               /* Pointer to buffer */
                sizeof(float),                          /* Size of one scalar */
                py::format_descriptor<float>::format(), /* Python struct-style format descriptor */
                2,                                      /* Number of dimensions */
                { m.rows(), m.cols() },                 /* Buffer dimensions */
                { sizeof(float) * m.cols(),             /* Strides (in bytes) for each index */
                  sizeof(float) }
            );
        });

Supporting the buffer protocol in a new type involves specifying the special py::buffer_protocol() tag in the py::class_ constructor and calling the def_buffer() method with a lambda function that creates a py::buffer_info description record on demand describing a given matrix instance. The contents of py::buffer_info mirror the Python buffer protocol specification.

    struct buffer_info {
        void *ptr;
        py::ssize_t itemsize;
        std::string format;
        py::ssize_t ndim;
        std::vector<py::ssize_t> shape;
        std::vector<py::ssize_t> strides;
    };

To create a C++ function that can take a Python buffer object as an argument, simply use the type py::buffer as one of its arguments. Buffers can exist in a great variety of configurations, hence some safety checks are usually necessary in the function body. Below, you can see a basic example on how to define a custom constructor for the Eigen double precision matrix (Eigen::MatrixXd) type, which supports initialization from compatible buffer objects (e.g. a NumPy matrix).

    /* Bind MatrixXd (or some other Eigen type) to Python */
    typedef Eigen::MatrixXd Matrix;
    typedef Matrix::Scalar Scalar;
    constexpr bool rowMajor = Matrix::Flags & Eigen::RowMajorBit;
    py::class_<Matrix>(m, "Matrix", py::buffer_protocol())
        .def(py::init([](py::buffer b) {
            typedef Eigen::Stride<Eigen::Dynamic, Eigen::Dynamic> Strides;
            /* Request a buffer descriptor from Python */
            py::buffer_info info = b.request();
            /* Some basic validation checks ... */
            if (!info.item_type_is_equivalent_to<Scalar>())
                throw std::runtime_error("Incompatible format: expected a double array!");
            if (info.ndim != 2)
                throw std::runtime_error("Incompatible buffer dimension!");
            auto strides = Strides(
                info.strides[rowMajor ? 0 : 1] / (py::ssize_t)sizeof(Scalar),
                info.strides[rowMajor ? 1 : 0] / (py::ssize_t)sizeof(Scalar));
            auto map = Eigen::Map<Matrix, 0, Strides>(
                static_cast<Scalar *>(info.ptr), info.shape[0], info.shape[1], strides);
            return Matrix(map);
        }));

For reference, the def_buffer() call for this Eigen data type should look as follows:

    .def_buffer([](Matrix &m) -> py::buffer_info {
        return py::buffer_info(
            m.data(),                                /* Pointer to buffer */
            sizeof(Scalar),                          /* Size of one scalar */
            py::format_descriptor<Scalar>::format(), /* Python struct-style format descriptor */
            2,                                       /* Number of dimensions */
            { m.rows(), m.cols() },                  /* Buffer dimensions */
            { sizeof(Scalar) * (rowMajor ? m.cols() : 1),
              sizeof(Scalar) * (rowMajor ? 1 : m.rows()) }
                                                     /* Strides (in bytes) for each index */
        );
     })

For a much easier approach of binding Eigen types (although with some limitations), refer to the section on Eigen.

SEE ALSO: The file tests/test_buffers.cpp contains a complete example that demonstrates using the buffer protocol with pybind11 in more detail.

[1] http://docs.python.org/3/c-api/buffer.html

  Arrays

By exchanging py::buffer with py::array in the above snippet, we can restrict the function so that it only accepts NumPy arrays (rather than any type of Python object satisfying the buffer protocol).

In many situations, we want to define a function which only accepts a NumPy array of a certain data type. This is possible via the py::array_t<T> template. For instance, the following function requires the argument to be a NumPy array containing double precision values.

    void f(py::array_t<double> array);

When it is invoked with a different type (e.g. an integer or a list of integers), the binding code will attempt to cast the input into a NumPy array of the requested type. This feature requires the pybind11/numpy.h header to be included. Note that pybind11/numpy.h does not depend on the NumPy headers, and thus can be used without declaring a build-time dependency on NumPy; NumPy>=1.7.0 is a runtime dependency.

Data in NumPy arrays is not guaranteed to packed in a dense manner; furthermore, entries can be separated by arbitrary column and row strides. Sometimes, it can be useful to require a function to only accept dense arrays using either the C (row-major) or Fortran (column-major) ordering. This can be accomplished via a second template argument with values py::array::c_style or py::array::f_style.

    void f(py::array_t<double, py::array::c_style | py::array::forcecast> array);

The py::array::forcecast argument is the default value of the second template parameter, and it ensures that non-conforming arguments are converted into an array satisfying the specified requirements instead of trying the next function overload.

There are several methods on arrays; the methods listed below under references work, as well as the following functions based on the NumPy API:

•   .dtype() returns the type of the contained values.

•   .strides() returns a pointer to the strides of the array (optionally pass an integer axis to get a number).

•   .flags() returns the flag settings. .writable() and .owndata() are directly available.

•   .offset_at() returns the offset (optionally pass indices).

•   .squeeze() returns a view with length-1 axes removed.

•   .view(dtype) returns a view of the array with a different dtype.

•   .reshape({i, j, ...}) returns a view of the array with a different shape. .resize({...}) is also available.

•   .index_at(i, j, ...) gets the count from the beginning to a given index.

    There are also several methods for getting references (described below).

  Structured types

In order for py::array_t to work with structured (record) types, we first need to register the memory layout of the type. This can be done via PYBIND11_NUMPY_DTYPE macro, called in the plugin definition code, which expects the type followed by field names:

    struct A {
        int x;
        double y;
    };
    struct B {
        int z;
        A a;
    };
    // ...
    PYBIND11_MODULE(test, m, py::mod_gil_not_used()) {
        // ...
        PYBIND11_NUMPY_DTYPE(A, x, y);
        PYBIND11_NUMPY_DTYPE(B, z, a);
        /* now both A and B can be used as template arguments to py::array_t */
    }

The structure should consist of fundamental arithmetic types, std::complex, previously registered substructures, and arrays of any of the above. Both C++ arrays and std::array are supported. While there is a static assertion to prevent many types of unsupported structures, it is still the user's responsibility to use only "plain" structures that can be safely manipulated as raw memory without violating invariants.

  Scalar types

In some cases we may want to accept or return NumPy scalar values such as np.float32 or np.float64. We hope to be able to handle single-precision and double-precision on the C-side. However, both are bound to Python's double-precision builtin float by default, so they cannot be processed separately. We used the py::buffer trick to implement the previous approach, which will cause the readability of the code to drop significantly.

Luckily, there's a helper type for this occasion - py::numpy_scalar:

    m.def("add", [](py::numpy_scalar<float> a, py::numpy_scalar<float> b) {
        return py::make_scalar(a + b);
    });
    m.def("add", [](py::numpy_scalar<double> a, py::numpy_scalar<double> b) {
        return py::make_scalar(a + b);
    });

This type is trivially convertible to and from the type it wraps; currently supported scalar types are NumPy arithmetic types: bool_, int8, int16, int32, int64, uint8, uint16, uint32, uint64, float32, float64, complex64, complex128, all of them mapping to respective C++ counterparts.

NOTE: py::numpy_scalar<T> strictly matches NumPy scalar types. For example, py::numpy_scalar<int64_t> will accept np.int64(123), but not a regular Python int like 123.

NOTE: Native C types are mapped to NumPy types in a platform specific way: for instance, char may be mapped to either np.int8 or np.uint8 and long may use 4 or 8 bytes depending on the platform. Unless you clearly understand the difference and your needs, please use <cstdint>.

  Vectorizing functions

Suppose we want to bind a function with the following signature to Python so that it can process arbitrary NumPy array arguments (vectors, matrices, general N-D arrays) in addition to its normal arguments:

    double my_func(int x, float y, double z);

After including the pybind11/numpy.h header, this is extremely simple:

    m.def("vectorized_func", py::vectorize(my_func));

Invoking the function like below causes 4 calls to be made to my_func with each of the array elements. The significant advantage of this compared to solutions like numpy.vectorize() is that the loop over the elements runs entirely on the C++ side and can be crunched down into a tight, optimized loop by the compiler. The result is returned as a NumPy array of type numpy.dtype.float64.

    >>> x = np.array([[1, 3], [5, 7]])
    >>> y = np.array([[2, 4], [6, 8]])
    >>> z = 3
    >>> result = vectorized_func(x, y, z)

The scalar argument z is transparently replicated 4 times. The input arrays x and y are automatically converted into the right types (they are of type numpy.dtype.int64 but need to be numpy.dtype.int32 and numpy.dtype.float32, respectively).

NOTE: Only arithmetic, complex, and POD types passed by value or by const & reference are vectorized; all other arguments are passed through as-is. Functions taking rvalue reference arguments cannot be vectorized.

In cases where the computation is too complicated to be reduced to vectorize, it will be necessary to create and access the buffer contents manually. The following snippet contains a complete example that shows how this works (the code is somewhat contrived, since it could have been done more simply using vectorize).

    #include <pybind11/pybind11.h>
    #include <pybind11/numpy.h>
    namespace py = pybind11;
    py::array_t<double> add_arrays(py::array_t<double> input1, py::array_t<double> input2) {
        py::buffer_info buf1 = input1.request(), buf2 = input2.request();
        if (buf1.ndim != 1 || buf2.ndim != 1)
            throw std::runtime_error("Number of dimensions must be one");
        if (buf1.size != buf2.size)
            throw std::runtime_error("Input shapes must match");
        /* No pointer is passed, so NumPy will allocate the buffer */
        auto result = py::array_t<double>(buf1.size);
        py::buffer_info buf3 = result.request();
        double *ptr1 = static_cast<double *>(buf1.ptr);
        double *ptr2 = static_cast<double *>(buf2.ptr);
        double *ptr3 = static_cast<double *>(buf3.ptr);
        for (size_t idx = 0; idx < buf1.shape[0]; idx++)
            ptr3[idx] = ptr1[idx] + ptr2[idx];
        return result;
    }
    PYBIND11_MODULE(test, m, py::mod_gil_not_used()) {
        m.def("add_arrays", &add_arrays, "Add two NumPy arrays");
    }

SEE ALSO: The file tests/test_numpy_vectorize.cpp contains a complete example that demonstrates using vectorize() in more detail.

  Direct access

For performance reasons, particularly when dealing with very large arrays, it is often desirable to directly access array elements without internal checking of dimensions and bounds on every access when indices are known to be already valid. To avoid such checks, the array class and array_t<T> template class offer an unchecked proxy object that can be used for this unchecked access through the unchecked<N> and mutable_unchecked<N> methods, where N gives the required dimensionality of the array:

    m.def("sum_3d", [](py::array_t<double> x) {
        auto r = x.unchecked<3>(); // x must have ndim = 3; can be non-writeable
        double sum = 0;
        for (py::ssize_t i = 0; i < r.shape(0); i++)
            for (py::ssize_t j = 0; j < r.shape(1); j++)
                for (py::ssize_t k = 0; k < r.shape(2); k++)
                    sum += r(i, j, k);
        return sum;
    });
    m.def("increment_3d", [](py::array_t<double> x) {
        auto r = x.mutable_unchecked<3>(); // Will throw if ndim != 3 or flags.writeable is false
        for (py::ssize_t i = 0; i < r.shape(0); i++)
            for (py::ssize_t j = 0; j < r.shape(1); j++)
                for (py::ssize_t k = 0; k < r.shape(2); k++)
                    r(i, j, k) += 1.0;
    }, py::arg().noconvert());

To obtain the proxy from an array object, you must specify both the data type and number of dimensions as template arguments, such as auto r = myarray.mutable_unchecked<float, 2>().

If the number of dimensions is not known at compile time, you can omit the dimensions template parameter (i.e. calling arr_t.unchecked() or arr.unchecked<T>(). This will give you a proxy object that works in the same way, but results in less optimizable code and thus a small efficiency loss in tight loops.

Note that the returned proxy object directly references the array's data, and only reads its shape, strides, and writeable flag when constructed. You must take care to ensure that the referenced array is not destroyed or reshaped for the duration of the returned object, typically by limiting the scope of the returned instance.

The returned proxy object supports some of the same methods as py::array so that it can be used as a drop-in replacement for some existing, index-checked uses of py::array:

•   .ndim() returns the number of dimensions

•   .data(1, 2, ...) and r.mutable_data(1, 2, ...)` returns a pointer to the const T or T data, respectively, at the given indices. The latter is only available to proxies obtained via a.mutable_unchecked().

•   .itemsize() returns the size of an item in bytes, i.e. sizeof(T).

•   .shape(n) returns the size of dimension n

•   .size() returns the total number of elements (i.e. the product of the shapes).

•   .nbytes() returns the number of bytes used by the referenced elements (i.e. itemsize() times size()).

    SEE ALSO: The file tests/test_numpy_array.cpp contains additional examples demonstrating the use of this feature.

  Ellipsis

Python provides a convenient ... ellipsis notation that is often used to slice multidimensional arrays. For instance, the following snippet extracts the middle dimensions of a tensor with the first and last index set to zero.

    a = ...  # a NumPy array
    b = a[0, ..., 0]

The function py::ellipsis() function can be used to perform the same operation on the C++ side:

    py::array a = /* A NumPy array */;
    py::array b = a[py::make_tuple(0, py::ellipsis(), 0)];

  Memory view

For a case when we simply want to provide a direct accessor to C/C++ buffer without a concrete class object, we can return a memoryview object. Suppose we wish to expose a memoryview for 2x4 uint8_t array, we can do the following:

    const uint8_t buffer[] = {
        0, 1, 2, 3,
        4, 5, 6, 7
    };
    m.def("get_memoryview2d", []() {
        return py::memoryview::from_buffer(
            buffer,                                    // buffer pointer
            { 2, 4 },                                  // shape (rows, cols)
            { sizeof(uint8_t) * 4, sizeof(uint8_t) }   // strides in bytes
        );
    });

This approach is meant for providing a memoryview for a C/C++ buffer not managed by Python. The user is responsible for managing the lifetime of the buffer. Using a memoryview created in this way after deleting the buffer in C++ side results in undefined behavior.

We can also use memoryview::from_memory for a simple 1D contiguous buffer:

    m.def("get_memoryview1d", []() {
        return py::memoryview::from_memory(
            buffer,               // buffer pointer
            sizeof(uint8_t) * 8   // buffer size
        );
    });

Changed in version 2.6: memoryview::from_memory added.

  Utilities

  Using Python's print function in C++

The usual way to write output in C++ is using std::cout while in Python one would use print. Since these methods use different buffers, mixing them can lead to output order issues. To resolve this, pybind11 modules can use the py::print() function which writes to Python's sys.stdout for consistency.

Python's print function is replicated in the C++ API including optional keyword arguments sep, end, file, flush. Everything works as expected in Python:

    py::print(1, 2.0, "three"); // 1 2.0 three
    py::print(1, 2.0, "three", "sep"_a="-"); // 1-2.0-three
    auto args = py::make_tuple("unpacked", true);
    py::print("->", *args, "end"_a="<-"); // -> unpacked True <-

  Capturing standard output from ostream

Often, a library will use the streams std::cout and std::cerr to print, but this does not play well with Python's standard sys.stdout and sys.stderr redirection. Replacing a library's printing with py::print <print> may not be feasible. This can be fixed using a guard around the library function that redirects output to the corresponding Python streams:

    #include <pybind11/iostream.h>
    ...
    // Add a scoped redirect for your noisy code
    m.def("noisy_func", []() {
        py::scoped_ostream_redirect stream(
            std::cout,                               // std::ostream&
            py::module_::import("sys").attr("stdout") // Python output
        );
        call_noisy_func();
    });

WARNING: The implementation in pybind11/iostream.h is NOT thread safe. Multiple threads writing to a redirected ostream concurrently cause data races and potentially buffer overflows. Therefore it is currently a requirement that all (possibly) concurrent redirected ostream writes are protected by a mutex. #HelpAppreciated: Work on iostream.h thread safety. For more background see the discussions under PR #2982 and PR #2995.

This method respects flushes on the output streams and will flush if needed when the scoped guard is destroyed. This allows the output to be redirected in real time, such as to a Jupyter notebook. The two arguments, the C++ stream and the Python output, are optional, and default to standard output if not given. An extra type, py::scoped_estream_redirect <scoped_estream_redirect>, is identical except for defaulting to std::cerr and sys.stderr; this can be useful with py::call_guard, which allows multiple items, but uses the default constructor:

    // Alternative: Call single function using call guard
    m.def("noisy_func", &call_noisy_function,
          py::call_guard<py::scoped_ostream_redirect,
                         py::scoped_estream_redirect>());

The redirection can also be done in Python with the addition of a context manager, using the py::add_ostream_redirect() <add_ostream_redirect> function:

    py::add_ostream_redirect(m, "ostream_redirect");

The name in Python defaults to ostream_redirect if no name is passed. This creates the following context manager in Python:

    with ostream_redirect(stdout=True, stderr=True):
        noisy_function()

It defaults to redirecting both streams, though you can use the keyword arguments to disable one of the streams if needed.

NOTE: The above methods will not redirect C-level output to file descriptors, such as fprintf. For those cases, you'll need to redirect the file descriptors either directly in C or with Python's os.dup2 function in an operating-system dependent way.

  Evaluating Python expressions from strings and files

pybind11 provides the eval, exec and eval_file functions to evaluate Python expressions and statements. The following example illustrates how they can be used.

    // At beginning of file
    #include <pybind11/eval.h>
    ...
    // Evaluate in scope of main module
    py::object scope = py::module_::import("__main__").attr("__dict__");
    // Evaluate an isolated expression
    int result = py::eval("my_variable + 10", scope).cast<int>();
    // Evaluate a sequence of statements
    py::exec(
        "print('Hello')\n"
        "print('world!');",
        scope);
    // Evaluate the statements in an separate Python file on disk
    py::eval_file("script.py", scope);

C++11 raw string literals are also supported and quite handy for this purpose. The only requirement is that the first statement must be on a new line following the raw string delimiter R"(, ensuring all lines have common leading indent:

    py::exec(R"(
        x = get_answer()
        if x == 42:
            print('Hello World!')
        else:
            print('Bye!')
        )", scope
    );

NOTE: eval and eval_file accept a template parameter that describes how the string/file should be interpreted. Possible choices include eval_expr (isolated expression), eval_single_statement (a single statement, return value is always none), and eval_statements (sequence of statements, return value is always none). eval defaults to eval_expr, eval_file defaults to eval_statements and exec is just a shortcut for eval<eval_statements>.

  Embedding the interpreter

While pybind11 is mainly focused on extending Python using C++, it's also possible to do the reverse: embed the Python interpreter into a C++ program. All of the other documentation pages still apply here, so refer to them for general pybind11 usage. This section will cover a few extra things required for embedding.

  Getting started

A basic executable with an embedded interpreter can be created with just a few lines of CMake and the pybind11::embed target, as shown below. For more information, see Build systems.

    cmake_minimum_required(VERSION 3.15...4.2)
    project(example)
    find_package(pybind11 REQUIRED)  # or `add_subdirectory(pybind11)`
    add_executable(example main.cpp)
    target_link_libraries(example PRIVATE pybind11::embed)

The essential structure of the main.cpp file looks like this:

    #include <pybind11/embed.h> // everything needed for embedding
    namespace py = pybind11;
    int main() {
        py::scoped_interpreter guard{}; // start the interpreter and keep it alive
        py::print("Hello, World!"); // use the Python API
    }

The interpreter must be initialized before using any Python API, which includes all the functions and classes in pybind11. The RAII guard class scoped_interpreter takes care of the interpreter lifetime. After the guard is destroyed, the interpreter shuts down and clears its memory. No Python functions can be called after this.

  Executing Python code

There are a few different ways to run Python code. One option is to use eval, exec or eval_file, as explained in Evaluating Python expressions from strings and files. Here is a quick example in the context of an executable with an embedded interpreter:

    #include <pybind11/embed.h>
    namespace py = pybind11;
    int main() {
        py::scoped_interpreter guard{};
        py::exec(R"(
            kwargs = dict(name="World", number=42)
            message = "Hello, {name}! The answer is {number}".format(**kwargs)
            print(message)
        )");
    }

Alternatively, similar results can be achieved using pybind11's API (see Python C++ interface for more details).

    #include <pybind11/embed.h>
    namespace py = pybind11;
    using namespace py::literals;
    int main() {
        py::scoped_interpreter guard{};
        auto kwargs = py::dict("name"_a="World", "number"_a=42);
        auto message = "Hello, {name}! The answer is {number}"_s.format(**kwargs);
        py::print(message);
    }

The two approaches can also be combined:

    #include <pybind11/embed.h>
    #include <iostream>
    namespace py = pybind11;
    using namespace py::literals;
    int main() {
        py::scoped_interpreter guard{};
        auto locals = py::dict("name"_a="World", "number"_a=42);
        py::exec(R"(
            message = "Hello, {name}! The answer is {number}".format(**locals())
        )", py::globals(), locals);
        auto message = locals["message"].cast<std::string>();
        std::cout << message;
    }

  Importing modules

Python modules can be imported using module_::import():

    py::module_ sys = py::module_::import("sys");
    py::print(sys.attr("path"));

For convenience, the current working directory is included in sys.path when embedding the interpreter. This makes it easy to import local Python files:

    """calc.py located in the working directory"""
    def add(i, j):
        return i + j

    py::module_ calc = py::module_::import("calc");
    py::object result = calc.attr("add")(1, 2);
    int n = result.cast<int>();
    assert(n == 3);

Modules can be reloaded using module_::reload() if the source is modified e.g. by an external process. This can be useful in scenarios where the application imports a user defined data processing script which needs to be updated after changes by the user. Note that this function does not reload modules recursively.

  Adding embedded modules

Embedded binary modules can be added using the PYBIND11_EMBEDDED_MODULE macro. Note that the definition must be placed at global scope. They can be imported like any other module.

    #include <pybind11/embed.h>
    namespace py = pybind11;
    PYBIND11_EMBEDDED_MODULE(fast_calc, m) {
        // `m` is a `py::module_` which is used to bind functions and classes
        m.def("add", [](int i, int j) {
            return i + j;
        });
    }
    int main() {
        py::scoped_interpreter guard{};
        auto fast_calc = py::module_::import("fast_calc");
        auto result = fast_calc.attr("add")(1, 2).cast<int>();
        assert(result == 3);
    }

Unlike extension modules where only a single binary module can be created, on the embedded side an unlimited number of modules can be added using multiple PYBIND11_EMBEDDED_MODULE definitions (as long as they have unique names).

These modules are added to Python's list of builtins, so they can also be imported in pure Python files loaded by the interpreter. Everything interacts naturally:

    """py_module.py located in the working directory"""
    import cpp_module
    a = cpp_module.a
    b = a + 1

    #include <pybind11/embed.h>
    namespace py = pybind11;
    PYBIND11_EMBEDDED_MODULE(cpp_module, m) {
        m.attr("a") = 1;
    }
    int main() {
        py::scoped_interpreter guard{};
        auto py_module = py::module_::import("py_module");
        auto locals = py::dict("fmt"_a="{} + {} = {}", **py_module.attr("__dict__"));
        assert(locals["a"].cast<int>() == 1);
        assert(locals["b"].cast<int>() == 2);
        py::exec(R"(
            c = a + b
            message = fmt.format(a, b, c)
        )", py::globals(), locals);
        assert(locals["c"].cast<int>() == 3);
        assert(locals["message"].cast<std::string>() == "1 + 2 = 3");
    }

PYBIND11_EMBEDDED_MODULE also accepts py::mod_gil_not_used(), py::multiple_interpreters::per_interpreter_gil(), and py::multiple_interpreters::shared_gil() tags just like PYBIND11_MODULE. See Sub-interpreter support and Free-threading support for more information.

  Interpreter lifetime

The Python interpreter shuts down when scoped_interpreter is destroyed. After this, creating a new instance will restart the interpreter. Alternatively, the initialize_interpreter / finalize_interpreter pair of functions can be used to directly set the state at any time.

Modules created with pybind11 can be safely re-initialized after the interpreter has been restarted. However, this may not apply to third-party extension modules. The issue is that Python itself cannot completely unload extension modules and there are several caveats with regard to interpreter restarting. In short, not all memory may be freed, either due to Python reference cycles or user-created global data. All the details can be found in the CPython documentation.

WARNING: Creating two concurrent scoped_interpreter guards is a fatal error. So is calling initialize_interpreter for a second time after the interpreter has already been initialized. Use scoped_subinterpreter to create a sub-interpreter. See Embedding Sub-interpreters for important details on sub-interpreters.

Do not use the raw CPython API functions Py_Initialize and Py_Finalize as these do not properly handle the lifetime of pybind11's internal data.

  Embedding Sub-interpreters

A sub-interpreter is a separate interpreter instance which provides a separate, isolated interpreter environment within the same process as the main interpreter. Sub-interpreters are created and managed with a separate API from the main interpreter. Beginning in Python 3.12, sub-interpreters each have their own Global Interpreter Lock (GIL), which means that running a sub-interpreter in a separate thread from the main interpreter can achieve true concurrency.

pybind11's sub-interpreter API can be found in pybind11/subinterpreter.h.

pybind11 subinterpreter instances can be safely moved and shared between threads as needed. However, managing multiple threads and the lifetimes of multiple interpreters and their GILs can be challenging. Proceed with caution (and lots of testing)!

The main interpreter must be initialized before creating a sub-interpreter, and the main interpreter must outlive all sub-interpreters. Sub-interpreters are managed through a different API than the main interpreter.

The subinterpreter class manages the lifetime of sub-interpreters. Instances are movable, but not copyable. Default constructing this class does not create a sub-interpreter (it creates an empty holder). To create a sub-interpreter, call subinterpreter::create().

WARNING: Sub-interpreter creation acquires (and subsequently releases) the main interpreter GIL. If another thread holds the main GIL, the function will block until the main GIL can be acquired.

Sub-interpreter destruction temporarily activates the sub-interpreter. The sub-interpreter must not be active (on any threads) at the time the subinterpreter destructor is called.

Both actions will re-acquire any interpreter's GIL that was held prior to the call before returning (or return to no active interpreter if none was active at the time of the call).

Each sub-interpreter will import a separate copy of each PYBIND11_EMBEDDED_MODULE when those modules specify a multiple_interpreters tag. If a module does not specify a multiple_interpreters tag, then Python will report an ImportError if it is imported in a sub-interpreter.

pybind11 also has a scoped_subinterpreter class, which creates and activates a sub-interpreter when it is constructed, and deactivates and deletes it when it goes out of scope.

  Activating a Sub-interpreter

Once a sub-interpreter is created, you can "activate" it on a thread (and acquire its GIL) by creating a subinterpreter_scoped_activate instance and passing it the sub-interpreter to be activated. The function will acquire the sub-interpreter's GIL and make the sub-interpreter the current active interpreter on the current thread for the lifetime of the instance. When the subinterpreter_scoped_activate instance goes out of scope, the sub-interpreter GIL is released and the prior interpreter that was active on the thread (if any) is reactivated and it's GIL is re-acquired.

When using subinterpreter_scoped_activate:

1.  If the thread holds any interpreter's GIL: - That GIL is released

2.  The new sub-interpreter's GIL is acquired

3.  The new sub-interpreter is made active.

4.

When the scope ends:

•   The sub-interpreter's GIL is released

•

If there was a previous interpreter:

•   The old interpreter's GIL is re-acquired

•   The old interpreter is made active

•   Otherwise, no interpreter is currently active and no GIL is held.

    Example:

        py::initialize_interpreter();
        // Main GIL is held
        {
            py::subinterpreter sub = py::subinterpreter::create();
            // Main interpreter is still active, main GIL re-acquired
            {
                py::subinterpreter_scoped_activate guard(sub);
                // Sub-interpreter active, thread holds sub's GIL
                {
                    py::subinterpreter_scoped_activate main_guard(py);
                    // Sub's GIL was automatically released
                    // Main interpreter active, thread holds main's GIL
                }
                // Back to sub-interpreter, thread holds sub's GIL again
            }
            // Main interpreter is active, main's GIL is held
        }

  GIL API for sub-interpreters

gil_scoped_release and gil_scoped_acquire can be used to manage the GIL of a sub-interpreter just as they do for the main interpreter. They both manage the GIL of the currently active interpreter, without the programmer having to do anything special or different. There is one important caveat:

NOTE: When no interpreter is active through a subinterpreter_scoped_activate instance (such as on a new thread), gil_scoped_acquire will acquire the main GIL and activate the main interpreter.

  Full Sub-interpreter example

Here is an example showing how to create and activate sub-interpreters:

    #include <iostream>
    #include <pybind11/embed.h>
    #include <pybind11/subinterpreter.h>
    namespace py = pybind11;
    PYBIND11_EMBEDDED_MODULE(printer, m, py::multiple_interpreters::per_interpreter_gil()) {
        m.def("which", [](const std::string& when) {
            std::cout << when << "; Current Interpreter is "
                    << py::subinterpreter::current().id()
                    << std::endl;
        });
    }
    int main() {
        py::scoped_interpreter main_interp;
        py::module_::import("printer").attr("which")("First init");
        {
            py::subinterpreter sub = py::subinterpreter::create();
            py::module_::import("printer").attr("which")("Created sub");
            {
                py::subinterpreter_scoped_activate guard(sub);
                try {
                    py::module_::import("printer").attr("which")("Activated sub");
                }
                catch (py::error_already_set &e) {
                    std::cerr << "EXCEPTION " << e.what() << std::endl;
                    return 1;
                }
            }
            py::module_::import("printer").attr("which")("Deactivated sub");
            {
                py::gil_scoped_release nogil;
                {
                    py::subinterpreter_scoped_activate guard(sub);
                    try {
                        {
                            py::subinterpreter_scoped_activate main_guard(py::subinterpreter::main());
                            try {
                                py::module_::import("printer").attr("which")("Main within sub");
                            }
                            catch (py::error_already_set &e) {
                                std::cerr << "EXCEPTION " << e.what() << std::endl;
                                return 1;
                            }
                        }
                        py::module_::import("printer").attr("which")("After Main, still within sub");
                    }
                    catch (py::error_already_set &e) {
                        std::cerr << "EXCEPTION " << e.what() << std::endl;
                        return 1;
                    }
                }
            }
        }
        py::module_::import("printer").attr("which")("At end");
        return 0;
    }

Expected output:

    First init; Current Interpreter is 0
    Created sub; Current Interpreter is 0
    Activated sub; Current Interpreter is 1
    Deactivated sub; Current Interpreter is 0
    Main within sub; Current Interpreter is 0
    After Main, still within sub; Current Interpreter is 1
    At end; Current Interpreter is 0

WARNING: In Python 3.12 sub-interpreters must be destroyed in the same OS thread that created them. Failure to follow this rule may result in deadlocks or crashes when destroying the sub-interpreter on the wrong thread.

This constraint is not present in Python 3.13+.

  Best Practices for sub-interpreter safety

•   Never share Python objects across different interpreters.

•   error_already_set objects contain a reference to the Python exception type, and error_already_set::what() acquires the GIL. So Python exceptions must never be allowed to propagate past the enclosing subinterpreter_scoped_activate instance! (So your try/catch should be just inside the scope covered by the subinterpreter_scoped_activate.)

•   Avoid global/static state whenever possible. Instead, keep state within each interpreter, such as within the interpreter state dict, which can be accessed via subinterpreter::current().state_dict(), or within instance members and tied to Python objects.

•   Avoid trying to "cache" Python objects in C++ variables across function calls (this is an easy way to accidentally introduce sub-interpreter bugs). In the code example above, note that we did not save the result of module_::import(), in order to avoid accidentally using the resulting Python object when the wrong interpreter was active.

•   Avoid moving or disarming RAII objects managing GIL and sub-interpreter lifetimes. Doing so can lead to confusion about lifetimes. (For example, accidentally extending a subinterpreter_scoped_activate past the lifetime of it's subinterpreter.)

•   While sub-interpreters each have their own GIL, there can now be multiple independent GILs in one program so you need to consider the possibility of deadlocks caused by multiple GILs and/or the interactions of the GIL(s) and your C++ code's own locking.

•   When using multiple threads to run independent sub-interpreters, the independent GILs allow concurrent calls from different interpreters into the same C++ code from different threads. So you must still consider the thread safety of your C++ code. Remember, in Python 3.12 sub-interpreters must be destroyed on the same thread that they were created on.

•   When using sub-interpreters in free-threaded python builds, note that creating and destroying sub-interpreters may initiate a "stop-the-world". Be sure to detach long-running C++ threads from Python thread state (similar to releasing the GIL) to avoid deadlocks.

•   Familiarize yourself with Concurrency and Parallelism in Python with pybind11.

  Miscellaneous

  General notes regarding convenience macros

pybind11 provides a few convenience macros such as PYBIND11_DECLARE_HOLDER_TYPE() and PYBIND11_OVERRIDE_*. Since these are "just" macros that are evaluated in the preprocessor (which has no concept of types), they will get confused by commas in a template argument; for example, consider:

    PYBIND11_OVERRIDE(MyReturnType<T1, T2>, Class<T3, T4>, func)

The limitation of the C preprocessor interprets this as five arguments (with new arguments beginning after each comma) rather than three. To get around this, there are two alternatives: you can use a type alias, or you can wrap the type using the PYBIND11_TYPE macro:

    // Version 1: using a type alias
    using ReturnType = MyReturnType<T1, T2>;
    using ClassType = Class<T3, T4>;
    PYBIND11_OVERRIDE(ReturnType, ClassType, func);
    // Version 2: using the PYBIND11_TYPE macro:
    PYBIND11_OVERRIDE(PYBIND11_TYPE(MyReturnType<T1, T2>),
                      PYBIND11_TYPE(Class<T3, T4>), func)

The PYBIND11_MAKE_OPAQUE macro does not require the above workarounds.

  Global Interpreter Lock (GIL)

The Python C API dictates that the Global Interpreter Lock (GIL) must always be held by the current thread to safely access Python objects. As a result, when Python calls into C++ via pybind11 the GIL must be held, and pybind11 will never implicitly release the GIL.

    void my_function() {
        /* GIL is held when this function is called from Python */
    }
    PYBIND11_MODULE(example, m) {
        m.def("my_function", &my_function);
    }

pybind11 will ensure that the GIL is held when it knows that it is calling Python code. For example, if a Python callback is passed to C++ code via std::function, when C++ code calls the function the built-in wrapper will acquire the GIL before calling the Python callback. Similarly, the PYBIND11_OVERRIDE family of macros will acquire the GIL before calling back into Python.

When writing C++ code that is called from other C++ code, if that code accesses Python state, it must explicitly acquire and release the GIL. A separate document on deadlocks [1] elaborates on a particularly subtle interaction with C++'s block-scope static variable initializer guard mutexes.

[1] See docs/advanced/deadlock.md

    The classes gil_scoped_release and gil_scoped_acquire can be used to acquire and release the global interpreter lock in the body of a C++ function call. In this way, long-running C++ code can be parallelized using multiple Python threads, but great care must be taken when any gil_scoped_release appear: if there is any way that the C++ code can access Python objects, gil_scoped_acquire should be used to reacquire the GIL. Taking Overriding virtual functions in Python as an example, this could be realized as follows (important changes highlighted):

        class PyAnimal : public Animal, public py::trampoline_self_life_support {
        public:
            /* Inherit the constructors */
            using Animal::Animal;
            /* Trampoline (need one for each virtual function) */
            std::string go(int n_times) {
                /* PYBIND11_OVERRIDE_PURE will acquire the GIL before accessing Python state */
                PYBIND11_OVERRIDE_PURE(
                    std::string, /* Return type */
                    Animal,      /* Parent class */
                    go,          /* Name of function */
                    n_times      /* Argument(s) */
                );
            }
        };
        PYBIND11_MODULE(example, m) {
            py::class_<Animal, PyAnimal, py::smart_holder> animal(m, "Animal");
            animal
                .def(py::init<>())
                .def("go", &Animal::go);
            py::class_<Dog, py::smart_holder>(m, "Dog", animal)
                .def(py::init<>());
            m.def("call_go", [](Animal *animal) -> std::string {
                // GIL is held when called from Python code. Release GIL before
                // calling into (potentially long-running) C++ code
                py::gil_scoped_release release;
                return call_go(animal);
            });
        }

    The call_go wrapper can also be simplified using the call_guard policy (see Additional call policies) which yields the same result:

        m.def("call_go", &call_go, py::call_guard<py::gil_scoped_release>());

  Common Sources Of Global Interpreter Lock Errors

Failing to properly hold the Global Interpreter Lock (GIL) is one of the more common sources of bugs within code that uses pybind11. If you are running into GIL related errors, we highly recommend you consult the following checklist.

•   Do you have any global variables that are pybind11 objects or invoke pybind11 functions in either their constructor or destructor? You are generally not allowed to invoke any Python function in a global static context. We recommend using lazy initialization and then intentionally leaking at the end of the program.

•   Do you have any pybind11 objects that are members of other C++ structures? One commonly overlooked requirement is that pybind11 objects have to increase their reference count whenever their copy constructor is called. Thus, you need to be holding the GIL to invoke the copy constructor of any C++ class that has a pybind11 member. This can sometimes be very tricky to track for complicated programs Think carefully when you make a pybind11 object a member in another struct.

•   C++ destructors that invoke Python functions can be particularly troublesome as destructors can sometimes get invoked in weird and unexpected circumstances as a result of exceptions.

•   C++ static block-scope variable initialization that calls back into Python can cause deadlocks; see [1] for a detailed discussion.

•   You should try running your code in a debug build. That will enable additional assertions within pybind11 that will throw exceptions on certain GIL handling errors (reference counting operations).

  Free-threading support

pybind11 supports the experimental free-threaded builds of Python versions 3.13+. pybind11's internal data structures are thread safe. To enable your modules to be used with free-threading, pass the mod_gil_not_used tag as the third argument to PYBIND11_MODULE.

For example:

    PYBIND11_MODULE(example, m, py::mod_gil_not_used()) {
        py::class_<Animal> animal(m, "Animal");
        // etc
    }

Importantly, enabling your module to be used with free-threading is also your promise that your code is thread safe. Modules must still be built against the Python free-threading branch to enable free-threading, even if they specify this tag. Adding this tag does not break compatibility with non-free-threaded Python.

  Sub-interpreter support

pybind11 supports isolated sub-interpreters, which are stable in Python 3.12+. pybind11's internal data structures are sub-interpreter safe. To enable your modules to be imported in isolated sub-interpreters, pass the multiple_interpreters::per_interpreter_gil() tag as the third or later argument to PYBIND11_MODULE.

For example:

    PYBIND11_MODULE(example, m, py::multiple_interpreters::per_interpreter_gil()) {
        py::class_<Animal> animal(m, "Animal");
        // etc
    }

Best Practices for Sub-interpreter Safety:

•   Your initialization function will run for each interpreter that imports your module.

•   Never share Python objects across different sub-interpreters.

•   Avoid global/static state whenever possible. Instead, keep state within each interpreter, such as in instance members tied to Python objects, globals(), and the interpreter state dict.

•   Modules without any global/static state in their C++ code may already be sub-interpreter safe without any additional work!

•   Avoid trying to "cache" Python objects in C++ variables across function calls (this is an easy way to accidentally introduce sub-interpreter bugs).

•   While sub-interpreters each have their own GIL, there can now be multiple independent GILs in one program, so concurrent calls into a module from two different sub-interpreters are still possible. Therefore, your module still needs to consider thread safety.

    pybind11 also supports "legacy" sub-interpreters which shared a single global GIL. You can enable legacy-only behavior by using the multiple_interpreters::shared_gil() tag in PYBIND11_MODULE.

    You can explicitly disable sub-interpreter support in your module by using the multiple_interpreters::not_supported() tag. This is the default behavior if you do not specify a multiple_interpreters tag.

  Concurrency and Parallelism in Python with pybind11

Sub-interpreter support does not imply free-threading support or vice versa. Free-threading safe modules can still have global/static state (as long as access to them is thread-safe), but sub-interpreter safe modules cannot. Likewise, sub-interpreter safe modules can still rely on the GIL, but free-threading safe modules cannot.

Here is a simple example module which has a function that calculates a value and returns the result of the previous calculation.

    PYBIND11_MODULE(example, m) {
        static size_t seed = 0;
        m.def("calc_next", []() {
            auto old = seed;
            seed = (seed + 1) * 10;
            return old;
        });

This module is not free-threading safe because there is no synchronization on the number variable. It is relatively easy to make this free-threading safe. One way is by using atomics, like this:

    PYBIND11_MODULE(example, m, py::mod_gil_not_used()) {
        static std::atomic<size_t> seed(0);
        m.def("calc_next", []() {
            size_t old, next;
            do {
                old = seed.load();
                next = (old + 1) * 10;
            } while (!seed.compare_exchange_weak(old, next));
            return old;
        });
    }

The atomic variable and the compare-exchange guarantee a consistent behavior from this function even when called currently from multiple threads at the same time.

However, the global/static integer is not sub-interpreter safe, because the calls in one sub-interpreter will change what is seen in another. To fix it, the state needs to be specific to each interpreter. One way to do that is by storing the state on another Python object, such as a member of a class. For this simple example, we will store it in globals().

    PYBIND11_MODULE(example, m, py::multiple_interpreters::per_interpreter_gil()) {
        m.def("calc_next", []() {
            if (!py::globals().contains("myseed"))
                py::globals()["myseed"] = 0;
            size_t old = py::globals()["myseed"];
            py::globals()["myseed"] = (old + 1) * 10;
            return old;
        });
    }

This module is sub-interpreter safe, for both shared_gil ("legacy") and per_interpreter_gil ("default") varieties. Multiple sub-interpreters could each call this same function concurrently from different threads. This is safe because each sub-interpreter's GIL protects it's own Python objects from concurrent access.

However, the module is no longer free-threading safe, for the same reason as before, because the calculation is not synchronized. We can synchronize it using a Python critical section. This will do nothing if not in free-threaded Python. You can have it lock one or two Python objects. You cannot nest it.

WARNING: When using a py::scoped_critical_section, make sure it is not nested and that no other synchronization primitives (such as a std::mutex) are held, which could lead to deadlocks. In 3.13, taking the same lock causes it to release then reacquire, which means you can't use it to, for example, read and write to a dictionary, because the dictionary uses a critical section internally in CPython. Use a std::mutex instead if you need this on Python 3.13. In 3.14, taking a lock on a locked object no longer releases and relocks as an optimization, which also fixes this case.

    #include <pybind11/critical_section.h>
    // ...
    PYBIND11_MODULE(example, m, py::multiple_interpreters::per_interpreter_gil(), py::mod_gil_not_used()) {
        m.def("calc_next", []() {
            size_t old;
            py::dict g = py::globals();
            py::scoped_critical_section guard(g);
            if (!g.contains("myseed"))
                g["myseed"] = 0;
            old = g["myseed"];
            g["myseed"] = (old + 1) * 10;
            return old;
        });
    }

The module is now both sub-interpreter safe and free-threading safe.

  Binding sequence data types, iterators, the slicing protocol, etc.

Please refer to the supplemental example for details.

SEE ALSO: The file tests/test_sequences_and_iterators.cpp contains a complete example that shows how to bind a sequence data type, including length queries (__len__), iterators (__iter__), the slicing protocol and other kinds of useful operations.

  Partitioning code over multiple extension modules

It's straightforward to split binding code over multiple extension modules, while referencing types that are declared elsewhere. Everything "just" works without any special precautions. One exception to this rule occurs when extending a type declared in another extension module. Recall the basic example from Section Inheritance and automatic downcasting.

    py::class_<Pet> pet(m, "Pet");
    pet.def(py::init<const std::string &>())
       .def_readwrite("name", &Pet::name);
    py::class_<Dog>(m, "Dog", pet /* <- specify parent */)
        .def(py::init<const std::string &>())
        .def("bark", &Dog::bark);

Suppose now that Pet bindings are defined in a module named basic, whereas the Dog bindings are defined somewhere else. The challenge is of course that the variable pet is not available anymore though it is needed to indicate the inheritance relationship to the constructor of py::class_<Dog>. However, it can be acquired as follows:

    py::object pet = (py::object) py::module_::import("basic").attr("Pet");
    py::class_<Dog>(m, "Dog", pet)
        .def(py::init<const std::string &>())
        .def("bark", &Dog::bark);

Alternatively, you can specify the base class as a template parameter option to py::class_, which performs an automated lookup of the corresponding Python type. Like the above code, however, this also requires invoking the import function once to ensure that the pybind11 binding code of the module basic has been executed:

    py::module_::import("basic");
    py::class_<Dog, Pet>(m, "Dog")
        .def(py::init<const std::string &>())
        .def("bark", &Dog::bark);

Naturally, both methods will fail when there are cyclic dependencies.

Note that pybind11 code compiled with hidden-by-default symbol visibility (e.g. via the command line flag -fvisibility=hidden on GCC/Clang), which is required for proper pybind11 functionality, can interfere with the ability to access types defined in another extension module. Working around this requires manually exporting types that are accessed by multiple extension modules; pybind11 provides a macro to do just this:

    class PYBIND11_EXPORT Dog : public Animal {
        ...
    };

Note also that it is possible (although would rarely be required) to share arbitrary C++ objects between extension modules at runtime. Internal library data is shared between modules using capsule machinery [2] which can be also utilized for storing, modifying and accessing user-defined data. Note that an extension module will "see" other extensions' data if and only if they were built with the same pybind11 version. Consider the following example:

    auto data = reinterpret_cast<MyData *>(py::get_shared_data("mydata"));
    if (!data)
        data = static_cast<MyData *>(py::set_shared_data("mydata", new MyData(42)));

If the above snippet was used in several separately compiled extension modules, the first one to be imported would create a MyData instance and associate a "mydata" key with a pointer to it. Extensions that are imported later would be then able to access the data behind the same pointer.

[2] https://docs.python.org/3/extending/extending.html#using-capsules

  Module Destructors

pybind11 does not provide an explicit mechanism to invoke cleanup code at module destruction time. In rare cases where such functionality is required, it is possible to emulate it using Python capsules or weak references with a destruction callback.

    auto cleanup_callback = []() {
        // perform cleanup here -- this function is called with the GIL held
    };
    m.add_object("_cleanup", py::capsule(cleanup_callback));

This approach has the potential downside that instances of classes exposed within the module may still be alive when the cleanup callback is invoked (whether this is acceptable will generally depend on the application).

Alternatively, the capsule may also be stashed within a type object, which ensures that it not called before all instances of that type have been collected:

    auto cleanup_callback = []() { /* ... */ };
    m.attr("BaseClass").attr("_cleanup") = py::capsule(cleanup_callback);

Both approaches also expose a potentially dangerous _cleanup attribute in Python, which may be undesirable from an API standpoint (a premature explicit call from Python might lead to undefined behavior). Yet another approach that avoids this issue involves weak reference with a cleanup callback:

    // Register a callback function that is invoked when the BaseClass object is collected
    py::cpp_function cleanup_callback(
        [](py::handle weakref) {
            // perform cleanup here -- this function is called with the GIL held
            weakref.dec_ref(); // release weak reference
        }
    );
    // Create a weak reference with a cleanup callback and initially leak it
    (void) py::weakref(m.attr("BaseClass"), cleanup_callback).release();

NOTE: PyPy does not garbage collect objects when the interpreter exits. An alternative approach (which also works on CPython) is to use the atexit module [3], for example:

    auto atexit = py::module_::import("atexit");
    atexit.attr("register")(py::cpp_function([]() {
        // perform cleanup here -- this function is called with the GIL held
    }));

[3] https://docs.python.org/3/library/atexit.html

  Generating documentation using Sphinx

Sphinx [4] has the ability to inspect the signatures and documentation strings in pybind11-based extension modules to automatically generate beautiful documentation in a variety formats. The python_example repository [5] contains a simple example repository which uses this approach.

There are two potential gotchas when using this approach: first, make sure that the resulting strings do not contain any TAB characters, which break the docstring parsing routines. You may want to use C++11 raw string literals, which are convenient for multi-line comments. Conveniently, any excess indentation will be automatically be removed by Sphinx. However, for this to work, it is important that all lines are indented consistently, i.e.:

    // ok
    m.def("foo", &foo, R"mydelimiter(
        The foo function
        Parameters
        ----------
    )mydelimiter");
    // *not ok*
    m.def("foo", &foo, R"mydelimiter(The foo function
        Parameters
        ----------
    )mydelimiter");

By default, pybind11 automatically generates and prepends a signature to the docstring of a function registered with module_::def() and class_::def(). Sometimes this behavior is not desirable, because you want to provide your own signature or remove the docstring completely to exclude the function from the Sphinx documentation. The class options allows you to selectively suppress auto-generated signatures:

    PYBIND11_MODULE(example, m) {
        py::options options;
        options.disable_function_signatures();
        m.def("add", [](int a, int b) { return a + b; }, "A function which adds two numbers");
    }

pybind11 also appends all members of an enum to the resulting enum docstring. This default behavior can be disabled by using the disable_enum_members_docstring() function of the options class.

With disable_user_defined_docstrings() all user defined docstrings of module_::def(), class_::def() and enum_() are disabled, but the function signatures and enum members are included in the docstring, unless they are disabled separately.

Note that changes to the settings affect only function bindings created during the lifetime of the options instance. When it goes out of scope at the end of the module's init function, the default settings are restored to prevent unwanted side effects.

[4] http://www.sphinx-doc.org

[5] http://github.com/pybind/python_example

  Avoiding C++ types in docstrings

Docstrings are generated at the time of the declaration, e.g. when .def(...) is called. At this point parameter and return types should be known to pybind11. If a custom type is not exposed yet through a py::class_ constructor or a custom type caster, its C++ type name will be used instead to generate the signature in the docstring:

    |  __init__(...)
    |      __init__(self: example.Foo, arg0: ns::Bar) -> None
                                             ^^^^^^^

This limitation can be circumvented by ensuring that C++ classes are registered with pybind11 before they are used as a parameter or return type of a function:

    PYBIND11_MODULE(example, m) {
        auto pyFoo = py::class_<ns::Foo>(m, "Foo");
        auto pyBar = py::class_<ns::Bar>(m, "Bar");
        pyFoo.def(py::init<const ns::Bar&>());
        pyBar.def(py::init<const ns::Foo&>());
    }

  Setting inner type hints in docstrings

When you use pybind11 wrappers for list, dict, and other generic python types, the docstring will just display the generic type. You can convey the inner types in the docstring by using a special 'typed' version of the generic type.

    PYBIND11_MODULE(example, m) {
        m.def("pass_list_of_str", [](py::typing::List<py::str> arg) {
            // arg can be used just like py::list
        ));
    }

The resulting docstring will be pass_list_of_str(arg0: list[str]) -> None.

The following special types are available in pybind11/typing.h:

•   py::Tuple<Args...>

•   py::Dict<K, V>

•   py::List<V>

•   py::Set<V>

•   py::Callable<Signature>

    WARNING: Just like in python, these are merely hints. They don't actually enforce the types of their contents at runtime or compile time.

  Deprecated

Support for Python 3.8 is deprecated and will be removed in 3.1.

Support for C++11 is deprecated and will be removed in a future version. Please use at least C++14.

Support for FindPythonLibs (not available in CMake 3.26+ mode) is deprecated and will be removed in a future version. The default mode is also going to change to "new" from "compat" in the future.

The following features were deprecated before pybind11 3.0, and may be removed in minor releases of pybind11 3.x.

  Deprecated Features
Feature | Deprecated Version | Year
py::metaclass() | 2.1 | 2017
PYBIND11_PLUGIN | 2.2 | 2017
py::set_error() replacing operator() | 2.12 | 2024
get_type_overload | 2.6 | 2020
call() | 2.0 | 2016
.str() | ? |
.get_type() | 2.6 |
== and != | 2.2 | 2017
.check() | ? |
object(handle, bool) | ? |
error_already_set.clear() | 2.2 | 2017
obj.attr(…) as bool | ? |
.contains | ? (maybe 2.4) |
py::capsule two-argument with destructor | ? |

  py::enum_

This is the original documentation for py::enum_, which is deprecated because it is not PEP 435 compatible (see also #2332). Please prefer py::native_enum (added with pybind11v3) when writing new bindings. See Enumerations and internal types for more information.

Let's suppose that we have an example class that contains internal types like enumerations, e.g.:

    struct Pet {
        enum Kind {
            Dog = 0,
            Cat
        };
        struct Attributes {
            float age = 0;
        };
        Pet(const std::string &name, Kind type) : name(name), type(type) { }
        std::string name;
        Kind type;
        Attributes attr;
    };

The binding code for this example looks as follows:

    py::class_<Pet> pet(m, "Pet");
    pet.def(py::init<const std::string &, Pet::Kind>())
        .def_readwrite("name", &Pet::name)
        .def_readwrite("type", &Pet::type)
        .def_readwrite("attr", &Pet::attr);
    py::enum_<Pet::Kind>(pet, "Kind")
        .value("Dog", Pet::Kind::Dog)
        .value("Cat", Pet::Kind::Cat)
        .export_values();
    py::class_<Pet::Attributes>(pet, "Attributes")
        .def(py::init<>())
        .def_readwrite("age", &Pet::Attributes::age);

To ensure that the nested types Kind and Attributes are created within the scope of Pet, the pet py::class_ instance must be supplied to the enum_ and py::class_ constructor. The enum_::export_values() function exports the enum entries into the parent scope, which should be skipped for newer C++11-style strongly typed enums.

    >>> p = Pet("Lucy", Pet.Cat)
    >>> p.type
    Kind.Cat
    >>> int(p.type)
    1L

The entries defined by the enumeration type are exposed in the __members__ property:

    >>> Pet.Kind.__members__
    {'Dog': Kind.Dog, 'Cat': Kind.Cat}

The name property returns the name of the enum value as a unicode string.

NOTE: It is also possible to use str(enum), however these accomplish different goals. The following shows how these two approaches differ.

    >>> p = Pet("Lucy", Pet.Cat)
    >>> pet_type = p.type
    >>> pet_type
    Pet.Cat
    >>> str(pet_type)
    'Pet.Cat'
    >>> pet_type.name
    'Cat'

NOTE: When the special tag py::arithmetic() is specified to the enum_ constructor, pybind11 creates an enumeration that also supports rudimentary arithmetic and bit-level operations like comparisons, and, or, xor, negation, etc.

    py::enum_<Pet::Kind>(pet, "Kind", py::arithmetic())
       ...

By default, these are omitted to conserve space.

WARNING: Contrary to Python customs, enum values from the wrappers should not be compared using is, but with == (see #1177 for background).

  Frequently asked questions

  "ImportError: dynamic module does not define init function"

1. Make sure that the name specified in PYBIND11_MODULE is identical to the filename of the extension library (without suffixes such as .so).

2. If the above did not fix the issue, you are likely using an incompatible version of Python that does not match what you compiled with.

  "Symbol not found: __Py_ZeroStruct / _PyInstanceMethod_Type"

See the first answer.

  "SystemError: dynamic module not initialized properly"

See the first answer.

  The Python interpreter immediately crashes when importing my module

See the first answer.

  Limitations involving reference arguments

In C++, it's fairly common to pass arguments using mutable references or mutable pointers, which allows both read and write access to the value supplied by the caller. This is sometimes done for efficiency reasons, or to realize functions that have multiple return values. Here are two very basic examples:

    void increment(int &i) { i++; }
    void increment_ptr(int *i) { (*i)++; }

In Python, all arguments are passed by reference, so there is no general issue in binding such code from Python.

However, certain basic Python types (like str, int, bool, float, etc.) are immutable. This means that the following attempt to port the function to Python doesn't have the same effect on the value provided by the caller -- in fact, it does nothing at all.

    def increment(i):
        i += 1  # nope..

pybind11 is also affected by such language-level conventions, which means that binding increment or increment_ptr will also create Python functions that don't modify their arguments.

Although inconvenient, one workaround is to encapsulate the immutable types in a custom type that does allow modifications.

An other alternative involves binding a small wrapper lambda function that returns a tuple with all output arguments (see the remainder of the documentation for examples on binding lambda functions). An example:

    int foo(int &i) { i++; return 123; }

and the binding code

    m.def("foo", [](int i) { int rv = foo(i); return std::make_tuple(rv, i); });

  How can I reduce the build time?

It's good practice to split binding code over multiple files, as in the following example:

example.cpp:

    void init_ex1(py::module_ &);
    void init_ex2(py::module_ &);
    /* ... */
    PYBIND11_MODULE(example, m, py::mod_gil_not_used()) {
        init_ex1(m);
        init_ex2(m);
        /* ... */
    }

ex1.cpp:

    void init_ex1(py::module_ &m) {
        m.def("add", [](int a, int b) { return a + b; });
    }

ex2.cpp:

    void init_ex2(py::module_ &m) {
        m.def("sub", [](int a, int b) { return a - b; });
    }

python:

    >>> import example
    >>> example.add(1, 2)
    3
    >>> example.sub(1, 1)
    0

As shown above, the various init_ex functions should be contained in separate files that can be compiled independently from one another, and then linked together into the same final shared object. Following this approach will:

1.  reduce memory requirements per compilation unit.

2.  enable parallel builds (if desired).

3.  allow for faster incremental builds. For instance, when a single class definition is changed, only a subset of the binding code will generally need to be recompiled.

  "recursive template instantiation exceeded maximum depth of 256"

If you receive an error about excessive recursive template evaluation, try specifying a larger value, e.g. -ftemplate-depth=1024 on GCC/Clang. The culprit is generally the generation of function signatures at compile time using C++14 template metaprogramming.

  "'SomeClass' declared with greater visibility than the type of its field 'SomeClass::member' [-Wattributes]"

This error typically indicates that you are compiling without the required -fvisibility flag. pybind11 code internally forces hidden visibility on all internal code, but if non-hidden (and thus exported) code attempts to include a pybind type (for example, py::object or py::list) you can run into this warning.

To avoid it, make sure you are specifying -fvisibility=hidden when compiling pybind code.

As to why -fvisibility=hidden is necessary, because pybind modules could have been compiled under different versions of pybind itself, it is also important that the symbols defined in one module do not clash with the potentially-incompatible symbols defined in another. While Python extension modules are usually loaded with localized symbols (under POSIX systems typically using dlopen with the RTLD_LOCAL flag), this Python default can be changed, but even if it isn't it is not always enough to guarantee complete independence of the symbols involved when not using -fvisibility=hidden.

Additionally, -fvisibility=hidden can deliver considerably binary size savings. (See the following section for more details.)

  How can I create smaller binaries?

To do its job, pybind11 extensively relies on a programming technique known as template metaprogramming, which is a way of performing computation at compile time using type information. Template metaprogramming usually instantiates code involving significant numbers of deeply nested types that are either completely removed or reduced to just a few instructions during the compiler's optimization phase. However, due to the nested nature of these types, the resulting symbol names in the compiled extension library can be extremely long. For instance, the included test suite contains the following symbol:

    __ZN8pybind1112cpp_functionC1Iv8Example2JRNSt3__16vectorINS3_12basic_stringIwNS3_11char_traitsIwEENS3_9allocatorIwEEEENS8_ISA_EEEEEJNS_4nameENS_7siblingENS_9is_methodEA28_cEEEMT0_FT_DpT1_EDpRKT2_

which is the mangled form of the following function type:

    pybind11::cpp_function::cpp_function<void, Example2, std::__1::vector<std::__1::basic_string<wchar_t, std::__1::char_traits<wchar_t>, std::__1::allocator<wchar_t> >, std::__1::allocator<std::__1::basic_string<wchar_t, std::__1::char_traits<wchar_t>, std::__1::allocator<wchar_t> > > >&, pybind11::name, pybind11::sibling, pybind11::is_method, char [28]>(void (Example2::*)(std::__1::vector<std::__1::basic_string<wchar_t, std::__1::char_traits<wchar_t>, std::__1::allocator<wchar_t> >, std::__1::allocator<std::__1::basic_string<wchar_t, std::__1::char_traits<wchar_t>, std::__1::allocator<wchar_t> > > >&), pybind11::name const&, pybind11::sibling const&, pybind11::is_method const&, char const (&) [28])

The memory needed to store just the mangled name of this function (196 bytes) is larger than the actual piece of code (111 bytes) it represents! On the other hand, it's silly to even give this function a name -- after all, it's just a tiny cog in a bigger piece of machinery that is not exposed to the outside world. So we'll generally only want to export symbols for those functions which are actually called from the outside.

This can be achieved by specifying the parameter -fvisibility=hidden to GCC and Clang, which sets the default symbol visibility to hidden, which has a tremendous impact on the final binary size of the resulting extension library. (On Visual Studio, symbols are already hidden by default, so nothing needs to be done there.)

In addition to decreasing binary size, -fvisibility=hidden also avoids potential serious issues when loading multiple modules and is required for proper pybind operation. See the previous FAQ entry for more details.

  How can I properly handle Ctrl-C in long-running functions?

Ctrl-C is received by the Python interpreter, and holds it until the GIL is released, so a long-running function won't be interrupted.

To interrupt from inside your function, you can use the PyErr_CheckSignals() function, that will tell if a signal has been raised on the Python side. This function merely checks a flag, so its impact is negligible. When a signal has been received, you must either explicitly interrupt execution by throwing py::error_already_set (which will propagate the existing KeyboardInterrupt), or clear the error (which you usually will not want):

    PYBIND11_MODULE(example, m, py::mod_gil_not_used()) {
        m.def("long running_func", []()
        {
            for (;;) {
                if (PyErr_CheckSignals() != 0)
                    throw py::error_already_set();
                // Long running iteration
            }
        });
    }

  What is a highly conclusive and simple way to find memory leaks (e.g. in pybind11 bindings)?

Use while True & top (Linux, macOS).

For example, locally change tests/test_type_caster_pyobject_ptr.py like this:

     def test_return_list_pyobject_ptr_reference():
    +  while True:
         vec_obj = m.return_list_pyobject_ptr_reference(ValueHolder)
         assert [e.value for e in vec_obj] == [93, 186]
         # Commenting out the next `assert` will leak the Python references.
         # An easy way to see evidence of the leaks:
         # Insert `while True:` as the first line of this function and monitor the
         # process RES (Resident Memory Size) with the Unix top command.
    -    assert m.dec_ref_each_pyobject_ptr(vec_obj) == 2
    +    # assert m.dec_ref_each_pyobject_ptr(vec_obj) == 2

Then run the test as you would normally do, which will go into the infinite loop.

In another shell, but on the same machine run:

    top

This will show:

        PID USER      PR  NI    VIRT    RES    SHR S  %CPU  %MEM     TIME+ COMMAND
    1266095 rwgk      20   0 5207496 611372  45696 R 100.0   0.3   0:08.01 test_type_caste

Look for the number under RES there. You'll see it going up very quickly.

Don't forget to Ctrl-C the test command before your machine becomes unresponsive due to swapping.

This method only takes a couple minutes of effort and is very conclusive. What you want to see is that the RES number is stable after a couple seconds.

  CMake doesn't detect the right Python version

The CMake-based build system will try to automatically detect the installed version of Python and link against that. When this fails, or when there are multiple versions of Python and it finds the wrong one, delete CMakeCache.txt and then add -DPYTHON_EXECUTABLE=$(which python) to your CMake configure line. (Replace $(which python) with a path to python if your prefer.)

You can alternatively try -DPYBIND11_FINDPYTHON=ON, which will activate the new CMake FindPython support instead of pybind11's custom search. Newer CMake, like, 3.18.2+, is recommended. You can set this in your CMakeLists.txt before adding or finding pybind11, as well.

  Inconsistent detection of Python version in CMake and pybind11

The functions find_package(PythonInterp) and find_package(PythonLibs) provided by CMake for Python version detection are modified by pybind11 due to unreliability and limitations that make them unsuitable for pybind11's needs. Instead pybind11 provides its own, more reliable Python detection CMake code. Conflicts can arise, however, when using pybind11 in a project that also uses the CMake Python detection in a system with several Python versions installed.

This difference may cause inconsistencies and errors if both mechanisms are used in the same project.

There are three possible solutions:

1.  Avoid using find_package(PythonInterp) and find_package(PythonLibs) from CMake and rely on pybind11 in detecting Python version. If this is not possible, the CMake machinery should be called before including pybind11.

2.  Set PYBIND11_FINDPYTHON to True or use find_package(Python COMPONENTS Interpreter Development) on modern CMake ( 3.18.2+ best). Pybind11 in these cases uses the new CMake FindPython instead of the old, deprecated search tools, and these modules are much better at finding the correct Python. If FindPythonLibs/Interp are not available (CMake 3.27+), then this will be ignored and FindPython will be used.

3.  Set PYBIND11_NOPYTHON to TRUE. Pybind11 will not search for Python. However, you will have to use the target-based system, and do more setup yourself, because it does not know about or include things that depend on Python, like pybind11_add_module. This might be ideal for integrating into an existing system, like scikit-build's Python helpers.

  How to cite this project?

We suggest the following BibTeX template to cite pybind11 in scientific discourse:

    @misc{pybind11,
       author = {Wenzel Jakob and Jason Rhinelander and Dean Moldovan},
       year = {2017},
       note = {https://github.com/pybind/pybind11},
       title = {pybind11 -- Seamless operability between C++11 and Python}
    }

  Benchmark

The following is the result of a synthetic benchmark comparing both compilation time and module size of pybind11 against Boost.Python. A detailed report about a Boost.Python to pybind11 conversion of a real project is available here: [1].

[1] http://graylab.jhu.edu/RosettaCon2016/PyRosetta-4.pdf

  Setup

A python script (see the docs/benchmark.py file) was used to generate a set of files with dummy classes whose count increases for each successive benchmark (between 1 and 2048 classes in powers of two). Each class has four methods with a randomly generated signature with a return value and four arguments. (There was no particular reason for this setup other than the desire to generate many unique function signatures whose count could be controlled in a simple way.)

Here is an example of the binding code for one class:

    ...
    class cl034 {
    public:
        cl279 *fn_000(cl084 *, cl057 *, cl065 *, cl042 *);
        cl025 *fn_001(cl098 *, cl262 *, cl414 *, cl121 *);
        cl085 *fn_002(cl445 *, cl297 *, cl145 *, cl421 *);
        cl470 *fn_003(cl200 *, cl323 *, cl332 *, cl492 *);
    };
    ...
    PYBIND11_MODULE(example, m, py::mod_gil_not_used()) {
        ...
        py::class_<cl034>(m, "cl034")
            .def("fn_000", &cl034::fn_000)
            .def("fn_001", &cl034::fn_001)
            .def("fn_002", &cl034::fn_002)
            .def("fn_003", &cl034::fn_003)
        ...
    }

The Boost.Python version looks almost identical except that a return value policy had to be specified as an argument to def(). For both libraries, compilation was done with

    Apple LLVM version 7.0.2 (clang-700.1.81)

and the following compilation flags

    g++ -Os -shared -rdynamic -undefined dynamic_lookup -fvisibility=hidden -std=c++14

  Compilation time

The following log-log plot shows how the compilation time grows for an increasing number of class and function declarations. pybind11 includes many fewer headers, which initially leads to shorter compilation times, but the performance is ultimately fairly similar (pybind11 is 19.8 seconds faster for the largest largest file with 2048 classes and a total of 8192 methods -- a modest 1.2x speedup relative to Boost.Python, which required 116.35 seconds). [image]

  Module size

Differences between the two libraries become much more pronounced when considering the file size of the generated Python plugin: for the largest file, the binary generated by Boost.Python required 16.8 MiB, which was 2.17 times / 9.1 megabytes larger than the output generated by pybind11. For very small inputs, Boost.Python has an edge in the plot below -- however, note that it stores many definitions in an external library, whose size was not included here, hence the comparison is slightly shifted in Boost.Python's favor. [image]

  Limitations

  Design choices

pybind11 strives to be a general solution to binding generation, but it also has certain limitations:

•   pybind11 casts away const-ness in function arguments and return values. This is in line with the Python language, which has no concept of const values. This means that some additional care is needed to avoid bugs that would be caught by the type checker in a traditional C++ program.

•   The NumPy interface pybind11::array greatly simplifies accessing numerical data from C++ (and vice versa), but it's not a full-blown array class like Eigen::Array or boost.multi_array. Eigen objects are directly supported, however, with pybind11/eigen.h.

    Large but useful features could be implemented in pybind11 but would lead to a significant increase in complexity. Pybind11 strives to be simple and compact. Users who require large new features are encouraged to write an extension to pybind11; see pybind11_json for an example.

  Known bugs

These are issues that hopefully will one day be fixed, but currently are unsolved. If you know how to help with one of these issues, contributions are welcome!

•   Intel 20.2 is currently having an issue with the test suite. #2573

•   Debug mode Python does not support 1-5 tests in the test suite currently. #2422

•   PyPy3 7.3.1 and 7.3.2 have issues with several tests on 32-bit Windows.

  Known limitations

These are issues that are probably solvable, but have not been fixed yet. A clean, well written patch would likely be accepted to solve them.

•   Type casters are not kept alive recursively. #2527 One consequence is that containers of char * are currently not supported. #2245

  Python 3.9.0 warning

Combining older versions of pybind11 (< 2.6.0) with Python on exactly 3.9.0 will trigger undefined behavior that typically manifests as crashes during interpreter shutdown (but could also destroy your data. You have been warned).

This issue was fixed in Python. As a mitigation for this bug, pybind11 2.6.0 or newer includes a workaround specifically when Python 3.9.0 is detected at runtime, leaking about 50 bytes of memory when a callback function is garbage collected. For reference, the pybind11 test suite has about 2,000 such callbacks, but only 49 are garbage collected before the end-of-process. Wheels (even if built with Python 3.9.0) will correctly avoid the leak when run in Python 3.9.1, and this does not affect other 3.X versions.

WARNING: Please be advised that the reference documentation discussing pybind11 internals is currently incomplete. Please refer to the previous sections and the pybind11 header files for the nitty gritty details.

  Reference

  Macros

PYBIND11_MODULE(name, variable, ...)
    This macro creates the entry point that will be invoked when the Python interpreter imports an extension module. The module name is given as the first argument and it should not be in quotes. The second macro argument defines a variable of type py::module_ which can be used to initialize the module.

    The entry point is marked as "maybe unused" to aid dead-code detection analysis: since the entry point is typically only looked up at runtime and not referenced during translation, it would otherwise appear as unused ("dead") code.

        PYBIND11_MODULE(example, m) {
            m.doc() = "pybind11 example module";
            // Add bindings here
            m.def("foo", []() {
                return "Hello, World!";
            });
        }

    The third and subsequent macro arguments are optional (available since 2.13.0), and can be used to mark the extension module as supporting various Python features.

•   mod_gil_not_used()

•   multiple_interpreters::per_interpreter_gil()

•   multiple_interpreters::shared_gil()

•   multiple_interpreters::not_supported()

        PYBIND11_MODULE(example, m, py::mod_gil_not_used()) {
            m.doc() = "pybind11 example module safe to run without the GIL";
            m.def("foo", []() {
                return "Hello, Free-threaded World!";
            });
        }

  Convenience classes for arbitrary Python types

  Common member functions

template<typename Derived> class object_api : public pyobject_tag
    A mixin class which adds common functions to handle, object and various accessors. The only requirement for Derived is to implement PyObject *Derived::ptr() const.

    Subclassed by accessor< accessor_policies::obj_attr >, accessor< accessor_policies::str_attr >, accessor< accessor_policies::generic_item >, accessor< accessor_policies::sequence_item >, accessor< accessor_policies::list_item >, accessor< accessor_policies::tuple_item >

    Public Functions

iterator begin() const
    Return an iterator equivalent to calling iter() in Python. The object must be a collection which supports the iteration protocol.

iterator end() const
    Return a sentinel which ends iteration.

item_accessor operator[](handle key) const
    Return an internal functor to invoke the object's sequence protocol. Casting the returned detail::item_accessor instance to a handle or object subclass causes a corresponding call to __getitem__. Assigning a handle or object subclass causes a call to __setitem__.

item_accessor operator[](object &&key) const
    See above (the only difference is that the key's reference is stolen).

item_accessor operator[](const char *key) const
    See above (the only difference is that the key is provided as a string literal).

obj_attr_accessor attr(handle key) const
    Return an internal functor to access the object's attributes. Casting the returned detail::obj_attr_accessor instance to a handle or object subclass causes a corresponding call to getattr. Assigning a handle or object subclass causes a call to setattr.

obj_attr_accessor attr(object &&key) const
    See above (the only difference is that the key's reference is stolen).

str_attr_accessor attr(const char *key) const
    See above (the only difference is that the key is provided as a string literal).

template<typename T> obj_attr_accessor attr_with_type_hint(handle key) const
    Similar to the above attr functions with the difference that the templated Type is used to set the __annotations__ dict value to the corresponding key. Worth noting that attr_with_type_hint is implemented in cast.h.

template<typename T> str_attr_accessor attr_with_type_hint(const char *key) const
    See above (the only difference is that the key is provided as a string literal).

args_proxy operator*() const
    Matches * unpacking in Python, e.g. to unpack arguments out of a tuple or list for a function call. Applying another * to the result yields ** unpacking, e.g. to unpack a dict as function keyword arguments. See Calling Python functions.

template<typename T> bool contains(T &&item) const
    Check if the given item is contained within this object, i.e. item in obj.

template<return_value_policy policy = return_value_policy::automatic_reference, typename ...Args> object operator()(Args&&... args) const
    Assuming the Python object is a function or implements the __call__ protocol, operator() invokes the underlying function, passing an arbitrary set of parameters. The result is returned as a object and may need to be converted back into a Python object using handle::cast().

    When some of the arguments cannot be converted to Python objects, the function will throw a cast_error exception. When the Python function call fails, a error_already_set exception is thrown.

inline bool is(object_api const &other) const
    Equivalent to obj is other in Python.

inline bool is_none() const
    Equivalent to obj is None in Python.

inline bool equal(object_api const &other) const
    Equivalent to obj == other in Python.

str_attr_accessor doc() const
    Get or set the object's docstring, i.e. obj.__doc__.

object annotations() const
    Get or set the object's annotations, i.e. obj.__annotations__.

inline ssize_t ref_count() const
    Return the object's current reference count.

  Without reference counting

class handle : public detail::object_api<handle>
    Holds a reference to a Python object (no reference counting)

    The handle class is a thin wrapper around an arbitrary Python object (i.e. a PyObject * in Python's C API). It does not perform any automatic reference counting and merely provides a basic C++ interface to various Python API functions.

    SEE ALSO: The object class inherits from handle and adds automatic reference counting features.

    Subclassed by args_proxy, kwargs_proxy, object

    Public Functions

handle() = default
    The default constructor creates a handle with a nullptr-valued pointer.

template<typename T, detail::enable_if_t<detail::is_pyobj_ptr_or_nullptr_t<T>::value, int> = 0> inline handle(T ptr)
    Enable implicit conversion from PyObject * and nullptr. Not using handle(PyObject *ptr) to avoid implicit conversion from 0.

template<typename T, detail::enable_if_t<detail::all_of<detail::none_of<std::is_base_of<handle, T>, detail::is_pyobj_ptr_or_nullptr_t<T>>, std::is_convertible<T, PyObject*>>::value, int> = 0> inline handle(T &obj)
    Enable implicit conversion through T::operator PyObject *().

inline PyObject *ptr() const
    Return the underlying PyObject * pointer.

inline const handle &inc_ref() const &
    Manually increase the reference count of the Python object. Usually, it is preferable to use the object class which derives from handle and calls this function automatically. Returns a reference to itself.

inline const handle &dec_ref() const &
    Manually decrease the reference count of the Python object. Usually, it is preferable to use the object class which derives from handle and calls this function automatically. Returns a reference to itself.

template<typename T> T cast() const
    Attempt to cast the Python object into the given C++ type. A cast_error will be throw upon failure.

inline explicit operator bool() const
    Return true when the handle wraps a valid Python object.

inline bool operator==(const handle &h) const
    Deprecated: Check that the underlying pointers are the same. Equivalent to obj1 is obj2 in Python.

  With reference counting

class object : public handle
    Holds a reference to a Python object (with reference counting)

    Like handle, the object class is a thin wrapper around an arbitrary Python object (i.e. a PyObject * in Python's C API). In contrast to handle, it optionally increases the object's reference count upon construction, and it always decreases the reference count when the object instance goes out of scope and is destructed. When using object instances consistently, it is much easier to get reference counting right at the first attempt.

    Subclassed by ClassVar< T >, Final< T >, Optional< T >, Union< Types >, anyset, bool_, buffer, bytearray, bytes, capsule, dict, dtype, ellipsis, exception< type >, float_, function, generic_type, int_, iterable, iterator, list, memoryview, module_, none, sequence, slice, staticmethod, str, tuple, type, weakref

    Public Functions

inline object(const object &o)
    Copy constructor; always increases the reference count.

inline object(object &&other) noexcept
    Move constructor; steals the object from other and preserves its reference count.

inline ~object()
    Destructor; automatically calls handle::dec_ref().

inline handle release()
    Resets the internal pointer to nullptr without decreasing the object's reference count. The function returns a raw handle to the original Python object.

template<typename T> T reinterpret_borrow(handle h)
    Declare that a handle or PyObject * is a certain type and borrow the reference. The target type T must be object or one of its derived classes. The function doesn't do any conversions or checks. It's up to the user to make sure that the target type is correct.

        PyObject *p = PyList_GetItem(obj, index);
        py::object o = reinterpret_borrow<py::object>(p);
        // or
        py::tuple t = reinterpret_borrow<py::tuple>(p); // <-- `p` must be already be a `tuple`

template<typename T> T reinterpret_steal(handle h)
    Like reinterpret_borrow(), but steals the reference.

        PyObject *p = PyObject_Str(obj);
        py::str s = reinterpret_steal<py::str>(p); // <-- `p` must be already be a `str`

  Convenience classes for specific Python types

class module_ : public object
    Wrapper for Python extension modules.

    Public Functions

inline explicit module_(const char *name, const char *doc = nullptr)
    Create a new top-level Python module with the given name and docstring.

template<typename Func, typename ...Extra> inline module_ &def(const char *name_, Func &&f, const Extra&... extra)
    Create Python binding for a new function within the module scope. Func can be a plain C++ function, a function pointer, or a lambda function. For details on the Extra&& ... extra argument, see section Passing extra arguments to def or py::class_.

inline module_ def_submodule(const char *name, const char *doc = nullptr)
    Create and return a new Python submodule with the given name and docstring. This also works recursively, i.e.

        py::module_ m("example", "pybind11 example plugin");
        py::module_ m2 = m.def_submodule("sub", "A submodule of 'example'");
        py::module_ m3 = m2.def_submodule("subsub", "A submodule of 'example.sub'");

inline void reload()
    Reload the module or throws error_already_set.

inline void add_object(const char *name, handle obj, bool overwrite = false)
    Adds an object to the module using the given name. Throws if an object with the given name already exists.

    overwrite should almost always be false: attempting to overwrite objects that pybind11 has established will, in most cases, break things.

    Public Static Functions

static inline module_ import(const char *name)
    Import and return a module or throws error_already_set.

static inline module_ create_extension_module(const char *name, const char *doc, PyModuleDef *def, mod_gil_not_used gil_not_used = mod_gil_not_used(false))
    Create a new top-level module that can be used as the main module of a C extension.

    def should point to a statically allocated PyModuleDef.

group Pytypes

    Functions

template<typename Unsigned> Unsigned as_unsigned(PyObject *o)

template<typename ...Args> bool args_are_all_keyword_or_ds()

class iterator : public object
    #include <pytypes.h>

    Wraps a Python iterator so that it can also be used as a C++ input iterator

    Caveat: copying an iterator does not (and cannot) clone the internal state of the Python iterable. This also applies to the post-increment operator. This iterator should only be used to retrieve the current value using operator*().

    Subclassed by Iterator< T >

    Public Static Functions

static inline iterator sentinel()
    The value which marks the end of the iteration. it == iterator::sentinel() is equivalent to catching StopIteration in Python.

        void foo(py::iterator it) {
            while (it != py::iterator::sentinel()) {
               // use `*it`
               ++it;
            }
        }

class type : public object
    Subclassed by Type< T >

    Public Static Functions

static inline handle handle_of(handle h)
    Return a type handle from a handle or an object.

static inline type of(handle h)
    Return a type object from a handle or an object.

template<typename T> static handle handle_of()
    Convert C++ type to handle if previously registered. Does not convert standard types, like int, float. etc. yet. See https://github.com/pybind/pybind11/issues/2486

template<typename T> static inline type of()
    Convert C++ type to type if previously registered. Does not convert standard types, like int, float. etc. yet. See https://github.com/pybind/pybind11/issues/2486

class iterable : public object
    Subclassed by Iterable< T >

class str : public object

    Public Functions

template<typename T, detail::enable_if_t<!std::is_base_of<object, detail::remove_cvref_t<T>>::value && std::is_constructible<handle, T>::value, int> = 0> inline explicit str(T &&h)
    Return a string representation of the object. This is analogous to the str() function in Python.

class bytes : public object

class bytearray : public object

class none : public object
    Subclassed by Never, NoReturn

class ellipsis : public object

class bool_ : public object
    Subclassed by TypeGuard< T >, TypeIs< T >

class int_ : public object

class float_ : public object

class weakref : public object

class slice : public object

class capsule : public object

class tuple : public object
    Subclassed by args, typing::Tuple< Types >

class dict : public object
    Subclassed by Dict< K, V >, kwargs

class sequence : public object

class list : public object
    Subclassed by List< T >

class args : public tuple
    Subclassed by Args< T >

class kwargs : public dict
    Subclassed by KWArgs< T >

template<typename T> class Args : public args

template<typename T> class KWArgs : public kwargs

class anyset : public object
    Subclassed by frozenset, set

class set : public anyset
    Subclassed by Set< T >

class frozenset : public anyset

class function : public object
    Subclassed by Callable< Return(Args...)>, cpp_function

class staticmethod : public object

class buffer : public object
    Subclassed by array

class memoryview : public object

    Public Functions

inline explicit memoryview(const buffer_info &info)
    Creates memoryview from buffer_info.

    buffer_info must be created from buffer::request(). Otherwise throws an exception.

    For creating a memoryview from objects that support buffer protocol, use memoryview(const object& obj) instead of this constructor.

    Public Static Functions

static memoryview from_buffer(void *ptr, ssize_t itemsize, const char *format, detail::any_container<ssize_t> shape, detail::any_container<ssize_t> strides, bool readonly = false)
    Creates memoryview from static buffer.

    This method is meant for providing a memoryview for C/C++ buffer not managed by Python. The caller is responsible for managing the lifetime of ptr and format, which MUST outlive the memoryview constructed here.

    See also: Python C API documentation for PyMemoryView_FromBuffer.

Parameters

•   ptr -- Pointer to the buffer.

•   itemsize -- Byte size of an element.

•   format -- Pointer to the null-terminated format string. For homogeneous Buffers, this should be set to format_descriptor<T>::value.

•   shape -- Shape of the tensor (1 entry per dimension).

•   strides -- Number of bytes between adjacent entries (for each per dimension).

•   readonly -- Flag to indicate if the underlying storage may be written to.

static inline memoryview from_memory(void *mem, ssize_t size, bool readonly = false)
    Creates memoryview from static memory.

    This method is meant for providing a memoryview for C/C++ buffer not managed by Python. The caller is responsible for managing the lifetime of mem, which MUST outlive the memoryview constructed here.

    See also: Python C API documentation for PyMemoryView_FromBuffer.

  Convenience functions converting to Python types

template<return_value_policy policy = return_value_policy::automatic_reference, typename ...Args> typing::Tuple<Args...> make_tuple(Args&&... args_)

template<return_value_policy Policy = return_value_policy::reference_internal, typename Iterator, typename Sentinel, typename ValueType = typename detail::iterator_access<Iterator>::result_type, typename ...Extra> typing::Iterator<ValueType> make_iterator(Iterator first, Sentinel last, Extra&&... extra)
    Makes a python iterator from a first and past-the-end C++ InputIterator.

    WARNING: doxygenfunction: Unable to resolve function "make_iterator" with arguments (Type&, Extra&&...) in doxygen xml output for project "pybind11" from directory: .build/doxygenxml/. Potential matches:

        - template<return_value_policy Policy = return_value_policy::reference_internal, typename Iterator, typename Sentinel, typename ValueType = typename detail::iterator_access<Iterator>::result_type, typename ...Extra> typing::Iterator<ValueType> make_iterator(Iterator first, Sentinel last, Extra&&... extra)
        - template<return_value_policy Policy = return_value_policy::reference_internal, typename Type, typename ValueType = typename detail::iterator_access<decltype(std::begin(std::declval<Type&>()))>::result_type, typename ...Extra> typing::Iterator<ValueType> make_iterator(Type &value, Extra&&... extra)

template<return_value_policy Policy = return_value_policy::reference_internal, typename Iterator, typename Sentinel, typename KeyType = typename detail::iterator_key_access<Iterator>::result_type, typename ...Extra> typing::Iterator<KeyType> make_key_iterator(Iterator first, Sentinel last, Extra&&... extra)
    Makes a python iterator over the keys (.first) of a iterator over pairs from a first and past-the-end InputIterator.

    WARNING: doxygenfunction: Unable to resolve function "make_key_iterator" with arguments (Type&, Extra&&...) in doxygen xml output for project "pybind11" from directory: .build/doxygenxml/. Potential matches:

        - template<return_value_policy Policy = return_value_policy::reference_internal, typename Iterator, typename Sentinel, typename KeyType = typename detail::iterator_key_access<Iterator>::result_type, typename ...Extra> typing::Iterator<KeyType> make_key_iterator(Iterator first, Sentinel last, Extra&&... extra)
        - template<return_value_policy Policy = return_value_policy::reference_internal, typename Type, typename KeyType = typename detail::iterator_key_access<decltype(std::begin(std::declval<Type&>()))>::result_type, typename ...Extra> typing::Iterator<KeyType> make_key_iterator(Type &value, Extra&&... extra)

template<return_value_policy Policy = return_value_policy::reference_internal, typename Iterator, typename Sentinel, typename ValueType = typename detail::iterator_value_access<Iterator>::result_type, typename ...Extra> typing::Iterator<ValueType> make_value_iterator(Iterator first, Sentinel last, Extra&&... extra)
    Makes a python iterator over the values (.second) of a iterator over pairs from a first and past-the-end InputIterator.

    WARNING: doxygenfunction: Unable to resolve function "make_value_iterator" with arguments (Type&, Extra&&...) in doxygen xml output for project "pybind11" from directory: .build/doxygenxml/. Potential matches:

        - template<return_value_policy Policy = return_value_policy::reference_internal, typename Iterator, typename Sentinel, typename ValueType = typename detail::iterator_value_access<Iterator>::result_type, typename ...Extra> typing::Iterator<ValueType> make_value_iterator(Iterator first, Sentinel last, Extra&&... extra)
        - template<return_value_policy Policy = return_value_policy::reference_internal, typename Type, typename ValueType = typename detail::iterator_value_access<decltype(std::begin(std::declval<Type&>()))>::result_type, typename ...Extra> typing::Iterator<ValueType> make_value_iterator(Type &value, Extra&&... extra)

  Passing extra arguments to def or py::class_

group Annotations

struct is_method
    #include <attr.h>

    Annotation for methods.

struct is_setter
    #include <attr.h>

    Annotation for setters.

struct is_operator
    #include <attr.h>

    Annotation for operators.

struct is_final
    #include <attr.h>

    Annotation for classes that cannot be subclassed.

struct scope
    #include <attr.h>

    Annotation for parent scope.

struct doc
    #include <attr.h>

    Annotation for documentation.

struct name
    #include <attr.h>

    Annotation for function names.

struct sibling
    #include <attr.h>

    Annotation indicating that a function is an overload associated with a given "sibling".

template<typename T> struct base
    #include <attr.h>

    Annotation indicating that a class derives from another given type.

template<size_t Nurse, size_t Patient> struct keep_alive
    #include <attr.h>

    Keep patient alive while nurse lives.

struct multiple_inheritance
    #include <attr.h>

    Annotation indicating that a class is involved in a multiple inheritance relationship.

struct dynamic_attr
    #include <attr.h>

    Annotation which enables dynamic attributes, i.e. adds __dict__ to a class.

struct buffer_protocol
    #include <attr.h>

    Annotation which enables the buffer protocol for a type.

struct release_gil_before_calling_cpp_dtor
    #include <attr.h>

    Annotation which enables releasing the GIL before calling the C++ destructor of wrapped instances (pybind/pybind11#1446).

struct metaclass
    #include <attr.h>

    Annotation which requests that a special metaclass is created for a type.

    Public Functions

inline explicit metaclass(handle value)
    Override pybind11's default metaclass.

struct custom_type_setup
    #include <attr.h>

    Specifies a custom callback with signature void (PyHeapTypeObject*) that may be used to customize the Python type.

    The callback is invoked immediately before PyType_Ready.

    Note: This is an advanced interface, and uses of it may require changes to work with later versions of pybind11. You may wish to consult the implementation of make_new_python_type in detail/classes.h to understand the context in which the callback will be run.

struct module_local
    #include <attr.h>

    Annotation that marks a class as local to the module:

struct arithmetic
    #include <attr.h>

    Annotation to mark enums as an arithmetic type.

struct prepend
    #include <attr.h>

    Mark a function for addition at the beginning of the existing overload chain instead of the end.

template<typename ...Ts> struct call_guard
    A call policy which places one or more guard variables (Ts...) around the function call.

    For example, this definition:

        m.def("foo", foo, py::call_guard<T>());

    is equivalent to the following pseudocode:

        m.def("foo", [](args...) {
            T scope_guard;
            return foo(args...); // forwarded arguments
        });

template<> struct call_guard<>

template<typename T> struct call_guard<T>

template<typename T, typename ...Ts> struct call_guard<T, Ts...>

template<> struct type

struct arg
    #include <cast.h>

    Annotation for arguments

    Subclassed by arg_v

    Public Functions

inline explicit constexpr arg(const char *name = nullptr)
    Constructs an argument with the name of the argument; if null or omitted, this is a positional argument.

template<typename T> arg_v operator=(T &&value) const
    Assign a value to this argument.

inline arg &noconvert(bool flag = true)
    Indicate that the type should not be converted in the type caster.

inline arg &none(bool flag = true)
    Indicates that the argument should/shouldn't allow None (e.g. for nullable pointer args).

    Public Members

const char *name
    If non-null, this is a named kwargs argument.

bool flag_noconvert
    If set, do not allow conversion (requires a supporting type caster!)

bool flag_none
    If set (the default), allow None to be passed to this argument.

struct arg_v : public arg
    #include <cast.h>

    Annotation for arguments with values

    Public Functions

template<typename T> inline arg_v(const char *name, T &&x, const char *descr = nullptr)
    Direct construction with name, default, and description.

template<typename T> inline arg_v(const arg &base, T &&x, const char *descr = nullptr)
    Called internally when invoking py::arg("a") = value.

inline arg_v &noconvert(bool flag = true)
    Same as arg::noconvert(), but returns *this as arg_v&, not arg&.

inline arg_v &none(bool flag = true)
    Same as arg::nonone(), but returns *this as arg_v&, not arg&.

    Public Members

object value
    The default value.

const char *descr
    The (optional) description of the default value.

std::string type
    The C++ type name of the default value (only available when compiled in debug mode).

struct kw_only
    #include <cast.h>

    Annotation indicating that all following arguments are keyword-only; the is the equivalent of an unnamed '*' argument

struct pos_only
    #include <cast.h>

    Annotation indicating that all previous arguments are positional-only; the is the equivalent of an unnamed '/' argument

  Embedding the interpreter

PYBIND11_EMBEDDED_MODULE(name, variable, ...)
    Add a new module to the table of builtins for the interpreter. Must be defined in global scope. The first macro parameter is the name of the module (without quotes). The second parameter is the variable which will be used as the interface to add functions and classes to the module.

        PYBIND11_EMBEDDED_MODULE(example, m) {
            // ... initialize functions and classes here
            m.def("foo", []() {
                return "Hello, World!";
            });
        }

    The third and subsequent macro arguments are optional, and can be used to mark the module as supporting various Python features.

•   mod_gil_not_used()

•   multiple_interpreters::per_interpreter_gil()

•   multiple_interpreters::shared_gil()

•   multiple_interpreters::not_supported()

        PYBIND11_EMBEDDED_MODULE(example, m, py::mod_gil_not_used()) {
            m.def("foo", []() {
                return "Hello, Free-threaded World!";
            });
        }

    WARNING: doxygenfunction: Unable to resolve function "initialize_interpreter" with arguments None in doxygen xml output for project "pybind11" from directory: .build/doxygenxml/. Potential matches:

        - void initialize_interpreter(PyConfig *config, int argc = 0, const char *const *argv = nullptr, bool add_program_dir_to_path = true)
        - void initialize_interpreter(bool init_signal_handlers = true, int argc = 0, const char *const *argv = nullptr, bool add_program_dir_to_path = true)

inline void finalize_interpreter()
    Shut down the Python interpreter. No pybind11 or CPython API functions can be called after this. In addition, pybind11 objects must not outlive the interpreter:

        { // BAD
            py::initialize_interpreter();
            auto hello = py::str("Hello, World!");
            py::finalize_interpreter();
        } // <-- BOOM, hello's destructor is called after interpreter shutdown
        { // GOOD
            py::initialize_interpreter();
            { // scoped
                auto hello = py::str("Hello, World!");
            } // <-- OK, hello is cleaned up properly
            py::finalize_interpreter();
        }
        { // BETTER
            py::scoped_interpreter guard{};
            auto hello = py::str("Hello, World!");
        }

    WARNING: The interpreter can be restarted by calling initialize_interpreter again. Modules created using pybind11 can be safely re-initialized. However, Python itself cannot completely unload binary extension modules and there are several caveats with regard to interpreter restarting. All the details can be found in the CPython documentation. In short, not all interpreter memory may be freed, either due to reference cycles or user-created global data.

class scoped_interpreter
    Scope guard version of initialize_interpreter and finalize_interpreter(). This a move-only guard and only a single instance can exist.

    See initialize_interpreter for a discussion of its constructor arguments.

        #include <pybind11/embed.h>
        int main() {
            py::scoped_interpreter guard{};
            py::print(Hello, World!);
        } // <-- interpreter shutdown

  Redirecting C++ streams

class scoped_ostream_redirect
    This a move-only guard that redirects output.

        #include <pybind11/iostream.h>
        ...
        {
            py::scoped_ostream_redirect output;
            std::cout << "Hello, World!"; // Python stdout
        } // <-- return std::cout to normal

    You can explicitly pass the c++ stream and the python object, for example to guard stderr instead.

        {
            py::scoped_ostream_redirect output{
                std::cerr, py::module::import("sys").attr("stderr")};
            std::cout << "Hello, World!";
        }

    Subclassed by scoped_estream_redirect

class scoped_estream_redirect : public scoped_ostream_redirect
    Like scoped_ostream_redirect, but redirects cerr by default. This class is provided primary to make py::call_guard easier to make.

        m.def("noisy_func", &noisy_func,
              py::call_guard<scoped_ostream_redirect,
                             scoped_estream_redirect>());

inline class_<detail::OstreamRedirect> add_ostream_redirect(module_ m, const std::string &name = "ostream_redirect")
    This is a helper function to add a C++ redirect context manager to Python instead of using a C++ guard. To use it, add the following to your binding code:

        #include <pybind11/iostream.h>
        ...
        py::add_ostream_redirect(m, "ostream_redirect");

    You now have a Python context manager that redirects your output:

        with m.ostream_redirect():
            m.print_to_cout_function()

    This manager can optionally be told which streams to operate on:

        with m.ostream_redirect(stdout=true, stderr=true):
            m.noisy_function_with_error_printing()

  Python built-in functions

group const_name
    Unless stated otherwise, the following C++ functions behave the same as their Python counterparts.

    Functions

inline dict globals()
    Return a dictionary representing the global variables in the current execution frame, or __main__.__dict__ if there is no frame (usually when the interpreter is embedded).

template<typename T, detail::enable_if_t<std::is_base_of<object, T>::value, int> = 0> bool isinstance(handle obj)
    Return true if obj is an instance of T. Type T must be a subclass of object or a class which was exposed to Python as py::class_<T>.

inline bool isinstance(handle obj, handle type)
    Return true if obj is an instance of the type.

inline bool hasattr(handle obj, handle name)

inline bool hasattr(handle obj, const char *name)

inline void delattr(handle obj, handle name)

inline void delattr(handle obj, const char *name)

inline object getattr(handle obj, handle name)

inline object getattr(handle obj, const char *name)

inline object getattr(handle obj, handle name, handle default_)

inline object getattr(handle obj, const char *name, handle default_)

inline void setattr(handle obj, handle name, handle value)

inline void setattr(handle obj, const char *name, handle value)

inline ssize_t hash(handle obj)

inline size_t len(handle h)
    Get the length of a Python object.

inline size_t len_hint(handle h)
    Get the length hint of a Python object. Returns 0 when this cannot be determined.

inline str repr(handle h)

inline iterator iter(handle obj)

  Inheritance

See Object-oriented code and Classes for more detail.

PYBIND11_OVERRIDE(ret_type, cname, fn, ...)
    Macro to populate the virtual method in the trampoline class. This macro tries to look up the method from the Python side, deals with the Global Interpreter Lock (GIL) and necessary argument conversions to call this method and return the appropriate type. This macro should be used if the method name in C and in Python are identical. See Overriding virtual functions in Python for more information.

        class PyAnimal : public Animal {
        public:
            // Inherit the constructors
            using Animal::Animal;
            // Trampoline (need one for each virtual function)
            std::string go(int n_times) override {
                PYBIND11_OVERRIDE_PURE(
                    std::string, // Return type (ret_type)
                    Animal,      // Parent class (cname)
                    go,          // Name of function in C++ (must match Python name) (fn)
                    n_times      // Argument(s) (...)
                );
            }
        };

PYBIND11_OVERRIDE_PURE(ret_type, cname, fn, ...)
    Macro for pure virtual functions, this function is identical to PYBIND11_OVERRIDE, except that it throws if no override can be found.

PYBIND11_OVERRIDE_NAME(ret_type, cname, name, fn, ...)
    Macro to populate the virtual method in the trampoline class. This macro tries to look up a method named 'fn' from the Python side, deals with the Global Interpreter Lock (GIL) and necessary argument conversions to call this method and return the appropriate type. See Overriding virtual functions in Python for more information. This macro should be used when the method name in C is not the same as the method name in Python. For example with __str__.

        std::string toString() override {
          PYBIND11_OVERRIDE_NAME(
              std::string, // Return type (ret_type)
              Animal,      // Parent class (cname)
              "__str__",   // Name of method in Python (name)
              toString,    // Name of function in C++ (fn)
          );
        }

PYBIND11_OVERRIDE_PURE_NAME(ret_type, cname, name, fn, ...)
    Macro for pure virtual functions, this function is identical to PYBIND11_OVERRIDE_NAME, except that it throws if no override can be found.

template<class T> function get_override(const T *this_ptr, const char *name)
    Try to retrieve a python method by the provided name from the instance pointed to by the this_ptr.

This_ptr
    The pointer to the object the overridden method should be retrieved for. This should be the first non-trampoline class encountered in the inheritance chain.

Name
    The name of the overridden Python method to retrieve.

Returns
    The Python method by this name from the object or an empty function wrapper.

  Exceptions

class error_already_set : public std::exception
    Fetch and hold an error which was already set in Python. An instance of this is typically thrown to propagate python-side errors back through C++ which can either be caught manually or else falls back to the function dispatcher (which then raises the captured error back to python).

    Public Functions

inline error_already_set()
    Fetches the current Python exception (using PyErr_Fetch()), which will clear the current Python error indicator.

inline const char *what() const noexcept override
    The what() result is built lazily on demand. WARNING: This member function needs to acquire the Python GIL. This can lead to crashes (undefined behavior) if the Python interpreter is finalizing.

inline void restore()
    Restores the currently-held Python error (which will clear the Python error indicator first if already set). NOTE: This member function will always restore the normalized exception, which may or may not be the original Python exception. WARNING: The GIL must be held when this member function is called!

inline void discard_as_unraisable(object err_context)
    If it is impossible to raise the currently-held error, such as in a destructor, we can write it out using Python's unraisable hook (sys.unraisablehook). The error context should be some object whose repr() helps identify the location of the error. Python already knows the type and value of the error, so there is no need to repeat that.

inline void discard_as_unraisable(const char *err_context)
    An alternate version of discard_as_unraisable(), where a string provides information on the location of the error. For example, __func__ could be helpful. WARNING: The GIL must be held when this member function is called!

inline bool matches(handle exc) const
    Check if the currently trapped error type matches the given Python exception class (or a subclass thereof). May also be passed a tuple to search for any exception class matches in the given tuple.

class builtin_exception : public std::runtime_error
    C++ bindings of builtin Python exceptions.

    Subclassed by attribute_error, buffer_error, cast_error, import_error, index_error, key_error, reference_cast_error, stop_iteration, type_error, value_error

    Public Functions

virtual void set_error() const = 0
    Set the error using the Python C API.

  Literals

namespace literals

  CMake helpers

Pybind11 can be used with add_subdirectory(extern/pybind11), or from an install with find_package(pybind11 CONFIG). The interface provided in either case is functionally identical.

  pybind11Config.cmake

  Exported variables

This module sets the following variables in your project:

pybind11_FOUND
    true if pybind11 and all required components found on the system

pybind11_VERSION
    pybind11 version in format Major.Minor.Release

pybind11_VERSION_TYPE
    pybind11 version type (dev* or empty for a release)

pybind11_INCLUDE_DIRS
    Directories where pybind11 and python headers are located.

pybind11_INCLUDE_DIR
    Directory where pybind11 headers are located.

pybind11_DEFINITIONS
    Definitions necessary to use pybind11, namely USING_pybind11.

pybind11_LIBRARIES
    Compile flags and python libraries (as needed) to link against.

pybind11_LIBRARY
    Empty.

    Available components: None

  Exported targets

If pybind11 is found, this module defines the following IMPORTED interface library targets:

pybind11::module
    for extension modules.

pybind11::embed
    for embedding the Python interpreter.

    Python headers, libraries (as needed by platform), and the C++ standard are attached to the target.

    Advanced targets are also supplied - these are primary for users building complex applications, and they are available in all modes:

pybind11::headers
    Just the pybind11 headers and minimum compile requirements.

pybind11::pybind11
    Python headers too.

pybind11::python_link_helper
    Just the "linking" part of pybind11:module, for CMake < 3.15.

pybind11::thin_lto
    An alternative to INTERPROCEDURAL_OPTIMIZATION.

pybind11::lto
    An alternative to INTERPROCEDURAL_OPTIMIZATION (also avoids thin LTO on clang).

pybind11::windows_extras
    Adds bigobj and mp for MSVC.

  Modes

There are two modes provided; classic, which is built on the old Python discovery packages in CMake, or the new FindPython mode, which uses FindPython from 3.12+ forward (3.15+ _highly_ recommended). If you set the minimum or maximum version of CMake to 3.27+, then FindPython is the default (since FindPythonInterp/FindPythonLibs has been removed via policy CMP0148).

Starting in pybind11 3.0, the new mode is the default, but we provide backward compatible names (PYTHON_* vs. the new Python_* names). Set the mode explicitly to avoid the compatibility defines. You can specify this mode explicitly by setting PYBIND11_FINDPYTHON to COMPAT, but if you are changing your CMakeLists anyway, please just use the ON mode. A future release may default to ON.

  New FindPython mode

To activate this mode, either call find_package(Python COMPONENTS Interpreter Development) before finding this package, or set the PYBIND11_FINDPYTHON variable to ON. In this mode, you can either use the basic targets, or use the FindPython tools:

    find_package(Python COMPONENTS Interpreter Development)
    find_package(pybind11 CONFIG)
    # pybind11 method:
    pybind11_add_module(MyModule1 src1.cpp)
    # Python method:
    Python_add_library(MyModule2 src2.cpp)
    target_link_libraries(MyModule2 PUBLIC pybind11::headers)
    set_target_properties(MyModule2 PROPERTIES
                                    INTERPROCEDURAL_OPTIMIZATION ON
                                    CXX_VISIBILITY_PRESET hidden
                                    VISIBILITY_INLINES_HIDDEN ON)

If you build targets yourself, you may be interested in stripping the output for reduced size; this is the one other feature that the helper function gives you.

  Classic mode

Set PythonLibsNew variables to influence python detection and CMAKE_CXX_STANDARD to influence standard setting.

    find_package(pybind11 CONFIG REQUIRED)
    # Create an extension module
    add_library(mylib MODULE main.cpp)
    target_link_libraries(mylib PUBLIC pybind11::module)
    # Or embed the Python interpreter into an executable
    add_executable(myexe main.cpp)
    target_link_libraries(myexe PUBLIC pybind11::embed)

  Hints

The following variables can be set to guide the search for this package:

pybind11_DIR
    CMake variable, set to directory containing this Config file.

CMAKE_PREFIX_PATH
    CMake variable, set to root directory of this package.

PATH
    Environment variable, set to bin directory of this package.

CMAKE_DISABLE_FIND_PACKAGE_pybind11
    CMake variable, disables find_package(pybind11) when not REQUIRED, perhaps to force internal build.

  Commands

  pybind11_add_module

This module defines the following commands to assist with creating Python modules:

    pybind11_add_module(<target>
      [STATIC|SHARED|MODULE]
      [THIN_LTO] [OPT_SIZE] [NO_EXTRAS] [WITHOUT_SOABI]
      <files>...
      )

Add a module and setup all helpers. You can select the type of the library; the default is MODULE. There are several options:

OPT_SIZE
    Optimize for size, even if the CMAKE_BUILD_TYPE is not MinSizeRel.

THIN_LTO
    Use thin LTO instead of regular if there's a choice (pybind11's selection is disabled if CMAKE_INTERPROCEDURAL_OPTIMIZATIONS is set).

WITHOUT_SOABI
    Disable the SOABI component (PYBIND11_FINDPYTHON mode only).

NO_EXTRAS
    Disable all extras, exit immediately after making the module.

  pybind11_strip

    pybind11_strip(<target>)

Strip a target after building it (linux/macOS), called by pybind11_add_module.

  pybind11_extension

    pybind11_extension(<target>)

Sets the Python extension name correctly for Python on your platform, called by pybind11_add_module.

  pybind11_find_import(module)

    pybind11_find_import(<module> [VERSION <number>] [REQUIRED] [QUIET])

See if a module is installed. Use the registered name (the one on PyPI). You can specify a VERSION, and you can specify REQUIRED or QUIET. Only available if NOPYTHON mode is not active. Sets module_VERSION and module_FOUND. Caches the result once a valid install is found.

  Suggested usage

Using find_package with version info is not recommended except for release versions.

    find_package(pybind11 CONFIG)
    find_package(pybind11 2.9 EXACT CONFIG REQUIRED)

AUTHOR
Wenzel Jakob

COPYRIGHT
2017, Wenzel Jakob