For downstream package authors

This document aims to explain some best practices for authoring a package that depends on NumPy.

Understanding NumPy’s versioning and API/ABI stability

NumPy uses a standard, PEP 440 compliant, versioning scheme: major.minor.bugfix. A major release is highly unusual (NumPy is still at version 1.xx) and if it happens it will likely indicate an ABI break. Minor versions are released regularly, typically every 6 months. Minor versions contain new features, deprecations, and removals of previously deprecated code. Bugfix releases are made even more frequently; they do not contain any new features or deprecations.

It is important to know that NumPy, like Python itself and most other well known scientific Python projects, does not use semantic versioning. Instead, backwards incompatible API changes require deprecation warnings for at least two releases. For more details, see NEP 23 — Backwards compatibility and deprecation policy.

NumPy has both a Python API and a C API. The C API can be used directly or via Cython, f2py, or other such tools. If your package uses the C API, then ABI (application binary interface) stability of NumPy is important. NumPy’s ABI is forward but not backward compatible. This means: binaries compiled against a given version of NumPy will still run correctly with newer NumPy versions, but not with older versions.

Testing against the NumPy main branch or pre-releases

For large, actively maintained packages that depend on NumPy, we recommend testing against the development version of NumPy in CI. To make this easy, nightly builds are provided as wheels at This helps detect regressions in NumPy that need fixing before the next NumPy release. Furthermore, we recommend to raise errors on warnings in CI for this job, either all warnings or otherwise at least DeprecationWarning and FutureWarning. This gives you an early warning about changes in NumPy to adapt your code.

Adding a dependency on NumPy

Build-time dependency

If a package either uses the NumPy C API directly or it uses some other tool that depends on it like Cython or Pythran, NumPy is a build-time dependency of the package. Because the NumPy ABI is only forward compatible, you must build your own binaries (wheels or other package formats) against the lowest NumPy version that you support (or an even older version).

Picking the correct NumPy version to build against for each Python version and platform can get complicated. There are a couple of ways to do this. Build-time dependencies are specified in pyproject.toml (see PEP 517), which is the file used to build wheels by PEP 517 compliant tools (e.g., when using pip wheel).

You can specify everything manually in pyproject.toml, or you can instead rely on the oldest-supported-numpy metapackage. oldest-supported-numpy will specify the correct NumPy version at build time for wheels, taking into account Python version, Python implementation (CPython or PyPy), operating system and hardware platform. It will specify the oldest NumPy version that supports that combination of characteristics. Note: for platforms for which NumPy provides wheels on PyPI, it will be the first version with wheels (even if some older NumPy version happens to build).

For conda-forge it’s a little less complicated: there’s dedicated handling for NumPy in build-time and runtime dependencies, so typically this is enough (see here for docs):

  - numpy
  - {{ pin_compatible('numpy') }}


pip has --no-use-pep517 and --no-build-isolation flags that may ignore pyproject.toml or treat it differently - if users use those flags, they are responsible for installing the correct build dependencies themselves.

conda will always use -no-build-isolation; dependencies for conda builds are given in the conda recipe (meta.yaml), the ones in pyproject.toml have no effect.

Please do not use setup_requires (it is deprecated and may invoke easy_install).

Because for NumPy you have to care about ABI compatibility, you specify the version with == to the lowest supported version. For your other build dependencies you can probably be looser, however it’s still important to set lower and upper bounds for each dependency. It’s fine to specify either a range or a specific version for a dependency like wheel or setuptools. It’s recommended to set the upper bound of the range to the latest already released version of wheel and setuptools - this prevents future releases from breaking your packages on PyPI.

Runtime dependency & version ranges

NumPy itself and many core scientific Python packages have agreed on a schedule for dropping support for old Python and NumPy versions: NEP 29 — Recommend Python and NumPy version support as a community policy standard. We recommend all packages depending on NumPy to follow the recommendations in NEP 29.

For run-time dependencies, you specify the range of versions in install_requires in (assuming you use numpy.distutils or setuptools to build). Getting the upper bound right for NumPy is slightly tricky. If we don’t set any bound, a too-new version will be pulled in a few years down the line, and NumPy may have deprecated and removed some API that your package depended on by then. On the other hand if you set the upper bound to the newest already-released version, then as soon as a new NumPy version is released there will be no matching version of your package that works with it.

What to do here depends on your release frequency. Given that NumPy releases come in a 6-monthly cadence and that features that get deprecated in NumPy should stay around for another two releases, a good upper bound is <1.(xx+3).0 - where xx is the minor version of the latest already-released NumPy. This is safe to do if you release at least once a year. If your own releases are much less frequent, you may set the upper bound a little further into the future - this is a trade-off between a future NumPy version _maybe_ removing something you rely on, and the upper bound being exceeded which _may_ lead to your package being hard to install in combination with other packages relying on the latest NumPy.


SciPy has more documentation on how it builds wheels and deals with its build-time and runtime dependencies here.

NumPy and SciPy wheel build CI may also be useful as a reference, it can be found here for NumPy and here for SciPy.