NEP 53 — Evolving the NumPy C-API for NumPy 2.0#


Sebastian Berg <>








The NumPy C-API is used in downstream projects (often through Cython) to extend NumPy functionality. Supporting these packages generally means that it is slow to evolve our C-API and some changes are not possible in a normal NumPy release because NumPy must guarantee backwards compatibility: A downstream package compiled against an old NumPy version (e.g. 1.17) will generally work with a new NumPy version (e.g. 1.25).

A NumPy 2.0 release allows to partially break this promise: We can accept that a SciPy version compiled with NumPy 1.17 (e.g. SciPy 1.10) will not work with NumPy 2.0. However, it must still be easy to create a single SciPy binary that is compatible with both NumPy 1.x and NumPy 2.0.

Given these constraints this NEP outlines a path forward to allow large changes to our C-API. Similar to Python API changes proposed for NumPy 2.0 the NEP aims to allow changes to an extend that most downstream packages are expected to need no or only minor code changes.

The implementation of this NEP consists would consist of two steps: 1. As part of a general improvement, starting with NumPy 1.25 building with

NumPy will by default export an older API version to allow backwards compatible builds with the newest available NumPy version. (New API is not available unless opted-in.)

  1. The NumPy 2.0 will:

    • require recompilation of downstream packages against NumPy 2.0 to be compatible with NumPy 2.0.

    • need a numpy2_compat as a dependency when running on NumPy 1.x.

    • require some downstream code changes to adapt to changed API.

Motivation and Scope#

The NumPy API conists of more than 300 functions and numerous macros. Many of these are outdated: some were only ever used within NumPy, exist only for compatibility with NumPy’s predecessors, or have no or only a single known downstream user (i.e. SciPy).

Further, many structs used by NumPy have always been public making it impossible to change them outside of a major release. Some changes have been planned for years and were the reason for NPY_NO_DEPRECATED_API and further deprecations as explained in C API Deprecations.

While we probably have little reason to change the layout of the array struct (PyArrayObject_fields) for example the development and improvement of dtypes would be made easier by changing the PyArray_Descr struct.

This NEP proposes a few concrete changes to our C-API mainly as examples. However, more changes will be handled on a case-by-case basis, and we do not aim to provide a full list of changes in this NEP.

Adding state is out of scope#

New developments such as CPython’s support for subinterpreters and the HPy API may require the NumPy C-API to evolve in a way that may require (or at least prefer) state to be passed in.

As of now, we do not aim to include changes for this here. We cannot expect users to do large code updates to pass in for example an HPy context to many NumPy functions.

While we could introduce a second API for this purpose in NumPy 2.0, we expect that this is unnecessary and that the provisions introduced here:

  • the ability to compile with the newest NumPy version but be compatible with older versions,

  • and the possibility of updating a numpy2_compat package.

should allow to add such an API also in a minor release.

Usage and Impact#

Backwards compatible builds#

Backwards compatible builds will be described in more details in the documentation. Briefly, we will allow users to use a definition like:


to select the version they wish to compile for (lowest version to be compatible with). By default the backwards compatibility will be such that the resulting binary is compatible with the oldest NumPy version which supports the same version of Python: NumPy 1.19.x was the first to support Python 3.9 and NumPy 1.25 supports Python 3.9 or greater, so NumPy 1.25 defaults to 1.19 compatibility. Thus, users of new API may be required to add the define, but users of who want to be compatible with older versions need not do anything unless they wish to have exceptionally long compatibility.

The API additions in the past years were so limited that such a change should be necessary at most for a hand-full of users worldwide.

This mechanism is much the same as the Python limited API since NumPy’s C-API has a similar need for ABI stability.

Breaking the C-API and changing the ABI#

NumPy has too many functions, many of which are aliases. The following lists examples of things we plan to remove and users will have to adapt to be compatible with NumPy 2.0:

  • PyArray_Mean and PyArray_Std are untested implementation similar to arr.mean() and arr.std(). We are planning on removing these as they can be replaced with method calls relatively easily.

  • The MapIter set of API functions (and struct) allows to implement advanced indexing like semantics downstream. There was a single historic known user of this (theano) and the use-case would be faster and easier to implement in a different way. The API is complicated, requires reaching deep into NumPy to be useful and its exposure makes the implementation more difficult. Unless new important use cases are found, we propose to remove it.

An example for an ABI change is to change the layout of PyArray_Descr (the struct of np.dtype instances) to allow a larger maximum itemsize and new flags (useful for future custom user DTypes). For this specific change, users who access the structs fields directly will have to change their code. A downstream search shows that this should not be very common, the main impacts are:

  • Access of the descr->elsize field (and others) would have to be replaced with a macro’s like PyDataType_ITEMSIZE(descr) (NumPy may include a version check when needed).

  • Implementers of user defined dtypes, will have to change a few lines of code and luckily, there are very few of such user defined dtypes. (The details are that we rename the struct to PyArray_DescrProto for the static definition and fetch the actual instance from NumPy explicitly.)

A last example is increasing NPY_MAXDIMS to 64. NPY_MAXDIMS is mainly used to statically allocate scratch space:

func(PyArrayObject *arr) {
    npy_intp shape[NPY_MAXDIMS];
    /* Work with a shape or strides from the array */

If NumPy changed it to 64 in a minor release, this would lead to undefined behavior if the code was compiled with NPY_MAXDIMS=32 but a 40 dimensional array is passed in. But the larger value is also a correct maximum on previous versions of NumPy making it generally safe for NumPy 2.0 change. (One can imagine code that wants to know the actual runtime value. We have not seen such code in practice, but it would need to be adjusted.)

Impact on Cython users#

Cython users may use the NumPy C-API via cimport numpy as cnp. Due to the uncertainty of Cython development, there are two scenarios for impact on Cython users.

If Cython 3 can be relied on, Cython users would be impacted less than C-API users, because Cython 3 allows us to hide struct layout changes (i.e. changes to PyArray_Descr). If this is not the case and we must support Cython 0.29.x (which is the historic branch before Cython 3), then Cython users will also have to use a function/macro like PyDataType_ITEMSIZE() (or use the Python object). This is unfortunately less typical in Cython code, but also unlikely to be a common pattern for dtype struct fields/attributes.

A further impact is that some future API additions such as new classes may need to placed in a distinct .pyd file to avoid Cython generating code that would fail on older NumPy versions.

End-user and packaging impact#

Packaging in a way that is compatible with NumPy 2.0 will require a recompilation of downstream libraries that rely on the NumPy C-API. This may take some time, although hopefully the process will start before NumPy 2.0 is itself released.

Further, to allow bigger changes more easily in NumPy 2.0, we expect to create a numpy2_compat package. When a library is build with NumPy 2.0 but wants to support NumPy 1.x it will have to depend on numpy2_compat. End-users should not need to be aware of this dependency and an informative error can be raised when the module is missing.

Some new API can be backported#

One large advantage of allowing users to compile with the newest version of NumPy is that in some cases we will be able to backport new API. Some new API functions can be written in terms of old ones or included directly.


It may be possible to make functions public that were present but private in NumPy 1.x public via the compatible numpy2_compat package.

This means that at some new API additions could be made available to downstreams users faster. They would require a new NumPy version for compilation but their wheels can be backwards compatible with earlier versions.


The first part of implementation (allowing building for an earlier API version) is very straight forward since the NumPy C-API evolved slowly for many years. Some struct fields will be hidden by default and functions introduced in a more recent version will be marked and hidden unless the user opted in to a newer API version. An implementation can be found in the PR 23528.

The second part is mainly about identifying and implementing the desired changes in a way that backwards compatibility will not be broken and API breaks remain manageable for downstream libraries. Every change we do must have a brief note on how to adapt to the API change (i.e. alternative functions).

NumPy 2 compatibility and API table changes#

To allow changing the API table, NumPy 2.0 would ship a different table than NumPy 1.x (a table is a list of functions and symbols).

For compatibility we would need to translate the 1.x table to the 2.0 table. This could be done in headers only in theory, but this seems unwieldy. We thus propose to add a numpy2_compat package. This package’s main purpose would be to provide a translation of the 1.x table to the 2.x one in a single place (filling in any necessary blanks).

Introducing this package solves the “transition” issue because it allows a user to:

  • Install a SciPy version that is compatible with 2.0 and 1.x

  • and keep using NumPy 1.x because of other packages they are using are not yet compatible.

The import of numpy2_compat (and an error when it is missing) will be inserted by the NumPy eaders as part of the import_array() call.


There are always possibilities to decide not to do certain changes (e.g. due to downstream users noting their continued need for it). For example, the function PyArray_Mean could be replaced by one to call array.mean() if necessary.

The NEP proposes to allow larger changes to our API table by introducing a compatibility package numpy2_compat. We could do many changes without introducing such a package.

The default API version could be chosen to be older or as the current one. An older version would be aimed at libraries who want a larger compatibility than NEP 29 suggests. Choosing the current would default to removing unnecessary compatibility shims for users who do not distribute wheels. The suggested default chooses to favors libraries that distribute wheels and wish a compatibility range similar to NEP 29. This is because compatibility shims should be light-weight and we expect few libraries require a longer compatibility.

Backward compatibility#

As mentioned above backwards compatibility is achieved by:

  1. Forcing downstream to recompile with NumPy 2.0

  2. Providing a numpy2_compat library.

But relies on users to adapt to changed C-API as described in the Usage and Impact section.


  • numpy/numpy#5888 brought up previously that it would be helpful to allow exporting of an older API version in our headers. This was never implemented, instead we relied on oldest-support-numpy.

  • A first draft of this proposal was presented at the NumPy 2.0 planning meeting 2023-04-03.

References and Footnotes#