NumPy roadmap#

This is a live snapshot of tasks and features we will be investing resources in. It may be used to encourage and inspire developers and to search for funding.


We aim to make it easier to interoperate with NumPy. There are many NumPy-like packages that add interesting new capabilities to the Python ecosystem, as well as many libraries that extend NumPy’s model in various ways. Work in NumPy to facilitate interoperability with all such packages, and the code that uses them, may include (among other things) interoperability protocols, better duck typing support and ndarray subclass handling.

The key goal is: make it easy for code written for NumPy to also work with other NumPy-like projects. This will enable GPU support via, e.g, CuPy or JAX, distributed array support via Dask, and writing special-purpose arrays (either from scratch, or as a numpy.ndarray subclass) that work well with SciPy, scikit-learn and other such packages.

The __array_ufunc__ and __array_function__ protocols are stable, but do not cover the whole API. New protocols for overriding other functionality in NumPy are needed. Work in this area aims to bring to completion one or more of the following proposals:

In addition we aim to provide ways to make it easier for other libraries to implement a NumPy-compatible API. This may include defining consistent subsets of the API, as discussed in this section of NEP 37.


Improvements to NumPy’s performance are important to many users. We have focused this effort on Universal SIMD (see NEP 38 — Using SIMD optimization instructions for performance) intrinsics which provide nice improvements across various hardware platforms via an abstraction layer. The infrastructure is in place, and we welcome follow-on PRs to add SIMD support across all relevant NumPy functions.

Other performance improvement ideas include:

  • A better story around parallel execution.

  • Optimizations in individual functions.

  • Reducing ufunc and __array_function__ overhead.

Furthermore we would like to improve the benchmarking system, in terms of coverage, easy of use, and publication of the results (now here) as part of the docs or website.

Documentation and website#

The NumPy documentation is of varying quality. The API documentation is in good shape; tutorials and high-level documentation on many topics are missing or outdated. See NEP 44 — Restructuring the NumPy documentation for planned improvements. Adding more tutorials is underway in the numpy-tutorials repo.

Our website ( was completely redesigned recently. We aim to further improve it by adding translations, more case studies and other high-level content, and more (see this tracking issue).


We aim to make it much easier to extend NumPy. The primary topic here is to improve the dtype system - see NEP 41 — First step towards a new datatype system and related NEPs linked from it. Concrete goals for the dtype system rewrite are:

  • Easier custom dtypes:

    • Simplify and/or wrap the current C-API

    • More consistent support for dtype metadata

    • Support for writing a dtype in Python

  • Allow adding (a) new string dtype(s). This could be encoded strings with fixed-width storage (e.g., utf8 or latin1), and/or a variable length string dtype. The latter could share an implementation with dtype=object, but be explicitly type-checked. One of these should probably be the default for text data. The current string dtype support is neither efficient nor user friendly.

User experience#

Type annotations#

NumPy 1.20 adds type annotations for most NumPy functionality, so users can use tools like mypy to type check their code and IDEs can improve their support for NumPy. Improving those type annotations, for example to support annotating array shapes and dtypes, is ongoing.

Platform support#

We aim to increase our support for different hardware architectures. This includes adding CI coverage when CI services are available, providing wheels on PyPI for POWER8/9 (ppc64le), providing better build and install documentation, and resolving build issues on other platforms like AIX.


  • MaskedArray needs to be improved, ideas include:

    • Rewrite masked arrays to not be a ndarray subclass – maybe in a separate project?

    • MaskedArray as a duck-array type, and/or

    • dtypes that support missing values

  • Fortran integration via numpy.f2py requires a number of improvements, see this tracking issue.

  • A backend system for numpy.fft (so that e.g. fft-mkl doesn’t need to monkeypatch numpy).

  • Write a strategy on how to deal with overlap between NumPy and SciPy for linalg.

  • Deprecate np.matrix (very slowly).

  • Add new indexing modes for “vectorized indexing” and “outer indexing” (see NEP 21 — Simplified and explicit advanced indexing).

  • Make the polynomial API easier to use.

  • Integrate an improved text file loader.

  • Ufunc and gufunc improvements, see gh-8892 and gh-11492.