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.
__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.
We aim to make it much easier to extend NumPy. The primary topic here is to improve the dtype system.
Easier custom dtypes:
Simplify and/or wrap the current C-API
More consistent support for dtype metadata
Support for writing a dtype in Python
New string dtype(s):
Encoded strings with fixed-width storage (utf8, latin1, …) and/or
Variable length strings (could share implementation with dtype=object, but are explicitly type-checked)
One of these should probably be the default for text data. The current behavior on Python 3 is neither efficient nor user friendly.
Improvements to NumPy’s performance are important to many users. The primary topic at the moment is better use of SIMD instructions, also on platforms other than x86 - see NEP 38 — Using SIMD optimization instructions for performance.
Other performance improvement ideas include:
Reducing ufunc and
Optimizations in individual functions.
A better story around parallel execution.
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.
Website and documentation¶
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 The API documentation is in good shape; tutorials and high-level documentation on many topics are missing or outdated.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.
Our website (https://numpy.org) was completely redesigned recently. We aim to further improve it by adding translations, better Hugo-Sphinx integration via a new Sphinx theme, and more (see this tracking issue).
We aim to add type annotations for all NumPy functionality, so users can use tools like mypy to type check their code and IDEs can improve their support for NumPy. The existing type stubs in the numpy-stubs repository are being improved and will be moved into the NumPy code base.
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 ARM64 (
aarch64) and POWER8/9 (
ppc64le), providing better
build and install documentation, and resolving build issues on other platforms
MaskedArrayneeds 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.f2pyrequires a number of improvements, see this tracking issue.
A backend system for
numpy.fft(so that e.g.
fft-mkldoesn’t need to monkeypatch numpy).
Write a strategy on how to deal with overlap between NumPy and SciPy for
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.