NumPy 2.1.0 Release Notes

NumPy 2.1.0 provides support for the upcoming Python 3.13 release and
drops support for Python 3.9. In addition to the usual bug fixes and
updated Python support, it helps get us back into our usual release
cycle after the extended development of 2.0. The highlights for this
release are:

• Support for the array-api 2023.12 standard.
• Support for Python 3.13.
• Preliminary support for free threaded Python 3.13.

Python versions 3.10-3.13 are supported in this release.

New functions

New function numpy.unstack

A new function np.unstack(array, axis=...) was added, which splits an
array into a tuple of arrays along an axis. It serves as the inverse of
[numpy.stack]{.title-ref}.

(gh-26579)

Deprecations

• The fix_imports keyword argument in numpy.save is deprecated.
  Since NumPy 1.17, numpy.save uses a pickle protocol that no longer
  supports Python 2, and ignored fix_imports keyword. This keyword
  is kept only for backward compatibility. It is now deprecated.

  (gh-26452)

• Passing non-integer inputs as the first argument of
  [bincount]{.title-ref} is now deprecated, because such inputs are
  silently cast to integers with no warning about loss of precision.

  (gh-27076)

Expired deprecations

• Scalars and 0D arrays are disallowed for numpy.nonzero and
  numpy.ndarray.nonzero.

  (gh-26268)

• set_string_function internal function was removed and
  PyArray_SetStringFunction was stubbed out.

  (gh-26611)

C API changes

API symbols now hidden but customizable

NumPy now defaults to hide the API symbols it adds to allow all NumPy
API usage. This means that by default you cannot dynamically fetch the
NumPy API from another library (this was never possible on windows).

If you are experiencing linking errors related to PyArray_API or
PyArray_RUNTIME_VERSION, you can define the NPY_API_SYMBOL_ATTRIBUTE
to opt-out of this change.

If you are experiencing problems due to an upstream header including
NumPy, the solution is to make sure you
#include "numpy/ndarrayobject.h" before their header and import NumPy
yourself based on including-the-c-api.

(gh-26103)

Many shims removed from npy_3kcompat.h

Many of the old shims and helper functions were removed from
npy_3kcompat.h. If you find yourself in need of these, vendor the
previous version of the file into your codebase.

(gh-26842)

New PyUFuncObject field process_core_dims_func

The field process_core_dims_func was added to the structure
PyUFuncObject. For generalized ufuncs, this field can be set to a
function of type PyUFunc_ProcessCoreDimsFunc that will be called when
the ufunc is called. It allows the ufunc author to check that core
dimensions satisfy additional constraints, and to set output core
dimension sizes if they have not been provided.

(gh-26908)

New Features

Preliminary Support for Free-Threaded CPython 3.13

CPython 3.13 will be available as an experimental free-threaded build.
See https://py-free-threading.github.io, PEP 703 and the
CPython 3.13 release notes for more detail about free-threaded Python.

NumPy 2.1 has preliminary support for the free-threaded build of CPython
3.13. This support was enabled by fixing a number of C thread-safety
issues in NumPy. Before NumPy 2.1, NumPy used a large number of C global
static variables to store runtime caches and other state. We have either
refactored to avoid the need for global state, converted the global
state to thread-local state, or added locking.

Support for free-threaded Python does not mean that NumPy is thread
safe. Read-only shared access to ndarray should be safe. NumPy exposes
shared mutable state and we have not added any locking to the array
object itself to serialize access to shared state. Care must be taken in
user code to avoid races if you would like to mutate the same array in
multiple threads. It is certainly possible to crash NumPy by mutating an
array simultaneously in multiple threads, for example by calling a ufunc
and the resize method simultaneously. For now our guidance is:
"don't do that". In the future we would like to provide stronger
guarantees.

Object arrays in particular need special care, since the GIL previously
provided locking for object array access and no longer does. See
Issue #27199 for more information about object
arrays in the free-threaded build.

If you are interested in free-threaded Python, for example because you
have a multiprocessing-based workflow that you are interested in running
with Python threads, we encourage testing and experimentation.

If you run into problems that you suspect are because of NumPy, please
open an issue,
checking first if the bug also occurs in the "regular" non-free-threaded CPython 3.13
build. Many threading bugs can also occur in code that releases
the GIL; disabling the GIL only makes it easier to hit threading bugs.

(gh-26157)

f2py can generate freethreading-compatible C extensions

Pass --freethreading-compatible to the f2py CLI tool to produce a C
extension marked as compatible with the free threading CPython
interpreter. Doing so prevents the interpreter from re-enabling the GIL
at runtime when it imports the C extension. Note that f2py does not
analyze fortran code for thread safety, so you must verify that the
wrapped fortran code is thread safe before marking the extension as
compatible.

(gh-26981)

• numpy.reshape and numpy.ndarray.reshape now support shape and
  copy arguments.

  (gh-26292)

• NumPy now supports DLPack v1, support for older versions will be
  deprecated in the future.

  (gh-26501)

• numpy.asanyarray now supports copy and device arguments,
  matching numpy.asarray.

  (gh-26580)

• numpy.printoptions, numpy.get_printoptions, and
  numpy.set_printoptions now support a new option, override_repr,
  for defining custom repr(array) behavior.

  (gh-26611)

• numpy.cumulative_sum and numpy.cumulative_prod were added as
  Array API compatible alternatives for numpy.cumsum and
  numpy.cumprod. The new functions can include a fixed initial
  (zeros for sum and ones for prod) in the result.

  (gh-26724)

• numpy.clip now supports max and min keyword arguments which
  are meant to replace a_min and a_max. Also, for np.clip(a) or
  np.clip(a, None, None) a copy of the input array will be returned
  instead of raising an error.

  (gh-26724)

• numpy.astype now supports device argument.

  (gh-26724)

Improvements

histogram auto-binning now returns bin sizes >=1 for integer input data

For integer input data, bin sizes smaller than 1 result in spurious
empty bins. This is now avoided when the number of bins is computed
using one of the algorithms provided by histogram_bin_edges.

(gh-12150)

ndarray shape-type parameter is now covariant and bound to tuple[int, ...]

Static typing for ndarray is a long-term effort that continues with
this change. It is a generic type with type parameters for the shape and
the data type. Previously, the shape type parameter could be any value.
This change restricts it to a tuple of ints, as one would expect from
using ndarray.shape. Further, the shape-type parameter has been
changed from invariant to covariant. This change also applies to the
subtypes of ndarray, e.g. numpy.ma.MaskedArray. See the
typing docs
for more information.

(gh-26081)

np.quantile with method closest_observation chooses nearest even order statistic

This changes the definition of nearest for border cases from the nearest
odd order statistic to nearest even order statistic. The numpy
implementation now matches other reference implementations.

(gh-26656)

lapack_lite is now thread safe

NumPy provides a minimal low-performance version of LAPACK named
lapack_lite that can be used if no BLAS/LAPACK system is detected at
build time.

Until now, lapack_lite was not thread safe. Single-threaded use cases
did not hit any issues, but running linear algebra operations in
multiple threads could lead to errors, incorrect results, or segfaults
due to data races.

We have added a global lock, serializing access to lapack_lite in
multiple threads.

(gh-26750)

The numpy.printoptions context manager is now thread and async-safe

In prior versions of NumPy, the printoptions were defined using a
combination of Python and C global variables. We have refactored so the
state is stored in a python ContextVar, making the context manager
thread and async-safe.

(gh-26846)

Type hinting numpy.polynomial

Starting from the 2.1 release, PEP 484 type annotations have been
included for the functions and convenience classes in numpy.polynomial
and its sub-packages.

(gh-26897)

Improved numpy.dtypes type hints

The type annotations for numpy.dtypes are now a better reflection of
the runtime: The numpy.dtype type-aliases have been replaced with
specialized dtype subtypes, and the previously missing annotations
for numpy.dtypes.StringDType have been added.

(gh-27008)

Performance improvements and changes

• numpy.save now uses pickle protocol version 4 for saving arrays
  with object dtype, which allows for pickle objects larger than 4GB
  and improves saving speed by about 5% for large arrays.

  (gh-26388)

• OpenBLAS on x86_64 and i686 is built with fewer kernels. Based on
  benchmarking, there are 5 clusters of performance around these
  kernels: PRESCOTT NEHALEM SANDYBRIDGE HASWELL SKYLAKEX.

  (gh-27147)

• OpenBLAS on windows is linked without quadmath, simplifying
  licensing

  (gh-27147)

• Due to a regression in OpenBLAS on windows, the performance
  improvements when using multiple threads for OpenBLAS 0.3.26 were
  reverted.

  (gh-27147)

ma.cov and ma.corrcoef are now significantly faster

The private function has been refactored along with ma.cov and
ma.corrcoef. They are now significantly faster, particularly on large,
masked arrays.

(gh-26285)

Changes

• As numpy.vecdot is now a ufunc it has a less precise signature.
  This is due to the limitations of ufunc's typing stub.

  (gh-26313)

• numpy.floor, numpy.ceil, and numpy.trunc now won't perform
  casting to a floating dtype for integer and boolean dtype input
  arrays.

  (gh-26766)

ma.corrcoef may return a slightly different result

A pairwise observation approach is currently used in ma.corrcoef to
calculate the standard deviations for each pair of variables. This has
been changed as it is being used to normalise the covariance, estimated
using ma.cov, which does not consider the observations for each
variable in a pairwise manner, rendering it unnecessary. The
normalisation has been replaced by the more appropriate standard
deviation for each variable, which significantly reduces the wall time,
but will return slightly different estimates of the correlation
coefficients in cases where the observations between a pair of variables
are not aligned. However, it will return the same estimates in all other
cases, including returning the same correlation matrix as corrcoef
when using a masked array with no masked values.

(gh-26285)

Cast-safety fixes in copyto and full

copyto now uses NEP 50 correctly and applies this to its cast safety.
Python integer to NumPy integer casts and Python float to NumPy float
casts are now considered "safe" even if assignment may fail or
precision may be lost. This means the following examples change
slightly:

• np.copyto(int8_arr, 1000) previously performed an unsafe/same-kind cast
  of the Python integer. It will now always raise, to achieve an
  unsafe cast you must pass an array or NumPy scalar.

• np.copyto(uint8_arr, 1000, casting="safe") will raise an
  OverflowError rather than a TypeError due to same-kind casting.

• np.copyto(float32_arr, 1e300, casting="safe") will overflow to
  inf (float32 cannot hold 1e300) rather raising a TypeError.

Further, only the dtype is used when assigning NumPy scalars (or 0-d
arrays), meaning that the following behaves differently:

• np.copyto(float32_arr, np.float64(3.0), casting="safe") raises.
• np.coptyo(int8_arr, np.int64(100), casting="safe") raises.
  Previously, NumPy checked whether the 100 fits the int8_arr.

This aligns copyto, full, and full_like with the correct NumPy 2
behavior.

(gh-27091)

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