NumPy 2.5.0rc1：移除 distutils，弃用多项 API

NumPy 2.5.0 Release Notes

Numpy 2.5.0 is a transitional release. It drops support for Python 3.11,

marking the end of distutils, and expires a large number of deprecations made

in the 2.0.x release. It also improves free threading and brings sorting into

compliance with the array-api standard with the addition of descending sorts.

Python 3.15 will be supported when it is released.

This release supports Python versions 3.12-3.14.

Highlights

• Distutils has been removed,
• Many expired deprecations, see below,
• Many new deprecations, see below,
• Many static typing improvements.
• Improved support for free threading,
• Support for descending sorts,

See New Features below for other additions.

Deprecations

• numpy.char.chararray is deprecated. Use an ndarray with a string or bytes dtype instead.
• (gh-30605)
• numpy.take now correctly checks if the result can be cast to the provided
• out=out under the same-kind rule. A DeprecationWarning is given now
• when this check fails. Previously, take incorrectly checked if out
• could be cast to the result (the wrong direction). This deprecation also
• affects compress and possibly other functions. (Future versions of NumPy
• may tighten the casting check further.)
• (gh-30615)
• The numpy.char.[as]array functions are deprecated. Use an
• numpy.[as]array with a string or bytes dtype instead.
• (gh-30802)
• Setting the dtype attribute is deprecated because mutating an array is unsafe
• if an array is shared, especially by multiple threads. As an alternative,
• you can create a view with a new dtype via array.view(dtype=new_dtype).
• (gh-29244)
• Setting the shape attribute is deprecated because mutating an array is
• unsafe if an array is shared, especially by multiple threads. As an
• alternative, you can create a new view via np.reshape or
• np.ndarray.reshape. For example: x = np.arange(15); x = np.reshape(x, (3, 5)).
• To ensure no copy is made from the data, one can use np.reshape(..., copy=False).
• While setting the shape on an array is discouraged, for cases where it is
• difficult to work around, e.g., in __array_finalize__, it is possible
• with the private method np.ndarray._set_shape.
• (gh-29536)
• Using the generic unit in numpy.timedelta64 is deprecated since this
• can lead to unexpected behavior such as non-transitive comparison, see
• gh-28287 for details. As
• an alternative, specify an explicit unit such as 's' (seconds) or 'D'
• (days) when constructing numpy.timedelta64. Due to this change, operations
• that implicitly rely on the generic unit are also deprecated. For
• example:

arr = np.array([1, 2, 3], dtype="m8[s]")
# `1` is implicitly converted to generic timedelta64
arr + 1

• (gh-29619)
• Resizing a Numpy array in place is deprecated since mutating an array is
• unsafe if an array is shared, especially by multiple threads. As an
• alternative, you can create a resized array via np.resize.
• (gh-30181)
• numpy.fix is deprecated, use numpy.trunc instead. It is faster and
• follows the Array API standard. Both functions provide identical
• functionality: rounding array elements towards zero.
• (gh-30644)
• numpy.ma.round_ is deprecated. numpy.ma.round can be used as a
• replacement.
• (gh-30738)
• numpy.typename is deprecated because the names returned by it were
• outdated and inconsistent. numpy.dtype.name can be used as a
• replacement.
• (gh-30774)
• Inputs other than integers are deprecated for numpy.triu_indices and
• numpy.tril_indices. Non-integer values for the M, k and N
• parameters of numpy.tri are deprecated. Non-integer values for the k
• parameter of both numpy.tril_indices_from and numpy.triu_indices_from
• are deprecated.
• (gh-30869)
• Deprecations in custom dtype property and __array_finalize__.
• Previously arr.view(dtype=new_dtype) called arr.dtype = new_dtype
• also for subclasses, i.e., the attribute setting. That path is now
• deprecated and refined, meaning that even subclasses that do not see this
• DeprecationWarning may wish to update their code.
• A subclass that does any dtype specific logic (i.e. verifying the dtype
• in __array_finalize__ or has a dtype property) should now:
• Set _set_dtype = None in which case arr.view(dtype=new_dtype)
• will call __array_finalize__ with the new dtype, ensuring that
• any validation __array_finalize__ will run is done.
• Or, for a quick fix, define _set_dtype as a function (calling
• ndarray._set_dtype() to avoid DeprecationWarnings.
• (Future versions might migrate towards the _set_dtype = None path.)
• Ideally, follow NumPy's deprecation to prevent dtype mutation by users.
• The use of ndarray._set_dtype() may be necessary for some subclass
• finalization patterns, but should otherwise be avoided.
• (gh-31293)

Expired deprecations

• numpy.distutils has been removed
• (gh-30340)
• Passing None as dtype to np.finfo will now raise a TypeError
• (deprecated since 1.25)
• (gh-30460)
• numpy.cross no longer supports 2-dimensional vectors.
• (Deprecated since 2.0)
• (gh-30461)
• numpy._core.numerictypes.maximum_sctype has been removed.
• (deprecated since 2.0)
• (gh-30462)
• numpy.row_stack has been removed in favor of numpy.vstack.
• (deprecated since 2.0)
• (gh-30463)
• get_array_wrap has been removed.
• (deprecated since 2.0)
• (gh-30463)
• recfromtxt and recfromcsv have been removed from numpy.lib._npyio
• in favor of numpy.genfromtxt.
• (deprecated since 2.0)
• (gh-30467)
• The numpy.chararray re-export of numpy.char.chararray has been removed.
• (deprecated since 2.0)
• (gh-30604)
• bincount now raises a TypeError for non-integer inputs.
• (deprecated since 2.1)
• (gh-30610)
• The numpy.lib.math alias for the standard library math module has
• been removed.
• (deprecated since 1.25)
• (gh-30612)
• Data type alias 'a' was removed in favor of 'S'.
• (deprecated since 2.0)
• (gh-30613)
• _add_newdoc_ufunc(ufunc, newdoc) has been removed in favor of
• ufunc.__doc__ = newdoc.
• (deprecated since 2.2)
• (gh-30614)

Compatibility notes

linalg.eig and linalg.eigvals now always return complex arrays

Previously, the return values depended on whether the eigenvalues happen to lie

on the real line (which, for a general, non-symmetric matrix, is not

guaranteed).

This change makes consistent what was a value-dependent result. To retain the

previous behavior, do:

w = eigvals(a)
if np.any(w.imag == 0):  # this is what NumPy used to do
    w = w.real

If your matrix is symmetrix/hermitian, use eigh and eigvalsh instead of

eig and eigvals. These are guaranteed to return real values. A common

case is covariance matrices, which are symmetric and positive definite by

construction.

(gh-30411)

MSVC support

NumPy now requires minimum MSVC 19.35 toolchain version on Windows platforms.

This corresponds to Visual Studio 2022 version 17.5 Preview 2 or newer.

(gh-30489)

Cython support

NumPy's Cython headers (accessed via cimport numpy) now require Cython 3.0

or newer to build. If you try to compile a project that depends on NumPy's

Cython headers using Cython 0.29 or older, you will see a message like this:

Error compiling Cython file:
------------------------------------------------------------
...
# versions.
#
# See __init__.cython-30.pxd for the real Cython header
#
DEF err = int('Build aborted: the NumPy Cython headers require Cython 3.0.0 or newer.')
  ------------------------------------------------------------
  /path/to/site-packages/numpy/__init__.pxd:11:13: Error in compile-time expression:
  ValueError: invalid literal for int() with base 10:
  'Build aborted: the NumPy Cython headers require Cython 3.0.0 or newer.'

Note that the invalid integer is not a bug in NumPy - we are intentionally

generating this error to avoid triggering a more obscure error later in the

build when an older Cython version tries to use a Cython feature that was not