v1.25.0

NumPy 1.25.0 Release Notes

The NumPy 1.25.0 release continues the ongoing work to improve the
handling and promotion of dtypes, increase the execution speed, and
clarify the documentation. There has also been work to prepare for the
future NumPy 2.0.0 release, resulting in a large number of new and
expired deprecation. Highlights are:

• Support for MUSL, there are now MUSL wheels.
• Support the Fujitsu C/C++ compiler.
• Object arrays are now supported in einsum
• Support for inplace matrix multiplication (@=).

We will be releasing a NumPy 1.26 when Python 3.12 comes out. That is
needed because distutils has been dropped by Python 3.12 and we will be
switching to using meson for future builds. The next mainline release
will be NumPy 2.0.0. We plan that the 2.0 series will still support
downstream projects built against earlier versions of NumPy.

The Python versions supported in this release are 3.9-3.11.

Deprecations

• np.core.MachAr is deprecated. It is private API. In names defined
  in np.core should generally be considered private.

  (gh-22638)

• np.finfo(None) is deprecated.

  (gh-23011)

• np.round_ is deprecated. Use np.round instead.

  (gh-23302)

• np.product is deprecated. Use np.prod instead.

  (gh-23314)

• np.cumproduct is deprecated. Use np.cumprod instead.

  (gh-23314)

• np.sometrue is deprecated. Use np.any instead.

  (gh-23314)

• np.alltrue is deprecated. Use np.all instead.

  (gh-23314)

• Only ndim-0 arrays are treated as scalars. NumPy used to treat all
  arrays of size 1 (e.g., np.array([3.14])) as scalars. In the
  future, this will be limited to arrays of ndim 0 (e.g.,
  np.array(3.14)). The following expressions will report a
  deprecation warning:

  a = np.array([3.14])
  float(a)  # better: a[0] to get the numpy.float or a.item()
  
  b = np.array([[3.14]])
  c = numpy.random.rand(10)
  c[0] = b  # better: c[0] = b[0, 0]

  (gh-10615)

• numpy.find_common_type is now deprecated and its use
  should be replaced with either numpy.result_type or
  numpy.promote_types. Most users leave the second
  scalar_types argument to find_common_type as [] in which case
  np.result_type and np.promote_types are both faster and more
  robust. When not using scalar_types the main difference is that
  the replacement intentionally converts non-native byte-order to
  native byte order. Further, find_common_type returns object
  dtype rather than failing promotion. This leads to differences when
  the inputs are not all numeric. Importantly, this also happens for
  e.g. timedelta/datetime for which NumPy promotion rules are
  currently sometimes surprising.

  When the scalar_types argument is not [] things are more
  complicated. In most cases, using np.result_type and passing the
  Python values 0, 0.0, or 0j has the same result as using
  int, float, or complex in scalar_types.

  When scalar_types is constructed, np.result_type is the correct
  replacement and it may be passed scalar values like
  np.float32(0.0). Passing values other than 0, may lead to
  value-inspecting behavior (which np.find_common_type never used
  and NEP 50 may change in the future). The main possible change in
  behavior in this case, is when the array types are signed integers
  and scalar types are unsigned.

  If you are unsure about how to replace a use of scalar_types or
  when non-numeric dtypes are likely, please do not hesitate to open a
  NumPy issue to ask for help.

  (gh-22539)

Expired deprecations

• np.core.machar and np.finfo.machar have been removed.

  (gh-22638)

• +arr will now raise an error when the dtype is not numeric (and
  positive is undefined).

  (gh-22998)

• A sequence must now be passed into the stacking family of functions
  (stack, vstack, hstack, dstack and column_stack).

  (gh-23019)

• np.clip now defaults to same-kind casting. Falling back to unsafe
  casting was deprecated in NumPy 1.17.

  (gh-23403)

• np.clip will now propagate np.nan values passed as min or
  max. Previously, a scalar NaN was usually ignored. This was
  deprecated in NumPy 1.17.

  (gh-23403)

• The np.dual submodule has been removed.

  (gh-23480)

• NumPy now always ignores sequence behavior for an array-like
  (defining one of the array protocols). (Deprecation started NumPy
  1.20)

  (gh-23660)

• The niche FutureWarning when casting to a subarray dtype in
  astype or the array creation functions such as asarray is now
  finalized. The behavior is now always the same as if the subarray
  dtype was wrapped into a single field (which was the workaround,
  previously). (FutureWarning since NumPy 1.20)

  (gh-23666)

• == and != warnings have been finalized. The == and !=
  operators on arrays now always:

  • raise errors that occur during comparisons such as when the
    arrays have incompatible shapes
    (np.array([1, 2]) == np.array([1, 2, 3])).

  • return an array of all True or all False when values are
    fundamentally not comparable (e.g. have different dtypes). An
    example is np.array(["a"]) == np.array([1]).

    This mimics the Python behavior of returning False and True
    when comparing incompatible types like "a" == 1 and
    "a" != 1. For a long time these gave DeprecationWarning or
    FutureWarning.

  (gh-22707)

• Nose support has been removed. NumPy switched to using pytest in
  2018 and nose has been unmaintained for many years. We have kept
  NumPy's nose support to avoid breaking downstream projects who
  might have been using it and not yet switched to pytest or some
  other testing framework. With the arrival of Python 3.12, unpatched
  nose will raise an error. It is time to move on.

  Decorators removed:

  • raises
  • slow
  • setastest
  • skipif
  • knownfailif
  • deprecated
  • parametrize
  • _needs_refcount

  These are not to be confused with pytest versions with similar
  names, e.g., pytest.mark.slow, pytest.mark.skipif,
  pytest.mark.parametrize.

  Functions removed:

  • Tester
  • import_nose
  • run_module_suite

  (gh-23041)

• The numpy.testing.utils shim has been removed. Importing from the
  numpy.testing.utils shim has been deprecated since 2019, the shim
  has now been removed. All imports should be made directly from
  numpy.testing.

  (gh-23060)

• The environment variable to disable dispatching has been removed.
  Support for the NUMPY_EXPERIMENTAL_ARRAY_FUNCTION environment
  variable has been removed. This variable disabled dispatching with
  __array_function__.

  (gh-23376)

• Support for y= as an alias of out= has been removed. The fix,
  isposinf and isneginf functions allowed using y= as a
  (deprecated) alias for out=. This is no longer supported.

  (gh-23376)

Compatibility notes

• The busday_count method now correctly handles cases where the
  begindates is later in time than the enddates. Previously, the
  enddates was included, even though the documentation states it is
  always excluded.

  (gh-23229)

• When comparing datetimes and timedelta using np.equal or
  np.not_equal numpy previously allowed the comparison with
  casting="unsafe". This operation now fails. Forcing the output
  dtype using the dtype kwarg can make the operation succeed, but we
  do not recommend it.

  (gh-22707)

• When loading data from a file handle using np.load, if the handle
  is at the end of file, as can happen when reading multiple arrays by
  calling np.load repeatedly, numpy previously raised ValueError
  if allow_pickle=False, and OSError if allow_pickle=True. Now
  it raises EOFError instead, in both cases.

  (gh-23105)

np.pad with mode=wrap pads with strict multiples of original data

Code based on earlier version of pad that uses mode="wrap" will
return different results when the padding size is larger than initial
array.

np.pad with mode=wrap now always fills the space with strict
multiples of original data even if the padding size is larger than the
initial array.

(gh-22575)

Cython long_t and ulong_t removed

long_t and ulong_t were aliases for longlong_t and ulonglong_t
and confusing (a remainder from of Python 2). This change may lead to
the errors:

'long_t' is not a type identifier
'ulong_t' is not a type identifier

We recommend use of bit-sized types such as cnp.int64_t or the use of
cnp.intp_t which is 32 bits on 32 bit systems and 64 bits on 64 bit
systems (this is most compatible with indexing). If C long is desired,
use plain long or npy_long. cnp.int_t is also long (NumPy's
default integer). However, long is 32 bit on 64 bit windows and we may
wish to adjust this even in NumPy. (Please do not hesitate to contact
NumPy developers if you are curious about this.)

(gh-22637)

Changed error message and type for bad axes argument to ufunc

The error message and type when a wrong axes value is passed to
ufunc(..., axes=[...]) has changed. The message is now more
indicative of the problem, and if the value is mismatched an
AxisError will be raised. A TypeError will still be raised for
invalidinput types.

(gh-22675)

Array-likes that define __array_ufunc__ can now override ufuncs if used as where

If the where keyword argument of a numpy.ufunc{.interpreted-text
role="class"} is a subclass of numpy.ndarray{.interpreted-text
role="class"} or is a duck type that defines
numpy.class.__array_ufunc__{.interpreted-text role="func"} it can
override the behavior of the ufunc using the same mechanism as the input
and output arguments. Note that for this to work properly, the
where.__array_ufunc__ implementation will have to unwrap the where
argument to pass it into the default implementation of the ufunc or,
for numpy.ndarray{.interpreted-text role="class"} subclasses before
using super().__array_ufunc__.

(gh-23240)

Compiling against the NumPy C API is now backwards compatible by default

NumPy now defaults to exposing a backwards compatible subset of the
C-API. This makes the use of oldest-supported-numpy unnecessary.
Libraries can override the default minimal version to be compatible with
using:

#define NPY_TARGET_VERSION NPY_1_22_API_VERSION

before including NumPy or by passing the equivalent -D option to the
compiler. The NumPy 1.25 default is NPY_1_19_API_VERSION. Because the
NumPy 1.19 C API was identical to the NumPy 1.16 one resulting programs
will be compatible with NumPy 1.16 (from a C-API perspective). This
default will be increased in future non-bugfix releases. You can still
compile against an older NumPy version and run on a newer one.

For more details please see
for-downstream-package-authors{.interpreted-text role="ref"}.

(gh-23528)

New Features

np.einsum now accepts arrays with object dtype

The code path will call python operators on object dtype arrays, much
like np.dot and np.matmul.

(gh-18053)

Add support for inplace matrix multiplication

It is now possible to perform inplace matrix multiplication via the @=
operator.

>>> import numpy as np

>>> a = np.arange(6).reshape(3, 2)
>>> print(a)
[[0 1]
 [2 3]
 [4 5]]

>>> b = np.ones((2, 2), dtype=int)
>>> a @= b
>>> print(a)
[[1 1]
 [5 5]
 [9 9]]

(gh-21120)

Added NPY_ENABLE_CPU_FEATURES environment variable

Users may now choose to enable only a subset of the built CPU features
at runtime by specifying the NPY_ENABLE_CPU_FEATURES
environment variable. Note that these specified features must be outside
the baseline, since those are always assumed. Errors will be raised if
attempting to enable a feature that is either not supported by your CPU,
or that NumPy was not built with.

(gh-22137)

NumPy now has an np.exceptions namespace

NumPy now has a dedicated namespace making most exceptions and warnings
available. All of these remain available in the main namespace, although
some may be moved slowly in the future. The main reason for this is to
increase discoverability and add future exceptions.

(gh-22644)

np.linalg functions return NamedTuples

np.linalg functions that return tuples now return namedtuples. These
functions are eig(), eigh(), qr(), slogdet(), and svd(). The
return type is unchanged in instances where these functions return
non-tuples with certain keyword arguments (like
svd(compute_uv=False)).

(gh-22786)

String functions in np.char are compatible with NEP 42 custom dtypes

Custom dtypes that represent unicode strings or byte strings can now be
passed to the string functions in np.char.

(gh-22863)

String dtype instances can be created from the string abstract dtype classes

It is now possible to create a string dtype instance with a size without
using the string name of the dtype. For example,
type(np.dtype('U'))(8) will create a dtype that is equivalent to
np.dtype('U8'). This feature is most useful when writing generic code
dealing with string dtype classes.

(gh-22963)

Fujitsu C/C++ compiler is now supported

Support for Fujitsu compiler has been added. To build with Fujitsu
compiler, run:

python setup.py build -c fujitsu

SSL2 is now supported

Support for SSL2 has been added. SSL2 is a library that provides
OpenBLAS compatible GEMM functions. To enable SSL2, it need to edit
site.cfg and build with Fujitsu compiler. See site.cfg.example.

(gh-22982)

Improvements

NDArrayOperatorsMixin specifies that it has no __slots__

The NDArrayOperatorsMixin class now specifies that it contains no
__slots__, ensuring that subclasses can now make use of this feature
in Python.

(gh-23113)

Fix power of complex zero

np.power now returns a different result for 0^{non-zero} for complex
numbers. Note that the value is only defined when the real part of the
exponent is larger than zero. Previously, NaN was returned unless the
imaginary part was strictly zero. The return value is either 0+0j or
0-0j.

(gh-18535)

New DTypePromotionError

NumPy now has a new DTypePromotionError which is used when two dtypes
cannot be promoted to a common one, for example:

np.result_type("M8[s]", np.complex128)

raises this new exception.

(gh-22707)

np.show_config uses information from Meson

Build and system information now contains information from Meson.
np.show_config now has a new optional parameter mode to
help customize the output.

(gh-22769)

Fix np.ma.diff not preserving the mask when called with arguments prepend/append.

Calling np.ma.diff with arguments prepend and/or append now returns a
MaskedArray with the input mask preserved.

Previously, a MaskedArray without the mask was returned.

(gh-22776)

Corrected error handling for NumPy C-API in Cython

Many NumPy C functions defined for use in Cython were lacking the
correct error indicator like except -1 or except *. These have now
been added.

(gh-22997)

Ability to directly spawn random number generators

numpy.random.Generator.spawn now allows to directly spawn new independent
child generators via the numpy.random.SeedSequence.spawn mechanism.
numpy.random.BitGenerator.spawn does the same for the underlying bit
generator.

Additionally, numpy.random.BitGenerator.seed_seq now gives
direct access to the seed sequence used for initializing the bit
generator. This allows for example:

seed = 0x2e09b90939db40c400f8f22dae617151
rng = np.random.default_rng(seed)
child_rng1, child_rng2 = rng.spawn(2)

# safely use rng, child_rng1, and child_rng2

Previously, this was hard to do without passing the SeedSequence
explicitly. Please see numpy.random.SeedSequence for more
information.

(gh-23195)

numpy.logspace now supports a non-scalar base argument

The base argument of numpy.logspace can now be array-like if it is
broadcastable against the start and stop arguments.

(gh-23275)

np.ma.dot() now supports for non-2d arrays

Previously np.ma.dot() only worked if a and b were both 2d. Now it
works for non-2d arrays as well as np.dot().

(gh-23322)

Explicitly show keys of .npz file in repr

NpzFile shows keys of loaded .npz file when printed.

>>> npzfile = np.load('arr.npz')
>>> npzfile
NpzFile 'arr.npz' with keys arr_0, arr_1, arr_2, arr_3, arr_4...

(gh-23357)

NumPy now exposes DType classes in np.dtypes

The new numpy.dtypes module now exposes DType classes and will contain
future dtype related functionality. Most users should have no need to
use these classes directly.

(gh-23358)

Drop dtype metadata before saving in .npy or .npz files

Currently, a *.npy file containing a table with a dtype with metadata cannot
be read back. Now, np.save and np.savez drop metadata before saving.

(gh-23371)

numpy.lib.recfunctions.structured_to_unstructured returns views in more cases

structured_to_unstructured now returns a view, if the stride between
the fields is constant. Prior, padding between the fields or a reversed
field would lead to a copy. This change only applies to ndarray,
memmap and recarray. For all other array subclasses, the behavior
remains unchanged.

(gh-23652)

Signed and unsigned integers always compare correctly

When uint64 and int64 are mixed in NumPy, NumPy typically promotes
both to float64. This behavior may be argued about but is confusing
for comparisons ==, <=, since the results returned can be incorrect
but the conversion is hidden since the result is a boolean. NumPy will
now return the correct results for these by avoiding the cast to float.

(gh-23713)

Performance improvements and changes

Faster np.argsort on AVX-512 enabled processors

32-bit and 64-bit quicksort algorithm for np.argsort gain up to 6x speed
up on processors that support AVX-512 instruction set.

Thanks to Intel corporation for sponsoring
this work.

(gh-23707)

Faster np.sort on AVX-512 enabled processors

Quicksort for 16-bit and 64-bit dtypes gain up to 15x and 9x speed up on
processors that support AVX-512 instruction set.

Thanks to Intel corporation for sponsoring
this work.

(gh-22315)

__array_function__ machinery is now much faster

The overhead of the majority of functions in NumPy is now smaller
especially when keyword arguments are used. This change significantly
speeds up many simple function calls.

(gh-23020)

ufunc.at can be much faster

Generic ufunc.at can be up to 9x faster. The conditions for this
speedup:

• operands are aligned
• no casting

If ufuncs with appropriate indexed loops on 1d arguments with the above
conditions, ufunc.at can be up to 60x faster (an additional 7x
speedup). Appropriate indexed loops have been added to add,
subtract, multiply, floor_divide, maximum, minimum, fmax,
and fmin.

The internal logic is similar to the logic used for regular ufuncs,
which also have fast paths.

Thanks to the D. E. Shaw group for sponsoring
this work.

(gh-23136)

Faster membership test on NpzFile

Membership test on NpzFile will no longer decompress the archive if it
is successful.

(gh-23661)

Changes

np.r_[] and np.c_[] with certain scalar values

In rare cases, using mainly np.r_ with scalars can lead to different
results. The main potential changes are highlighted by the following:

>>> np.r_[np.arange(5, dtype=np.uint8), -1].dtype
int16  # rather than the default integer (int64 or int32)
>>> np.r_[np.arange(5, dtype=np.int8), 255]
array([  0,   1,   2,   3,   4, 255], dtype=int16)

Where the second example returned:

array([ 0,  1,  2,  3,  4, -1], dtype=int8)

The first one is due to a signed integer scalar with an unsigned integer
array, while the second is due to 255 not fitting into int8 and
NumPy currently inspecting values to make this work. (Note that the
second example is expected to change in the future due to
NEP 50 <NEP50>{.interpreted-text role="ref"}; it will then raise an
error.)

(gh-22539)

Most NumPy functions are wrapped into a C-callable

To speed up the __array_function__ dispatching, most NumPy functions
are now wrapped into C-callables and are not proper Python functions or
C methods. They still look and feel the same as before (like a Python
function), and this should only improve performance and user experience
(cleaner tracebacks). However, please inform the NumPy developers if
this change confuses your program for some reason.

(gh-23020)

C++ standard library usage

NumPy builds now depend on the C++ standard library, because the
numpy.core._multiarray_umath extension is linked with the C++ linker.

(gh-23601)

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