NumPy 1.25.0 Release Notes#


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


  • np.finfo(None) is deprecated.


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


  • np.product is deprecated. Use instead.

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

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

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


Expired deprecations#

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


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


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


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

  • 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.


== and != warnings 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.


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


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.


Environment variable to disable dispatching removed#

Support for the NUMPY_EXPERIMENTAL_ARRAY_FUNCTION environment variable has been removed. This variable disabled dispatching with __array_function__.

Support for y= as an alias of out= removed#

The fix, isposinf and isneginf functions allowed using y= as a (deprecated) alias for out=. This is no longer supported.


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.


  • 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.


  • 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.


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.


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.)


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 invalid input types.


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

If the where keyword argument of a numpy.ufunc is a subclass of numpy.ndarray or is a duck type that defines numpy.class.__array_ufunc__ 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 subclasses before using super().__array_ufunc__.


New Features#

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 discoverably and add future exceptions.


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.


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.


Fujitsu C/C++ compiler is now supported#

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

python 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.



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


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.


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.


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.


Fix not preserving the mask when called with arguments prepend/append.#

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

Previously, a MaskedArray without the mask was returned.


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.


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.


Performance improvements and changes#

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.


__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) can be much faster#

Generic 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, 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.



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.