NumPy 1.25.0 Release Notes — NumPy v2.0 Manual (2024)

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

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

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

(gh-22575)

Cython long_t and ulong_t removed#

long_t and ulong_t were aliases for longlong_t and ulonglong_tand 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 ofcnp.intp_t which is 32 bits on 32 bit systems and 64 bits on 64 bitsystems (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, longis 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 toufunc(..., axes=[...])` has changed. The message is now more indicative ofthe problem, and if the value is mismatched an AxisError will be raised.A TypeError will still be raised for invalid input 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 is a subclass ofnumpy.ndarray or is a duck type that definesnumpy.class.__array_ufunc__ it can override the behavior of the ufuncusing 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 thedefault implementation of the ufunc or, for numpy.ndarraysubclasses 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 withusing:

#define NPY_TARGET_VERSION NPY_1_22_API_VERSION

before including NumPy or by passing the equivalent -D option to thecompiler.The NumPy 1.25 default is NPY_1_19_API_VERSION. Because the NumPy 1.19C API was identical to the NumPy 1.16 one resulting programs will be compatiblewith 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.

(gh-23528)

np.einsum now accepts arrays with object dtype#

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

(gh-18053)

Add support for inplace matrix multiplication#

It is now possible to perform inplace matrix multiplicationvia 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 atruntime by specifying the NPY_ENABLE_CPU_FEATURES environment variable.Note that these specified features must be outside the baseline, since thoseare always assumed. Errors will be raised if attempting to enable a featurethat 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 exceptionsand warnings available. All of these remain available in themain namespace, although some may be moved slowly in the future.The main reason for this is to increase discoverability and addfuture exceptions.

(gh-22644)

np.linalg functions return NamedTuples#

np.linalg functions that return tuples now return namedtuples. Thesefunctions are eig(), eigh(), qr(), slogdet(), and svd().The return type is unchanged in instances where these functions returnnon-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 bepassed 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 withoutusing 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 featureis most useful when writing generic code dealing with string dtypeclasses.

(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 OpenBLAScompatible GEMM functions. To enable SSL2, it need to edit site.cfg and buildwith Fujitsu compiler. See site.cfg.example.

(gh-22982)

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 inPython.

(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 whenthe real part of the exponent is larger than zero.Previously, NaN was returned unless the imaginary part was strictlyzero. The return value is either 0+0j or 0-0j.

(gh-18535)

New DTypePromotionError#

NumPy now has a new DTypePromotionError which is used when twodtypes 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 helpcustomize 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 aMaskedArray 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 thecorrect 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 newindependent child generators via the numpy.random.SeedSequence.spawnmechanism.numpy.random.BitGenerator.spawn does the same for the underlyingbit generator.

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

seed = 0x2e09b90939db40c400f8f22dae617151rng = 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 SeedSequenceexplicitly. 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 isbroadcastable 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')>>> npzfileNpzFile '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 andwill 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 withmetadata 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 thefields is constant. Prior, padding between the fields or a reversed fieldwould lead to a copy.This change only applies to ndarray, memmap and recarray. For allother array subclasses, the behavior remains unchanged.

(gh-23652)

Signed and unsigned integers always compare correctly#

When uint64 and int64 are mixed in NumPy, NumPy typicallypromotes both to float64. This behavior may be argued aboutbut is confusing for comparisons ==, <=, since the resultsreturned can be incorrect but the conversion is hidden since theresult is a boolean.NumPy will now return the correct results for these by avoidingthe cast to float.

(gh-23713)

Faster np.argsort on AVX-512 enabled processors#

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

Thanks to Intel corporation for sponsoring thiswork.

(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 onprocessors that support AVX-512 instruction set.

Thanks to Intel corporation for sponsoring thiswork.

(gh-22315)

__array_function__ machinery is now much faster#

The overhead of the majority of functions in NumPy is now smallerespecially when keyword arguments are used. This change significantlyspeeds 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 aboveconditions, 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, andfmin.

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

Thanks to the D. E. Shaw group for sponsoring thiswork.

(gh-23136)

Faster membership test on NpzFile#

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

(gh-23661)

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

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

>>> np.r_[np.arange(5, dtype=np.uint8), -1].dtypeint16 # 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 integerarray, while the second is due to 255 not fitting into int8 andNumPy currently inspecting values to make this work.(Note that the second example is expected to change in the future due toNEP 50; 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 functionsare now wrapped into C-callables and are not proper Python functions orC methods.They still look and feel the same as before (like a Python function), and thisshould only improve performance and user experience (cleaner tracebacks).However, please inform the NumPy developers if this change confuses yourprogram for some reason.

(gh-23020)

C++ standard library usage#

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

(gh-23601)