NumPy 1.19.0 Release Notes — NumPy v2.0 Manual (2024)

numpy.insert and numpy.delete can no longer be passed an axis on 0d arrays#

This concludes a deprecation from 1.9, where when an axis argument waspassed to a call to ~numpy.insert and ~numpy.delete on a 0d array, theaxis and obj argument and indices would be completely ignored.In these cases, insert(arr, "nonsense", 42, axis=0) would actually overwrite theentire array, while delete(arr, "nonsense", axis=0) would be arr.copy()

Now passing axis on a 0d array raises ~numpy.AxisError.

(gh-15802)

numpy.delete no longer ignores out-of-bounds indices#

This concludes deprecations from 1.8 and 1.9, where np.delete would ignoreboth negative and out-of-bounds items in a sequence of indices. This was atodds with its behavior when passed a single index.

Now out-of-bounds items throw IndexError, and negative items index from theend.

(gh-15804)

numpy.insert and numpy.delete no longer accept non-integral indices#

This concludes a deprecation from 1.9, where sequences of non-integers indiceswere allowed and cast to integers. Now passing sequences of non-integralindices raises IndexError, just like it does when passing a singlenon-integral scalar.

(gh-15805)

numpy.delete no longer casts boolean indices to integers#

This concludes a deprecation from 1.8, where np.delete would cast booleanarrays and scalars passed as an index argument into integer indices. Thebehavior now is to treat boolean arrays as a mask, and to raise an erroron boolean scalars.

(gh-15815)

Changed random variate stream from numpy.random.Generator.dirichlet#

A bug in the generation of random variates for the Dirichlet distributionwith small ‘alpha’ values was fixed by using a different algorithm whenmax(alpha) < 0.1. Because of the change, the stream of variatesgenerated by dirichlet in this case will be different from previousreleases.

(gh-14924)

Scalar promotion in PyArray_ConvertToCommonType#

The promotion of mixed scalars and arrays in PyArray_ConvertToCommonTypehas been changed to adhere to those used by np.result_type.This means that input such as (1000, np.array([1], dtype=np.uint8)))will now return uint16 dtypes. In most cases the behaviour is unchanged.Note that the use of this C-API function is generally discouraged.This also fixes np.choose to behave the same way as the rest of NumPyin this respect.

(gh-14933)

Fasttake and fastputmask slots are deprecated and NULL’ed#

The fasttake and fastputmask slots are now never used andmust always be set to NULL. This will result in no change in behaviour.However, if a user dtype should set one of these a DeprecationWarningwill be given.

(gh-14942)

np.ediff1d casting behaviour with to_end and to_begin#

np.ediff1d now uses the "same_kind" casting rule forits additional to_end and to_begin arguments. Thisensures type safety except when the input array has a smallerinteger type than to_begin or to_end.In rare cases, the behaviour will be more strict than it waspreviously in 1.16 and 1.17. This is necessary to solve issueswith floating point NaN.

(gh-14981)

Converting of empty array-like objects to NumPy arrays#

Objects with len(obj) == 0 which implement an “array-like” interface,meaning an object implementing obj.__array__(),obj.__array_interface__, obj.__array_struct__, or the pythonbuffer interface and which are also sequences (i.e. Pandas objects)will now always retain there shape correctly when converted to an array.If such an object has a shape of (0, 1) previously, it couldbe converted into an array of shape (0,) (losing all dimensionsafter the first 0).

(gh-14995)

Removed multiarray.int_asbuffer#

As part of the continued removal of Python 2 compatibility,multiarray.int_asbuffer was removed. On Python 3, it threw aNotImplementedError and was unused internally. It is expected that thereare no downstream use cases for this method with Python 3.

(gh-15229)

numpy.distutils.compat has been removed#

This module contained only the function get_exception(), which was used as:

try: ...except Exception: e = get_exception()

Its purpose was to handle the change in syntax introduced in Python 2.6, fromexcept Exception, e: to except Exception as e:, meaning it was onlynecessary for codebases supporting Python 2.5 and older.

(gh-15255)

issubdtype no longer interprets float as np.floating#

numpy.issubdtype had a FutureWarning since NumPy 1.14 whichhas expired now. This means that certain input where the secondargument was neither a datatype nor a NumPy scalar type(such as a string or a python type like int or float)will now be consistent with passing in np.dtype(arg2).type.This makes the result consistent with expectations and leads toa false result in some cases which previously returned true.

(gh-15773)

Change output of round on scalars to be consistent with Python#

Output of the __round__ dunder method and consequently the Pythonbuilt-in round has been changed to be a Python int to be consistentwith calling it on Python float objects when called with no arguments.Previously, it would return a scalar of the np.dtype that was passed in.

(gh-15840)

The numpy.ndarray constructor no longer interprets strides=() as strides=None#

The former has changed to have the expected meaning of settingnumpy.ndarray.strides to (), while the latter continues to result instrides being chosen automatically.

(gh-15882)

C-Level string to datetime casts changed#

The C-level casts from strings were simplified. This changedalso fixes string to datetime and timedelta casts to behavecorrectly (i.e. like Python casts using string_arr.astype("M8")while previously the cast would behave likestring_arr.astype(np.int_).astype("M8").This only affects code using low-level C-API to do manual casts(not full array casts) of single scalar values or using e.g.PyArray_GetCastFunc, and should thus not affect the vast majorityof users.

(gh-16068)

SeedSequence with small seeds no longer conflicts with spawning#

Small seeds (less than 2**96) were previously implicitly 0-padded out to128 bits, the size of the internal entropy pool. When spawned, the spawn keywas concatenated before the 0-padding. Since the first spawn key is (0,),small seeds before the spawn created the same states as the first spawnedSeedSequence. Now, the seed is explicitly 0-padded out to the internalpool size before concatenating the spawn key. Spawned SeedSequences willproduce different results than in the previous release. UnspawnedSeedSequences will still produce the same results.

(gh-16551)

Deprecate automatic dtype=object for ragged input#

Calling np.array([[1, [1, 2, 3]]) will issue a DeprecationWarning asper NEP 34. Users should explicitly use dtype=object to avoid thewarning.

(gh-15119)

Passing shape=0 to factory functions in numpy.rec is deprecated#

0 is treated as a special case and is aliased to None in the functions:

• numpy.core.records.fromarrays

• numpy.core.records.fromrecords

• numpy.core.records.fromstring

• numpy.core.records.fromfile

In future, 0 will not be special cased, and will be treated as an arraylength like any other integer.

(gh-15217)

Deprecation of probably unused C-API functions#

The following C-API functions are probably unused and have beendeprecated:

• PyArray_GetArrayParamsFromObject

• PyUFunc_GenericFunction

  See Also

  MATLAB MANUAL AND INTRODUCTORY TUTORIALSweb.unbc.ca/~ytang/MatlabTutorial.pdfMATLAB MANUAL AND INTRODUCTORY TUTORIALS Ivan Graham, with some revisions by Nick Britton, Mathematical - [PDF Document]

• PyUFunc_SetUsesArraysAsData

In most cases PyArray_GetArrayParamsFromObject should be replacedby converting to an array, while PyUFunc_GenericFunction can bereplaced with PyObject_Call (see documentation for details).

(gh-15427)

Converting certain types to dtypes is Deprecated#

The super classes of scalar types, such as np.integer, np.generic,or np.inexact will now give a deprecation warning when convertedto a dtype (or used in a dtype keyword argument).The reason for this is that np.integer is converted to np.int_,while it would be expected to represent any integer (e.g. alsoint8, int16, etc.For example, dtype=np.floating is currently identical todtype=np.float64, even though also np.float32 is a subclass ofnp.floating.

(gh-15534)

Deprecation of round for np.complexfloating scalars#

Output of the __round__ dunder method and consequently the Python built-inround has been deprecated on complex scalars. This does not affectnp.round.

(gh-15840)

numpy.ndarray.tostring() is deprecated in favor of tobytes()#

~numpy.ndarray.tobytes has existed since the 1.9 release, but until thisrelease ~numpy.ndarray.tostring emitted no warning. The change to emit awarning brings NumPy in line with the builtin array.array methods of thesame name.

(gh-15867)

Better support for const dimensions in API functions#

The following functions now accept a constant array of npy_intp:

• PyArray_BroadcastToShape

• PyArray_IntTupleFromIntp

• PyArray_OverflowMultiplyList

Previously the caller would have to cast away the const-ness to call thesefunctions.

(gh-15251)

Const qualify UFunc inner loops#

UFuncGenericFunction now expects pointers to const dimension andstrides as arguments. This means inner loops may no longer modifyeither dimension or strides. This change leads to anincompatible-pointer-types warning forcing users to either ignorethe compiler warnings or to const qualify their own loop signatures.

(gh-15355)

numpy.frompyfunc now accepts an identity argument#

This allows the numpy.ufunc.identity attribute to be set on theresulting ufunc, meaning it can be used for empty and multi-dimensionalcalls to numpy.ufunc.reduce.

(gh-8255)

np.str_ scalars now support the buffer protocol#

np.str_ arrays are always stored as UCS4, so the corresponding scalarsnow expose this through the buffer interface, meaningmemoryview(np.str_('test')) now works.

(gh-15385)

subok option for numpy.copy#

A new kwarg, subok, was added to numpy.copy to allow users to togglethe behavior of numpy.copy with respect to array subclasses. The defaultvalue is False which is consistent with the behavior of numpy.copy forprevious numpy versions. To create a copy that preserves an array subclass withnumpy.copy, call np.copy(arr, subok=True). This addition betterdocuments that the default behavior of numpy.copy differs from thenumpy.ndarray.copy method which respects array subclasses by default.

(gh-15685)

numpy.linalg.multi_dot now accepts an out argument#

out can be used to avoid creating unnecessary copies of the final productcomputed by numpy.linalg.multidot.

(gh-15715)

keepdims parameter for numpy.count_nonzero#

The parameter keepdims was added to numpy.count_nonzero. Theparameter has the same meaning as it does in reduction functions suchas numpy.sum or numpy.mean.

(gh-15870)

equal_nan parameter for numpy.array_equal#

The keyword argument equal_nan was added to numpy.array_equal.equal_nan is a boolean value that toggles whether or not nan values areconsidered equal in comparison (default is False). This matches API used inrelated functions such as numpy.isclose and numpy.allclose.

(gh-16128)

Use 64-bit integer size on 64-bit platforms in fallback lapack_lite#

Use 64-bit integer size on 64-bit platforms in the fallback LAPACK library,which is used when the system has no LAPACK installed, allowing it to deal withlinear algebra for large arrays.

(gh-15218)

Use AVX512 intrinsic to implement np.exp when input is np.float64#

Use AVX512 intrinsic to implement np.exp when input is np.float64,which can improve the performance of np.exp with np.float64 input 5-7xfaster than before. The _multiarray_umath.so module has grown about 63 KBon linux64.

(gh-15648)

Ability to disable madvise hugepages#

On Linux NumPy has previously added support for madavise hugepages which canimprove performance for very large arrays. Unfortunately, on older Kernelversions this led to performance regressions, thus by default the support hasbeen disabled on kernels before version 4.6. To override the default, you canuse the environment variable:

NUMPY_MADVISE_HUGEPAGE=0

or set it to 1 to force enabling support. Note that this only makesa difference if the operating system is set up to use madvisetransparent hugepage.

(gh-15769)

numpy.einsum accepts NumPy int64 type in subscript list#

There is no longer a type error thrown when numpy.einsum is passeda NumPy int64 array as its subscript list.

(gh-16080)

np.logaddexp2.identity changed to -inf#

The ufunc ~numpy.logaddexp2 now has an identity of -inf, allowing it tobe called on empty sequences. This matches the identity of ~numpy.logaddexp.

(gh-16102)

Remove handling of extra argument to __array__#

A code path and test have been in the code since NumPy 0.4 for a two-argumentvariant of __array__(dtype=None, context=None). It was activated whencalling ufunc(op) or ufunc.reduce(op) if op.__array__ existed.However that variant is not documented, and it is not clear what the intentionwas for its use. It has been removed.

(gh-15118)

numpy.random._bit_generator moved to numpy.random.bit_generator#

In order to expose numpy.random.BitGenerator andnumpy.random.SeedSequence to Cython, the _bitgenerator module is nowpublic as numpy.random.bit_generator

Cython access to the random distributions is provided via a pxd file#

c_distributions.pxd provides access to the c functions behind many of therandom distributions from Cython, making it convenient to use and extend them.

(gh-15463)

Fixed eigh and cholesky methods in numpy.random.multivariate_normal#

Previously, when passing method='eigh' or method='cholesky',numpy.random.multivariate_normal produced samples from the wrongdistribution. This is now fixed.

(gh-15872)

Fixed the jumping implementation in MT19937.jumped#

This fix changes the stream produced from jumped MT19937 generators. It doesnot affect the stream produced using RandomState or MT19937 thatare directly seeded.

The translation of the jumping code for the MT19937 contained a reversed loopordering. MT19937.jumped matches the Makoto Matsumoto’s originalimplementation of the Horner and Sliding Window jump methods.

(gh-16153)