NumPy 1.23.4 Release Notes

NumPy 1.23.4 is a maintenance release that fixes bugs discovered after the 1.23.3 release and keeps the build infrastructure current. The main improvements are fixes for some annotation corner cases, a fix for a long time nested_iters memory leak, and a fix of complex vector dot for very large arrays. The Python versions supported for this release are 3.8-3.11.

Note that the mypy version needs to be 0.981+ if you test using Python 3.10.7, otherwise the typing tests will fail.

Contributors

A total of 8 people contributed to this release. People with a "+" by their names contributed a patch for the first time.

• Bas van Beek
• Charles Harris
• Matthew Barber
• Matti Picus
• Ralf Gommers
• Ross Barnowski
• Sebastian Berg
• Sicheng Zeng +

Pull requests merged

A total of 13 pull requests were merged for this release.

• #22368: BUG: Add __array_api_version__ to numpy.array_api namespace
• #22370: MAINT: update sde toolkit to 9.0, fix download link
• #22382: BLD: use macos-11 image on azure, macos-1015 is deprecated
• #22383: MAINT: random: remove get_info from "extending with Cython"...
• #22384: BUG: Fix complex vector dot with more than NPY_CBLAS_CHUNK elements
• #22387: REV: Loosen lookfor's import try/except again
• #22388: TYP,ENH: Mark numpy.typing protocols as runtime checkable
• #22389: TYP,MAINT: Change more overloads to play nice with pyright
• #22390: TST,TYP: Bump mypy to 0.981
• #22391: DOC: Update delimiter param description.
• #22392: BUG: Memory leaks in numpy.nested_iters
• #22413: REL: Prepare for the NumPy 1.23.4 release.
• #22424: TST: Fix failing aarch64 wheel builds.

Checksums

MD5

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NumPy 1.23.3 Release Notes

NumPy 1.23.3 is a maintenance release that fixes bugs discovered after the 1.23.2 release. There is no major theme for this release, the main improvements are for some downstream builds and some annotation corner cases. The Python versions supported for this release are 3.8-3.11.

Note that we will move to MacOS 11 for the NumPy 1.23.4 release, the 10.15 version currently used will no longer be supported by our build infrastructure at that point.

Contributors

A total of 16 people contributed to this release. People with a "+" by their names contributed a patch for the first time.

• Aaron Meurer
• Bas van Beek
• Charles Harris
• Ganesh Kathiresan
• Gavin Zhang +
• Iantra Solari+
• Jyn Spring 琴春 +
• Matti Picus
• Rafael Cardoso Fernandes Sousa
• Rafael Sousa +
• Ralf Gommers
• Rin Cat (鈴猫) +
• Saransh Chopra +
• Sayed Adel
• Sebastian Berg
• Serge Guelton

Pull requests merged

A total of 14 pull requests were merged for this release.

• #22136: BLD: Add Python 3.11 wheels to aarch64 build
• #22148: MAINT: Update setup.py for Python 3.11.
• #22155: CI: Test NumPy build against old versions of GCC(6, 7, 8)
• #22156: MAINT: support IBM i system
• #22195: BUG: Fix circleci build
• #22214: BUG: Expose heapsort algorithms in a shared header
• #22215: BUG: Support using libunwind for backtrack
• #22216: MAINT: fix an incorrect pointer type usage in f2py
• #22220: BUG: change overloads to play nice with pyright.
• #22221: TST,BUG: Use fork context to fix MacOS savez test
• #22222: TYP,BUG: Reduce argument validation in C-based __class_getitem__
• #22223: TST: ensure np.equal.reduce raises a TypeError
• #22224: BUG: Fix the implementation of numpy.array_api.vecdot
• #22230: BUG: Better report integer division overflow (backport)

Checksums

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SHA256

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NumPy 1.23.2 Release Notes

NumPy 1.23.2 is a maintenance release that fixes bugs discovered after the 1.23.1 release. Notable features are:

• Typing changes needed for Python 3.11
• Wheels for Python 3.11.0rc1

The Python versions supported for this release are 3.8-3.11.

Contributors

A total of 9 people contributed to this release. People with a "+" by their names contributed a patch for the first time.

• Alexander Grund +
• Bas van Beek
• Charles Harris
• Jon Cusick +
• Matti Picus
• Michael Osthege +
• Pal Barta +
• Ross Barnowski
• Sebastian Berg

Pull requests merged

A total of 15 pull requests were merged for this release.

• #22030: ENH: Add __array_ufunc__ typing support to the nin=1 ufuncs
• #22031: MAINT, TYP: Fix np.angle dtype-overloads
• #22032: MAINT: Do not let _GenericAlias wrap the underlying classes'...
• #22033: TYP,MAINT: Allow einsum subscripts to be passed via integer...
• #22034: MAINT,TYP: Add object-overloads for the np.generic rich comparisons
• #22035: MAINT,TYP: Allow the squeeze and transpose method to...
• #22036: BUG: Fix subarray to object cast ownership details
• #22037: BUG: Use Popen to silently invoke f77 -v
• #22038: BUG: Avoid errors on NULL during deepcopy
• #22039: DOC: Add versionchanged for converter callable behavior.
• #22057: MAINT: Quiet the anaconda uploads.
• #22078: ENH: reorder includes for testing on top of system installations...
• #22106: TST: fix test_linear_interpolation_formula_symmetric
• #22107: BUG: Fix skip condition for test_loss_of_precision[complex256]
• #22115: BLD: Build python3.11.0rc1 wheels.

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NumPy 1.23.1 Release Notes

The NumPy 1.23.1 is a maintenance release that fixes bugs discovered after the 1.23.0 release. Notable fixes are:

• Fix searchsorted for float16 NaNs
• Fix compilation on Apple M1
• Fix KeyError in crackfortran operator support (Slycot)

The Python version supported for this release are 3.8-3.10.

Contributors

A total of 7 people contributed to this release. People with a "+" by their names contributed a patch for the first time.

• Charles Harris
• Matthias Koeppe +
• Pranab Das +
• Rohit Goswami
• Sebastian Berg
• Serge Guelton
• Srimukh Sripada +

Pull requests merged

A total of 8 pull requests were merged for this release.

• #21866: BUG: Fix discovered MachAr (still used within valgrind)
• #21867: BUG: Handle NaNs correctly for float16 during sorting
• #21868: BUG: Use keepdims during normalization in np.average and...
• #21869: DOC: mention changes to max_rows behaviour in np.loadtxt
• #21870: BUG: Reject non integer array-likes with size 1 in delete
• #21949: BLD: Make can_link_svml return False for 32bit builds on x86_64
• #21951: BUG: Reorder extern "C" to only apply to function declarations...
• #21952: BUG: Fix KeyError in crackfortran operator support

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NumPy 1.23.0 Release Notes

The NumPy 1.23.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase the execution speed, clarify the documentation, and expire old deprecations. The highlights are:

• Implementation of loadtxt in C, greatly improving its performance.
• Exposing DLPack at the Python level for easy data exchange.
• Changes to the promotion and comparisons of structured dtypes.
• Improvements to f2py.

See below for the details,

New functions

• A masked array specialization of ndenumerate is now available as numpy.ma.ndenumerate. It provides an alternative to numpy.ndenumerate and skips masked values by default.

  (gh-20020)

• numpy.from_dlpack has been added to allow easy exchange of data using the DLPack protocol. It accepts Python objects that implement the __dlpack__ and __dlpack_device__ methods and returns a ndarray object which is generally the view of the data of the input object.

  (gh-21145)

Deprecations

• Setting __array_finalize__ to None is deprecated. It must now be a method and may wish to call super().__array_finalize__(obj) after checking for None or if the NumPy version is sufficiently new.

  (gh-20766)

• Using axis=32 (axis=np.MAXDIMS) in many cases had the same meaning as axis=None. This is deprecated and axis=None must be used instead.

  (gh-20920)

• The hook function PyDataMem_SetEventHook has been deprecated and the demonstration of its use in tool/allocation_tracking has been removed. The ability to track allocations is now built-in to python via tracemalloc.

  (gh-20394)

• numpy.distutils has been deprecated, as a result of distutils itself being deprecated. It will not be present in NumPy for Python >= 3.12, and will be removed completely 2 years after the release of Python 3.12 For more details, see distutils-status-migration{.interpreted-text role="ref"}.

  (gh-20875)

• numpy.loadtxt will now give a DeprecationWarning when an integer dtype is requested but the value is formatted as a floating point number.

  (gh-21663)

Expired deprecations

• The NpzFile.iteritems() and NpzFile.iterkeys() methods have been removed as part of the continued removal of Python 2 compatibility. This concludes the deprecation from 1.15.

  (gh-16830)

• The alen and asscalar functions have been removed.

  (gh-20414)

• The UPDATEIFCOPY array flag has been removed together with the enum NPY_ARRAY_UPDATEIFCOPY. The associated (and deprecated) PyArray_XDECREF_ERR was also removed. These were all deprecated in 1.14. They are replaced by WRITEBACKIFCOPY, that requires calling PyArray_ResoveWritebackIfCopy before the array is deallocated.

  (gh-20589)

• Exceptions will be raised during array-like creation. When an object raised an exception during access of the special attributes __array__ or __array_interface__, this exception was usually ignored. This behaviour was deprecated in 1.21, and the exception will now be raised.

  (gh-20835)

• Multidimensional indexing with non-tuple values is not allowed. Previously, code such as arr[ind] where ind = [[0, 1], [0, 1]] produced a FutureWarning and was interpreted as a multidimensional index (i.e., arr[tuple(ind)]). Now this example is treated like an array index over a single dimension (arr[array(ind)]). Multidimensional indexing with anything but a tuple was deprecated in NumPy 1.15.

  (gh-21029)

• Changing to a dtype of different size in F-contiguous arrays is no longer permitted. Deprecated since Numpy 1.11.0. See below for an extended explanation of the effects of this change.

  (gh-20722)

New Features

crackfortran has support for operator and assignment overloading

crackfortran parser now understands operator and assignment definitions in a module. They are added in the body list of the module which contains a new key implementedby listing the names of the subroutines or functions implementing the operator or assignment.

(gh-15006)

f2py supports reading access type attributes from derived type statements

As a result, one does not need to use public or private statements to specify derived type access properties.

(gh-15844)

New parameter ndmin added to genfromtxt

This parameter behaves the same as ndmin from numpy.loadtxt.

(gh-20500)

np.loadtxt now supports quote character and single converter function

numpy.loadtxt now supports an additional quotechar keyword argument which is not set by default. Using quotechar='"' will read quoted fields as used by the Excel CSV dialect.

Further, it is now possible to pass a single callable rather than a dictionary for the converters argument.

(gh-20580)

Changing to dtype of a different size now requires contiguity of only the last axis

Previously, viewing an array with a dtype of a different item size required that the entire array be C-contiguous. This limitation would unnecessarily force the user to make contiguous copies of non-contiguous arrays before being able to change the dtype.

This change affects not only ndarray.view, but other construction mechanisms, including the discouraged direct assignment to ndarray.dtype.

This change expires the deprecation regarding the viewing of F-contiguous arrays, described elsewhere in the release notes.

(gh-20722)

Deterministic output files for F2PY

For F77 inputs, f2py will generate modname-f2pywrappers.f unconditionally, though these may be empty. For free-form inputs, modname-f2pywrappers.f, modname-f2pywrappers2.f90 will both be generated unconditionally, and may be empty. This allows writing generic output rules in cmake or meson and other build systems. Older behavior can be restored by passing --skip-empty-wrappers to f2py. f2py-meson{.interpreted-text role="ref"} details usage.

(gh-21187)

keepdims parameter for average

The parameter keepdims was added to the functions numpy.average and numpy.ma.average. The parameter has the same meaning as it does in reduction functions such as numpy.sum or numpy.mean.

(gh-21485)

New parameter equal_nan added to np.unique

np.unique was changed in 1.21 to treat all NaN values as equal and return a single NaN. Setting equal_nan=False will restore pre-1.21 behavior to treat NaNs as unique. Defaults to True.

(gh-21623)

Compatibility notes

1D np.linalg.norm preserves float input types, even for scalar results

Previously, this would promote to float64 when the ord argument was not one of the explicitly listed values, e.g. ord=3:

>>> f32 = np.float32([1, 2])
>>> np.linalg.norm(f32, 2).dtype
dtype('float32')
>>> np.linalg.norm(f32, 3)
dtype('float64')  # numpy 1.22
dtype('float32')  # numpy 1.23

This change affects only float32 and float16 vectors with ord other than -Inf, 0, 1, 2, and Inf.

(gh-17709)

Changes to structured (void) dtype promotion and comparisons

In general, NumPy now defines correct, but slightly limited, promotion for structured dtypes by promoting the subtypes of each field instead of raising an exception:

>>> np.result_type(np.dtype("i,i"), np.dtype("i,d"))
dtype([('f0', '<i4'), ('f1', '<f8')])

For promotion matching field names, order, and titles are enforced, however padding is ignored. Promotion involving structured dtypes now always ensures native byte-order for all fields (which may change the result of np.concatenate) and ensures that the result will be "packed", i.e. all fields are ordered contiguously and padding is removed. See structured_dtype_comparison_and_promotion{.interpreted-text role="ref"} for further details.

The repr of aligned structures will now never print the long form including offsets and itemsize unless the structure includes padding not guaranteed by align=True.

In alignment with the above changes to the promotion logic, the casting safety has been updated:

• "equiv" enforces matching names and titles. The itemsize is allowed to differ due to padding.
• "safe" allows mismatching field names and titles
• The cast safety is limited by the cast safety of each included field.
• The order of fields is used to decide cast safety of each individual field. Previously, the field names were used and only unsafe casts were possible when names mismatched.

The main important change here is that name mismatches are now considered "safe" casts.

(gh-19226)

NPY_RELAXED_STRIDES_CHECKING has been removed

NumPy cannot be compiled with NPY_RELAXED_STRIDES_CHECKING=0 anymore. Relaxed strides have been the default for many years and the option was initially introduced to allow a smoother transition.

(gh-20220)

np.loadtxt has recieved several changes

The row counting of numpy.loadtxt was fixed. loadtxt ignores fully empty lines in the file, but counted them towards max_rows. When max_rows is used and the file contains empty lines, these will now not be counted. Previously, it was possible that the result contained fewer than max_rows rows even though more data was available to be read. If the old behaviour is required, itertools.islice may be used:

import itertools
lines = itertools.islice(open("file"), 0, max_rows)
result = np.loadtxt(lines, ...)

While generally much faster and improved, numpy.loadtxt may now fail to converter certain strings to numbers that were previously successfully read. The most important cases for this are:

• Parsing floating point values such as 1.0 into integers is now deprecated.
• Parsing hexadecimal floats such as 0x3p3 will fail
• An _ was previously accepted as a thousands delimiter 100_000. This will now result in an error.

If you experience these limitations, they can all be worked around by passing appropriate converters=. NumPy now supports passing a single converter to be used for all columns to make this more convenient. For example, converters=float.fromhex can read hexadecimal float numbers and converters=int will be able to read 100_000.

Further, the error messages have been generally improved. However, this means that error types may differ. In particularly, a ValueError is now always raised when parsing of a single entry fails.

(gh-20580)

Improvements

ndarray.__array_finalize__ is now callable

This means subclasses can now use super().__array_finalize__(obj) without worrying whether ndarray is their superclass or not. The actual call remains a no-op.

(gh-20766)

Add support for VSX4/Power10

With VSX4/Power10 enablement, the new instructions available in Power ISA 3.1 can be used to accelerate some NumPy operations, e.g., floor_divide, modulo, etc.

(gh-20821)

np.fromiter now accepts objects and subarrays

The numpy.fromiter function now supports object and subarray dtypes. Please see he function documentation for examples.

(gh-20993)

Math C library feature detection now uses correct signatures

Compiling is preceded by a detection phase to determine whether the underlying libc supports certain math operations. Previously this code did not respect the proper signatures. Fixing this enables compilation for the wasm-ld backend (compilation for web assembly) and reduces the number of warnings.

(gh-21154)

np.kron now maintains subclass information

np.kron maintains subclass information now such as masked arrays while computing the Kronecker product of the inputs

>>> x = ma.array([[1, 2], [3, 4]], mask=[[0, 1], [1, 0]])
>>> np.kron(x,x)
masked_array(
  data=[[1, --, --, --],
        [--, 4, --, --],
        [--, --, 4, --],
        [--, --, --, 16]],
  mask=[[False,  True,  True,  True],
        [ True, False,  True,  True],
        [ True,  True, False,  True],
        [ True,  True,  True, False]],
  fill_value=999999)

:warning: Warning, np.kron output now follows ufunc ordering (multiply) to determine the output class type

>>> class myarr(np.ndarray):
>>>    __array_priority__ = -1
>>> a = np.ones([2, 2])
>>> ma = myarray(a.shape, a.dtype, a.data)
>>> type(np.kron(a, ma)) == np.ndarray
False # Before it was True
>>> type(np.kron(a, ma)) == myarr
True

(gh-21262)

Performance improvements and changes

Faster np.loadtxt

numpy.loadtxt is now generally much faster than previously as most of it is now implemented in C.

(gh-20580)

Faster reduction operators

Reduction operations like numpy.sum, numpy.prod, numpy.add.reduce, numpy.logical_and.reduce on contiguous integer-based arrays are now much faster.

(gh-21001)

Faster np.where

numpy.where is now much faster than previously on unpredictable/random input data.

(gh-21130)

Faster operations on NumPy scalars

Many operations on NumPy scalars are now significantly faster, although rare operations (e.g. with 0-D arrays rather than scalars) may be slower in some cases. However, even with these improvements users who want the best performance for their scalars, may want to convert a known NumPy scalar into a Python one using scalar.item().

(gh-21188)

Faster np.kron

numpy.kron is about 80% faster as the product is now computed using broadcasting.

(gh-21354)

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NumPy 1.23.0 Release Notes

The NumPy 1.23.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase the execution speed, clarify the documentation, and expire old deprecations. The highlights are:

• Implementation of loadtxt in C, greatly improving its performance.
• Exposing DLPack at the Python level for easy data exchange.
• Changes to the promotion and comparisons of structured dtypes.
• Improvements to f2py.

See below for the details,

New functions

• A masked array specialization of ndenumerate is now available as numpy.ma.ndenumerate. It provides an alternative to numpy.ndenumerate and skips masked values by default.

  (gh-20020)

• numpy.from_dlpack has been added to allow easy exchange of data using the DLPack protocol. It accepts Python objects that implement the __dlpack__ and __dlpack_device__ methods and returns a ndarray object which is generally the view of the data of the input object.

  (gh-21145)

Deprecations

• Setting __array_finalize__ to None is deprecated. It must now be a method and may wish to call super().__array_finalize__(obj) after checking for None or if the NumPy version is sufficiently new.

  (gh-20766)

• Using axis=32 (axis=np.MAXDIMS) in many cases had the same meaning as axis=None. This is deprecated and axis=None must be used instead.

  (gh-20920)

• The hook function PyDataMem_SetEventHook has been deprecated and the demonstration of its use in tool/allocation_tracking has been removed. The ability to track allocations is now built-in to python via tracemalloc.

  (gh-20394)

• numpy.distutils has been deprecated, as a result of distutils itself being deprecated. It will not be present in NumPy for Python >= 3.12, and will be removed completely 2 years after the release of Python 3.12 For more details, see distutils-status-migration{.interpreted-text role="ref"}.

  (gh-20875)

• numpy.loadtxt will now give a DeprecationWarning when an integer dtype is requested but the value is formatted as a floating point number.

  (gh-21663)

Expired deprecations

• The NpzFile.iteritems() and NpzFile.iterkeys() methods have been removed as part of the continued removal of Python 2 compatibility. This concludes the deprecation from 1.15.

  (gh-16830)

• The alen and asscalar functions have been removed.

  (gh-20414)

• The UPDATEIFCOPY array flag has been removed together with the enum NPY_ARRAY_UPDATEIFCOPY. The associated (and deprecated) PyArray_XDECREF_ERR was also removed. These were all deprecated in 1.14. They are replaced by WRITEBACKIFCOPY, that requires calling PyArray_ResoveWritebackIfCopy before the array is deallocated.

  (gh-20589)

• Exceptions will be raised during array-like creation. When an object raised an exception during access of the special attributes __array__ or __array_interface__, this exception was usually ignored. This behaviour was deprecated in 1.21, and the exception will now be raised.

  (gh-20835)

• Multidimensional indexing with non-tuple values is not allowed. Previously, code such as arr[ind] where ind = [[0, 1], [0, 1]] produced a FutureWarning and was interpreted as a multidimensional index (i.e., arr[tuple(ind)]). Now this example is treated like an array index over a single dimension (arr[array(ind)]). Multidimensional indexing with anything but a tuple was deprecated in NumPy 1.15.

  (gh-21029)

• Changing to a dtype of different size in F-contiguous arrays is no longer permitted. Deprecated since Numpy 1.11.0. See below for an extended explanation of the effects of this change.

  (gh-20722)

New Features

crackfortran has support for operator and assignment overloading

crackfortran parser now understands operator and assignment definitions in a module. They are added in the body list of the module which contains a new key implementedby listing the names of the subroutines or functions implementing the operator or assignment.

(gh-15006)

f2py supports reading access type attributes from derived type statements

As a result, one does not need to use public or private statements to specify derived type access properties.

(gh-15844)

New parameter ndmin added to genfromtxt

This parameter behaves the same as ndmin from numpy.loadtxt.

(gh-20500)

np.loadtxt now supports quote character and single converter function

numpy.loadtxt now supports an additional quotechar keyword argument which is not set by default. Using quotechar='"' will read quoted fields as used by the Excel CSV dialect.

Further, it is now possible to pass a single callable rather than a dictionary for the converters argument.

(gh-20580)

Changing to dtype of a different size now requires contiguity of only the last axis

Previously, viewing an array with a dtype of a different item size required that the entire array be C-contiguous. This limitation would unnecessarily force the user to make contiguous copies of non-contiguous arrays before being able to change the dtype.

This change affects not only ndarray.view, but other construction mechanisms, including the discouraged direct assignment to ndarray.dtype.

This change expires the deprecation regarding the viewing of F-contiguous arrays, described elsewhere in the release notes.

(gh-20722)

Deterministic output files for F2PY

For F77 inputs, f2py will generate modname-f2pywrappers.f unconditionally, though these may be empty. For free-form inputs, modname-f2pywrappers.f, modname-f2pywrappers2.f90 will both be generated unconditionally, and may be empty. This allows writing generic output rules in cmake or meson and other build systems. Older behavior can be restored by passing --skip-empty-wrappers to f2py. f2py-meson{.interpreted-text role="ref"} details usage.

(gh-21187)

keepdims parameter for average

The parameter keepdims was added to the functions numpy.average and numpy.ma.average. The parameter has the same meaning as it does in reduction functions such as numpy.sum or numpy.mean.

(gh-21485)

New parameter equal_nan added to np.unique

np.unique was changed in 1.21 to treat all NaN values as equal and return a single NaN. Setting equal_nan=False will restore pre-1.21 behavior to treat NaNs as unique. Defaults to True.

(gh-21623)

Compatibility notes

1D np.linalg.norm preserves float input types, even for scalar results

Previously, this would promote to float64 when the ord argument was not one of the explicitly listed values, e.g. ord=3:

>>> f32 = np.float32([1, 2])
>>> np.linalg.norm(f32, 2).dtype
dtype('float32')
>>> np.linalg.norm(f32, 3)
dtype('float64')  # numpy 1.22
dtype('float32')  # numpy 1.23

This change affects only float32 and float16 vectors with ord other than -Inf, 0, 1, 2, and Inf.

(gh-17709)

Changes to structured (void) dtype promotion and comparisons

In general, NumPy now defines correct, but slightly limited, promotion for structured dtypes by promoting the subtypes of each field instead of raising an exception:

>>> np.result_type(np.dtype("i,i"), np.dtype("i,d"))
dtype([('f0', '<i4'), ('f1', '<f8')])

For promotion matching field names, order, and titles are enforced, however padding is ignored. Promotion involving structured dtypes now always ensures native byte-order for all fields (which may change the result of np.concatenate) and ensures that the result will be "packed", i.e. all fields are ordered contiguously and padding is removed. See structured_dtype_comparison_and_promotion{.interpreted-text role="ref"} for further details.

The repr of aligned structures will now never print the long form including offsets and itemsize unless the structure includes padding not guaranteed by align=True.

In alignment with the above changes to the promotion logic, the casting safety has been updated:

• "equiv" enforces matching names and titles. The itemsize is allowed to differ due to padding.
• "safe" allows mismatching field names and titles
• The cast safety is limited by the cast safety of each included field.
• The order of fields is used to decide cast safety of each individual field. Previously, the field names were used and only unsafe casts were possible when names mismatched.

The main important change here is that name mismatches are now considered "safe" casts.

(gh-19226)

NPY_RELAXED_STRIDES_CHECKING has been removed

NumPy cannot be compiled with NPY_RELAXED_STRIDES_CHECKING=0 anymore. Relaxed strides have been the default for many years and the option was initially introduced to allow a smoother transition.

(gh-20220)

np.loadtxt has recieved several changes

The row counting of numpy.loadtxt was fixed. loadtxt ignores fully empty lines in the file, but counted them towards max_rows. When max_rows is used and the file contains empty lines, these will now not be counted. Previously, it was possible that the result contained fewer than max_rows rows even though more data was available to be read. If the old behaviour is required, itertools.islice may be used:

import itertools
lines = itertools.islice(open("file"), 0, max_rows)
result = np.loadtxt(lines, ...)

While generally much faster and improved, numpy.loadtxt may now fail to converter certain strings to numbers that were previously successfully read. The most important cases for this are:

• Parsing floating point values such as 1.0 into integers is now deprecated.
• Parsing hexadecimal floats such as 0x3p3 will fail
• An _ was previously accepted as a thousands delimiter 100_000. This will now result in an error.

If you experience these limitations, they can all be worked around by passing appropriate converters=. NumPy now supports passing a single converter to be used for all columns to make this more convenient. For example, converters=float.fromhex can read hexadecimal float numbers and converters=int will be able to read 100_000.

Further, the error messages have been generally improved. However, this means that error types may differ. In particularly, a ValueError is now always raised when parsing of a single entry fails.

(gh-20580)

Improvements

ndarray.__array_finalize__ is now callable

This means subclasses can now use super().__array_finalize__(obj) without worrying whether ndarray is their superclass or not. The actual call remains a no-op.

(gh-20766)

Add support for VSX4/Power10

With VSX4/Power10 enablement, the new instructions available in Power ISA 3.1 can be used to accelerate some NumPy operations, e.g., floor_divide, modulo, etc.

(gh-20821)

np.fromiter now accepts objects and subarrays

The numpy.fromiter function now supports object and subarray dtypes. Please see he function documentation for examples.

(gh-20993)

Math C library feature detection now uses correct signatures

Compiling is preceded by a detection phase to determine whether the underlying libc supports certain math operations. Previously this code did not respect the proper signatures. Fixing this enables compilation for the wasm-ld backend (compilation for web assembly) and reduces the number of warnings.

(gh-21154)

np.kron now maintains subclass information

np.kron maintains subclass information now such as masked arrays while computing the Kronecker product of the inputs

>>> x = ma.array([[1, 2], [3, 4]], mask=[[0, 1], [1, 0]])
>>> np.kron(x,x)
masked_array(
  data=[[1, --, --, --],
        [--, 4, --, --],
        [--, --, 4, --],
        [--, --, --, 16]],
  mask=[[False,  True,  True,  True],
        [ True, False,  True,  True],
        [ True,  True, False,  True],
        [ True,  True,  True, False]],
  fill_value=999999)

:warning: Warning, np.kron output now follows ufunc ordering (multiply) to determine the output class type

>>> class myarr(np.ndarray):
>>>    __array_priority__ = -1
>>> a = np.ones([2, 2])
>>> ma = myarray(a.shape, a.dtype, a.data)
>>> type(np.kron(a, ma)) == np.ndarray
False # Before it was True
>>> type(np.kron(a, ma)) == myarr
True

(gh-21262)

String comparisons now supported in ufuncs

The comparison ufuncs [np.equal]{.title-ref}, [np.greater]{.title-ref}, etc. now support unicode and byte string inputs (dtypes S and U). Due to this change a FutureWarning is now given when comparing unicode to byte strings. Such comparisons always returned False and continue to do so at this time.

(gh-21716)

Performance improvements and changes

Faster np.loadtxt

numpy.loadtxt is now generally much faster than previously as most of it is now implemented in C.

(gh-20580)

Faster reduction operators

Reduction operations like numpy.sum, numpy.prod, numpy.add.reduce, numpy.logical_and.reduce on contiguous integer-based arrays are now much faster.

(gh-21001)

Faster np.where

numpy.where is now much faster than previously on unpredictable/random input data.

(gh-21130)

Faster operations on NumPy scalars

Many operations on NumPy scalars are now significantly faster, although rare operations (e.g. with 0-D arrays rather than scalars) may be slower in some cases. However, even with these improvements users who want the best performance for their scalars, may want to convert a known NumPy scalar into a Python one using scalar.item().

(gh-21188)

Faster np.kron

numpy.kron is about 80% faster as the product is now computed using broadcasting.

(gh-21354)

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NumPy 1.23.0 Release Notes

The NumPy 1.23.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase the execution speed, clarify the documentation, and expire old deprecations. The highlights are:

• Implementation of loadtxt in C, greatly improving its performance.
• Exposing DLPack at the Python level for easy data exchange.
• Changes to the promotion and comparisons of structured dtypes.
• Improvements to f2py.

See below for the details,

New functions

• A masked array specialization of ndenumerate is now available as numpy.ma.ndenumerate. It provides an alternative to numpy.ndenumerate and skips masked values by default.

  (gh-20020)

• numpy.from_dlpack has been added to allow easy exchange of data using the DLPack protocol. It accepts Python objects that implement the __dlpack__ and __dlpack_device__ methods and returns a ndarray object which is generally the view of the data of the input object.

  (gh-21145)

Deprecations

• Setting __array_finalize__ to None is deprecated. It must now be a method and may wish to call super().__array_finalize__(obj) after checking for None or if the NumPy version is sufficiently new.

  (gh-20766)

• Using axis=32 (axis=np.MAXDIMS) in many cases had the same meaning as axis=None. This is deprecated and axis=None must be used instead.

  (gh-20920)

• The hook function PyDataMem_SetEventHook has been deprecated and the demonstration of its use in tool/allocation_tracking has been removed. The ability to track allocations is now built-in to python via tracemalloc.

  (gh-20394)

• numpy.distutils has been deprecated, as a result of distutils itself being deprecated. It will not be present in NumPy for Python >= 3.12, and will be removed completely 2 years after the release of Python 3.12 For more details, see distutils-status-migration{.interpreted-text role="ref"}.

  (gh-20875)

Expired deprecations

• The NpzFile.iteritems() and NpzFile.iterkeys() methods have been removed as part of the continued removal of Python 2 compatibility. This concludes the deprecation from 1.15.

  (gh-16830)

• The alen and asscalar functions have been removed.

  (gh-20414)

• The UPDATEIFCOPY array flag has been removed together with the enum NPY_ARRAY_UPDATEIFCOPY. The associated (and deprecated) PyArray_XDECREF_ERR was also removed. These were all deprecated in 1.14. They are replaced by WRITEBACKIFCOPY, that requires calling PyArray_ResoveWritebackIfCopy before the array is deallocated.

  (gh-20589)

• Exceptions will be raised during array-like creation. When an object raised an exception during access of the special attributes __array__ or __array_interface__, this exception was usually ignored. This behaviour was deprecated in 1.21, and the exception will now be raised.

  (gh-20835)

• Multidimensional indexing with non-tuple values is not allowed. Previously, code such as arr[ind] where ind = [[0, 1], [0, 1]] produced a FutureWarning and was interpreted as a multidimensional index (i.e., arr[tuple(ind)]). Now this example is treated like an array index over a single dimension (arr[array(ind)]). Multidimensional indexing with anything but a tuple was deprecated in NumPy 1.15.

  (gh-21029)

• Changing to a dtype of different size in F-contiguous arrays is no longer permitted. Deprecated since Numpy 1.11.0. See below for an extended explanation of the effects of this change.

  (gh-20722)

New Features

crackfortran has support for operator and assignment overloading

crackfortran parser now understands operator and assignment definitions in a module. They are added in the body list of the module which contains a new key implementedby listing the names of the subroutines or functions implementing the operator or assignment.

(gh-15006)

f2py supports reading access type attributes from derived type statements

As a result, one does not need to use public or private statements to specify derived type access properties.

(gh-15844)

New parameter ndmin added to genfromtxt

This parameter behaves the same as ndmin from numpy.loadtxt.

(gh-20500)

np.loadtxt now supports quote character and single converter function

numpy.loadtxt now supports an additional quotechar keyword argument which is not set by default. Using quotechar='"' will read quoted fields as used by the Excel CSV dialect.

Further, it is now possible to pass a single callable rather than a dictionary for the converters argument.

(gh-20580)

Changing to dtype of a different size now requires contiguity of only the last axis

Previously, viewing an array with a dtype of a different item size required that the entire array be C-contiguous. This limitation would unnecessarily force the user to make contiguous copies of non-contiguous arrays before being able to change the dtype.

This change affects not only ndarray.view, but other construction mechanisms, including the discouraged direct assignment to ndarray.dtype.

This change expires the deprecation regarding the viewing of F-contiguous arrays, described elsewhere in the release notes.

(gh-20722)

Deterministic output files for F2PY

For F77 inputs, f2py will generate modname-f2pywrappers.f unconditionally, though these may be empty. For free-form inputs, modname-f2pywrappers.f, modname-f2pywrappers2.f90 will both be generated unconditionally, and may be empty. This allows writing generic output rules in cmake or meson and other build systems. Older behavior can be restored by passing --skip-empty-wrappers to f2py. f2py-meson{.interpreted-text role="ref"} details usage.

(gh-21187)

keepdims parameter for average

The parameter keepdims was added to the functions numpy.average and numpy.ma.average. The parameter has the same meaning as it does in reduction functions such as numpy.sum or numpy.mean.

(gh-21485)

Compatibility notes

1D np.linalg.norm preserves float input types, even for scalar results

Previously, this would promote to float64 when the ord argument was not one of the explicitly listed values, e.g. ord=3:

>>> f32 = np.float32([1, 2])
>>> np.linalg.norm(f32, 2).dtype
dtype('float32')
>>> np.linalg.norm(f32, 3)
dtype('float64')  # numpy 1.22
dtype('float32')  # numpy 1.23

This change affects only float32 and float16 vectors with ord other than -Inf, 0, 1, 2, and Inf.

(gh-17709)

Changes to structured (void) dtype promotion and comparisons

In general, NumPy now defines correct, but slightly limited, promotion for structured dtypes by promoting the subtypes of each field instead of raising an exception:

>>> np.result_type(np.dtype("i,i"), np.dtype("i,d"))
dtype([('f0', '<i4'), ('f1', '<f8')])

For promotion matching field names, order, and titles are enforced, however padding is ignored. Promotion involving structured dtypes now always ensures native byte-order for all fields (which may change the result of np.concatenate) and ensures that the result will be "packed", i.e. all fields are ordered contiguously and padding is removed. See structured_dtype_comparison_and_promotion{.interpreted-text role="ref"} for further details.

The repr of aligned structures will now never print the long form including offsets and itemsize unless the structure includes padding not guaranteed by align=True.

In alignment with the above changes to the promotion logic, the casting safety has been updated:

• "equiv" enforces matching names and titles. The itemsize is allowed to differ due to padding.
• "safe" allows mismatching field names and titles
• The cast safety is limited by the cast safety of each included field.
• The order of fields is used to decide cast safety of each individual field. Previously, the field names were used and only unsafe casts were possible when names mismatched.

The main important change here is that name mismatches are now considered "safe" casts.

(gh-19226)

NPY_RELAXED_STRIDES_CHECKING has been removed

NumPy cannot be compiled with NPY_RELAXED_STRIDES_CHECKING=0 anymore. Relaxed strides have been the default for many years and the option was initially introduced to allow a smoother transition.

(gh-20220)

np.loadtxt has recieved several changes

The row counting of numpy.loadtxt was fixed. loadtxt ignores fully empty lines in the file, but counted them towards max_rows. When max_rows is used and the file contains empty lines, these will now not be counted. Previously, it was possible that the result contained fewer than max_rows rows even though more data was available to be read. If the old behaviour is required, itertools.islice may be used:

import itertools
lines = itertools.islice(open("file"), 0, max_rows)
result = np.loadtxt(lines, ...)

While generally much faster and improved, numpy.loadtxt may now fail to converter certain strings to numbers that were previously successfully read. The most important cases for this are:

• Parsing floating point values such as 1.0 into integers will now fail
• Parsing hexadecimal floats such as 0x3p3 will fail
• An _ was previously accepted as a thousands delimiter 100_000. This will now result in an error.

If you experience these limitations, they can all be worked around by passing appropriate converters=. NumPy now supports passing a single converter to be used for all columns to make this more convenient. For example, converters=float.fromhex can read hexadecimal float numbers and converters=int will be able to read 100_000.

Further, the error messages have been generally improved. However, this means that error types may differ. In particularly, a ValueError is now always raised when parsing of a single entry fails.

(gh-20580)

Improvements

ndarray.__array_finalize__ is now callable

This means subclasses can now use super().__array_finalize__(obj) without worrying whether ndarray is their superclass or not. The actual call remains a no-op.

(gh-20766)

Add support for VSX4/Power10

With VSX4/Power10 enablement, the new instructions available in Power ISA 3.1 can be used to accelerate some NumPy operations, e.g., floor_divide, modulo, etc.

(gh-20821)

np.fromiter now accepts objects and subarrays

The numpy.fromiter function now supports object and subarray dtypes. Please see he function documentation for examples.

(gh-20993)

Math C library feature detection now uses correct signatures

Compiling is preceded by a detection phase to determine whether the underlying libc supports certain math operations. Previously this code did not respect the proper signatures. Fixing this enables compilation for the wasm-ld backend (compilation for web assembly) and reduces the number of warnings.

(gh-21154)

np.kron now maintains subclass information

np.kron maintains subclass information now such as masked arrays while computing the Kronecker product of the inputs

>>> x = ma.array([[1, 2], [3, 4]], mask=[[0, 1], [1, 0]])
>>> np.kron(x,x)
masked_array(
  data=[[1, --, --, --],
        [--, 4, --, --],
        [--, --, 4, --],
        [--, --, --, 16]],
  mask=[[False,  True,  True,  True],
        [ True, False,  True,  True],
        [ True,  True, False,  True],
        [ True,  True,  True, False]],
  fill_value=999999)

:warning: Warning, np.kron output now follows ufunc ordering (multiply) to determine the output class type

>>> class myarr(np.ndarray):
>>>    __array_priority__ = -1
>>> a = np.ones([2, 2])
>>> ma = myarray(a.shape, a.dtype, a.data)
>>> type(np.kron(a, ma)) == np.ndarray
False # Before it was True
>>> type(np.kron(a, ma)) == myarr
True

(gh-21262)

Performance improvements and changes

Faster np.loadtxt

numpy.loadtxt is now generally much faster than previously as most of it is now implemented in C.

(gh-20580)

Faster reduction operators

Reduction operations like numpy.sum, numpy.prod, numpy.add.reduce, numpy.logical_and.reduce on contiguous integer-based arrays are now much faster.

(gh-21001)

Faster np.where

numpy.where is now much faster than previously on unpredictable/random input data.

(gh-21130)

Faster operations on NumPy scalars

Many operations on NumPy scalars are now significantly faster, although rare operations (e.g. with 0-D arrays rather than scalars) may be slower in some cases. However, even with these improvements users who want the best performance for their scalars, may want to convert a known NumPy scalar into a Python one using scalar.item().

(gh-21188)

Faster np.kron

numpy.kron is about 80% faster as the product is now computed using broadcasting.

(gh-21354)

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NumPy 1.23.0 Release Notes

The NumPy 1.23.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase the execution speed, clarify the documentation, and expire old deprecations. The highlights are:

• Implementation of loadtxt in C, greatly improving its performance.
• Exposing DLPack at the Python level for easy data exchange.
• Changes to the promotion and comparisons of structured dtypes.
• Improvements to f2py.

See below for the details,

New functions

• A masked array specialization of ndenumerate is now available as numpy.ma.ndenumerate. It provides an alternative to numpy.ndenumerate and skips masked values by default.

  (gh-20020)

• numpy.from_dlpack has been added to allow easy exchange of data using the DLPack protocol. It accepts Python objects that implement the __dlpack__ and __dlpack_device__ methods and returns a ndarray object which is generally the view of the data of the input object.

  (gh-21145)

Deprecations

• Setting __array_finalize__ to None is deprecated. It must now be a method and may wish to call super().__array_finalize__(obj) after checking for None or if the NumPy version is sufficiently new.

  (gh-20766)

• Using axis=32 (axis=np.MAXDIMS) in many cases had the same meaning as axis=None. This is deprecated and axis=None must be used instead.

  (gh-20920)

• The hook function PyDataMem_SetEventHook has been deprecated and the demonstration of its use in tool/allocation_tracking has been removed. The ability to track allocations is now built-in to python via tracemalloc.

  (gh-20394)

• numpy.distutils has been deprecated, as a result of distutils itself being deprecated. It will not be present in NumPy for Python >= 3.12, and will be removed completely 2 years after the release of Python 3.12 For more details, see distutils-status-migration{.interpreted-text role="ref"}.

  (gh-20875)

Expired deprecations

• The NpzFile.iteritems() and NpzFile.iterkeys() methods have been removed as part of the continued removal of Python 2 compatibility. This concludes the deprecation from 1.15.

  (gh-16830)

• The alen and asscalar functions have been removed.

  (gh-20414)

• The UPDATEIFCOPY array flag has been removed together with the enum NPY_ARRAY_UPDATEIFCOPY. The associated (and deprecated) PyArray_XDECREF_ERR was also removed. These were all deprecated in 1.14. They are replaced by WRITEBACKIFCOPY, that requires calling PyArray_ResoveWritebackIfCopy before the array is deallocated.

  (gh-20589)

• Exceptions will be raised during array-like creation. When an object raised an exception during access of the special attributes __array__ or __array_interface__, this exception was usually ignored. This behaviour was deprecated in 1.21, and the exception will now be raised.

  (gh-20835)

• Multidimensional indexing with non-tuple values is not allowed. Previously, code such as arr[ind] where ind = [[0, 1], [0, 1]] produced a FutureWarning and was interpreted as a multidimensional index (i.e., arr[tuple(ind)]). Now this example is treated like an array index over a single dimension (arr[array(ind)]). Multidimensional indexing with anything but a tuple was deprecated in NumPy 1.15.

  (gh-21029)

• Changing to a dtype of different size in F-contiguous arrays is no longer permitted. Deprecated since Numpy 1.11.0. See below for an extended explanation of the effects of this change.

  (gh-20722)

New Features

crackfortran has support for operator and assignment overloading

crackfortran parser now understands operator and assignment definitions in a module. They are added in the body list of the module which contains a new key implementedby listing the names of the subroutines or functions implementing the operator or assignment.

(gh-15006)

f2py supports reading access type attributes from derived type statements

As a result, one does not need to use public or private statements to specify derived type access properties.

(gh-15844)

New parameter ndmin added to genfromtxt

This parameter behaves the same as ndmin from numpy.loadtxt.

(gh-20500)

np.loadtxt now supports quote character and single converter function

numpy.loadtxt now supports an additional quotechar keyword argument which is not set by default. Using quotechar='"' will read quoted fields as used by the Excel CSV dialect.

Further, it is now possible to pass a single callable rather than a dictionary for the converters argument.

(gh-20580)

Changing to dtype of a different size now requires contiguity of only the last axis

Previously, viewing an array with a dtype of a different item size required that the entire array be C-contiguous. This limitation would unnecessarily force the user to make contiguous copies of non-contiguous arrays before being able to change the dtype.

This change affects not only ndarray.view, but other construction mechanisms, including the discouraged direct assignment to ndarray.dtype.

This change expires the deprecation regarding the viewing of F-contiguous arrays, described elsewhere in the release notes.

(gh-20722)

Deterministic output files for F2PY

For F77 inputs, f2py will generate modname-f2pywrappers.f unconditionally, though these may be empty. For free-form inputs, modname-f2pywrappers.f, modname-f2pywrappers2.f90 will both be generated unconditionally, and may be empty. This allows writing generic output rules in cmake or meson and other build systems. Older behavior can be restored by passing --skip-empty-wrappers to f2py. f2py-meson{.interpreted-text role="ref"} details usage.

(gh-21187)

keepdims parameter for average

The parameter keepdims was added to the functions numpy.average and numpy.ma.average. The parameter has the same meaning as it does in reduction functions such as numpy.sum or numpy.mean.

(gh-21485)

Compatibility notes

1D np.linalg.norm preserves float input types, even for scalar results

Previously, this would promote to float64 when the ord argument was not one of the explicitly listed values, e.g. ord=3:

>>> f32 = np.float32([1, 2])
>>> np.linalg.norm(f32, 2).dtype
dtype('float32')
>>> np.linalg.norm(f32, 3)
dtype('float64')  # numpy 1.22
dtype('float32')  # numpy 1.23

This change affects only float32 and float16 vectors with ord other than -Inf, 0, 1, 2, and Inf.

(gh-17709)

Changes to structured (void) dtype promotion and comparisons

In general, NumPy now defines correct, but slightly limited, promotion for structured dtypes by promoting the subtypes of each field instead of raising an exception:

>>> np.result_type(np.dtype("i,i"), np.dtype("i,d"))
dtype([('f0', '<i4'), ('f1', '<f8')])

For promotion matching field names, order, and titles are enforced, however padding is ignored. Promotion involving structured dtypes now always ensures native byte-order for all fields (which may change the result of np.concatenate) and ensures that the result will be "packed", i.e. all fields are ordered contiguously and padding is removed. See structured_dtype_comparison_and_promotion{.interpreted-text role="ref"} for further details.

The repr of aligned structures will now never print the long form including offsets and itemsize unless the structure includes padding not guaranteed by align=True.

In alignment with the above changes to the promotion logic, the casting safety has been updated:

• "equiv" enforces matching names and titles. The itemsize is allowed to differ due to padding.
• "safe" allows mismatching field names and titles
• The cast safety is limited by the cast safety of each included field.
• The order of fields is used to decide cast safety of each individual field. Previously, the field names were used and only unsafe casts were possible when names mismatched.

The main important change here is that name mismatches are now considered "safe" casts.

(gh-19226)

NPY_RELAXED_STRIDES_CHECKING has been removed

NumPy cannot be compiled with NPY_RELAXED_STRIDES_CHECKING=0 anymore. Relaxed strides have been the default for many years and the option was initially introduced to allow a smoother transition.

(gh-20220)

np.loadtxt has recieved several changes

The row counting of numpy.loadtxt was fixed. loadtxt ignores fully empty lines in the file, but counted them towards max_rows. When max_rows is used and the file contains empty lines, these will now not be counted. Previously, it was possible that the result contained fewer than max_rows rows even though more data was available to be read. If the old behaviour is required, itertools.islice may be used:

import itertools
lines = itertools.islice(open("file"), 0, max_rows)
result = np.loadtxt(lines, ...)

While generally much faster and improved, numpy.loadtxt may now fail to converter certain strings to numbers that were previously successfully read. The most important cases for this are:

• Parsing floating point values such as 1.0 into integers will now fail
• Parsing hexadecimal floats such as 0x3p3 will fail
• An _ was previously accepted as a thousands delimiter 100_000. This will now result in an error.

If you experience these limitations, they can all be worked around by passing appropriate converters=. NumPy now supports passing a single converter to be used for all columns to make this more convenient. For example, converters=float.fromhex can read hexadecimal float numbers and converters=int will be able to read 100_000.

Further, the error messages have been generally improved. However, this means that error types may differ. In particularly, a ValueError is now always raised when parsing of a single entry fails.

(gh-20580)

Improvements

ndarray.__array_finalize__ is now callable

This means subclasses can now use super().__array_finalize__(obj) without worrying whether ndarray is their superclass or not. The actual call remains a no-op.

(gh-20766)

Add support for VSX4/Power10

With VSX4/Power10 enablement, the new instructions available in Power ISA 3.1 can be used to accelerate some NumPy operations, e.g., floor_divide, modulo, etc.

(gh-20821)

np.fromiter now accepts objects and subarrays

The numpy.fromiter function now supports object and subarray dtypes. Please see he function documentation for examples.

(gh-20993)

Math C library feature detection now uses correct signatures

Compiling is preceded by a detection phase to determine whether the underlying libc supports certain math operations. Previously this code did not respect the proper signatures. Fixing this enables compilation for the wasm-ld backend (compilation for web assembly) and reduces the number of warnings.

(gh-21154)

np.kron now maintains subclass information

np.kron maintains subclass information now such as masked arrays while computing the Kronecker product of the inputs

>>> x = ma.array([[1, 2], [3, 4]], mask=[[0, 1], [1, 0]])
>>> np.kron(x,x)
masked_array(
  data=[[1, --, --, --],
        [--, 4, --, --],
        [--, --, 4, --],
        [--, --, --, 16]],
  mask=[[False,  True,  True,  True],
        [ True, False,  True,  True],
        [ True,  True, False,  True],
        [ True,  True,  True, False]],
  fill_value=999999)

:warning: Warning, np.kron output now follows ufunc ordering (multiply) to determine the output class type

>>> class myarr(np.ndarray):
>>>    __array_priority__ = -1
>>> a = np.ones([2, 2])
>>> ma = myarray(a.shape, a.dtype, a.data)
>>> type(np.kron(a, ma)) == np.ndarray
False # Before it was True
>>> type(np.kron(a, ma)) == myarr
True

(gh-21262)

Performance improvements and changes

Faster np.loadtxt

numpy.loadtxt is now generally much faster than previously as most of it is now implemented in C.

(gh-20580)

Faster reduction operators

Reduction operations like numpy.sum, numpy.prod, numpy.add.reduce, numpy.logical_and.reduce on contiguous integer-based arrays are now much faster.

(gh-21001)

Faster np.where

numpy.where is now much faster than previously on unpredictable/random input data.

(gh-21130)

Faster operations on NumPy scalars

Many operations on NumPy scalars are now significantly faster, although rare operations (e.g. with 0-D arrays rather than scalars) may be slower in some cases. However, even with these improvements users who want the best performance for their scalars, may want to convert a known NumPy scalar into a Python one using scalar.item().

(gh-21188)

Faster np.kron

numpy.kron is about 80% faster as the product is now computed using broadcasting.

(gh-21354)

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NumPy 1.22.4 Release Notes

NumPy 1.22.4 is a maintenance release that fixes bugs discovered after the 1.22.3 release. In addition, the wheels for this release are built using the recently released Cython 0.29.30, which should fix the reported problems with debugging.

The Python versions supported for this release are 3.8-3.10. Note that the Mac wheels are now based on OS X 10.15 rather than 10.6 that was used in previous NumPy release cycles.

Contributors

A total of 12 people contributed to this release. People with a "+" by their names contributed a patch for the first time.

• Alexander Shadchin
• Bas van Beek
• Charles Harris
• Hood Chatham
• Jarrod Millman
• John-Mark Gurney +
• Junyan Ou +
• Mariusz Felisiak +
• Ross Barnowski
• Sebastian Berg
• Serge Guelton
• Stefan van der Walt

Pull requests merged

A total of 22 pull requests were merged for this release.

• #21191: TYP, BUG: Fix np.lib.stride_tricks re-exported under the...
• #21192: TST: Bump mypy from 0.931 to 0.940
• #21243: MAINT: Explicitly re-export the types in numpy._typing
• #21245: MAINT: Specify sphinx, numpydoc versions for CI doc builds
• #21275: BUG: Fix typos
• #21277: ENH, BLD: Fix math feature detection for wasm
• #21350: MAINT: Fix failing simd and cygwin tests.
• #21438: MAINT: Fix failing Python 3.8 32-bit Windows test.
• #21444: BUG: add linux guard per #21386
• #21445: BUG: Allow legacy dtypes to cast to datetime again
• #21446: BUG: Make mmap handling safer in frombuffer
• #21447: BUG: Stop using PyBytesObject.ob_shash deprecated in Python 3.11.
• #21448: ENH: Introduce numpy.core.setup_common.NPY_CXX_FLAGS
• #21472: BUG: Ensure compile errors are raised correclty
• #21473: BUG: Fix segmentation fault
• #21474: MAINT: Update doc requirements
• #21475: MAINT: Mark npy_memchr with no_sanitize("alignment") on clang
• #21512: DOC: Proposal - make the doc landing page cards more similar...
• #21525: MAINT: Update Cython version to 0.29.30.
• #21536: BUG: Fix GCC error during build configuration
• #21541: REL: Prepare for the NumPy 1.22.4 release.
• #21547: MAINT: Skip tests that fail on PyPy.

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MD5

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09b3a41ea0b9bc20bd1691cf88f0b0d3  numpy-1.22.4.tar.gz
b44849506fbb54cdef9dbb435b2b1987  numpy-1.22.4.zip

SHA256

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37431a77ceb9307c28382c9773da9f306435135fae6b80b62a11c53cfedd8802  numpy-1.22.4-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
cc7f00008eb7d3f2489fca6f334ec19ca63e31371be28fd5dad955b16ec285bd  numpy-1.22.4-cp39-cp39-win32.whl
f0725df166cf4785c0bc4cbfb320203182b1ecd30fee6e541c8752a92df6aa32  numpy-1.22.4-cp39-cp39-win_amd64.whl
0791fbd1e43bf74b3502133207e378901272f3c156c4df4954cad833b1380207  numpy-1.22.4-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
b4308198d0e41efaa108e57d69973398439c7299a9d551680cdd603cf6d20709  numpy-1.22.4.tar.gz
425b390e4619f58d8526b3dcf656dde069133ae5c240229821f01b5f44ea07af  numpy-1.22.4.zip

NumPy 1.21.6 Release Notes

NumPy 1.21.6 is a very small release that achieves two things:

• Backs out the mistaken backport of C++ code into 1.21.5.
• Provides a 32 bit Windows wheel for Python 3.10.

The provision of the 32 bit wheel is intended to make life easier for oldest-supported-numpy.

Checksums

MD5

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04aea95dcb1d256d13a45df42173aa1e  numpy-1.21.6.zip

SHA256

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