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)

Checksums

MD5

e82b403f04ff9696804199b727c76a16  numpy-1.23.0rc2-cp310-cp310-macosx_10_9_x86_64.whl
8c54a8d758645f3cc4cd38d3e8749b53  numpy-1.23.0rc2-cp310-cp310-macosx_11_0_arm64.whl
9cb69a99ecc262a0618d1022abefddf3  numpy-1.23.0rc2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
fdc8f769f9d7f49621810dd6e93c4b7b  numpy-1.23.0rc2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
b4e4780b9893cafb7fdf9e77a917621a  numpy-1.23.0rc2-cp310-cp310-win32.whl
72522edc1356681cb2282c0ab846e185  numpy-1.23.0rc2-cp310-cp310-win_amd64.whl
e41ddd480f1a4df9fbd19613e8fe0279  numpy-1.23.0rc2-cp38-cp38-macosx_10_9_x86_64.whl
c072a129bed27a890f40fe4ae92c85d1  numpy-1.23.0rc2-cp38-cp38-macosx_11_0_arm64.whl
3403871b2b11afb71e157da8b5e77bde  numpy-1.23.0rc2-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
4bfc243c9d418c3012790c916821b1a1  numpy-1.23.0rc2-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
71da5294cd1c7787c502b5dd3868e810  numpy-1.23.0rc2-cp38-cp38-win32.whl
82050d9ffdd0c949bdbb431cadd1e594  numpy-1.23.0rc2-cp38-cp38-win_amd64.whl
ba5b01751b659d1d7db38ebd98c43230  numpy-1.23.0rc2-cp39-cp39-macosx_10_9_x86_64.whl
3f135b3b0e0bb45739c422c3a7eaf71d  numpy-1.23.0rc2-cp39-cp39-macosx_11_0_arm64.whl
d4c2891a953d0bbc746aaaf21e3b39b2  numpy-1.23.0rc2-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
cd9afa01451169e5b09037db846948fb  numpy-1.23.0rc2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
e58528377aabfde966bb965fc4df24d3  numpy-1.23.0rc2-cp39-cp39-win32.whl
b07488787ebc63d8ba7885625231f14a  numpy-1.23.0rc2-cp39-cp39-win_amd64.whl
11c6aa572a7337e0245043bd7e442db1  numpy-1.23.0rc2-pp38-pypy38_pp73-macosx_10_9_x86_64.whl
9ab414aa7e01e45faf2e28c7312d16e6  numpy-1.23.0rc2-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
d1f5b0c80a9eea0b57df5a5b03d2a981  numpy-1.23.0rc2-pp38-pypy38_pp73-win_amd64.whl
00884782fc17920a470641ae5c19ad31  numpy-1.23.0rc2.tar.gz

SHA256

2e66decdea13ae8091ba480209dd5ce31261fa3b021ec06b30bd2f4a304861b7  numpy-1.23.0rc2-cp310-cp310-macosx_10_9_x86_64.whl
fde47931544086a648b12ee7c9ccf30edd6c6db776005fb07e4a019a04980042  numpy-1.23.0rc2-cp310-cp310-macosx_11_0_arm64.whl
c308afc8ec782badd073999385a6c93c27ee68e6c0991697394d4fd56566af1f  numpy-1.23.0rc2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
1f22f6f3cb7094ad77c8d352e4bfd2c1db1c38bc08d0b6c74e9b46343c53b052  numpy-1.23.0rc2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
279dce16b143bc50d49bab52dc279d6ab5b0edc7f4d2cc7edaf6a547586bda7e  numpy-1.23.0rc2-cp310-cp310-win32.whl
04e4dbe6b777e977813e7ff5f43aa030ef4f6f75cbc1a4504d3135942b5c12fe  numpy-1.23.0rc2-cp310-cp310-win_amd64.whl
9793feff4758c68502f7652fab08e5ec427d9973d26014767cc15c1b1d885f56  numpy-1.23.0rc2-cp38-cp38-macosx_10_9_x86_64.whl
804293d9bdf33f9c9fb0b4a753f9e84114bb0ad538d184fc579b30782326c827  numpy-1.23.0rc2-cp38-cp38-macosx_11_0_arm64.whl
c335800064f04e0b474b64779ab234ae23c0a5b2f5a06284bb07d297d73692bd  numpy-1.23.0rc2-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
9810b840a751b6f0c73c21fb2a50e306d7d0be4114cded4c7d069e142ce488cf  numpy-1.23.0rc2-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
dbc987d14f46ae4c476068543d3ad2a20e7ebcb06b211eb4292224dc136eb01d  numpy-1.23.0rc2-cp38-cp38-win32.whl
020218fc82390f1d537cb193d6f1449a919ec97df69b5a64c0a1d017486e0032  numpy-1.23.0rc2-cp38-cp38-win_amd64.whl
785d6520f7bf10ff188762bc460579d6a31c11f960976b2a29efc383b0346572  numpy-1.23.0rc2-cp39-cp39-macosx_10_9_x86_64.whl
ae7e8801b93124a6b0becedc06285ddbaca2daab2d30e35ea413d3bec252717a  numpy-1.23.0rc2-cp39-cp39-macosx_11_0_arm64.whl
1c881827ff0ad7d607047c19a075a7d7c7125cc103fb969a9200bad26175fb9d  numpy-1.23.0rc2-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
6fbd492bead87ab83240c56b3490ac301595ab1399ace3e3c1b7c130e3529358  numpy-1.23.0rc2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
82e69890c394a4e1cbcaf12b47d8477bbac4635866fc46a77670abbe4bb4085d  numpy-1.23.0rc2-cp39-cp39-win32.whl
f5a1c7c45ff29db501f9e38a360aedd833e355c14c75155ba2bd46ee3799e30a  numpy-1.23.0rc2-cp39-cp39-win_amd64.whl
b7be00b0a76384490845395714e62f597e64bc6dc8f8a14be0e96034dde3667c  numpy-1.23.0rc2-pp38-pypy38_pp73-macosx_10_9_x86_64.whl
d17f7feb2cca596daa4b3dae86b611a13e9ace061e6583a8db21841f529ca891  numpy-1.23.0rc2-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
548f4d86aa259a448f2da0c07df070bf1f71b68c1f84b1356d4a2ed832598758  numpy-1.23.0rc2-pp38-pypy38_pp73-win_amd64.whl
8e8a88657c028b8b77f3df6f266a5e6ffb4419cbc3dfb525cbbb80ba710f5da2  numpy-1.23.0rc2.tar.gz