numpy
NumPy 1.22.0 is a big release featuring the work of 153 contributers spread over 609 pull requests. There have been many improvements, highlights are:
- Annotations of the main namespace are essentially complete. Upstream is a moving target, so there will likely be further improvements, but the major work is done. This is probably the most user visible enhancement in this release.
- A preliminary version of the proposed Array-API is provided. This is a step in creating a standard collection of functions that can be used across applications such as CuPy and JAX.
- NumPy now has a DLPack backend. DLPack provides a common interchange format for array (tensor) data.
- New methods for
quantile
,percentile
, and related functions. The new methods provide a complete set of the methods commonly found in the literature. - A new configurable allocator for use by downstream projects.
- The universal functions have been refactored to implement most of
NEP 43 <NEP43>
. This also unlocks the ability to experiment with the future DType API.
These are in addition to the ongoing work to provide SIMD support for commonly used functions, improvements to F2PY, and better documentation.
The Python versions supported in this release are 3.8-3.10, Python 3.7 has been dropped. Note that 32 bit wheels are only provided for Python 3.8 and 3.9 on Windows, all other wheels are 64 bits on account of Ubuntu, Fedora, and other Linux distributions dropping 32 bit support. The Mac wheels are now based on OS X 10.14 rather than 10.6 that was used in previous NumPy release cycles. 10.14 is the oldest release supported by Apple. All 64 bit wheels are also linked with 64 bit integer OpenBLAS, which should fix the occasional problems encountered by folks using truly huge arrays.
Using the strings "Bytes0"
, "Datetime64"
, "Str0"
, "Uint32"
, and "Uint64"
as a dtype will now raise a TypeError
.
(gh-19539)
numpy.loads
was deprecated in v1.15, with the recommendation that users use pickle.loads
instead. ndfromtxt
and mafromtxt
were both deprecated in v1.17 - users should use numpy.genfromtxt
instead with the appropriate value for the usemask
parameter.
(gh-19615)
The misspelled keyword argument delimitor
of numpy.ma.mrecords.fromtextfile()
has been changed to delimiter
, using it will emit a deprecation warning.
(gh-19921)
numpy.partition
and numpy.argpartition
would previously accept boolean values for the kth
parameter, which would subsequently be converted into integers. This behavior has now been deprecated.
(gh-20000)
The numpy.MachAr
class and finfo.machar <numpy.finfo>
attribute have been deprecated. Users are encouraged to access the property if interest directly from the corresponding numpy.finfo
attribute.
(gh-20201)
NumPy now sets the -ftrapping-math
option on clang to enforce correct floating point error handling for universal functions. Clang defaults to non-IEEE and C99 conform behaviour otherwise. This change (using the equivalent but newer -ffp-exception-behavior=strict
) was attempted in NumPy 1.21, but was effectively never used.
(gh-19479)
Floor division of complex types will now result in a TypeError
>>> a = np.arange(10) + 1j* np.arange(10)
>>> a // 1
TypeError: ufunc 'floor_divide' not supported for the input types...
(gh-19135)
When a function that respects numpy.ndarray
subclasses is vectorized using numpy.vectorize
, the vectorized function will now be subclass-safe also for cases that a signature is given (i.e., when creating a gufunc
): the output class will be the same as that returned by the first call to the underlying function.
(gh-19356)
Python support has been dropped. This is rather strict, there are changes that require Python >= 3.8.
(gh-19665)
The repr of np.dtype({"names": ["a"], "formats": [int], "offsets": [2]})
is now dtype({'names': ['a'], 'formats': ['<i8'], 'offsets': [2], 'itemsize': 10})
, whereas spaces where previously omitted after colons and between fields.
The old behavior can be restored via np.set_printoptions(legacy="1.21")
.
(gh-19687)
Fixed a bug in the advance
method of PCG64DSXM
and PCG64
. The bug only affects results when the step was larger than 264 on platforms that do not support 128-bit integers(e.g., Windows and 32-bit Linux).
(gh-20049)
There was bug in the generation of 32 bit floating point values from the uniform distribution that would result in the least significant bit of the random variate always being 0. This has been fixed.
This change affects the variates produced by the random.Generator
methods random
, standard_normal
, standard_exponential
, and standard_gamma
, but only when the dtype is specified as numpy.float32
.
(gh-20314)
The masked inner-loop selector is now never used. A warning will be given in the unlikely event that it was customized.
We do not expect that any code uses this. If you do use it, you must unset the selector on newer NumPy version. Please also contact the NumPy developers, we do anticipate providing a new, more specific, mechanism.
The customization was part of a never-implemented feature to allow for faster masked operations.
(gh-19259)
The new header experimental_public_dtype_api.h
allows to experiment with future API for improved universal function and especially user DType support. At this time it is advisable to experiment using the development version of NumPy since some changes are expected and new features will be unlocked.
(gh-19919)
As detailed in NEP 49, the function used for allocation of the data segment of a ndarray can be changed. The policy can be set globally or in a context. For more information see the NEP and the data_memory
reference docs. Also add a NUMPY_WARN_IF_NO_MEM_POLICY
override to warn on dangerous use of transfering ownership by setting NPY_ARRAY_OWNDATA
.
(gh-17582)
An initial implementation of NEP 47 (adoption the array API standard) has been added as numpy.array_api
. The implementation is experimental and will issue a UserWarning on import, as the array API standard is still in draft state. numpy.array_api
is a conforming implementation of the array API standard, which is also minimal, meaning that only those functions and behaviors that are required by the standard are implemented (see the NEP for more info). Libraries wishing to make use of the array API standard are encouraged to use numpy.array_api
to check that they are only using functionality that is guaranteed to be present in standard conforming implementations.
(gh-18585)
This feature depends on Doxygen in the generation process and on Breathe to integrate it with Sphinx.
(gh-18884)
The mypy plugin, introduced in numpy/numpy#17843, has again been expanded: the plugin now is now responsible for setting the platform-specific precision of numpy.ctypeslib.c_intp
, the latter being used as data type for various numpy.ndarray.ctypes
attributes.
Without the plugin, aforementioned type will default to ctypes.c_int64
.
To enable the plugin, one must add it to their mypy configuration file:
[mypy]
plugins = numpy.typing.mypy_plugin
(gh-19062)
Add a ndarray.__dlpack__()
method which returns a dlpack
C structure wrapped in a PyCapsule
. Also add a np._from_dlpack(obj)
function, where obj
supports __dlpack__()
, and returns an ndarray
.
(gh-19083)
keepdims
argument is added to numpy.argmin
, numpy.argmax
. If set to True
, the axes which are reduced are left in the result as dimensions with size one. The resulting array has the same number of dimensions and will broadcast with the input array.
(gh-19211)
Computes the number of 1-bits in the absolute value of the input. This works on all the numpy integer types. Analogous to the builtin int.bit_count
or popcount
in C++.
>>> np.uint32(1023).bit_count()
10
>>> np.int32(-127).bit_count()
7
(gh-19355)
The ndim
and axis
parameters are now also stored as attributes within each numpy.AxisError
instance.
(gh-19459)
numpy
added support for windows/arm64 target. Please note OpenBLAS
support is not yet available for windows/arm64 target.
(gh-19513)
LoongArch is a new instruction set, numpy compilation failure on LoongArch architecture, so add the commit.
(gh-19527)
Clang-format is a C/C++ code formatter, together with the added .clang-format
file, it produces code close enough to the NumPy C_STYLE_GUIDE for general use. Clang-format version 12+ is required due to the use of several new features, it is available in Fedora 34 and Ubuntu Focal among other distributions.
(gh-19754)
Based on its counterpart in Python float
and int
, the numpy floating point and integer types now support float.is_integer
. Returns True
if the number is finite with integral value, and False
otherwise.
>>> np.float32(-2.0).is_integer()
True
>>> np.float64(3.2).is_integer()
False
>>> np.int32(-2).is_integer()
True
(gh-19803)
A new symbolic parser has been added to f2py in order to correctly parse dimension specifications. The parser is the basis for future improvements and provides compatibility with Draft Fortran 202x.
(gh-19805)
Mimicking 585
, the numpy.ndarray
, numpy.dtype
and numpy.number
classes are now subscriptable for python 3.9 and later. Consequently, expressions that were previously only allowed in .pyi stub files or with the help of from __future__ import annotations
are now also legal during runtime.
>>> import numpy as np
>>> from typing import Any
>>> np.ndarray[Any, np.dtype[np.float64]]
numpy.ndarray[typing.Any, numpy.dtype[numpy.float64]]
(gh-19879)
All parameters in the can now take any python:path-like object
. This includes the likes of strings, bytes and objects implementing the __fspath__<os.PathLike.__fspath__>
protocol.
(gh-17530)
The attributes smallest_normal
and smallest_subnormal
are available as an extension of finfo
class for any floating-point data type. To use these new attributes, write np.finfo(np.float64).smallest_normal
or np.finfo(np.float64).smallest_subnormal
.
(gh-18536)
numpy.linalg.qr
is able to produce results for stacked matrices as inputs. Moreover, the implementation of QR decomposition has been shifted to C from Python.
(gh-19151)
numpy.fromregex
now accepts objects implementing the __fspath__<os.PathLike>
protocol, e.g. pathlib.Path
.
(gh-19680)
quantile
and percentile
now have have a method=
keyword argument supporting 13 different methods. This replaces the interpolation=
keyword argument.
The methods are now aligned with nine methods which can be found in scientific literature and the R language. The remaining methods are the previous discontinuous variations of the default "linear" one.
Please see the documentation of numpy.percentile
for more information.
(gh-19857)
A number of the nan<x>
functions previously lacked parameters that were present in their <x>
-based counterpart, e.g. the where
parameter was present in numpy.mean
but absent from numpy.nanmean
.
The following parameters have now been added to the nan<x>
functions:
- nanmin:
initial
&where
- nanmax:
initial
&where
- nanargmin:
keepdims
&out
- nanargmax:
keepdims
&out
- nansum:
initial
&where
- nanprod:
initial
&where
- nanmean:
where
- nanvar:
where
- nanstd:
where
(gh-20027)
Starting from the 1.20 release, PEP 484 type annotations have been included for parts of the NumPy library; annotating the remaining functions being a work in progress. With the release of 1.22 this process has been completed for the main NumPy namespace, which is now fully annotated.
Besides the main namespace, a limited number of sub-packages contain annotations as well. This includes, among others, numpy.testing
, numpy.linalg
and numpy.random
(available since 1.21).
(gh-20217)
By leveraging Intel Short Vector Math Library (SVML), 18 umath functions (exp2
, log2
, log10
, expm1
, log1p
, cbrt
, sin
, cos
, tan
, arcsin
, arccos
, arctan
, sinh
, cosh
, tanh
, arcsinh
, arccosh
, arctanh
) are vectorized using AVX-512 instruction set for both single and double precision implementations. This change is currently enabled only for Linux users and on processors with AVX-512 instruction set. It provides an average speed up of 32x and 14x for single and double precision functions respectively.
(gh-19478)
Update the OpenBLAS used in testing and in wheels to v0.3.18
(gh-20058)