Update dependency numpy to v2
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Review changes -
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Download -
Patches
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Plain diff
This MR contains the following updates:
| Package | Type | Update | Change |
|---|---|---|---|
| numpy (changelog) | dependencies | major |
<2.0.0 -> <2.3.0
|
Release Notes
numpy/numpy
v2.2.3
NumPy 2.2.3 Release Notes
NumPy 2.2.3 is a patch release that fixes bugs found after the 2.2.2 release. The majority of the changes are typing improvements and fixes for free threaded Python. Both of those areas are still under development, so if you discover new problems, please report them.
This release supports Python versions 3.10-3.13.
Contributors
A total of 9 people contributed to this release. People with a "+" by their names contributed a patch for the first time.
- !amotzop
- Charles Harris
- Chris Sidebottom
- Joren Hammudoglu
- Matthew Brett
- Nathan Goldbaum
- Raghuveer Devulapalli
- Sebastian Berg
- Yakov Danishevsky +
Pull requests merged
A total of 21 pull requests were merged for this release.
- #28185: MAINT: Prepare 2.2.x for further development
- #28201: BUG: fix data race in a more minimal way on stable branch
-
#28208: BUG: Fix
from_float_positionalerrors for huge pads - #28209: BUG: fix data race in np.repeat
- #28212: MAINT: Use VQSORT_COMPILER_COMPATIBLE to determine if we should...
- #28224: MAINT: update highway to latest
- #28236: BUG: Add cpp atomic support (#28234)
- #28237: BLD: Compile fix for clang-cl on WoA
-
#28243: TYP: Avoid upcasting
float64in the set-ops - #28249: BLD: better fix for clang / ARM compiles
-
#28266: TYP: Fix
timedelta64.__divmod__andtimedelta64.__mod__... - #28274: TYP: Fixed missing typing information of set_printoptions
- #28278: BUG: backport resource cleanup bugfix from gh-28273
- #28282: BUG: fix incorrect bytes to stringdtype coercion
- #28283: TYP: Fix scalar constructors
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#28284: TYP: stub
numpy.matlib -
#28285: TYP: stub the missing
numpy.testingmodules -
#28286: CI: Fix the github label for
TYP:MR's and issues - #28305: TYP: Backport typing updates from main
- #28321: BUG: fix race initializing legacy dtype casts
- #28324: CI: update test_moderately_small_alpha
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v2.2.2
NumPy 2.2.2 Release Notes
NumPy 2.2.2 is a patch release that fixes bugs found after the 2.2.1 release. The number of typing fixes/updates is notable. This release supports Python versions 3.10-3.13.
Contributors
A total of 8 people contributed to this release. People with a "+" by their names contributed a patch for the first time.
- Alicia Boya García +
- Charles Harris
- Joren Hammudoglu
- Kai Germaschewski +
- Nathan Goldbaum
- PTUsumit +
- Rohit Goswami
- Sebastian Berg
Pull requests merged
A total of 16 pull requests were merged for this release.
- #28050: MAINT: Prepare 2.2.x for further development
-
#28055: TYP: fix
voidarrays not acceptingstrkeys in__setitem__ -
#28066: TYP: fix unnecessarily broad
integerbinop return types (#28065) -
#28112: TYP: Better
ndarraybinop return types forfloat64&... -
#28113: TYP: Return the correct
boolfromissubdtype -
#28114: TYP: Always accept
date[time]in thedatetime64constructor - #28120: BUG: Fix auxdata initialization in ufunc slow path
- #28131: BUG: move reduction initialization to ufunc initialization
-
#28132: TYP: Fix
interpto accept and return scalars - #28137: BUG: call PyType_Ready in f2py to avoid data races
- #28145: BUG: remove unnecessary call to PyArray_UpdateFlags
- #28160: BUG: Avoid data race in PyArray_CheckFromAny_int
- #28175: BUG: Fix f2py directives and --lower casing
- #28176: TYP: Fix overlapping overloads issue in 2->1 ufuncs
- #28177: TYP: preserve shape-type in ndarray.astype()
- #28178: TYP: Fix missing and spurious top-level exports
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v2.2.1
NumPy 2.2.1 Release Notes
NumPy 2.2.1 is a patch release following 2.2.0. It fixes bugs found after the 2.2.0 release and has several maintenance pins to work around upstream changes.
There was some breakage in downstream projects following the 2.2.0 release due to updates to NumPy typing. Because of problems due to MyPy defects, we recommend using basedpyright for type checking, it can be installed from PyPI. The Pylance extension for Visual Studio Code is also based on Pyright. Problems that persist when using basedpyright should be reported as issues on the NumPy github site.
This release supports Python 3.10-3.13.
Contributors
A total of 9 people contributed to this release. People with a "+" by their names contributed a patch for the first time.
- Charles Harris
- Joren Hammudoglu
- Matti Picus
- Nathan Goldbaum
- Peter Hawkins
- Simon Altrogge
- Thomas A Caswell
- Warren Weckesser
- Yang Wang +
Pull requests merged
A total of 12 pull requests were merged for this release.
- #27935: MAINT: Prepare 2.2.x for further development
- #27950: TEST: cleanups
- #27958: BUG: fix use-after-free error in npy_hashtable.cpp (#27955)
- #27959: BLD: add missing include
- #27982: BUG:fix compile error libatomic link test to meson.build
-
#27990: TYP: Fix falsely rejected value types in
ndarray.__setitem__ -
#27991: MAINT: Don't wrap
#include <Python.h>withextern "C" - #27993: BUG: Fix segfault in stringdtype lexsort
- #28006: MAINT: random: Tweak module code in mtrand.pyx to fix a Cython...
- #28007: BUG: Cython API was missing NPY_UINTP.
- #28021: CI: pin scipy-doctest to 1.5.1
-
#28044: TYP: allow
Nonein operand sequence of nditer
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v2.2.0
NumPy 2.2.0 Release Notes
The NumPy 2.2.0 release is quick release that brings us back into sync with the usual twice yearly release cycle. There have been an number of small cleanups, as well as work bringing the new StringDType to completion and improving support for free threaded Python. Highlights are:
- New functions
matvecandvecmat, see below. - Many improved annotations.
- Improved support for the new StringDType.
- Improved support for free threaded Python
- Fixes for f2py
This release supports Python versions 3.10-3.13.
Deprecations
-
_add_newdoc_ufuncis now deprecated.ufunc.__doc__ = newdocshould be used instead.(gh-27735)
Expired deprecations
-
bool(np.array([]))and other empty arrays will now raise an error. Usearr.size > 0instead to check whether an array has no elements.(gh-27160)
Compatibility notes
-
numpy.covnow properly transposes single-row (2d array) design matrices whenrowvar=False. Previously, single-row design matrices would return a scalar in this scenario, which is not correct, so this is a behavior change and an array of the appropriate shape will now be returned.(gh-27661)
New Features
-
New functions for matrix-vector and vector-matrix products
Two new generalized ufuncs were defined:
-
numpy.matvec- matrix-vector product, treating the arguments as stacks of matrices and column vectors, respectively. -
numpy.vecmat- vector-matrix product, treating the arguments as stacks of column vectors and matrices, respectively. For complex vectors, the conjugate is taken.
These add to the existing
numpy.matmulas well as tonumpy.vecdot, which was added in numpy 2.0.Note that
numpy.matmulnever takes a complex conjugate, also not when its left input is a vector, while bothnumpy.vecdotandnumpy.vecmatdo take the conjugate for complex vectors on the left-hand side (which are taken to be the ones that are transposed, following the physics convention).(gh-25675)
-
-
np.complexfloating[T, T]can now also be written asnp.complexfloating[T](gh-27420)
-
UFuncs now support
__dict__attribute and allow overriding__doc__(either directly or viaufunc.__dict__["__doc__"]).__dict__can be used to also override other properties, such as__module__or__qualname__.(gh-27735)
-
The "nbit" type parameter of
np.numberand its subtypes now defaults totyping.Any. This way, type-checkers will infer annotations such asx: np.floatingasx: np.floating[Any], even in strict mode.(gh-27736)
Improvements
-
The
datetime64andtimedelta64hashes now correctly match the Pythons builtindatetimeandtimedeltaones. The hashes now evaluated equal even for equal values with different time units.(gh-14622)
-
Fixed a number of issues around promotion for string ufuncs with StringDType arguments. Mixing StringDType and the fixed-width DTypes using the string ufuncs should now generate much more uniform results.
(gh-27636)
-
Improved support for empty
memmap. Previously an emptymemmapwould fail unless a non-zerooffsetwas set. Now a zero-sizememmapis supported even ifoffset=0. To achieve this, if amemmapis mapped to an empty file that file is padded with a single byte.(gh-27723)
-
A regression has been fixed which allows F2PY users to expose variables to Python in modules with only assignments, and also fixes situations where multiple modules are present within a single source file.
(gh-27695)
Performance improvements and changes
-
Improved multithreaded scaling on the free-threaded build when many threads simultaneously call the same ufunc operations.
(gh-27896)
-
NumPy now uses fast-on-failure attribute lookups for protocols. This can greatly reduce overheads of function calls or array creation especially with custom Python objects. The largest improvements will be seen on Python 3.12 or newer.
(gh-27119)
-
OpenBLAS on x86_64 and i686 is built with fewer kernels. Based on benchmarking, there are 5 clusters of performance around these kernels:
MRESCOTT NEHALEM SANDYBRIDGE HASWELL SKYLAKEX. -
OpenBLAS on windows is linked without quadmath, simplifying licensing
-
Due to a regression in OpenBLAS on windows, the performance improvements when using multiple threads for OpenBLAS 0.3.26 were reverted.
(gh-27147)
-
NumPy now indicates hugepages also for large
np.zerosallocations on linux. Thus should generally improve performance.(gh-27808)
Changes
-
numpy.fixnow won't perform casting to a floating data-type for integer and boolean data-type input arrays.(gh-26766)
-
The type annotations of
numpy.float64andnumpy.complex128now reflect that they are also subtypes of the built-infloatandcomplextypes, respectively. This update prevents static type-checkers from reporting errors in cases such as:x: float = numpy.float64(6.28) # valid z: complex = numpy.complex128(-1j) # valid(gh-27334)
-
The
reprof arrays large enough to be summarized (i.e., where elements are replaced with...) now includes theshapeof the array, similar to what already was the case for arrays with zero size and non-obvious shape. With this change, the shape is always given when it cannot be inferred from the values. Note that while written asshape=..., this argument cannot actually be passed in to thenp.arrayconstructor. If you encounter problems, e.g., due to failing doctests, you can use the print optionlegacy=2.1to get the old behaviour.(gh-27482)
-
Calling
__array_wrap__directly on NumPy arrays or scalars now does the right thing whenreturn_scalaris passed (Added in NumPy 2). It is further safe now to call the scalar__array_wrap__on a non-scalar result.(gh-27807)
-
Bump the musllinux CI image and wheels to 1_2 from 1_1. This is because 1_1 is end of life.
(gh-27088)
-
The NEP 50 promotion state settings are now removed. They were always meant as temporary means for testing. A warning will be given if the environment variable is set to anything but
NPY_MROMOTION_STATE=weakwhile_set_promotion_stateand_get_promotion_stateare removed. In case code used_no_nep50_warning, acontextlib.nullcontextcould be used to replace it when not available.(gh-27156)
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140dd80ff8981a583a60980be1a655068f8adebf7a45a06a6858c873fcdcd4a0 numpy-2.2.0.tar.gz
v2.1.3
NumPy 2.1.3 Release Notes
NumPy 2.1.3 is a maintenance release that fixes bugs and regressions discovered after the 2.1.2 release. This release also adds support for free threaded Python 3.13 on Windows.
The Python versions supported by this release are 3.10-3.13.
Improvements
-
Fixed a number of issues around promotion for string ufuncs with StringDType arguments. Mixing StringDType and the fixed-width DTypes using the string ufuncs should now generate much more uniform results.
(gh-27636)
Changes
-
numpy.fixnow won't perform casting to a floating data-type for integer and boolean data-type input arrays.(gh-26766)
Contributors
A total of 15 people contributed to this release. People with a "+" by their names contributed a patch for the first time.
- Abhishek Kumar +
- Austin +
- Benjamin A. Beasley +
- Charles Harris
- Christian Lorentzen
- Marcel Telka +
- Matti Picus
- Michael Davidsaver +
- Nathan Goldbaum
- Peter Hawkins
- Raghuveer Devulapalli
- Ralf Gommers
- Sebastian Berg
- dependabot[bot]
- kp2pml30 +
Pull requests merged
A total of 21 pull requests were merged for this release.
- #27512: MAINT: prepare 2.1.x for further development
- #27537: MAINT: Bump actions/cache from 4.0.2 to 4.1.1
- #27538: MAINT: Bump pypa/cibuildwheel from 2.21.2 to 2.21.3
- #27539: MAINT: MSVC does not support #warning directive
- #27543: BUG: Fix user dtype can-cast with python scalar during promotion
-
#27561: DEV: bump
pythonto 3.12 in environment.yml - #27562: BLD: update vendored Meson to 1.5.2
- #27563: BUG: weighted quantile for some zero weights (#27549)
- #27565: MAINT: Use miniforge for macos conda test.
- #27566: BUILD: satisfy gcc-13 pendantic errors
- #27569: BUG: handle possible error for PyTraceMallocTrack
- #27570: BLD: start building Windows free-threaded wheels [wheel build]
- #27571: BUILD: vendor tempita from Cython
- #27574: BUG: Fix warning "differs in levels of indirection" in npy_atomic.h...
- #27592: MAINT: Update Highway to latest
- #27593: BUG: Adjust numpy.i for SWIG 4.3 compatibility
- #27616: BUG: Fix Linux QEMU CI workflow
- #27668: BLD: Do not set __STDC_VERSION__ to zero during build
- #27669: ENH: fix wasm32 runtime type error in numpy._core
- #27672: BUG: Fix a reference count leak in npy_find_descr_for_scalar.
- #27673: BUG: fixes for StringDType/unicode promoters
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v2.1.2
NumPy 2.1.2 Release Notes
NumPy 2.1.2 is a maintenance release that fixes bugs and regressions discovered after the 2.1.1 release.
The Python versions supported by this release are 3.10-3.13.
Contributors
A total of 11 people contributed to this release. People with a "+" by their names contributed a patch for the first time.
- Charles Harris
- Chris Sidebottom
- Ishan Koradia +
- João Eiras +
- Katie Rust +
- Marten van Kerkwijk
- Matti Picus
- Nathan Goldbaum
- Peter Hawkins
- Pieter Eendebak
- Slava Gorloff +
Pull requests merged
A total of 14 pull requests were merged for this release.
- #27333: MAINT: prepare 2.1.x for further development
- #27400: BUG: apply critical sections around populating the dispatch cache
- #27406: BUG: Stub out get_build_msvc_version if distutils.msvccompiler...
- #27416: BUILD: fix missing include for std::ptrdiff_t for C++23 language...
- #27433: BLD: pin setuptools to avoid breaking numpy.distutils
- #27437: BUG: Allow unsigned shift argument for np.roll
- #27439: BUG: Disable SVE VQSort
- #27471: BUG: rfftn axis bug
- #27479: BUG: Fix extra decref of PyArray_UInt8DType.
- #27480: CI: use PyPI not scientific-python-nightly-wheels for CI doc...
- #27481: MAINT: Check for SVE support on demand
- #27484: BUG: initialize the promotion state to be weak
- #27501: MAINT: Bump pypa/cibuildwheel from 2.20.0 to 2.21.2
- #27506: BUG: avoid segfault on bad arguments in ndarray.__array_function__
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v2.1.1
NumPy 2.1.1 Release Notes
NumPy 2.1.1 is a maintenance release that fixes bugs and regressions discovered after the 2.1.0 release.
The Python versions supported by this release are 3.10-3.13.
Contributors
A total of 7 people contributed to this release. People with a "+" by their names contributed a patch for the first time.
- Andrew Nelson
- Charles Harris
- Mateusz Sokół
- Maximilian Weigand +
- Nathan Goldbaum
- Pieter Eendebak
- Sebastian Berg
Pull requests merged
A total of 10 pull requests were merged for this release.
- #27236: REL: Prepare for the NumPy 2.1.0 release [wheel build]
- #27252: MAINT: prepare 2.1.x for further development
- #27259: BUG: revert unintended change in the return value of set_printoptions
- #27266: BUG: fix reference counting bug in __array_interface__ implementation...
- #27267: TST: Add regression test for missing descr in array-interface
- #27276: BUG: Fix #27256 and #27257
- #27278: BUG: Fix array_equal for numeric and non-numeric scalar types
- #27287: MAINT: Update maintenance/2.1.x after the 2.0.2 release
- #27303: BLD: cp311- macosx_arm64 wheels [wheel build]
- #27304: BUG: f2py: better handle filtering of public/private subroutines
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v2.1.0
NumPy 2.1.0 Release Notes
NumPy 2.1.0 provides support for the upcoming Python 3.13 release and drops support for Python 3.9. In addition to the usual bug fixes and updated Python support, it helps get us back into our usual release cycle after the extended development of 2.0. The highlights for this release are:
- Support for the array-api 2023.12 standard.
- Support for Python 3.13.
- Preliminary support for free threaded Python 3.13.
Python versions 3.10-3.13 are supported in this release.
New functions
New function numpy.unstack
A new function np.unstack(array, axis=...) was added, which splits an
array into a tuple of arrays along an axis. It serves as the inverse of
[numpy.stack]{.title-ref}.
(gh-26579)
Deprecations
-
The
fix_importskeyword argument innumpy.saveis deprecated. Since NumPy 1.17,numpy.saveuses a pickle protocol that no longer supports Python 2, and ignoredfix_importskeyword. This keyword is kept only for backward compatibility. It is now deprecated.(gh-26452)
-
Passing non-integer inputs as the first argument of [bincount]{.title-ref} is now deprecated, because such inputs are silently cast to integers with no warning about loss of precision.
(gh-27076)
Expired deprecations
-
Scalars and 0D arrays are disallowed for
numpy.nonzeroandnumpy.ndarray.nonzero.(gh-26268)
-
set_string_functioninternal function was removed andPyArray_SetStringFunctionwas stubbed out.(gh-26611)
C API changes
API symbols now hidden but customizable
NumPy now defaults to hide the API symbols it adds to allow all NumPy API usage. This means that by default you cannot dynamically fetch the NumPy API from another library (this was never possible on windows).
If you are experiencing linking errors related to PyArray_API or
PyArray_RUNTIME_VERSION, you can define the NPY_API_SYMBOL_ATTRIBUTE
to opt-out of this change.
If you are experiencing problems due to an upstream header including
NumPy, the solution is to make sure you
#include "numpy/ndarrayobject.h" before their header and import NumPy
yourself based on including-the-c-api.
(gh-26103)
Many shims removed from npy_3kcompat.h
Many of the old shims and helper functions were removed from
npy_3kcompat.h. If you find yourself in need of these, vendor the
previous version of the file into your codebase.
(gh-26842)
New PyUFuncObject field process_core_dims_func
The field process_core_dims_func was added to the structure
PyUFuncObject. For generalized ufuncs, this field can be set to a
function of type PyUFunc_ProcessCoreDimsFunc that will be called when
the ufunc is called. It allows the ufunc author to check that core
dimensions satisfy additional constraints, and to set output core
dimension sizes if they have not been provided.
(gh-26908)
New Features
Preliminary Support for Free-Threaded CPython 3.13
CPython 3.13 will be available as an experimental free-threaded build. See https://py-free-threading.github.io, PEP 703 and the CPython 3.13 release notes for more detail about free-threaded Python.
NumPy 2.1 has preliminary support for the free-threaded build of CPython 3.13. This support was enabled by fixing a number of C thread-safety issues in NumPy. Before NumPy 2.1, NumPy used a large number of C global static variables to store runtime caches and other state. We have either refactored to avoid the need for global state, converted the global state to thread-local state, or added locking.
Support for free-threaded Python does not mean that NumPy is thread
safe. Read-only shared access to ndarray should be safe. NumPy exposes
shared mutable state and we have not added any locking to the array
object itself to serialize access to shared state. Care must be taken in
user code to avoid races if you would like to mutate the same array in
multiple threads. It is certainly possible to crash NumPy by mutating an
array simultaneously in multiple threads, for example by calling a ufunc
and the resize method simultaneously. For now our guidance is:
"don't do that". In the future we would like to provide stronger
guarantees.
Object arrays in particular need special care, since the GIL previously provided locking for object array access and no longer does. See Issue #27199 for more information about object arrays in the free-threaded build.
If you are interested in free-threaded Python, for example because you have a multiprocessing-based workflow that you are interested in running with Python threads, we encourage testing and experimentation.
If you run into problems that you suspect are because of NumPy, please open an issue, checking first if the bug also occurs in the "regular" non-free-threaded CPython 3.13 build. Many threading bugs can also occur in code that releases the GIL; disabling the GIL only makes it easier to hit threading bugs.
(gh-26157)
f2py can generate freethreading-compatible C extensions
Pass --freethreading-compatible to the f2py CLI tool to produce a C
extension marked as compatible with the free threading CPython
interpreter. Doing so prevents the interpreter from re-enabling the GIL
at runtime when it imports the C extension. Note that f2py does not
analyze fortran code for thread safety, so you must verify that the
wrapped fortran code is thread safe before marking the extension as
compatible.
(gh-26981)
-
numpy.reshapeandnumpy.ndarray.reshapenow supportshapeandcopyarguments.(gh-26292)
-
NumPy now supports DLPack v1, support for older versions will be deprecated in the future.
(gh-26501)
-
numpy.asanyarraynow supportscopyanddevicearguments, matchingnumpy.asarray.(gh-26580)
-
numpy.printoptions,numpy.get_printoptions, andnumpy.set_printoptionsnow support a new option,override_repr, for defining customrepr(array)behavior.(gh-26611)
-
numpy.cumulative_sumandnumpy.cumulative_prodwere added as Array API compatible alternatives fornumpy.cumsumandnumpy.cumprod. The new functions can include a fixed initial (zeros forsumand ones forprod) in the result.(gh-26724)
-
numpy.clipnow supportsmaxandminkeyword arguments which are meant to replacea_minanda_max. Also, fornp.clip(a)ornp.clip(a, None, None)a copy of the input array will be returned instead of raising an error.(gh-26724)
-
numpy.astypenow supportsdeviceargument.(gh-26724)
Improvements
histogram auto-binning now returns bin sizes >=1 for integer input data
For integer input data, bin sizes smaller than 1 result in spurious
empty bins. This is now avoided when the number of bins is computed
using one of the algorithms provided by histogram_bin_edges.
(gh-12150)
ndarray shape-type parameter is now covariant and bound to tuple[int, ...]
Static typing for ndarray is a long-term effort that continues with
this change. It is a generic type with type parameters for the shape and
the data type. Previously, the shape type parameter could be any value.
This change restricts it to a tuple of ints, as one would expect from
using ndarray.shape. Further, the shape-type parameter has been
changed from invariant to covariant. This change also applies to the
subtypes of ndarray, e.g. numpy.ma.MaskedArray. See the
typing docs
for more information.
(gh-26081)
np.quantile with method closest_observation chooses nearest even order statistic
This changes the definition of nearest for border cases from the nearest odd order statistic to nearest even order statistic. The numpy implementation now matches other reference implementations.
(gh-26656)
lapack_lite is now thread safe
NumPy provides a minimal low-performance version of LAPACK named
lapack_lite that can be used if no BLAS/LAPACK system is detected at
build time.
Until now, lapack_lite was not thread safe. Single-threaded use cases
did not hit any issues, but running linear algebra operations in
multiple threads could lead to errors, incorrect results, or segfaults
due to data races.
We have added a global lock, serializing access to lapack_lite in
multiple threads.
(gh-26750)
The numpy.printoptions context manager is now thread and async-safe
In prior versions of NumPy, the printoptions were defined using a
combination of Python and C global variables. We have refactored so the
state is stored in a python ContextVar, making the context manager
thread and async-safe.
(gh-26846)
Type hinting numpy.polynomial
Starting from the 2.1 release, PEP 484 type annotations have been
included for the functions and convenience classes in numpy.polynomial
and its sub-packages.
(gh-26897)
Improved numpy.dtypes type hints
The type annotations for numpy.dtypes are now a better reflection of
the runtime: The numpy.dtype type-aliases have been replaced with
specialized dtype subtypes, and the previously missing annotations
for numpy.dtypes.StringDType have been added.
(gh-27008)
Performance improvements and changes
-
numpy.savenow uses pickle protocol version 4 for saving arrays with object dtype, which allows for pickle objects larger than 4GB and improves saving speed by about 5% for large arrays.(gh-26388)
-
OpenBLAS on x86_64 and i686 is built with fewer kernels. Based on benchmarking, there are 5 clusters of performance around these kernels:
MRESCOTT NEHALEM SANDYBRIDGE HASWELL SKYLAKEX.(gh-27147)
-
OpenBLAS on windows is linked without quadmath, simplifying licensing
(gh-27147)
-
Due to a regression in OpenBLAS on windows, the performance improvements when using multiple threads for OpenBLAS 0.3.26 were reverted.
(gh-27147)
ma.cov and ma.corrcoef are now significantly faster
The private function has been refactored along with ma.cov and
ma.corrcoef. They are now significantly faster, particularly on large,
masked arrays.
(gh-26285)
Changes
-
As
numpy.vecdotis now a ufunc it has a less precise signature. This is due to the limitations of ufunc's typing stub.(gh-26313)
-
numpy.floor,numpy.ceil, andnumpy.truncnow won't perform casting to a floating dtype for integer and boolean dtype input arrays.(gh-26766)
ma.corrcoef may return a slightly different result
A pairwise observation approach is currently used in ma.corrcoef to
calculate the standard deviations for each pair of variables. This has
been changed as it is being used to normalise the covariance, estimated
using ma.cov, which does not consider the observations for each
variable in a pairwise manner, rendering it unnecessary. The
normalisation has been replaced by the more appropriate standard
deviation for each variable, which significantly reduces the wall time,
but will return slightly different estimates of the correlation
coefficients in cases where the observations between a pair of variables
are not aligned. However, it will return the same estimates in all other
cases, including returning the same correlation matrix as corrcoef
when using a masked array with no masked values.
(gh-26285)
Cast-safety fixes in copyto and full
copyto now uses NEP 50 correctly and applies this to its cast safety.
Python integer to NumPy integer casts and Python float to NumPy float
casts are now considered "safe" even if assignment may fail or
precision may be lost. This means the following examples change
slightly:
-
np.copyto(int8_arr, 1000)previously performed an unsafe/same-kind cast of the Python integer. It will now always raise, to achieve an unsafe cast you must pass an array or NumPy scalar. -
np.copyto(uint8_arr, 1000, casting="safe")will raise an OverflowError rather than a TypeError due to same-kind casting. -
np.copyto(float32_arr, 1e300, casting="safe")will overflow toinf(float32 cannot hold1e300) rather raising a TypeError.
Further, only the dtype is used when assigning NumPy scalars (or 0-d arrays), meaning that the following behaves differently:
-
np.copyto(float32_arr, np.float64(3.0), casting="safe")raises. -
np.coptyo(int8_arr, np.int64(100), casting="safe")raises. Previously, NumPy checked whether the 100 fits theint8_arr.
This aligns copyto, full, and full_like with the correct NumPy 2
behavior.
(gh-27091)
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v2.0.2
NumPy 2.0.2 Release Notes
NumPy 2.0.2 is a maintenance release that fixes bugs and regressions discovered after the 2.0.1 release.
The Python versions supported by this release are 3.9-3.12.
Contributors
A total of 13 people contributed to this release. People with a "+" by their names contributed a patch for the first time.
- Bruno Oliveira +
- Charles Harris
- Chris Sidebottom
- Christian Heimes +
- Christopher Sidebottom
- Mateusz Sokół
- Matti Picus
- Nathan Goldbaum
- Pieter Eendebak
- Raghuveer Devulapalli
- Ralf Gommers
- Sebastian Berg
- Yair Chuchem +
Pull requests merged
A total of 19 pull requests were merged for this release.
- #27000: REL: Prepare for the NumPy 2.0.1 release [wheel build]
- #27001: MAINT: prepare 2.0.x for further development
- #27021: BUG: cfuncs.py: fix crash when sys.stderr is not available
-
#27022: DOC: Fix migration note for
alltrueandsometrue - #27061: BUG: use proper input and output descriptor in array_assign_subscript...
- #27073: BUG: Mirror VQSORT_ENABLED logic in Quicksort
- #27074: BUG: Bump Highway to latest master
- #27077: BUG: Off by one in memory overlap check
-
#27122: BUG: Use the new
npyv_loadable_stride_functions for ldexp and... - #27126: BUG: Bump Highway to latest
- #27128: BUG: add missing error handling in public_dtype_api.c
- #27129: BUG: fix another cast setup in array_assign_subscript
- #27130: BUG: Fix building NumPy in FIPS mode
- #27131: BLD: update vendored Meson for cross-compilation patches
- #27146: MAINT: Scipy openblas 0.3.27.44.4
-
#27151: BUG: Do not accidentally store dtype metadata in
np.save - #27195: REV: Revert undef I and document it
- #27213: BUG: Fix NPY_RAVEL_AXIS on backwards compatible NumPy 2 builds
- #27279: BUG: Fix array_equal for numeric and non-numeric scalar types
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v2.0.1
NumPy 2.0.1 Release Notes
NumPy 2.0.1 is a maintenance release that fixes bugs and regressions discovered after the 2.0.0 release. NumPy 2.0.1 is the last planned release in the 2.0.x series, 2.1.0rc1 should be out shortly.
The Python versions supported by this release are 3.9-3.12.
NOTE: Do not use the GitHub generated "Source code" files listed in the "Assets", they are garbage.
Improvements
np.quantile with method closest_observation chooses nearest even order statistic
This changes the definition of nearest for border cases from the nearest odd order statistic to nearest even order statistic. The numpy implementation now matches other reference implementations.
(gh-26656)
Contributors
A total of 15 people contributed to this release. People with a "+" by their names contributed a patch for the first time.
- @vahidmech +
- Alex Herbert +
- Charles Harris
- Giovanni Del Monte +
- Leo Singer
- Lysandros Nikolaou
- Matti Picus
- Nathan Goldbaum
- Patrick J. Roddy +
- Raghuveer Devulapalli
- Ralf Gommers
- Rostan Tabet +
- Sebastian Berg
- Tyler Reddy
- Yannik Wicke +
Pull requests merged
A total of 24 pull requests were merged for this release.
- #26711: MAINT: prepare 2.0.x for further development
-
#26792: TYP: fix incorrect import in
ma/extras.pyistub -
#26793: DOC: Mention '1.25' legacy printing mode in
set_printoptions - #26794: DOC: Remove mention of NaN and NAN aliases from constants
- #26821: BLD: Fix x86-simd-sort build failure on openBSD
- #26822: BUG: Ensure output order follows input in numpy.fft
- #26823: TYP: fix missing sys import in numeric.pyi
- #26832: DOC: remove hack to override _add_newdocs_scalars
- #26835: BUG: avoid side-effect of 'include complex.h'
-
#26836: BUG: fix max_rows and chunked string/datetime reading in
loadtxt - #26837: BUG: fix PyArray_ImportNumPyAPI under -Werror=strict-prototypes
- #26856: DOC: Update some documentation
- #26868: BUG: fancy indexing copy
-
#26869: BUG: Mismatched allocation domains in
PyArray_FillWithScalar - #26870: BUG: Handle --f77flags and --f90flags for meson [wheel build]
- #26887: BUG: Fix new DTypes and new string promotion when signature is...
- #26888: BUG: remove numpy.f2py from excludedimports
- #26959: BUG: Quantile closest_observation to round to nearest even order
- #26960: BUG: Fix off-by-one error in amount of characters in strip
- #26961: API: Partially revert unique with return_inverse
- #26962: BUG,MAINT: Fix utf-8 character stripping memory access
- #26963: BUG: Fix out-of-bound minimum offset for in1d table method
- #26971: BUG: fix f2py tests to work with v2 API
- #26995: BUG: Add object cast to avoid warning with limited API
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v2.0.0
NumPy 2.0.0 Release Notes
NumPy 2.0.0 is the first major release since 2006. It is the result of 11 months of development since the last feature release and is the work of 212 contributors spread over 1078 pull requests. It contains a large number of exciting new features as well as changes to both the Python and C APIs.
This major release includes breaking changes that could not happen in a regular minor (feature) release - including an ABI break, changes to type promotion rules, and API changes which may not have been emitting deprecation warnings in 1.26.x. Key documents related to how to adapt to changes in NumPy 2.0, in addition to these release notes, include:
- The numpy-2-migration-guide
- The Numpy 2.0-specific advice in for downstream package authors
Highlights
Highlights of this release include:
- New features:
- A new variable-length string dtype,
numpy.dtypes.StringDTypeand a newnumpy.stringsnamespace with performant ufuncs for string operations, - Support for
float32andlongdoublein allnumpy.fftfunctions, - Support for the array API standard in the main
numpynamespace.
- A new variable-length string dtype,
- Performance improvements:
- Sorting functions
sort,argsort,partition,argpartitionhave been accelerated through the use of the Intel x86-simd-sort and Google Highway libraries, and may see large (hardware-specific) speedups, - macOS Accelerate support and binary wheels for macOS >=14, with significant performance improvements for linear algebra operations on macOS, and wheels that are about 3 times smaller,
-
numpy.charfixed-length string operations have been accelerated by implementing ufuncs that also supportnumpy.dtypes.StringDTypein addition to the fixed-length string dtypes, - A new tracing and introspection API,
numpy.lib.introspect.opt_func_info, to determine which hardware-specific kernels are available and will be dispatched to. -
numpy.savenow uses pickle protocol version 4 for saving arrays with object dtype, which allows for pickle objects larger than 4GB and improves saving speed by about 5% for large arrays.
- Sorting functions
- Python API improvements:
- A clear split between public and private API, with a new module structure and each public function now available in a single place.
- Many removals of non-recommended functions and aliases. This
should make it easier to learn and use NumPy. The number of
objects in the main namespace decreased by ~10% and in
numpy.libby ~80%. -
Canonical dtype names and a newnumpy.isdtype` introspection function,
- C API improvements:
- A new public C API for creating custom dtypes,
- Many outdated functions and macros removed, and private internals hidden to ease future extensibility,
- New, easier to use, initialization functions:
PyArray_ImportNumPyAPIandPyUFunc_ImportUFuncAPI.
- Improved behavior:
- Improvements to type promotion behavior was changed by adopting NEP 50. This fixes many user surprises about promotions which previously often depended on data values of input arrays rather than only their dtypes. Please see the NEP and the numpy-2-migration-guide for details as this change can lead to changes in output dtypes and lower precision results for mixed-dtype operations.
- The default integer type on Windows is now
int64rather thanint32, matching the behavior on other platforms, - The maximum number of array dimensions is changed from 32 to 64
- Documentation:
- The reference guide navigation was significantly improved, and there is now documentation on NumPy's module structure,
- The building from source documentation was completely rewritten,
Furthermore there are many changes to NumPy internals, including continuing to migrate code from C to C++, that will make it easier to improve and maintain NumPy in the future.
The "no free lunch" theorem dictates that there is a price to pay for all these API and behavior improvements and better future extensibility. This price is:
-
Backwards compatibility. There are a significant number of breaking changes to both the Python and C APIs. In the majority of cases, there are clear error messages that will inform the user how to adapt their code. However, there are also changes in behavior for which it was not possible to give such an error message - these cases are all covered in the Deprecation and Compatibility sections below, and in the numpy-2-migration-guide.
Note that there is a
ruffmode to auto-fix many things in Python code. -
Breaking changes to the NumPy ABI. As a result, binaries of packages that use the NumPy C API and were built against a NumPy 1.xx release will not work with NumPy 2.0. On import, such packages will see an
ImportErrorwith a message about binary incompatibility.It is possible to build binaries against NumPy 2.0 that will work at runtime with both NumPy 2.0 and 1.x. See numpy-2-abi-handling for more details.
All downstream packages that depend on the NumPy ABI are advised to do a new release built against NumPy 2.0 and verify that that release works with both 2.0 and 1.26 - ideally in the period between 2.0.0rc1 (which will be ABI-stable) and the final 2.0.0 release to avoid problems for their users.
The Python versions supported by this release are 3.9-3.12.
NumPy 2.0 Python API removals
-
np.geterrobj,np.seterrobjand the related ufunc keyword argumentextobj=have been removed. The preferred replacement for all of these is using the context managerwith np.errstate():.(gh-23922)
-
np.casthas been removed. The literal replacement fornp.cast[dtype](arg)isnp.asarray(arg, dtype=dtype). -
np.sourcehas been removed. The preferred replacement isinspect.getsource. -
np.lookforhas been removed.(gh-24144)
-
numpy.whohas been removed. As an alternative for the removed functionality, one can use a variable explorer that is available in IDEs such as Spyder or Jupyter Notebook.(gh-24321)
-
Warnings and exceptions present in
numpy.exceptions, e.g,numpy.exceptions.ComplexWarning,numpy.exceptions.VisibleDeprecationWarning, are no longer exposed in the main namespace. -
Multiple niche enums, expired members and functions have been removed from the main namespace, such as:
ERR_*,SHIFT_*,np.fastCopyAndTranspose,np.kernel_version,np.numarray,np.oldnumericandnp.set_numeric_ops.(gh-24316)
-
Replaced
from ... import *in thenumpy/__init__.pywith explicit imports. As a result, these main namespace members got removed:np.FLOATING_POINT_SUPPORT,np.FPE_*,np.NINF,np.PINF,np.NZERO,np.PZERO,np.CLIP,np.WRAP,np.WRAP,np.RAISE,np.BUFSIZE,np.UFUNC_BUFSIZE_DEFAULT,np.UFUNC_PYVALS_NAME,np.ALLOW_THREADS,np.MAXDIMS,np.MAY_SHARE_EXACT,np.MAY_SHARE_BOUNDS,add_newdoc,np.add_docstringandnp.add_newdoc_ufunc.(gh-24357)
-
Alias
np.float_has been removed. Usenp.float64instead. -
Alias
np.complex_has been removed. Usenp.complex128instead. -
Alias
np.longfloathas been removed. Usenp.longdoubleinstead. -
Alias
np.singlecomplexhas been removed. Usenp.complex64instead. -
Alias
np.cfloathas been removed. Usenp.complex128instead. -
Alias
np.longcomplexhas been removed. Usenp.clongdoubleinstead. -
Alias
np.clongfloathas been removed. Usenp.clongdoubleinstead. -
Alias
np.string_has been removed. Usenp.bytes_instead. -
Alias
np.unicode_has been removed. Usenp.str_instead. -
Alias
np.Infhas been removed. Usenp.infinstead. -
Alias
np.Infinityhas been removed. Usenp.infinstead. -
Alias
np.NaNhas been removed. Usenp.naninstead. -
Alias
np.inftyhas been removed. Usenp.infinstead. -
Alias
np.mathas been removed. Usenp.asmatrixinstead. -
np.issubclass_has been removed. Use theissubclassbuiltin instead. -
np.asfarrayhas been removed. Usenp.asarraywith a proper dtype instead. -
np.set_string_functionhas been removed. Usenp.set_printoptionsinstead with a formatter for custom printing of NumPy objects. -
np.tracemalloc_domainis now only available fromnp.lib. -
np.recfromcsvandrecfromtxtare now only available fromnp.lib.npyio. -
np.issctype,np.maximum_sctype,np.obj2sctype,np.sctype2char,np.sctypes,np.issubsctypewere all removed from the main namespace without replacement, as they where niche members. -
Deprecated
np.deprecateandnp.deprecate_with_dochas been removed from the main namespace. UseDeprecationWarninginstead. -
Deprecated
np.safe_evalhas been removed from the main namespace. Useast.literal_evalinstead.(gh-24376)
-
np.find_common_typehas been removed. Usenumpy.promote_typesornumpy.result_typeinstead. To achieve semantics for thescalar_typesargument, usenumpy.result_typeand pass0,0.0, or0jas a Python scalar instead. -
np.round_has been removed. Usenp.roundinstead. -
np.nbyteshas been removed. Usenp.dtype(<dtype>).itemsizeinstead.(gh-24477)
-
np.compare_chararrayshas been removed from the main namespace. Usenp.char.compare_chararraysinstead. -
The
charrarrayin the main namespace has been deprecated. It can be imported without a deprecation warning fromnp.char.chararrayfor now, but we are planning to fully deprecate and removechararrayin the future. -
np.format_parserhas been removed from the main namespace. Usenp.rec.format_parserinstead.(gh-24587)
-
Support for seven data type string aliases has been removed from
np.dtype:int0,uint0,void0,object0,str0,bytes0andbool8.(gh-24807)
-
The experimental
numpy.array_apisubmodule has been removed. Use the mainnumpynamespace for regular usage instead, or the separatearray-api-strictpackage for the compliance testing use case for whichnumpy.array_apiwas mostly used.(gh-25911)
__array_prepare__ is removed
UFuncs called __array_prepare__ before running computations for normal
ufunc calls (not generalized ufuncs, reductions, etc.). The function was
also called instead of __array_wrap__ on the results of some linear
algebra functions.
It is now removed. If you use it, migrate to __array_ufunc__ or rely
on __array_wrap__ which is called with a context in all cases,
although only after the result array is filled. In those code paths,
__array_wrap__ will now be passed a base class, rather than a subclass
array.
(gh-25105)
Deprecations
-
np.compathas been deprecated, as Python 2 is no longer supported. -
numpy.int8and similar classes will no longer support conversion of out of bounds python integers to integer arrays. For example, conversion of 255 to int8 will not return -1.numpy.iinfo(dtype)can be used to check the machine limits for data types. For example,np.iinfo(np.uint16)returns min = 0 and max = 65535.np.array(value).astype(dtype)will give the desired result. -
np.safe_evalhas been deprecated.ast.literal_evalshould be used instead.(gh-23830)
-
np.recfromcsv,np.recfromtxt,np.disp,np.get_array_wrap,np.maximum_sctype,np.deprecateandnp.deprecate_with_dochave been deprecated.(gh-24154)
-
np.trapzhas been deprecated. Usenp.trapezoidor ascipy.integratefunction instead. -
np.in1dhas been deprecated. Usenp.isininstead. -
Alias
np.row_stackhas been deprecated. Usenp.vstackdirectly.(gh-24445)
-
__array_wrap__is now passedarr, context, return_scalarand support for implementations not accepting all three are deprecated. Its signature should be__array_wrap__(self, arr, context=None, return_scalar=False)(gh-25409)
-
Arrays of 2-dimensional vectors for
np.crosshave been deprecated. Use arrays of 3-dimensional vectors instead.(gh-24818)
-
np.dtype("a")alias fornp.dtype(np.bytes_)was deprecated. Usenp.dtype("S")alias instead.(gh-24854)
-
Use of keyword arguments
xandywith functionsassert_array_equalandassert_array_almost_equalhas been deprecated. Pass the first two arguments as positional arguments instead.(gh-24978)
numpy.fft deprecations for n-D transforms with None values in arguments
Using fftn, ifftn, rfftn, irfftn, fft2, ifft2, rfft2 or
irfft2 with the s parameter set to a value that is not None and
the axes parameter set to None has been deprecated, in line with the
array API standard. To retain current behaviour, pass a sequence [0,
..., k-1] to axes for an array of dimension k.
Furthermore, passing an array to s which contains None values is
deprecated as the parameter is documented to accept a sequence of
integers in both the NumPy docs and the array API specification. To use
the default behaviour of the corresponding 1-D transform, pass the value
matching the default for its n parameter. To use the default behaviour
for every axis, the s argument can be omitted.
(gh-25495)
np.linalg.lstsq now defaults to a new rcond value
numpy.linalg.lstsq now uses the new rcond value of the
machine precision times max(M, N). Previously, the machine precision
was used but a FutureWarning was given to notify that this change will
happen eventually. That old behavior can still be achieved by passing
rcond=-1.
(gh-25721)
Expired deprecations
-
The
np.core.umath_testssubmodule has been removed from the public API. (Deprecated in NumPy 1.15)(gh-23809)
-
The
PyDataMem_SetEventHookdeprecation has expired and it is removed. Usetracemallocand thenp.lib.tracemalloc_domaindomain. (Deprecated in NumPy 1.23)(gh-23921)
-
The deprecation of
set_numeric_opsand the C functionsPyArray_SetNumericOpsandPyArray_GetNumericOpshas been expired and the functions removed. (Deprecated in NumPy 1.16)(gh-23998)
-
The
fasttake,fastclip, andfastputmaskArrFuncsdeprecation is now finalized. -
The deprecated function
fastCopyAndTransposeand its C counterpart are now removed. -
The deprecation of
PyArray_ScalarFromObjectis now finalized.(gh-24312)
-
np.msorthas been removed. For a replacement,np.sort(a, axis=0)should be used instead.(gh-24494)
-
np.dtype(("f8", 1)will now return a shape 1 subarray dtype rather than a non-subarray one.(gh-25761)
-
Assigning to the
.dataattribute of an ndarray is disallowed and will raise. -
np.binary_repr(a, width)will raise if width is too small. -
Using
NPY_CHARinPyArray_DescrFromType()will raise, useNPY_STRINGNPY_UNICODE, orNPY_VSTRINGinstead.(gh-25794)
Compatibility notes
loadtxt and genfromtxt default encoding changed
loadtxt and genfromtxt now both default to encoding=None which may
mainly modify how converters work. These will now be passed str
rather than bytes. Pass the encoding explicitly to always get the new
or old behavior. For genfromtxt the change also means that returned
values will now be unicode strings rather than bytes.
(gh-25158)
f2py compatibility notes
-
f2pywill no longer accept ambiguous-mand.pyfCLI combinations. When more than one.pyffile is passed, an error is raised. When both-mand a.pyfis passed, a warning is emitted and the-mprovided name is ignored.(gh-25181)
-
The
f2py.compile()helper has been removed because it leaked memory, has been marked as experimental for several years now, and was implemented as a thinsubprocess.runwrapper. It was also one of the test bottlenecks. See gh-25122 for the full rationale. It also used severalnp.distutilsfeatures which are too fragile to be ported to work withmeson. -
Users are urged to replace calls to
f2py.compilewith calls tosubprocess.run("python", "-m", "numpy.f2py",...instead, and to use environment variables to interact withmeson. Native files are also an option.(gh-25193)
Minor changes in behavior of sorting functions
Due to algorithmic changes and use of SIMD code, sorting functions with
methods that aren't stable may return slightly different results in
2.0.0 compared to 1.26.x. This includes the default method of
numpy.argsort and numpy.argpartition.
Removed ambiguity when broadcasting in np.solve
The broadcasting rules for np.solve(a, b) were ambiguous when b had
1 fewer dimensions than a. This has been resolved in a
backward-incompatible way and is now compliant with the Array API. The
old behaviour can be reconstructed by using
np.solve(a, b[..., None])[..., 0].
(gh-25914)
Modified representation for Polynomial
The representation method for
numpy.polynomial.polynomial.Polynomial was updated to
include the domain in the representation. The plain text and latex
representations are now consistent. For example the output of
str(np.polynomial.Polynomial([1, 1], domain=[.1, .2])) used to be
1.0 + 1.0 x, but now is 1.0 + 1.0 (-3.0000000000000004 + 20.0 x).
(gh-21760)
C API changes
-
The
PyArray_CGT,PyArray_CLT,PyArray_CGE,PyArray_CLE,PyArray_CEQ,PyArray_CNEmacros have been removed. -
PyArray_MINandPyArray_MAXhave been moved fromndarraytypes.htonpy_math.h.(gh-24258)
-
A C API for working with
numpy.dtypes.StringDTypearrays has been exposed. This includes functions for acquiring and releasing mutexes which lock access to the string data, as well as packing and unpacking UTF-8 bytestreams from array entries. -
NPY_NTYPEShas been renamed toNPY_NTYPES_LEGACYas it does not include new NumPy built-in DTypes. In particular the new string DType will likely not work correctly with code that handles legacy DTypes.(gh-25347)
-
The C-API now only exports the static inline function versions of the array accessors (previously this depended on using "deprecated API"). While we discourage it, the struct fields can still be used directly.
(gh-25789)
-
NumPy now defines
PyArray_Packto set an individual memory address. UnlikePyArray_SETITEMthis function is equivalent to setting an individual array item and does not require a NumPy array input.(gh-25954)
-
The
->fslot has been removed fromPyArray_Descr. If you use this slot, replace accessing it withPyDataType_GetArrFuncs(see its documentation and thenumpy-2-migration-guide). In some cases using other functions likePyArray_GETITEMmay be an alternatives. -
PyArray_GETITEMandPyArray_SETITEMnow require the import of the NumPy API table to be used and are no longer defined inndarraytypes.h.(gh-25812)
-
Due to runtime dependencies, the definition for functionality accessing the dtype flags was moved from
numpy/ndarraytypes.hand is only available after includingnumpy/ndarrayobject.has it requiresimport_array(). This includesPyDataType_FLAGCHK,PyDataType_REFCHKandNPY_BEGIN_THREADS_DESCR. -
The dtype flags on
PyArray_Descrmust now be accessed through thePyDataType_FLAGSinline function to be compatible with both 1.x and 2.x. This function is defined innpy_2_compat.hto allow backporting. Most or all users should usePyDataType_FLAGCHKwhich is available on 1.x and does not require backporting. Cython users should use Cython 3. Otherwise access will go through Python unless they usePyDataType_FLAGCHKinstead.(gh-25816)
Datetime functionality exposed in the C API and Cython bindings
The functions NpyDatetime_ConvertDatetime64ToDatetimeStruct,
NpyDatetime_ConvertDatetimeStructToDatetime64,
NpyDatetime_ConvertPyDateTimeToDatetimeStruct,
NpyDatetime_GetDatetimeISO8601StrLen,
NpyDatetime_MakeISO8601Datetime, and
NpyDatetime_ParseISO8601Datetime have been added to the C API to
facilitate converting between strings, Python datetimes, and NumPy
datetimes in external libraries.
(gh-21199)
Const correctness for the generalized ufunc C API
The NumPy C API's functions for constructing generalized ufuncs
(PyUFunc_FromFuncAndData, PyUFunc_FromFuncAndDataAndSignature,
PyUFunc_FromFuncAndDataAndSignatureAndIdentity) take types and
data arguments that are not modified by NumPy's internals. Like the
name and doc arguments, third-party Python extension modules are
likely to supply these arguments from static constants. The types and
data arguments are now const-correct: they are declared as
const char *types and void *const *data, respectively. C code should
not be affected, but C++ code may be.
(gh-23847)
Larger NPY_MAXDIMS and NPY_MAXARGS, NPY_RAVEL_AXIS introduced
NPY_MAXDIMS is now 64, you may want to review its use. This is usually
used in a stack allocation, where the increase should be safe. However,
we do encourage generally to remove any use of NPY_MAXDIMS and
NPY_MAXARGS to eventually allow removing the constraint completely.
For the conversion helper and C-API functions mirroring Python ones such as
take, NPY_MAXDIMS was used to mean axis=None. Such usage must be replaced
with NPY_RAVEL_AXIS. See also migration_maxdims.
(gh-25149)
NPY_MAXARGS not constant and PyArrayMultiIterObject size change
Since NPY_MAXARGS was increased, it is now a runtime constant and not
compile-time constant anymore. We expect almost no users to notice this.
But if used for stack allocations it now must be replaced with a custom
constant using NPY_MAXARGS as an additional runtime check.
The sizeof(PyArrayMultiIterObject) no longer includes the full size of
the object. We expect nobody to notice this change. It was necessary to
avoid issues with Cython.
(gh-25271)
Required changes for custom legacy user dtypes
In order to improve our DTypes it is unfortunately necessary to break
the ABI, which requires some changes for dtypes registered with
PyArray_RegisterDataType. Please see the documentation of
PyArray_RegisterDataType for how to adapt your code and achieve
compatibility with both 1.x and 2.x.
(gh-25792)
New Public DType API
The C implementation of the NEP 42 DType API is now public. While the
DType API has shipped in NumPy for a few versions, it was only usable in
sessions with a special environment variable set. It is now possible to
write custom DTypes outside of NumPy using the new DType API and the
normal import_array() mechanism for importing the numpy C API.
See dtype-api for more details about the API. As always with a new feature,
please report any bugs you run into implementing or using a new DType. It is
likely that downstream C code that works with dtypes will need to be updated to
work correctly with new DTypes.
(gh-25754)
New C-API import functions
We have now added PyArray_ImportNumPyAPI and PyUFunc_ImportUFuncAPI
as static inline functions to import the NumPy C-API tables. The new
functions have two advantages over import_array and import_ufunc:
- They check whether the import was already performed and are light-weight if not, allowing to add them judiciously (although this is not preferable in most cases).
- The old mechanisms were macros rather than functions which included
a
returnstatement.
The PyArray_ImportNumPyAPI() function is included in npy_2_compat.h
for simpler backporting.
(gh-25866)
Structured dtype information access through functions
The dtype structures fields c_metadata, names, fields, and
subarray must now be accessed through new functions following the same
names, such as PyDataType_NAMES. Direct access of the fields is not
valid as they do not exist for all PyArray_Descr instances. The
metadata field is kept, but the macro version should also be
preferred.
(gh-25802)
Descriptor elsize and alignment access
Unless compiling only with NumPy 2 support, the elsize and aligment
fields must now be accessed via PyDataType_ELSIZE,
PyDataType_SET_ELSIZE, and PyDataType_ALIGNMENT. In cases where the
descriptor is attached to an array, we advise using PyArray_ITEMSIZE
as it exists on all NumPy versions. Please see
migration_c_descr for more information.
(gh-25943)
NumPy 2.0 C API removals
-
npy_interrupt.hand the corresponding macros likeNPY_SIGINT_ONhave been removed. We recommend queryingPyErr_CheckSignals()orPyOS_InterruptOccurred()periodically (these do currently require holding the GIL though). -
The
noprefix.hheader has been removed. Replace missing symbols with their prefixed counterparts (usually an addedNPY_ornpy_).(gh-23919)
-
PyUFunc_GetPyVals,PyUFunc_handlefperr, andPyUFunc_checkfperrhave been removed. If needed, a new backwards compatible function to raise floating point errors could be restored. Reason for removal: there are no known users and the functions would have madewith np.errstate()fixes much more difficult).(gh-23922)
-
The
numpy/old_defines.hwhich was part of the API deprecated since NumPy 1.7 has been removed. This removes macros of the formPyArray_CONSTANT. The replace_old_macros.sed script may be useful to convert them to theNPY_CONSTANTversion.(gh-24011)
-
The
legacy_inner_loop_selectormember of the ufunc struct is removed to simplify improvements to the dispatching system. There are no known users overriding or directly accessing this member.(gh-24271)
-
NPY_INTPLTRhas been removed to avoid confusion (seeintpredefinition).(gh-24888)
-
The advanced indexing
MapIterand related API has been removed. The (truly) public part of it was not well tested and had only one known user (Theano). Making it private will simplify improvements to speed upufunc.at, make advanced indexing more maintainable, and was important for increasing the maximum number of dimensions of arrays to 64. Please let us know if this API is important to you so we can find a solution together.(gh-25138)
-
The
NPY_MAX_ELSIZEmacro has been removed, as it only ever reflected builtin numeric types and served no internal purpose.(gh-25149)
-
PyArray_REFCNTandNPY_REFCOUNTare removed. UsePy_REFCNTinstead.(gh-25156)
-
PyArrayFlags_TypeandPyArray_NewFlagsObjectas well asPyArrayFlagsObjectare private now. There is no known use-case; use the Python API if needed. -
PyArray_MoveInto,PyArray_CastTo,PyArray_CastAnyToare removed usePyArray_CopyIntoand if absolutely neededPyArray_CopyAnyInto(the latter does a flat copy). -
PyArray_FillObjectArrayis removed, its only true use was for implementingnp.empty. Create a new empty array or usePyArray_FillWithScalar()(decrefs existing objects). -
PyArray_CompareUCS4andPyArray_CompareStringare removed. Use the standard C string comparison functions. -
PyArray_ISPYTHONis removed as it is misleading, has no known use-cases, and is easy to replace. -
PyArray_FieldNamesis removed, as it is unclear what it would be useful for. It also has incorrect semantics in some possible use-cases. -
PyArray_TypestrConvertis removed, since it seems a misnomer and unlikely to be used by anyone. If you know the size or are limited to few types, just use it explicitly, otherwise go via Python strings.(gh-25292)
-
PyDataType_GetDatetimeMetaDatais removed, it did not actually do anything since at least NumPy 1.7.(gh-25802)
-
PyArray_GetCastFuncis removed. Note that custom legacy user dtypes can still provide a castfunc as their implementation, but any access to them is now removed. The reason for this is that NumPy never used these internally for many years. If you use simple numeric types, please just use C casts directly. In case you require an alternative, please let us know so we can create new API such asPyArray_CastBuffer()which could use old or new cast functions depending on the NumPy version.(gh-25161)
New Features
np.add was extended to work with unicode and bytes dtypes.
(gh-24858)
A new bitwise_count function
This new function counts the number of 1-bits in a number.
numpy.bitwise_count works on all the numpy integer types
and integer-like objects.
>>> a = np.array([2**i - 1 for i in range(16)])
>>> np.bitwise_count(a)
array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15],
dtype=uint8)(gh-19355)
macOS Accelerate support, including the ILP64
Support for the updated Accelerate BLAS/LAPACK library, including ILP64 (64-bit integer) support, in macOS 13.3 has been added. This brings arm64 support, and significant performance improvements of up to 10x for commonly used linear algebra operations. When Accelerate is selected at build time, or if no explicit BLAS library selection is done, the 13.3+ version will automatically be used if available.
(gh-24053)
Binary wheels are also available. On macOS >=14.0, users who install NumPy from PyPI will get wheels built against Accelerate rather than OpenBLAS.
(gh-25255)
Option to use weights for quantile and percentile functions
A weights keyword is now available for numpy.quantile, numpy.percentile,
numpy.nanquantile and numpy.nanpercentile. Only method="inverted_cdf"
supports weights.
(gh-24254)
Improved CPU optimization tracking
A new tracer mechanism is available which enables tracking of the enabled targets for each optimized function (i.e., that uses hardware-specific SIMD instructions) in the NumPy library. With this enhancement, it becomes possible to precisely monitor the enabled CPU dispatch targets for the dispatched functions.
A new function named opt_func_info has been added to the new namespace
numpy.lib.introspect, offering this tracing capability. This function allows
you to retrieve information about the enabled targets based on function names
and data type signatures.
(gh-24420)
A new Meson backend for f2py
f2py in compile mode (i.e. f2py -c) now accepts the
--backend meson option. This is the default option for Python >=3.12.
For older Python versions, f2py will still default to
--backend distutils.
To support this in realistic use-cases, in compile mode f2py takes a
--dep flag one or many times which maps to dependency() calls in the
meson backend, and does nothing in the distutils backend.
There are no changes for users of f2py only as a code generator, i.e.
without -c.
(gh-24532)
bind(c) support for f2py
Both functions and subroutines can be annotated with bind(c). f2py
will handle both the correct type mapping, and preserve the unique label
for other C interfaces.
Note: bind(c, name = 'routine_name_other_than_fortran_routine') is
not honored by the f2py bindings by design, since bind(c) with the
name is meant to guarantee only the same name in C and Fortran, not in
Python and Fortran.
(gh-24555)
A new strict option for several testing functions
The strict keyword is now available for numpy.testing.assert_allclose,
numpy.testing.assert_equal, and numpy.testing.assert_array_less. Setting
strict=True will disable the broadcasting behaviour for scalars and ensure
that input arrays have the same data type.
(gh-24680, gh-24770, gh-24775)
Add np.core.umath.find and np.core.umath.rfind UFuncs
Add two find and rfind UFuncs that operate on unicode or byte
strings and are used in np.char. They operate similar to str.find
and str.rfind.
(gh-24868)
diagonal and trace for numpy.linalg
numpy.linalg.diagonal and numpy.linalg.trace have been added, which are
array API standard-compatible variants of numpy.diagonal and numpy.trace.
They differ in the default axis selection which define 2-D sub-arrays.
(gh-24887)
New long and ulong dtypes
numpy.long and numpy.ulong have been added as NumPy integers mapping to
C's long and unsigned long. Prior to NumPy 1.24, numpy.long was an alias
to Python's int.
(gh-24922)
svdvals for numpy.linalg
numpy.linalg.svdvals has been added. It computes singular values for (a stack
of) matrices. Executing np.svdvals(x) is the same as calling np.svd(x, compute_uv=False, hermitian=False). This function is compatible with the array
API standard.
(gh-24940)
A new isdtype function
numpy.isdtype was added to provide a canonical way to classify NumPy's
dtypes in compliance with the array API standard.
(gh-25054)
A new astype function
numpy.astype was added to provide an array API standard-compatible
alternative to the numpy.ndarray.astype method.
(gh-25079)
Array API compatible functions' aliases
13 aliases for existing functions were added to improve compatibility with the array API standard:
- Trigonometry:
acos,acosh,asin,asinh,atan,atanh,atan2. - Bitwise:
bitwise_left_shift,bitwise_invert,bitwise_right_shift. - Misc:
concat,permute_dims,pow. - In
numpy.linalg:tensordot,matmul.
(gh-25086)
New unique_* functions
The numpy.unique_all, numpy.unique_counts, numpy.unique_inverse, and
numpy.unique_values functions have been added. They provide functionality of
numpy.unique with different sets of flags. They are array API
standard-compatible, and because the number of arrays they return does not
depend on the values of input arguments, they are easier to target for JIT
compilation.
(gh-25088)
Matrix transpose support for ndarrays
NumPy now offers support for calculating the matrix transpose of an
array (or stack of arrays). The matrix transpose is equivalent to
swapping the last two axes of an array. Both np.ndarray and
np.ma.MaskedArray now expose a .mT attribute, and there is a
matching new numpy.matrix_transpose function.
(gh-23762)
Array API compatible functions for numpy.linalg
Six new functions and two aliases were added to improve compatibility with the Array API standard for `numpy.linalg`:
-
numpy.linalg.matrix_norm- Computes the matrix norm of a matrix (or a stack of matrices). -
numpy.linalg.vector_norm- Computes the vector norm of a vector (or batch of vectors). -
numpy.vecdot- Computes the (vector) dot product of two arrays. -
numpy.linalg.vecdot- An alias fornumpy.vecdot. -
numpy.linalg.matrix_transpose- An alias fornumpy.matrix_transpose.(gh-25155)
-
numpy.linalg.outerhas been added. It computes the outer product of two vectors. It differs fromnumpy.outerby accepting one-dimensional arrays only. This function is compatible with the array API standard.(gh-25101)
-
numpy.linalg.crosshas been added. It computes the cross product of two (arrays of) 3-dimensional vectors. It differs fromnumpy.crossby accepting three-dimensional vectors only. This function is compatible with the array API standard.(gh-25145)
A correction argument for var and std
A correction argument was added to numpy.var and numpy.std, which is an
array API standard compatible alternative to ddof. As both arguments serve a
similar purpose, only one of them can be provided at the same time.
(gh-25169)
ndarray.device and ndarray.to_device
An ndarray.device attribute and ndarray.to_device method were added
to numpy.ndarray for array API standard compatibility.
Additionally, device keyword-only arguments were added to:
numpy.asarray, numpy.arange, numpy.empty, numpy.empty_like,
numpy.eye, numpy.full, numpy.full_like, numpy.linspace, numpy.ones,
numpy.ones_like, numpy.zeros, and numpy.zeros_like.
For all these new arguments, only device="cpu" is supported.
(gh-25233)
StringDType has been added to NumPy
We have added a new variable-width UTF-8 encoded string data type, implementing a "NumPy array of Python strings", including support for a user-provided missing data sentinel. It is intended as a drop-in replacement for arrays of Python strings and missing data sentinels using the object dtype. See NEP 55 and the documentation of stringdtype for more details.
(gh-25347)
New keywords for cholesky and pinv
The upper and rtol keywords were added to
numpy.linalg.cholesky and numpy.linalg.pinv,
respectively, to improve array API standard compatibility.
For numpy.linalg.pinv, if neither rcond nor rtol is
specified, the rcond's default is used. We plan to deprecate and
remove rcond in the future.
(gh-25388)
New keywords for sort, argsort and linalg.matrix_rank
New keyword parameters were added to improve array API standard compatibility:
-
rtolwas added tonumpy.linalg.matrix_rank. -
stablewas added tonumpy.sortandnumpy.argsort.
(gh-25437)
New numpy.strings namespace for string ufuncs
NumPy now implements some string operations as ufuncs. The old np.char
namespace is still available, and where possible the string manipulation
functions in that namespace have been updated to use the new ufuncs,
substantially improving their performance.
Where possible, we suggest updating code to use functions in
np.strings instead of np.char. In the future we may deprecate
np.char in favor of np.strings.
(gh-25463)
numpy.fft support for different precisions and in-place calculations
The various FFT routines in numpy.fft now do their
calculations natively in float, double, or long double precision,
depending on the input precision, instead of always calculating in
double precision. Hence, the calculation will now be less precise for
single and more precise for long double precision. The data type of the
output array will now be adjusted accordingly.
Furthermore, all FFT routines have gained an out argument that can be
used for in-place calculations.
(gh-25536)
configtool and pkg-config support
A new numpy-config CLI script is available that can be queried for the
NumPy version and for compile flags needed to use the NumPy C API. This
will allow build systems to better support the use of NumPy as a
dependency. Also, a numpy.pc pkg-config file is now included with
Numpy. In order to find its location for use with PKG_CONFIG_PATH, use
numpy-config --pkgconfigdir.
(gh-25730)
Array API standard support in the main namespace
The main numpy namespace now supports the array API standard. See
array-api-standard-compatibility for
details.
(gh-25911)
Improvements
Strings are now supported by any, all, and the logical ufuncs.
(gh-25651)
Integer sequences as the shape argument for memmap
numpy.memmap can now be created with any integer sequence
as the shape argument, such as a list or numpy array of integers.
Previously, only the types of tuple and int could be used without
raising an error.
(gh-23729)
errstate is now faster and context safe
The numpy.errstate context manager/decorator is now faster
and safer. Previously, it was not context safe and had (rare) issues
with thread-safety.
(gh-23936)
AArch64 quicksort speed improved by using Highway's VQSort
The first introduction of the Google Highway library, using VQSort on AArch64. Execution time is improved by up to 16x in some cases, see the MR for benchmark results. Extensions to other platforms will be done in the future.
(gh-24018)
Complex types - underlying C type changes
-
The underlying C types for all of NumPy's complex types have been changed to use C99 complex types.
-
While this change does not affect the memory layout of complex types, it changes the API to be used to directly retrieve or write the real or complex part of the complex number, since direct field access (as in
c.realorc.imag) is no longer an option. You can now use utilities provided innumpy/npy_math.hto do these operations, like this:npy_cdouble c; npy_csetreal(&c, 1.0); npy_csetimag(&c, 0.0); printf("%d + %di\n", npy_creal(c), npy_cimag(c)); -
To ease cross-version compatibility, equivalent macros and a compatibility layer have been added which can be used by downstream packages to continue to support both NumPy 1.x and 2.x. See
complex-numbersfor more info. -
numpy/npy_common.hnow includescomplex.h, which means thatcomplexis now a reserved keyword.
(gh-24085)
iso_c_binding support and improved common blocks for f2py
Previously, users would have to define their own custom f2cmap file to
use type mappings defined by the Fortran2003 iso_c_binding intrinsic
module. These type maps are now natively supported by f2py
(gh-24555)
f2py now handles common blocks which have kind specifications from
modules. This further expands the usability of intrinsics like
iso_fortran_env and iso_c_binding.
(gh-25186)
Call str automatically on third argument to functions like assert_equal
The third argument to functions like
numpy.testing.assert_equal now has str called on it
automatically. This way it mimics the built-in assert statement, where
assert_equal(a, b, obj) works like assert a == b, obj.
(gh-24877)
Support for array-like atol/rtol in isclose, allclose
The keywords atol and rtol in numpy.isclose and
numpy.allclose now accept both scalars and arrays. An
array, if given, must broadcast to the shapes of the first two array
arguments.
(gh-24878)
Consistent failure messages in test functions
Previously, some numpy.testing assertions printed messages
that referred to the actual and desired results as x and y. Now,
these values are consistently referred to as ACTUAL and DESIRED.
(gh-24931)
n-D FFT transforms allow s[i] == -1
The numpy.fft.fftn, numpy.fft.ifftn,
numpy.fft.rfftn, numpy.fft.irfftn,
numpy.fft.fft2, numpy.fft.ifft2,
numpy.fft.rfft2 and numpy.fft.irfft2
functions now use the whole input array along the axis i if
s[i] == -1, in line with the array API standard.
(gh-25495)
Guard PyArrayScalar_VAL and PyUnicodeScalarObject for the limited API
PyUnicodeScalarObject holds a PyUnicodeObject, which is not
available when using Py_LIMITED_API. Add guards to hide it and
consequently also make the PyArrayScalar_VAL macro hidden.
(gh-25531)
Changes
-
np.gradient()now returns a tuple rather than a list making the return value immutable.(gh-23861)
-
Being fully context and thread-safe,
np.errstatecan only be entered once now. -
np.setbufsizeis now tied tonp.errstate(): leaving annp.errstatecontext will also reset thebufsize.(gh-23936)
-
A new public
np.lib.array_utilssubmodule has been introduced and it currently contains three functions:byte_bounds(moved fromnp.lib.utils),normalize_axis_tupleandnormalize_axis_index.(gh-24540)
-
Introduce
numpy.boolas the new canonical name for NumPy's boolean dtype, and makenumpy.bool\_an alias to it. Note that until NumPy 1.24,np.boolwas an alias to Python's builtinbool. The new name helps with array API standard compatibility and is a more intuitive name.(gh-25080)
-
The
dtype.flagsvalue was previously stored as a signed integer. This means that the aligned dtype struct flag lead to negative flags being set (-128 rather than 128). This flag is now stored unsigned (positive). Code which checks flags manually may need to adapt. This may include code compiled with Cython 0.29.x.(gh-25816)
Representation of NumPy scalars changed
As per NEP 51, the scalar representation has been updated to include the type information to avoid confusion with Python scalars.
Scalars are now printed as np.float64(3.0) rather than just 3.0.
This may disrupt workflows that store representations of numbers (e.g.,
to files) making it harder to read them. They should be stored as
explicit strings, for example by using str() or f"{scalar!s}". For
the time being, affected users can use
np.set_printoptions(legacy="1.25") to get the old behavior (with
possibly a few exceptions). Documentation of downstream projects may
require larger updates, if code snippets are tested. We are working on
tooling for
doctest-plus
to facilitate updates.
(gh-22449)
Truthiness of NumPy strings changed
NumPy strings previously were inconsistent about how they defined if the
string is True or False and the definition did not match the one
used by Python. Strings are now considered True when they are
non-empty and False when they are empty. This changes the following
distinct cases:
- Casts from string to boolean were previously roughly equivalent to
string_array.astype(np.int64).astype(bool), meaning that only valid integers could be cast. Now a string of"0"will be consideredTruesince it is not empty. If you need the old behavior, you may use the above step (casting to integer first) orstring_array == "0"(if the input is only ever0or1). To get the new result on old NumPy versions usestring_array != "". -
np.nonzero(string_array)previously ignored whitespace so that a string only containing whitespace was consideredFalse. Whitespace is now consideredTrue.
This change does not affect np.loadtxt, np.fromstring, or
np.genfromtxt. The first two still use the integer definition, while
genfromtxt continues to match for "true" (ignoring case). However,
if np.bool_ is used as a converter the result will change.
The change does affect np.fromregex as it uses direct assignments.
(gh-23871)
A mean keyword was added to var and std function
Often when the standard deviation is needed the mean is also needed. The
same holds for the variance and the mean. Until now the mean is then
calculated twice, the change introduced here for the numpy.var and
numpy.std functions allows for passing in a precalculated mean as an keyword
argument. See the docstrings for details and an example illustrating the
speed-up.
(gh-24126)
Remove datetime64 deprecation warning when constructing with timezone
The numpy.datetime64 method now issues a UserWarning rather than a
DeprecationWarning whenever a timezone is included in the datetime string that
is provided.
(gh-24193)
Default integer dtype is now 64-bit on 64-bit Windows
The default NumPy integer is now 64-bit on all 64-bit systems as the
historic 32-bit default on Windows was a common source of issues. Most
users should not notice this. The main issues may occur with code
interfacing with libraries written in a compiled language like C. For
more information see migration_windows_int64.
(gh-24224)
Renamed numpy.core to numpy._core
Accessing numpy.core now emits a DeprecationWarning. In practice we
have found that most downstream usage of numpy.core was to access
functionality that is available in the main numpy namespace. If for
some reason you are using functionality in numpy.core that is not
available in the main numpy namespace, this means you are likely using
private NumPy internals. You can still access these internals via
numpy._core without a deprecation warning but we do not provide any
backward compatibility guarantees for NumPy internals. Please open an
issue if you think a mistake was made and something needs to be made
public.
(gh-24634)
The "relaxed strides" debug build option, which was previously enabled
through the NPY_RELAXED_STRIDES_DEBUG environment variable or the
-Drelaxed-strides-debug config-settings flag has been removed.
(gh-24717)
Redefinition of np.intp/np.uintp (almost never a change)
Due to the actual use of these types almost always matching the use of
size_t/Py_ssize_t this is now the definition in C. Previously, it
matched intptr_t and uintptr_t which would often have been subtly
incorrect. This has no effect on the vast majority of machines since the
size of these types only differ on extremely niche platforms.
However, it means that:
- Pointers may not necessarily fit into an
intptyped array anymore. ThepandPcharacter codes can still be used, however. - Creating
intptr_toruintptr_ttyped arrays in C remains possible in a cross-platform way viaPyArray_DescrFromType('p'). - The new character codes
nNwere introduced. - It is now correct to use the Python C-API functions when parsing to
npy_intptyped arguments.
(gh-24888)
numpy.fft.helper made private
numpy.fft.helper was renamed to numpy.fft._helper to indicate that
it is a private submodule. All public functions exported by it should be
accessed from numpy.fft.
(gh-24945)
numpy.linalg.linalg made private
numpy.linalg.linalg was renamed to numpy.linalg._linalg to indicate
that it is a private submodule. All public functions exported by it
should be accessed from numpy.linalg.
(gh-24946)
Out-of-bound axis not the same as axis=None
In some cases axis=32 or for concatenate any large value was the same
as axis=None. Except for concatenate this was deprecate. Any out of
bound axis value will now error, make sure to use axis=None.
(gh-25149)
New copy keyword meaning for array and asarray constructors
Now numpy.array and numpy.asarray support
three values for copy parameter:
-
None- A copy will only be made if it is necessary. -
True- Always make a copy. -
False- Never make a copy. If a copy is required aValueErroris raised.
The meaning of False changed as it now raises an exception if a copy
is needed.
(gh-25168)
The __array__ special method now takes a copy keyword argument.
NumPy will pass copy to the __array__ special method in situations
where it would be set to a non-default value (e.g. in a call to
np.asarray(some_object, copy=False)). Currently, if an unexpected
keyword argument error is raised after this, NumPy will print a warning
and re-try without the copy keyword argument. Implementations of
objects implementing the __array__ protocol should accept a copy
keyword argument with the same meaning as when passed to
numpy.array or numpy.asarray.
(gh-25168)
Cleanup of initialization of numpy.dtype with strings with commas
The interpretation of strings with commas is changed slightly, in that a
trailing comma will now always create a structured dtype. E.g., where
previously np.dtype("i") and np.dtype("i,") were treated as
identical, now np.dtype("i,") will create a structured dtype, with a
single field. This is analogous to np.dtype("i,i") creating a
structured dtype with two fields, and makes the behaviour consistent
with that expected of tuples.
At the same time, the use of single number surrounded by parenthesis to
indicate a sub-array shape, like in np.dtype("(2)i,"), is deprecated.
Instead; one should use np.dtype("(2,)i") or np.dtype("2i").
Eventually, using a number in parentheses will raise an exception, like
is the case for initializations without a comma, like
np.dtype("(2)i").
(gh-25434)
Change in how complex sign is calculated
Following the array API standard, the complex sign is now calculated as
z / |z| (instead of the rather less logical case where the sign of the
real part was taken, unless the real part was zero, in which case the
sign of the imaginary part was returned). Like for real numbers, zero is
returned if z==0.
(gh-25441)
Return types of functions that returned a list of arrays
Functions that returned a list of ndarrays have been changed to return a
tuple of ndarrays instead. Returning tuples consistently whenever a
sequence of arrays is returned makes it easier for JIT compilers like
Numba, as well as for static type checkers in some cases, to support
these functions. Changed functions are: numpy.atleast_1d, numpy.atleast_2d,
numpy.atleast_3d, numpy.broadcast_arrays, numpy.meshgrid,
numpy.ogrid, numpy.histogramdd.
np.unique return_inverse shape for multi-dimensional inputs
When multi-dimensional inputs are passed to np.unique with
return_inverse=True, the unique_inverse output is now shaped such
that the input can be reconstructed directly using
np.take(unique, unique_inverse) when axis=None, and
np.take_along_axis(unique, unique_inverse, axis=axis) otherwise.
any and all return booleans for object arrays
The any and all functions and methods now return booleans also for
object arrays. Previously, they did a reduction which behaved like the
Python or and and operators which evaluates to one of the arguments.
You can use np.logical_or.reduce and np.logical_and.reduce to
achieve the previous behavior.
(gh-25712)
np.can_cast cannot be called on Python int, float, or complex
np.can_cast cannot be called with Python int, float, or complex
instances anymore. This is because NEP 50 means that the result of
can_cast must not depend on the value passed in. Unfortunately, for
Python scalars whether a cast should be considered "same_kind" or
"safe" may depend on the context and value so that this is currently
not implemented. In some cases, this means you may have to add a
specific path for: if type(obj) in (int, float, complex): ....
(gh-26393)
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