NumPy 1.25.1 Release Notes

NumPy 1.25.1 is a maintenance release that fixes bugs and regressions discovered after the 1.25.0 release. The Python versions supported by this release are 3.9-3.11.

Contributors

A total of 10 people contributed to this release. People with a "+" by their names contributed a patch for the first time.

• Andrew Nelson
• Charles Harris
• Developer-Ecosystem-Engineering
• Hood Chatham
• Nathan Goldbaum
• Rohit Goswami
• Sebastian Berg
• Tim Paine +
• dependabot[bot]
• matoro +

Pull requests merged

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

• #23968: MAINT: prepare 1.25.x for further development
• #24036: BLD: Port long double identification to C for meson
• #24037: BUG: Fix reduction return NULL to be goto fail
• #24038: BUG: Avoid undefined behavior in array.astype()
• #24039: BUG: Ensure __array_ufunc__ works without any kwargs passed
• #24117: MAINT: Pin urllib3 to avoid anaconda-client bug.
• #24118: TST: Pin pydantic<2 in Pyodide workflow
• #24119: MAINT: Bump pypa/cibuildwheel from 2.13.0 to 2.13.1
• #24120: MAINT: Bump actions/checkout from 3.5.2 to 3.5.3
• #24122: BUG: Multiply or Divides using SIMD without a full vector can...
• #24127: MAINT: testing for IS_MUSL closes #24074
• #24128: BUG: Only replace dtype temporarily if dimensions changed
• #24129: MAINT: Bump actions/setup-node from 3.6.0 to 3.7.0
• #24134: BUG: Fix private procedures in f2py modules

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NumPy 1.24.4 Release Notes

NumPy 1.24.4 is a maintenance release that fixes a few bugs discovered after the 1.24.3 release. It is the last planned release in the 1.24.x cycle. The Python versions supported by this release are 3.8-3.11.

Contributors

A total of 4 people contributed to this release. People with a "+" by their names contributed a patch for the first time.

• Bas van Beek
• Charles Harris
• Sebastian Berg
• Hongyang Peng +

Pull requests merged

A total of 6 pull requests were merged for this release.

• #23720: MAINT, BLD: Pin rtools to version 4.0 for Windows builds.
• #23739: BUG: fix the method for checking local files for 1.24.x
• #23760: MAINT: Copy rtools installation from install-rtools.
• #23761: BUG: Fix masked array ravel order for A (and somewhat K)
• #23890: TYP,DOC: Annotate and document the metadata parameter of...
• #23994: MAINT: Update rtools installation

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NumPy 1.25.0 Release Notes

The NumPy 1.25.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase the execution speed, and clarify the documentation. There has also been work to prepare for the future NumPy 2.0.0 release, resulting in a large number of new and expired deprecation. Highlights are:

• Support for MUSL, there are now MUSL wheels.
• Support the Fujitsu C/C++ compiler.
• Object arrays are now supported in einsum
• Support for inplace matrix multiplication (@=).

We will be releasing a NumPy 1.26 when Python 3.12 comes out. That is needed because distutils has been dropped by Python 3.12 and we will be switching to using meson for future builds. The next mainline release will be NumPy 2.0.0. We plan that the 2.0 series will still support downstream projects built against earlier versions of NumPy.

The Python versions supported in this release are 3.9-3.11.

Deprecations

• np.core.MachAr is deprecated. It is private API. In names defined in np.core should generally be considered private.

  (gh-22638)

• np.finfo(None) is deprecated.

  (gh-23011)

• np.round_ is deprecated. Use np.round instead.

  (gh-23302)

• np.product is deprecated. Use np.prod instead.

  (gh-23314)

• np.cumproduct is deprecated. Use np.cumprod instead.

  (gh-23314)

• np.sometrue is deprecated. Use np.any instead.

  (gh-23314)

• np.alltrue is deprecated. Use np.all instead.

  (gh-23314)

• Only ndim-0 arrays are treated as scalars. NumPy used to treat all arrays of size 1 (e.g., np.array([3.14])) as scalars. In the future, this will be limited to arrays of ndim 0 (e.g., np.array(3.14)). The following expressions will report a deprecation warning:

  a = np.array([3.14])
  float(a)  # better: a[0] to get the numpy.float or a.item()
  
  b = np.array([[3.14]])
  c = numpy.random.rand(10)
  c[0] = b  # better: c[0] = b[0, 0]
  

  (gh-10615)

• numpy.find_common_type is now deprecated and its use should be replaced with either numpy.result_type or numpy.promote_types. Most users leave the second scalar_types argument to find_common_type as [] in which case np.result_type and np.promote_types are both faster and more robust. When not using scalar_types the main difference is that the replacement intentionally converts non-native byte-order to native byte order. Further, find_common_type returns object dtype rather than failing promotion. This leads to differences when the inputs are not all numeric. Importantly, this also happens for e.g. timedelta/datetime for which NumPy promotion rules are currently sometimes surprising.

  When the scalar_types argument is not [] things are more complicated. In most cases, using np.result_type and passing the Python values 0, 0.0, or 0j has the same result as using int, float, or complex in scalar_types.

  When scalar_types is constructed, np.result_type is the correct replacement and it may be passed scalar values like np.float32(0.0). Passing values other than 0, may lead to value-inspecting behavior (which np.find_common_type never used and NEP 50 may change in the future). The main possible change in behavior in this case, is when the array types are signed integers and scalar types are unsigned.

  If you are unsure about how to replace a use of scalar_types or when non-numeric dtypes are likely, please do not hesitate to open a NumPy issue to ask for help.

  (gh-22539)

Expired deprecations

• np.core.machar and np.finfo.machar have been removed.

  (gh-22638)

• +arr will now raise an error when the dtype is not numeric (and positive is undefined).

  (gh-22998)

• A sequence must now be passed into the stacking family of functions (stack, vstack, hstack, dstack and column_stack).

  (gh-23019)

• np.clip now defaults to same-kind casting. Falling back to unsafe casting was deprecated in NumPy 1.17.

  (gh-23403)

• np.clip will now propagate np.nan values passed as min or max. Previously, a scalar NaN was usually ignored. This was deprecated in NumPy 1.17.

  (gh-23403)

• The np.dual submodule has been removed.

  (gh-23480)

• NumPy now always ignores sequence behavior for an array-like (defining one of the array protocols). (Deprecation started NumPy 1.20)

  (gh-23660)

• The niche FutureWarning when casting to a subarray dtype in astype or the array creation functions such as asarray is now finalized. The behavior is now always the same as if the subarray dtype was wrapped into a single field (which was the workaround, previously). (FutureWarning since NumPy 1.20)

  (gh-23666)

• == and != warnings have been finalized. The == and != operators on arrays now always:

  • raise errors that occur during comparisons such as when the arrays have incompatible shapes (np.array([1, 2]) == np.array([1, 2, 3])).

  • return an array of all True or all False when values are fundamentally not comparable (e.g. have different dtypes). An example is np.array(["a"]) == np.array([1]).

    This mimics the Python behavior of returning False and True when comparing incompatible types like "a" == 1 and "a" != 1. For a long time these gave DeprecationWarning or FutureWarning.

  (gh-22707)

• Nose support has been removed. NumPy switched to using pytest in 2018 and nose has been unmaintained for many years. We have kept NumPy's nose support to avoid breaking downstream projects who might have been using it and not yet switched to pytest or some other testing framework. With the arrival of Python 3.12, unpatched nose will raise an error. It is time to move on.

  Decorators removed:

  • raises
  • slow
  • setastest
  • skipif
  • knownfailif
  • deprecated
  • parametrize
  • _needs_refcount

  These are not to be confused with pytest versions with similar names, e.g., pytest.mark.slow, pytest.mark.skipif, pytest.mark.parametrize.

  Functions removed:

  • Tester
  • import_nose
  • run_module_suite

  (gh-23041)

• The numpy.testing.utils shim has been removed. Importing from the numpy.testing.utils shim has been deprecated since 2019, the shim has now been removed. All imports should be made directly from numpy.testing.

  (gh-23060)

• The environment variable to disable dispatching has been removed. Support for the NUMPY_EXPERIMENTAL_ARRAY_FUNCTION environment variable has been removed. This variable disabled dispatching with __array_function__.

  (gh-23376)

• Support for y= as an alias of out= has been removed. The fix, isposinf and isneginf functions allowed using y= as a (deprecated) alias for out=. This is no longer supported.

  (gh-23376)

Compatibility notes

• The busday_count method now correctly handles cases where the begindates is later in time than the enddates. Previously, the enddates was included, even though the documentation states it is always excluded.

  (gh-23229)

• When comparing datetimes and timedelta using np.equal or np.not_equal numpy previously allowed the comparison with casting="unsafe". This operation now fails. Forcing the output dtype using the dtype kwarg can make the operation succeed, but we do not recommend it.

  (gh-22707)

• When loading data from a file handle using np.load, if the handle is at the end of file, as can happen when reading multiple arrays by calling np.load repeatedly, numpy previously raised ValueError if allow_pickle=False, and OSError if allow_pickle=True. Now it raises EOFError instead, in both cases.

  (gh-23105)

np.pad with mode=wrap pads with strict multiples of original data

Code based on earlier version of pad that uses mode="wrap" will return different results when the padding size is larger than initial array.

np.pad with mode=wrap now always fills the space with strict multiples of original data even if the padding size is larger than the initial array.

(gh-22575)

Cython long_t and ulong_t removed

long_t and ulong_t were aliases for longlong_t and ulonglong_t and confusing (a remainder from of Python 2). This change may lead to the errors:

'long_t' is not a type identifier
'ulong_t' is not a type identifier

We recommend use of bit-sized types such as cnp.int64_t or the use of cnp.intp_t which is 32 bits on 32 bit systems and 64 bits on 64 bit systems (this is most compatible with indexing). If C long is desired, use plain long or npy_long. cnp.int_t is also long (NumPy's default integer). However, long is 32 bit on 64 bit windows and we may wish to adjust this even in NumPy. (Please do not hesitate to contact NumPy developers if you are curious about this.)

(gh-22637)

Changed error message and type for bad axes argument to ufunc

The error message and type when a wrong axes value is passed to ufunc(..., axes=[...]) has changed. The message is now more indicative of the problem, and if the value is mismatched an AxisError will be raised. A TypeError will still be raised for invalidinput types.

(gh-22675)

Array-likes that define __array_ufunc__ can now override ufuncs if used as where

If the where keyword argument of a numpy.ufunc{.interpreted-text role="class"} is a subclass of numpy.ndarray{.interpreted-text role="class"} or is a duck type that defines numpy.class.__array_ufunc__{.interpreted-text role="func"} it can override the behavior of the ufunc using the same mechanism as the input and output arguments. Note that for this to work properly, the where.__array_ufunc__ implementation will have to unwrap the where argument to pass it into the default implementation of the ufunc or, for numpy.ndarray{.interpreted-text role="class"} subclasses before using super().__array_ufunc__.

(gh-23240)

Compiling against the NumPy C API is now backwards compatible by default

NumPy now defaults to exposing a backwards compatible subset of the C-API. This makes the use of oldest-supported-numpy unnecessary. Libraries can override the default minimal version to be compatible with using:

#define NPY_TARGET_VERSION NPY_1_22_API_VERSION

before including NumPy or by passing the equivalent -D option to the compiler. The NumPy 1.25 default is NPY_1_19_API_VERSION. Because the NumPy 1.19 C API was identical to the NumPy 1.16 one resulting programs will be compatible with NumPy 1.16 (from a C-API perspective). This default will be increased in future non-bugfix releases. You can still compile against an older NumPy version and run on a newer one.

For more details please see for-downstream-package-authors{.interpreted-text role="ref"}.

(gh-23528)

New Features

np.einsum now accepts arrays with object dtype

The code path will call python operators on object dtype arrays, much like np.dot and np.matmul.

(gh-18053)

Add support for inplace matrix multiplication

It is now possible to perform inplace matrix multiplication via the @= operator.

>>> import numpy as np

>>> a = np.arange(6).reshape(3, 2)
>>> print(a)
[[0 1]
 [2 3]
 [4 5]]

>>> b = np.ones((2, 2), dtype=int)
>>> a @= b
>>> print(a)
[[1 1]
 [5 5]
 [9 9]]

(gh-21120)

Added NPY_ENABLE_CPU_FEATURES environment variable

Users may now choose to enable only a subset of the built CPU features at runtime by specifying the NPY_ENABLE_CPU_FEATURES environment variable. Note that these specified features must be outside the baseline, since those are always assumed. Errors will be raised if attempting to enable a feature that is either not supported by your CPU, or that NumPy was not built with.

(gh-22137)

NumPy now has an np.exceptions namespace

NumPy now has a dedicated namespace making most exceptions and warnings available. All of these remain available in the main namespace, although some may be moved slowly in the future. The main reason for this is to increase discoverability and add future exceptions.

(gh-22644)

np.linalg functions return NamedTuples

np.linalg functions that return tuples now return namedtuples. These functions are eig(), eigh(), qr(), slogdet(), and svd(). The return type is unchanged in instances where these functions return non-tuples with certain keyword arguments (like svd(compute_uv=False)).

(gh-22786)

String functions in np.char are compatible with NEP 42 custom dtypes

Custom dtypes that represent unicode strings or byte strings can now be passed to the string functions in np.char.

(gh-22863)

String dtype instances can be created from the string abstract dtype classes

It is now possible to create a string dtype instance with a size without using the string name of the dtype. For example, type(np.dtype('U'))(8) will create a dtype that is equivalent to np.dtype('U8'). This feature is most useful when writing generic code dealing with string dtype classes.

(gh-22963)

Fujitsu C/C++ compiler is now supported

Support for Fujitsu compiler has been added. To build with Fujitsu compiler, run:

python setup.py build -c fujitsu

SSL2 is now supported

Support for SSL2 has been added. SSL2 is a library that provides OpenBLAS compatible GEMM functions. To enable SSL2, it need to edit site.cfg and build with Fujitsu compiler. See site.cfg.example.

(gh-22982)

Improvements

NDArrayOperatorsMixin specifies that it has no __slots__

The NDArrayOperatorsMixin class now specifies that it contains no __slots__, ensuring that subclasses can now make use of this feature in Python.

(gh-23113)

Fix power of complex zero

np.power now returns a different result for 0^{non-zero} for complex numbers. Note that the value is only defined when the real part of the exponent is larger than zero. Previously, NaN was returned unless the imaginary part was strictly zero. The return value is either 0+0j or 0-0j.

(gh-18535)

New DTypePromotionError

NumPy now has a new DTypePromotionError which is used when two dtypes cannot be promoted to a common one, for example:

np.result_type("M8[s]", np.complex128)

raises this new exception.

(gh-22707)

np.show_config uses information from Meson

Build and system information now contains information from Meson. np.show_config now has a new optional parameter mode to help customize the output.

(gh-22769)

Fix np.ma.diff not preserving the mask when called with arguments prepend/append.

Calling np.ma.diff with arguments prepend and/or append now returns a MaskedArray with the input mask preserved.

Previously, a MaskedArray without the mask was returned.

(gh-22776)

Corrected error handling for NumPy C-API in Cython

Many NumPy C functions defined for use in Cython were lacking the correct error indicator like except -1 or except *. These have now been added.

(gh-22997)

Ability to directly spawn random number generators

numpy.random.Generator.spawn now allows to directly spawn new independent child generators via the numpy.random.SeedSequence.spawn mechanism. numpy.random.BitGenerator.spawn does the same for the underlying bit generator.

Additionally, numpy.random.BitGenerator.seed_seq now gives direct access to the seed sequence used for initializing the bit generator. This allows for example:

seed = 0x2e09b90939db40c400f8f22dae617151
rng = np.random.default_rng(seed)
child_rng1, child_rng2 = rng.spawn(2)

# safely use rng, child_rng1, and child_rng2

Previously, this was hard to do without passing the SeedSequence explicitly. Please see numpy.random.SeedSequence for more information.

(gh-23195)

numpy.logspace now supports a non-scalar base argument

The base argument of numpy.logspace can now be array-like if it is broadcastable against the start and stop arguments.

(gh-23275)

np.ma.dot() now supports for non-2d arrays

Previously np.ma.dot() only worked if a and b were both 2d. Now it works for non-2d arrays as well as np.dot().

(gh-23322)

Explicitly show keys of .npz file in repr

NpzFile shows keys of loaded .npz file when printed.

>>> npzfile = np.load('arr.npz')
>>> npzfile
NpzFile 'arr.npz' with keys arr_0, arr_1, arr_2, arr_3, arr_4...

(gh-23357)

NumPy now exposes DType classes in np.dtypes

The new numpy.dtypes module now exposes DType classes and will contain future dtype related functionality. Most users should have no need to use these classes directly.

(gh-23358)

Drop dtype metadata before saving in .npy or .npz files

Currently, a *.npy file containing a table with a dtype with metadata cannot be read back. Now, np.save and np.savez drop metadata before saving.

(gh-23371)

numpy.lib.recfunctions.structured_to_unstructured returns views in more cases

structured_to_unstructured now returns a view, if the stride between the fields is constant. Prior, padding between the fields or a reversed field would lead to a copy. This change only applies to ndarray, memmap and recarray. For all other array subclasses, the behavior remains unchanged.

(gh-23652)

Signed and unsigned integers always compare correctly

When uint64 and int64 are mixed in NumPy, NumPy typically promotes both to float64. This behavior may be argued about but is confusing for comparisons ==, <=, since the results returned can be incorrect but the conversion is hidden since the result is a boolean. NumPy will now return the correct results for these by avoiding the cast to float.

(gh-23713)

Performance improvements and changes

Faster np.argsort on AVX-512 enabled processors

32-bit and 64-bit quicksort algorithm for np.argsort gain up to 6x speed up on processors that support AVX-512 instruction set.

Thanks to Intel corporation for sponsoring this work.

(gh-23707)

Faster np.sort on AVX-512 enabled processors

Quicksort for 16-bit and 64-bit dtypes gain up to 15x and 9x speed up on processors that support AVX-512 instruction set.

Thanks to Intel corporation for sponsoring this work.

(gh-22315)

__array_function__ machinery is now much faster

The overhead of the majority of functions in NumPy is now smaller especially when keyword arguments are used. This change significantly speeds up many simple function calls.

(gh-23020)

ufunc.at can be much faster

Generic ufunc.at can be up to 9x faster. The conditions for this speedup:

• operands are aligned
• no casting

If ufuncs with appropriate indexed loops on 1d arguments with the above conditions, ufunc.at can be up to 60x faster (an additional 7x speedup). Appropriate indexed loops have been added to add, subtract, multiply, floor_divide, maximum, minimum, fmax, and fmin.

The internal logic is similar to the logic used for regular ufuncs, which also have fast paths.

Thanks to the D. E. Shaw group for sponsoring this work.

(gh-23136)

Faster membership test on NpzFile

Membership test on NpzFile will no longer decompress the archive if it is successful.

(gh-23661)

Changes

np.r_[] and np.c_[] with certain scalar values

In rare cases, using mainly np.r_ with scalars can lead to different results. The main potential changes are highlighted by the following:

>>> np.r_[np.arange(5, dtype=np.uint8), -1].dtype
int16  # rather than the default integer (int64 or int32)
>>> np.r_[np.arange(5, dtype=np.int8), 255]
array([  0,   1,   2,   3,   4, 255], dtype=int16)

Where the second example returned:

array([ 0,  1,  2,  3,  4, -1], dtype=int8)

The first one is due to a signed integer scalar with an unsigned integer array, while the second is due to 255 not fitting into int8 and NumPy currently inspecting values to make this work. (Note that the second example is expected to change in the future due to NEP 50 <NEP50>{.interpreted-text role="ref"}; it will then raise an error.)

(gh-22539)

Most NumPy functions are wrapped into a C-callable

To speed up the __array_function__ dispatching, most NumPy functions are now wrapped into C-callables and are not proper Python functions or C methods. They still look and feel the same as before (like a Python function), and this should only improve performance and user experience (cleaner tracebacks). However, please inform the NumPy developers if this change confuses your program for some reason.

(gh-23020)

C++ standard library usage

NumPy builds now depend on the C++ standard library, because the numpy.core._multiarray_umath extension is linked with the C++ linker.

(gh-23601)

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NumPy 1.24.3 Release Notes

NumPy 1.24.3 is a maintenance release that fixes bugs and regressions discovered after the 1.24.2 release. The Python versions supported by this release are 3.8-3.11.

Contributors

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

• Aleksei Nikiforov +
• Alexander Heger
• Bas van Beek
• Bob Eldering
• Brock Mendel
• Charles Harris
• Kyle Sunden
• Peter Hawkins
• Rohit Goswami
• Sebastian Berg
• Warren Weckesser
• dependabot[bot]

Pull requests merged

A total of 17 pull requests were merged for this release.

• #23206: BUG: fix for f2py string scalars (#23194)
• #23207: BUG: datetime64/timedelta64 comparisons return NotImplemented
• #23208: MAINT: Pin matplotlib to version 3.6.3 for refguide checks
• #23221: DOC: Fix matplotlib error in documentation
• #23226: CI: Ensure submodules are initialized in gitpod.
• #23341: TYP: Replace duplicate reduce in ufunc type signature with reduceat.
• #23342: TYP: Remove duplicate CLIP/WRAP/RAISE in __init__.pyi.
• #23343: TYP: Mark d argument to fftfreq and rfftfreq as optional...
• #23344: TYP: Add type annotations for comparison operators to MaskedArray.
• #23345: TYP: Remove some stray type-check-only imports of msort
• #23370: BUG: Ensure like is only stripped for like= dispatched functions
• #23543: BUG: fix loading and storing big arrays on s390x
• #23544: MAINT: Bump larsoner/circleci-artifacts-redirector-action
• #23634: BUG: Ignore invalid and overflow warnings in masked setitem
• #23635: BUG: Fix masked array raveling when order="A" or order="K"
• #23636: MAINT: Update conftest for newer hypothesis versions
• #23637: BUG: Fix bug in parsing F77 style string arrays.

Checksums

MD5

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SHA256

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NumPy 1.24.2 Release Notes

NumPy 1.24.2 is a maintenance release that fixes bugs and regressions discovered after the 1.24.1 release. The Python versions supported by this release are 3.8-3.11.

Contributors

A total of 14 people contributed to this release. People with a "+" by their names contributed a patch for the first time.

• Bas van Beek
• Charles Harris
• Khem Raj +
• Mark Harfouche
• Matti Picus
• Panagiotis Zestanakis +
• Peter Hawkins
• Pradipta Ghosh
• Ross Barnowski
• Sayed Adel
• Sebastian Berg
• Syam Gadde +
• dmbelov +
• pkubaj +

Pull requests merged

A total of 17 pull requests were merged for this release.

• #22965: MAINT: Update python 3.11-dev to 3.11.
• #22966: DOC: Remove dangling deprecation warning
• #22967: ENH: Detect CPU features on FreeBSD/powerpc64*
• #22968: BUG: np.loadtxt cannot load text file with quoted fields separated...
• #22969: TST: Add fixture to avoid issue with randomizing test order.
• #22970: BUG: Fix fill violating read-only flag. (#22959)
• #22971: MAINT: Add additional information to missing scalar AttributeError
• #22972: MAINT: Move export for scipy arm64 helper into main module
• #22976: BUG, SIMD: Fix spurious invalid exception for sin/cos on arm64/clang
• #22989: BUG: Ensure correct loop order in sin, cos, and arctan2
• #23030: DOC: Add version added information for the strict parameter in...
• #23031: BUG: use _Alignof rather than offsetof() on most compilers
• #23147: BUG: Fix for npyv__trunc_s32_f32 (VXE)
• #23148: BUG: Fix integer / float scalar promotion
• #23149: BUG: Add missing <type_traits> header.
• #23150: TYP, MAINT: Add a missing explicit Any parameter to the npt.ArrayLike...
• #23161: BLD: remove redundant definition of npy_nextafter [wheel build]

Checksums

MD5

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SHA256

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NumPy 1.24.1 Release Notes

NumPy 1.24.1 is a maintenance release that fixes bugs and regressions discovered after the 1.24.0 release. The Python versions supported by this release are 3.8-3.11.

Contributors

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

• Andrew Nelson
• Ben Greiner +
• Charles Harris
• Clément Robert
• Matteo Raso
• Matti Picus
• Melissa Weber Mendonça
• Miles Cranmer
• Ralf Gommers
• Rohit Goswami
• Sayed Adel
• Sebastian Berg

Pull requests merged

A total of 18 pull requests were merged for this release.

• #22820: BLD: add workaround in setup.py for newer setuptools
• #22830: BLD: CIRRUS_TAG redux
• #22831: DOC: fix a couple typos in 1.23 notes
• #22832: BUG: Fix refcounting errors found using pytest-leaks
• #22834: BUG, SIMD: Fix invalid value encountered in several ufuncs
• #22837: TST: ignore more np.distutils.log imports
• #22839: BUG: Do not use getdata() in np.ma.masked_invalid
• #22847: BUG: Ensure correct behavior for rows ending in delimiter in...
• #22848: BUG, SIMD: Fix the bitmask of the boolean comparison
• #22857: BLD: Help raspian arm + clang 13 about __builtin_mul_overflow
• #22858: API: Ensure a full mask is returned for masked_invalid
• #22866: BUG: Polynomials now copy properly (#22669)
• #22867: BUG, SIMD: Fix memory overlap in ufunc comparison loops
• #22868: BUG: Fortify string casts against floating point warnings
• #22875: TST: Ignore nan-warnings in randomized out tests
• #22883: MAINT: restore npymath implementations needed for freebsd
• #22884: BUG: Fix integer overflow in in1d for mixed integer dtypes #22877
• #22887: BUG: Use whole file for encoding checks with charset_normalizer.

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NumPy 1.24 Release Notes

The NumPy 1.24.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase the execution speed, and clarify the documentation. There are also a large number of new and expired deprecations due to changes in promotion and cleanups. This might be called a deprecation release. Highlights are

• Many new deprecations, check them out.
• Many expired deprecations,
• New F2PY features and fixes.
• New "dtype" and "casting" keywords for stacking functions.

See below for the details,

This release supports Python versions 3.8-3.11.

Deprecations

Deprecate fastCopyAndTranspose and PyArray_CopyAndTranspose

The numpy.fastCopyAndTranspose function has been deprecated. Use the corresponding copy and transpose methods directly:

arr.T.copy()

The underlying C function PyArray_CopyAndTranspose has also been deprecated from the NumPy C-API.

(gh-22313)

Conversion of out-of-bound Python integers

Attempting a conversion from a Python integer to a NumPy value will now always check whether the result can be represented by NumPy. This means the following examples will fail in the future and give a DeprecationWarning now:

np.uint8(-1)
np.array([3000], dtype=np.int8)

Many of these did succeed before. Such code was mainly useful for unsigned integers with negative values such as np.uint8(-1) giving np.iinfo(np.uint8).max.

Note that conversion between NumPy integers is unaffected, so that np.array(-1).astype(np.uint8) continues to work and use C integer overflow logic. For negative values, it will also work to view the array: np.array(-1, dtype=np.int8).view(np.uint8). In some cases, using np.iinfo(np.uint8).max or val % 2**8 may also work well.

In rare cases input data may mix both negative values and very large unsigned values (i.e. -1 and 2**63). There it is unfortunately necessary to use % on the Python value or use signed or unsigned conversion depending on whether negative values are expected.

(gh-22385)

Deprecate msort

The numpy.msort function is deprecated. Use np.sort(a, axis=0) instead.

(gh-22456)

np.str0 and similar are now deprecated

The scalar type aliases ending in a 0 bit size: np.object0, np.str0, np.bytes0, np.void0, np.int0, np.uint0 as well as np.bool8 are now deprecated and will eventually be removed.

(gh-22607)

Expired deprecations

• The normed keyword argument has been removed from [np.histogram]{.title-ref}, [np.histogram2d]{.title-ref}, and [np.histogramdd]{.title-ref}. Use density instead. If normed was passed by position, density is now used.

  (gh-21645)

• Ragged array creation will now always raise a ValueError unless dtype=object is passed. This includes very deeply nested sequences.

  (gh-22004)

• Support for Visual Studio 2015 and earlier has been removed.

• Support for the Windows Interix POSIX interop layer has been removed.

  (gh-22139)

• Support for Cygwin < 3.3 has been removed.

  (gh-22159)

• The mini() method of np.ma.MaskedArray has been removed. Use either np.ma.MaskedArray.min() or np.ma.minimum.reduce().

• The single-argument form of np.ma.minimum and np.ma.maximum has been removed. Use np.ma.minimum.reduce() or np.ma.maximum.reduce() instead.

  (gh-22228)

• Passing dtype instances other than the canonical (mainly native byte-order) ones to dtype= or signature= in ufuncs will now raise a TypeError. We recommend passing the strings "int8" or scalar types np.int8 since the byte-order, datetime/timedelta unit, etc. are never enforced. (Initially deprecated in NumPy 1.21.)

  (gh-22540)

• The dtype= argument to comparison ufuncs is now applied correctly. That means that only bool and object are valid values and dtype=object is enforced.

  (gh-22541)

• The deprecation for the aliases np.object, np.bool, np.float, np.complex, np.str, and np.int is expired (introduces NumPy 1.20). Some of these will now give a FutureWarning in addition to raising an error since they will be mapped to the NumPy scalars in the future.

  (gh-22607)

Compatibility notes

array.fill(scalar) may behave slightly different

numpy.ndarray.fill may in some cases behave slightly different now due to the fact that the logic is aligned with item assignment:

arr = np.array([1])  # with any dtype/value
arr.fill(scalar)
# is now identical to:
arr[0] = scalar

Previously casting may have produced slightly different answers when using values that could not be represented in the target dtype or when the target had object dtype.

(gh-20924)

Subarray to object cast now copies

Casting a dtype that includes a subarray to an object will now ensure a copy of the subarray. Previously an unsafe view was returned:

arr = np.ones(3, dtype=[("f", "i", 3)])
subarray_fields = arr.astype(object)[0]
subarray = subarray_fields[0]  # "f" field

np.may_share_memory(subarray, arr)

Is now always false. While previously it was true for the specific cast.

(gh-21925)

Returned arrays respect uniqueness of dtype kwarg objects

When the dtype keyword argument is used with :pynp.array(){.interpreted-text role="func"} or :pyasarray(){.interpreted-text role="func"}, the dtype of the returned array now always exactly matches the dtype provided by the caller.

In some cases this change means that a view rather than the input array is returned. The following is an example for this on 64bit Linux where long and longlong are the same precision but different dtypes:

>>> arr = np.array([1, 2, 3], dtype="long")
>>> new_dtype = np.dtype("longlong")
>>> new = np.asarray(arr, dtype=new_dtype)
>>> new.dtype is new_dtype
True
>>> new is arr
False

Before the change, the dtype did not match because new is arr was True.

(gh-21995)

DLPack export raises BufferError

When an array buffer cannot be exported via DLPack a BufferError is now always raised where previously TypeError or RuntimeError was raised. This allows falling back to the buffer protocol or __array_interface__ when DLPack was tried first.

(gh-22542)

NumPy builds are no longer tested on GCC-6

Ubuntu 18.04 is deprecated for GitHub actions and GCC-6 is not available on Ubuntu 20.04, so builds using that compiler are no longer tested. We still test builds using GCC-7 and GCC-8.

(gh-22598)

New Features

New attribute symbol added to polynomial classes

The polynomial classes in the numpy.polynomial package have a new symbol attribute which is used to represent the indeterminate of the polynomial. This can be used to change the value of the variable when printing:

>>> P_y = np.polynomial.Polynomial([1, 0, -1], symbol="y")
>>> print(P_y)
1.0 + 0.0·y¹ - 1.0·y²

Note that the polynomial classes only support 1D polynomials, so operations that involve polynomials with different symbols are disallowed when the result would be multivariate:

>>> P = np.polynomial.Polynomial([1, -1])  # default symbol is "x"
>>> P_z = np.polynomial.Polynomial([1, 1], symbol="z")
>>> P * P_z
Traceback (most recent call last)
   ...
ValueError: Polynomial symbols differ

The symbol can be any valid Python identifier. The default is symbol=x, consistent with existing behavior.

(gh-16154)

F2PY support for Fortran character strings

F2PY now supports wrapping Fortran functions with:

• character (e.g. character x)
• character array (e.g. character, dimension(n) :: x)
• character string (e.g. character(len=10) x)
• and character string array (e.g. character(len=10), dimension(n, m) :: x)

arguments, including passing Python unicode strings as Fortran character string arguments.

(gh-19388)

New function np.show_runtime

A new function numpy.show_runtime has been added to display the runtime information of the machine in addition to numpy.show_config which displays the build-related information.

(gh-21468)

strict option for testing.assert_array_equal

The strict option is now available for testing.assert_array_equal. Setting strict=True will disable the broadcasting behaviour for scalars and ensure that input arrays have the same data type.

(gh-21595)

New parameter equal_nan added to np.unique

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

(gh-21623)

casting and dtype keyword arguments for numpy.stack

The casting and dtype keyword arguments are now available for numpy.stack. To use them, write np.stack(..., dtype=None, casting='same_kind').

casting and dtype keyword arguments for numpy.vstack

The casting and dtype keyword arguments are now available for numpy.vstack. To use them, write np.vstack(..., dtype=None, casting='same_kind').

casting and dtype keyword arguments for numpy.hstack

The casting and dtype keyword arguments are now available for numpy.hstack. To use them, write np.hstack(..., dtype=None, casting='same_kind').

(gh-21627)

The bit generator underlying the singleton RandomState can be changed

The singleton RandomState instance exposed in the numpy.random module is initialized at startup with the MT19937 bit generator. The new function set_bit_generator allows the default bit generator to be replaced with a user-provided bit generator. This function has been introduced to provide a method allowing seamless integration of a high-quality, modern bit generator in new code with existing code that makes use of the singleton-provided random variate generating functions. The companion function get_bit_generator returns the current bit generator being used by the singleton RandomState. This is provided to simplify restoring the original source of randomness if required.

The preferred method to generate reproducible random numbers is to use a modern bit generator in an instance of Generator. The function default_rng simplifies instantiation:

>>> rg = np.random.default_rng(3728973198)
>>> rg.random()

The same bit generator can then be shared with the singleton instance so that calling functions in the random module will use the same bit generator:

>>> orig_bit_gen = np.random.get_bit_generator()
>>> np.random.set_bit_generator(rg.bit_generator)
>>> np.random.normal()

The swap is permanent (until reversed) and so any call to functions in the random module will use the new bit generator. The original can be restored if required for code to run correctly:

>>> np.random.set_bit_generator(orig_bit_gen)

(gh-21976)

np.void now has a dtype argument

NumPy now allows constructing structured void scalars directly by passing the dtype argument to np.void.

(gh-22316)

Improvements

F2PY Improvements

• The generated extension modules don't use the deprecated NumPy-C API anymore
• Improved f2py generated exception messages
• Numerous bug and flake8 warning fixes
• various CPP macros that one can use within C-expressions of signature files are prefixed with f2py_. For example, one should use f2py_len(x) instead of len(x)
• A new construct character(f2py_len=...) is introduced to support returning assumed length character strings (e.g. character(len=*)) from wrapper functions

A hook to support rewriting f2py internal data structures after reading all its input files is introduced. This is required, for instance, for BC of SciPy support where character arguments are treated as character strings arguments in C expressions.

(gh-19388)

IBM zSystems Vector Extension Facility (SIMD)

Added support for SIMD extensions of zSystem (z13, z14, z15), through the universal intrinsics interface. This support leads to performance improvements for all SIMD kernels implemented using the universal intrinsics, including the following operations: rint, floor, trunc, ceil, sqrt, absolute, square, reciprocal, tanh, sin, cos, equal, not_equal, greater, greater_equal, less, less_equal, maximum, minimum, fmax, fmin, argmax, argmin, add, subtract, multiply, divide.

(gh-20913)

NumPy now gives floating point errors in casts

In most cases, NumPy previously did not give floating point warnings or errors when these happened during casts. For examples, casts like:

np.array([2e300]).astype(np.float32)  # overflow for float32
np.array([np.inf]).astype(np.int64)

Should now generally give floating point warnings. These warnings should warn that floating point overflow occurred. For errors when converting floating point values to integers users should expect invalid value warnings.

Users can modify the behavior of these warnings using np.errstate.

Note that for float to int casts, the exact warnings that are given may be platform dependent. For example:

arr = np.full(100, value=1000, dtype=np.float64)
arr.astype(np.int8)

May give a result equivalent to (the intermediate cast means no warning is given):

arr.astype(np.int64).astype(np.int8)

May return an undefined result, with a warning set:

RuntimeWarning: invalid value encountered in cast

The precise behavior is subject to the C99 standard and its implementation in both software and hardware.

(gh-21437)

F2PY supports the value attribute

The Fortran standard requires that variables declared with the value attribute must be passed by value instead of reference. F2PY now supports this use pattern correctly. So integer, intent(in), value :: x in Fortran codes will have correct wrappers generated.

(gh-21807)

Added pickle support for third-party BitGenerators

The pickle format for bit generators was extended to allow each bit generator to supply its own constructor when during pickling. Previous versions of NumPy only supported unpickling Generator instances created with one of the core set of bit generators supplied with NumPy. Attempting to unpickle a Generator that used a third-party bit generators would fail since the constructor used during the unpickling was only aware of the bit generators included in NumPy.

(gh-22014)

arange() now explicitly fails with dtype=str

Previously, the np.arange(n, dtype=str) function worked for n=1 and n=2, but would raise a non-specific exception message for other values of n. Now, it raises a [TypeError]{.title-ref} informing that arange does not support string dtypes:

>>> np.arange(2, dtype=str)
Traceback (most recent call last)
   ...
TypeError: arange() not supported for inputs with DType <class 'numpy.dtype[str_]'>.

(gh-22055)

numpy.typing protocols are now runtime checkable

The protocols used in numpy.typing.ArrayLike and numpy.typing.DTypeLike are now properly marked as runtime checkable, making them easier to use for runtime type checkers.

(gh-22357)

Performance improvements and changes

Faster version of np.isin and np.in1d for integer arrays

np.in1d (used by np.isin) can now switch to a faster algorithm (up to >10x faster) when it is passed two integer arrays. This is often automatically used, but you can use kind="sort" or kind="table" to force the old or new method, respectively.

(gh-12065)

Faster comparison operators

The comparison functions (numpy.equal, numpy.not_equal, numpy.less, numpy.less_equal, numpy.greater and numpy.greater_equal) are now much faster as they are now vectorized with universal intrinsics. For a CPU with SIMD extension AVX512BW, the performance gain is up to 2.57x, 1.65x and 19.15x for integer, float and boolean data types, respectively (with N=50000).

(gh-21483)

Changes

Better reporting of integer division overflow

Integer division overflow of scalars and arrays used to provide a RuntimeWarning and the return value was undefined leading to crashes at rare occasions:

>>> np.array([np.iinfo(np.int32).min]*10, dtype=np.int32) // np.int32(-1)
<stdin>:1: RuntimeWarning: divide by zero encountered in floor_divide
array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0], dtype=int32)

Integer division overflow now returns the input dtype's minimum value and raise the following RuntimeWarning:

>>> np.array([np.iinfo(np.int32).min]*10, dtype=np.int32) // np.int32(-1)
<stdin>:1: RuntimeWarning: overflow encountered in floor_divide
array([-2147483648, -2147483648, -2147483648, -2147483648, -2147483648,
       -2147483648, -2147483648, -2147483648, -2147483648, -2147483648],
      dtype=int32)

(gh-21506)

masked_invalid now modifies the mask in-place

When used with copy=False, numpy.ma.masked_invalid now modifies the input masked array in-place. This makes it behave identically to masked_where and better matches the documentation.

(gh-22046)

nditer/NpyIter allows all allocating all operands

The NumPy iterator available through np.nditer in Python and as NpyIter in C now supports allocating all arrays. The iterator shape defaults to () in this case. The operands dtype must be provided, since a "common dtype" cannot be inferred from the other inputs.

(gh-22457)

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NumPy 1.24 Release Notes

The NumPy 1.24.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase the execution speed, and clarify the documentation. There are also a large number of new and expired deprecations due to changes in promotion and cleanups. This might be called a deprecation release. Highlights are

• Many new deprecations, check them out.
• Many expired deprecations,
• New F2PY features and fixes.
• New "dtype" and "casting" keywords for stacking functions.

See below for the details,

Deprecations

Deprecate fastCopyAndTranspose and PyArray_CopyAndTranspose

The numpy.fastCopyAndTranspose function has been deprecated. Use the corresponding copy and transpose methods directly:

arr.T.copy()

The underlying C function PyArray_CopyAndTranspose has also been deprecated from the NumPy C-API.

(gh-22313)

Conversion of out-of-bound Python integers

Attempting a conversion from a Python integer to a NumPy value will now always check whether the result can be represented by NumPy. This means the following examples will fail in the future and give a DeprecationWarning now:

np.uint8(-1)
np.array([3000], dtype=np.int8)

Many of these did succeed before. Such code was mainly useful for unsigned integers with negative values such as np.uint8(-1) giving np.iinfo(np.uint8).max.

Note that conversion between NumPy integers is unaffected, so that np.array(-1).astype(np.uint8) continues to work and use C integer overflow logic.

(gh-22393)

Deprecate msort

The numpy.msort function is deprecated. Use np.sort(a, axis=0) instead.

(gh-22456)

np.str0 and similar are now deprecated

The scalar type aliases ending in a 0 bit size: np.object0, np.str0, np.bytes0, np.void0, np.int0, np.uint0 as well as np.bool8 are now deprecated and will eventually be removed.

(gh-22607)

Expired deprecations

• The normed keyword argument has been removed from [np.histogram]{.title-ref}, [np.histogram2d]{.title-ref}, and [np.histogramdd]{.title-ref}. Use density instead. If normed was passed by position, density is now used.

  (gh-21645)

• Ragged array creation will now always raise a ValueError unless dtype=object is passed. This includes very deeply nested sequences.

  (gh-22004)

• Support for Visual Studio 2015 and earlier has been removed.

• Support for the Windows Interix POSIX interop layer has been removed.

  (gh-22139)

• Support for cygwin < 3.3 has been removed.

  (gh-22159)

• The mini() method of np.ma.MaskedArray has been removed. Use either np.ma.MaskedArray.min() or np.ma.minimum.reduce().

• The single-argument form of np.ma.minimum and np.ma.maximum has been removed. Use np.ma.minimum.reduce() or np.ma.maximum.reduce() instead.

  (gh-22228)

• Passing dtype instances other than the canonical (mainly native byte-order) ones to dtype= or signature= in ufuncs will now raise a TypeError. We recommend passing the strings "int8" or scalar types np.int8 since the byte-order, datetime/timedelta unit, etc. are never enforced. (Initially deprecated in NumPy 1.21.)

  (gh-22540)

• The dtype= argument to comparison ufuncs is now applied correctly. That means that only bool and object are valid values and dtype=object is enforced.

  (gh-22541)

• The deprecation for the aliases np.object, np.bool, np.float, np.complex, np.str, and np.int is expired (introduces NumPy 1.20). Some of these will now give a FutureWarning in addition to raising an error since they will be mapped to the NumPy scalars in the future.

  (gh-22607)

Compatibility notes

array.fill(scalar) may behave slightly different

numpy.ndarray.fill may in some cases behave slightly different now due to the fact that the logic is aligned with item assignment:

arr = np.array([1])  # with any dtype/value
arr.fill(scalar)
# is now identical to:
arr[0] = scalar

Previously casting may have produced slightly different answers when using values that could not be represented in the target dtype or when the target had object dtype.

(gh-20924)

Subarray to object cast now copies

Casting a dtype that includes a subarray to an object will now ensure a copy of the subarray. Previously an unsafe view was returned:

arr = np.ones(3, dtype=[("f", "i", 3)])
subarray_fields = arr.astype(object)[0]
subarray = subarray_fields[0]  # "f" field

np.may_share_memory(subarray, arr)

Is now always false. While previously it was true for the specific cast.

(gh-21925)

Returned arrays respect uniqueness of dtype kwarg objects

When the dtype keyword argument is used with :pynp.array(){.interpreted-text role="func"} or :pyasarray(){.interpreted-text role="func"}, the dtype of the returned array now always exactly matches the dtype provided by the caller.

In some cases this change means that a view rather than the input array is returned. The following is an example for this on 64bit Linux where long and longlong are the same precision but different dtypes:

>>> arr = np.array([1, 2, 3], dtype="long")
>>> new_dtype = np.dtype("longlong")
>>> new = np.asarray(arr, dtype=new_dtype)
>>> new.dtype is new_dtype
True
>>> new is arr
False

Before the change, the dtype did not match because new is arr was True.

(gh-21995)

DLPack export raises BufferError

When an array buffer cannot be exported via DLPack a BufferError is now always raised where previously TypeError or RuntimeError was raised. This allows falling back to the buffer protocol or __array_interface__ when DLPack was tried first.

(gh-22542)

NumPy builds are no longer tested on GCC-6

Ubuntu 18.04 is deprecated for GitHub actions and GCC-6 is not available on Ubuntu 20.04, so builds using that compiler are no longer tested. We still test builds using GCC-7 and GCC-8.

(gh-22598)

New Features

New attribute symbol added to polynomial classes

The polynomial classes in the numpy.polynomial package have a new symbol attribute which is used to represent the indeterminate of the polynomial. This can be used to change the value of the variable when printing:

>>> P_y = np.polynomial.Polynomial([1, 0, -1], symbol="y")
>>> print(P_y)
1.0 + 0.0·y¹ - 1.0·y²

Note that the polynomial classes only support 1D polynomials, so operations that involve polynomials with different symbols are disallowed when the result would be multivariate:

>>> P = np.polynomial.Polynomial([1, -1])  # default symbol is "x"
>>> P_z = np.polynomial.Polynomial([1, 1], symbol="z")
>>> P * P_z
Traceback (most recent call last)
   ...
ValueError: Polynomial symbols differ

The symbol can be any valid Python identifier. The default is symbol=x, consistent with existing behavior.

(gh-16154)

F2PY support for Fortran character strings

F2PY now supports wrapping Fortran functions with:

• character (e.g. character x)
• character array (e.g. character, dimension(n) :: x)
• character string (e.g. character(len=10) x)
• and character string array (e.g. character(len=10), dimension(n, m) :: x)

arguments, including passing Python unicode strings as Fortran character string arguments.

(gh-19388)

New function np.show_runtime

A new function numpy.show_runtime has been added to display the runtime information of the machine in addition to numpy.show_config which displays the build-related information.

(gh-21468)

strict option for testing.assert_array_equal

The strict option is now available for testing.assert_array_equal. Setting strict=True will disable the broadcasting behaviour for scalars and ensure that input arrays have the same data type.

(gh-21595)

New parameter equal_nan added to np.unique

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

(gh-21623)

casting and dtype keyword arguments for numpy.stack

The casting and dtype keyword arguments are now available for numpy.stack. To use them, write np.stack(..., dtype=None, casting='same_kind').

casting and dtype keyword arguments for numpy.vstack

The casting and dtype keyword arguments are now available for numpy.vstack. To use them, write np.vstack(..., dtype=None, casting='same_kind').

casting and dtype keyword arguments for numpy.hstack

The casting and dtype keyword arguments are now available for numpy.hstack. To use them, write np.hstack(..., dtype=None, casting='same_kind').

(gh-21627)

The bit generator underlying the singleton RandomState can be changed

The singleton RandomState instance exposed in the numpy.random module is initialized at startup with the MT19937 bit generator. The new function set_bit_generator allows the default bit generator to be replaced with a user-provided bit generator. This function has been introduced to provide a method allowing seamless integration of a high-quality, modern bit generator in new code with existing code that makes use of the singleton-provided random variate generating functions. The companion function get_bit_generator returns the current bit generator being used by the singleton RandomState. This is provided to simplify restoring the original source of randomness if required.

The preferred method to generate reproducible random numbers is to use a modern bit generator in an instance of Generator. The function default_rng simplifies instantiation:

>>> rg = np.random.default_rng(3728973198)
>>> rg.random()

The same bit generator can then be shared with the singleton instance so that calling functions in the random module will use the same bit generator:

>>> orig_bit_gen = np.random.get_bit_generator()
>>> np.random.set_bit_generator(rg.bit_generator)
>>> np.random.normal()

The swap is permanent (until reversed) and so any call to functions in the random module will use the new bit generator. The original can be restored if required for code to run correctly:

>>> np.random.set_bit_generator(orig_bit_gen)

(gh-21976)

np.void now has a dtype argument

NumPy now allows constructing structured void scalars directly by passing the dtype argument to np.void.

(gh-22316)

Improvements

F2PY Improvements

• The generated extension modules don't use the deprecated NumPy-C API anymore
• Improved f2py generated exception messages
• Numerous bug and flake8 warning fixes
• various CPP macros that one can use within C-expressions of signature files are prefixed with f2py_. For example, one should use f2py_len(x) instead of len(x)
• A new construct character(f2py_len=...) is introduced to support returning assumed length character strings (e.g. character(len=*)) from wrapper functions

A hook to support rewriting f2py internal data structures after reading all its input files is introduced. This is required, for instance, for BC of SciPy support where character arguments are treated as character strings arguments in C expressions.

(gh-19388)

IBM zSystems Vector Extension Facility (SIMD)

Added support for SIMD extensions of zSystem (z13, z14, z15), through the universal intrinsics interface. This support leads to performance improvements for all SIMD kernels implemented using the universal intrinsics, including the following operations: rint, floor, trunc, ceil, sqrt, absolute, square, reciprocal, tanh, sin, cos, equal, not_equal, greater, greater_equal, less, less_equal, maximum, minimum, fmax, fmin, argmax, argmin, add, subtract, multiply, divide.

(gh-20913)

NumPy now gives floating point errors in casts

In most cases, NumPy previously did not give floating point warnings or errors when these happened during casts. For examples, casts like:

np.array([2e300]).astype(np.float32)  # overflow for float32
np.array([np.inf]).astype(np.int64)

Should now generally give floating point warnings. These warnings should warn that floating point overflow occurred. For errors when converting floating point values to integers users should expect invalid value warnings.

Users can modify the behavior of these warnings using np.errstate.

Note that for float to int casts, the exact warnings that are given may be platform dependent. For example:

arr = np.full(100, value=1000, dtype=np.float64)
arr.astype(np.int8)

May give a result equivalent to (the intermediate cast means no warning is given):

arr.astype(np.int64).astype(np.int8)

May return an undefined result, with a warning set:

RuntimeWarning: invalid value encountered in cast

The precise behavior is subject to the C99 standard and its implementation in both software and hardware.

(gh-21437)

F2PY supports the value attribute

The Fortran standard requires that variables declared with the value attribute must be passed by value instead of reference. F2PY now supports this use pattern correctly. So integer, intent(in), value :: x in Fortran codes will have correct wrappers generated.

(gh-21807)

Added pickle support for third-party BitGenerators

The pickle format for bit generators was extended to allow each bit generator to supply its own constructor when during pickling. Previous versions of NumPy only supported unpickling Generator instances created with one of the core set of bit generators supplied with NumPy. Attempting to unpickle a Generator that used a third-party bit generators would fail since the constructor used during the unpickling was only aware of the bit generators included in NumPy.

(gh-22014)

arange() now explicitly fails with dtype=str

Previously, the np.arange(n, dtype=str) function worked for n=1 and n=2, but would raise a non-specific exception message for other values of n. Now, it raises a [TypeError]{.title-ref} informing that arange does not support string dtypes:

>>> np.arange(2, dtype=str)
Traceback (most recent call last)
   ...
TypeError: arange() not supported for inputs with DType <class 'numpy.dtype[str_]'>.

(gh-22055)

numpy.typing protocols are now runtime checkable

The protocols used in numpy.typing.ArrayLike and numpy.typing.DTypeLike are now properly marked as runtime checkable, making them easier to use for runtime type checkers.

(gh-22357)

Performance improvements and changes

Faster version of np.isin and np.in1d for integer arrays

np.in1d (used by np.isin) can now switch to a faster algorithm (up to >10x faster) when it is passed two integer arrays. This is often automatically used, but you can use kind="sort" or kind="table" to force the old or new method, respectively.

(gh-12065)

Faster comparison operators

The comparison functions (numpy.equal, numpy.not_equal, numpy.less, numpy.less_equal, numpy.greater and numpy.greater_equal) are now much faster as they are now vectorized with universal intrinsics. For a CPU with SIMD extension AVX512BW, the performance gain is up to 2.57x, 1.65x and 19.15x for integer, float and boolean data types, respectively (with N=50000).

(gh-21483)

Changes

Better reporting of integer division overflow

Integer division overflow of scalars and arrays used to provide a RuntimeWarning and the return value was undefined leading to crashes at rare occasions:

>>> np.array([np.iinfo(np.int32).min]*10, dtype=np.int32) // np.int32(-1)
<stdin>:1: RuntimeWarning: divide by zero encountered in floor_divide
array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0], dtype=int32)

Integer division overflow now returns the input dtype's minimum value and raise the following RuntimeWarning:

>>> np.array([np.iinfo(np.int32).min]*10, dtype=np.int32) // np.int32(-1)
<stdin>:1: RuntimeWarning: overflow encountered in floor_divide
array([-2147483648, -2147483648, -2147483648, -2147483648, -2147483648,
       -2147483648, -2147483648, -2147483648, -2147483648, -2147483648],
      dtype=int32)

(gh-21506)

masked_invalid now modifies the mask in-place

When used with copy=False, numpy.ma.masked_invalid now modifies the input masked array in-place. This makes it behave identically to masked_where and better matches the documentation.

(gh-22046)

nditer/NpyIter allows all allocating all operands

The NumPy iterator available through np.nditer in Python and as NpyIter in C now supports allocating all arrays. The iterator shape defaults to () in this case. The operands dtype must be provided, since a "common dtype" cannot be inferred from the other inputs.

(gh-22457)

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MD5

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NumPy 1.24 Release Notes

The NumPy 1.24.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase the execution speed, and clarify the documentation. There are also a large number of new and expired deprecations due to changes in promotion and cleanups. This might be called a deprecation release. Highlights are

• Many new deprecations, check them out.
• Many expired deprecations,
• New F2PY features and fixes.
• New "dtype" and "casting" keywords for stacking functions.

See below for the details,

Deprecations

Deprecate fastCopyAndTranspose and PyArray_CopyAndTranspose

The numpy.fastCopyAndTranspose function has been deprecated. Use the corresponding copy and transpose methods directly:

arr.T.copy()

The underlying C function PyArray_CopyAndTranspose has also been deprecated from the NumPy C-API.

(gh-22313)

Conversion of out-of-bound Python integers

Attempting a conversion from a Python integer to a NumPy value will now always check whether the result can be represented by NumPy. This means the following examples will fail in the future and give a DeprecationWarning now:

np.uint8(-1)
np.array([3000], dtype=np.int8)

Many of these did succeed before. Such code was mainly useful for unsigned integers with negative values such as np.uint8(-1) giving np.iinfo(np.uint8).max.

Note that conversion between NumPy integers is unaffected, so that np.array(-1).astype(np.uint8) continues to work and use C integer overflow logic.

(gh-22393)

Deprecate msort

The numpy.msort function is deprecated. Use np.sort(a, axis=0) instead.

(gh-22456)

np.str0 and similar are now deprecated

The scalar type aliases ending in a 0 bit size: np.object0, np.str0, np.bytes0, np.void0, np.int0, np.uint0 as well as np.bool8 are now deprecated and will eventually be removed.

(gh-22607)

Expired deprecations

• The normed keyword argument has been removed from [np.histogram]{.title-ref}, [np.histogram2d]{.title-ref}, and [np.histogramdd]{.title-ref}. Use density instead. If normed was passed by position, density is now used.

  (gh-21645)

• Ragged array creation will now always raise a ValueError unless dtype=object is passed. This includes very deeply nested sequences.

  (gh-22004)

• Support for Visual Studio 2015 and earlier has been removed.

• Support for the Windows Interix POSIX interop layer has been removed.

  (gh-22139)

• Support for cygwin < 3.3 has been removed.

  (gh-22159)

• The mini() method of np.ma.MaskedArray has been removed. Use either np.ma.MaskedArray.min() or np.ma.minimum.reduce().

• The single-argument form of np.ma.minimum and np.ma.maximum has been removed. Use np.ma.minimum.reduce() or np.ma.maximum.reduce() instead.

  (gh-22228)

• Passing dtype instances other than the canonical (mainly native byte-order) ones to dtype= or signature= in ufuncs will now raise a TypeError. We recommend passing the strings "int8" or scalar types np.int8 since the byte-order, datetime/timedelta unit, etc. are never enforced. (Initially deprecated in NumPy 1.21.)

  (gh-22540)

• The dtype= argument to comparison ufuncs is now applied correctly. That means that only bool and object are valid values and dtype=object is enforced.

  (gh-22541)

• The deprecation for the aliases np.object, np.bool, np.float, np.complex, np.str, and np.int is expired (introduces NumPy 1.20). Some of these will now give a FutureWarning in addition to raising an error since they will be mapped to the NumPy scalars in the future.

  (gh-22607)

Compatibility notes

array.fill(scalar) may behave slightly different

numpy.ndarray.fill may in some cases behave slightly different now due to the fact that the logic is aligned with item assignment:

arr = np.array([1])  # with any dtype/value
arr.fill(scalar)
# is now identical to:
arr[0] = scalar

Previously casting may have produced slightly different answers when using values that could not be represented in the target dtype or when the target had object dtype.

(gh-20924)

Subarray to object cast now copies

Casting a dtype that includes a subarray to an object will now ensure a copy of the subarray. Previously an unsafe view was returned:

arr = np.ones(3, dtype=[("f", "i", 3)])
subarray_fields = arr.astype(object)[0]
subarray = subarray_fields[0]  # "f" field

np.may_share_memory(subarray, arr)

Is now always false. While previously it was true for the specific cast.

(gh-21925)

Returned arrays respect uniqueness of dtype kwarg objects

When the dtype keyword argument is used with :pynp.array(){.interpreted-text role="func"} or :pyasarray(){.interpreted-text role="func"}, the dtype of the returned array now always exactly matches the dtype provided by the caller.

In some cases this change means that a view rather than the input array is returned. The following is an example for this on 64bit Linux where long and longlong are the same precision but different dtypes:

>>> arr = np.array([1, 2, 3], dtype="long")
>>> new_dtype = np.dtype("longlong")
>>> new = np.asarray(arr, dtype=new_dtype)
>>> new.dtype is new_dtype
True
>>> new is arr
False

Before the change, the dtype did not match because new is arr was True.

(gh-21995)

DLPack export raises BufferError

When an array buffer cannot be exported via DLPack a BufferError is now always raised where previously TypeError or RuntimeError was raised. This allows falling back to the buffer protocol or __array_interface__ when DLPack was tried first.

(gh-22542)

NumPy builds are no longer tested on GCC-6

Ubuntu 18.04 is deprecated for GitHub actions and GCC-6 is not available on Ubuntu 20.04, so builds using that compiler are no longer tested. We still test builds using GCC-7 and GCC-8.

(gh-22598)

New Features

New attribute symbol added to polynomial classes

The polynomial classes in the numpy.polynomial package have a new symbol attribute which is used to represent the indeterminate of the polynomial. This can be used to change the value of the variable when printing:

>>> P_y = np.polynomial.Polynomial([1, 0, -1], symbol="y")
>>> print(P_y)
1.0 + 0.0·y¹ - 1.0·y²

Note that the polynomial classes only support 1D polynomials, so operations that involve polynomials with different symbols are disallowed when the result would be multivariate:

>>> P = np.polynomial.Polynomial([1, -1])  # default symbol is "x"
>>> P_z = np.polynomial.Polynomial([1, 1], symbol="z")
>>> P * P_z
Traceback (most recent call last)
   ...
ValueError: Polynomial symbols differ

The symbol can be any valid Python identifier. The default is symbol=x, consistent with existing behavior.

(gh-16154)

F2PY support for Fortran character strings

F2PY now supports wrapping Fortran functions with:

• character (e.g. character x)
• character array (e.g. character, dimension(n) :: x)
• character string (e.g. character(len=10) x)
• and character string array (e.g. character(len=10), dimension(n, m) :: x)

arguments, including passing Python unicode strings as Fortran character string arguments.

(gh-19388)

New function np.show_runtime

A new function numpy.show_runtime has been added to display the runtime information of the machine in addition to numpy.show_config which displays the build-related information.

(gh-21468)

strict option for testing.assert_array_equal

The strict option is now available for testing.assert_array_equal. Setting strict=True will disable the broadcasting behaviour for scalars and ensure that input arrays have the same data type.

(gh-21595)

New parameter equal_nan added to np.unique

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

(gh-21623)

casting and dtype keyword arguments for numpy.stack

The casting and dtype keyword arguments are now available for numpy.stack. To use them, write np.stack(..., dtype=None, casting='same_kind').

casting and dtype keyword arguments for numpy.vstack

The casting and dtype keyword arguments are now available for numpy.vstack. To use them, write np.vstack(..., dtype=None, casting='same_kind').

casting and dtype keyword arguments for numpy.hstack

The casting and dtype keyword arguments are now available for numpy.hstack. To use them, write np.hstack(..., dtype=None, casting='same_kind').

(gh-21627)

The bit generator underlying the singleton RandomState can be changed

The singleton RandomState instance exposed in the numpy.random module is initialized at startup with the MT19937 bit generator. The new function set_bit_generator allows the default bit generator to be replaced with a user-provided bit generator. This function has been introduced to provide a method allowing seamless integration of a high-quality, modern bit generator in new code with existing code that makes use of the singleton-provided random variate generating functions. The companion function get_bit_generator returns the current bit generator being used by the singleton RandomState. This is provided to simplify restoring the original source of randomness if required.

The preferred method to generate reproducible random numbers is to use a modern bit generator in an instance of Generator. The function default_rng simplifies instantiation:

>>> rg = np.random.default_rng(3728973198)
>>> rg.random()

The same bit generator can then be shared with the singleton instance so that calling functions in the random module will use the same bit generator:

>>> orig_bit_gen = np.random.get_bit_generator()
>>> np.random.set_bit_generator(rg.bit_generator)
>>> np.random.normal()

The swap is permanent (until reversed) and so any call to functions in the random module will use the new bit generator. The original can be restored if required for code to run correctly:

>>> np.random.set_bit_generator(orig_bit_gen)

(gh-21976)

np.void now has a dtype argument

NumPy now allows constructing structured void scalars directly by passing the dtype argument to np.void.

(gh-22316)

Improvements

F2PY Improvements

• The generated extension modules don't use the deprecated NumPy-C API anymore
• Improved f2py generated exception messages
• Numerous bug and flake8 warning fixes
• various CPP macros that one can use within C-expressions of signature files are prefixed with f2py_. For example, one should use f2py_len(x) instead of len(x)
• A new construct character(f2py_len=...) is introduced to support returning assumed length character strings (e.g. character(len=*)) from wrapper functions

A hook to support rewriting f2py internal data structures after reading all its input files is introduced. This is required, for instance, for BC of SciPy support where character arguments are treated as character strings arguments in C expressions.

(gh-19388)

IBM zSystems Vector Extension Facility (SIMD)

Added support for SIMD extensions of zSystem (z13, z14, z15), through the universal intrinsics interface. This support leads to performance improvements for all SIMD kernels implemented using the universal intrinsics, including the following operations: rint, floor, trunc, ceil, sqrt, absolute, square, reciprocal, tanh, sin, cos, equal, not_equal, greater, greater_equal, less, less_equal, maximum, minimum, fmax, fmin, argmax, argmin, add, subtract, multiply, divide.

(gh-20913)

NumPy now gives floating point errors in casts

In most cases, NumPy previously did not give floating point warnings or errors when these happened during casts. For examples, casts like:

np.array([2e300]).astype(np.float32)  # overflow for float32
np.array([np.inf]).astype(np.int64)

Should now generally give floating point warnings. These warnings should warn that floating point overflow occurred. For errors when converting floating point values to integers users should expect invalid value warnings.

Users can modify the behavior of these warnings using np.errstate.

Note that for float to int casts, the exact warnings that are given may be platform dependent. For example:

arr = np.full(100, value=1000, dtype=np.float64)
arr.astype(np.int8)

May give a result equivalent to (the intermediate cast means no warning is given):

arr.astype(np.int64).astype(np.int8)

May return an undefined result, with a warning set:

RuntimeWarning: invalid value encountered in cast

The precise behavior is subject to the C99 standard and its implementation in both software and hardware.

(gh-21437)

F2PY supports the value attribute

The Fortran standard requires that variables declared with the value attribute must be passed by value instead of reference. F2PY now supports this use pattern correctly. So integer, intent(in), value :: x in Fortran codes will have correct wrappers generated.

(gh-21807)

Added pickle support for third-party BitGenerators

The pickle format for bit generators was extended to allow each bit generator to supply its own constructor when during pickling. Previous versions of NumPy only supported unpickling Generator instances created with one of the core set of bit generators supplied with NumPy. Attempting to unpickle a Generator that used a third-party bit generators would fail since the constructor used during the unpickling was only aware of the bit generators included in NumPy.

(gh-22014)

arange() now explicitly fails with dtype=str

Previously, the np.arange(n, dtype=str) function worked for n=1 and n=2, but would raise a non-specific exception message for other values of n. Now, it raises a [TypeError]{.title-ref} informing that arange does not support string dtypes:

>>> np.arange(2, dtype=str)
Traceback (most recent call last)
   ...
TypeError: arange() not supported for inputs with DType <class 'numpy.dtype[str_]'>.

(gh-22055)

numpy.typing protocols are now runtime checkable

The protocols used in numpy.typing.ArrayLike and numpy.typing.DTypeLike are now properly marked as runtime checkable, making them easier to use for runtime type checkers.

(gh-22357)

Performance improvements and changes

Faster version of np.isin and np.in1d for integer arrays

np.in1d (used by np.isin) can now switch to a faster algorithm (up to >10x faster) when it is passed two integer arrays. This is often automatically used, but you can use kind="sort" or kind="table" to force the old or new method, respectively.

(gh-12065)

Faster comparison operators

The comparison functions (numpy.equal, numpy.not_equal, numpy.less, numpy.less_equal, numpy.greater and numpy.greater_equal) are now much faster as they are now vectorized with universal intrinsics. For a CPU with SIMD extension AVX512BW, the performance gain is up to 2.57x, 1.65x and 19.15x for integer, float and boolean data types, respectively (with N=50000).

(gh-21483)

Changes

Better reporting of integer division overflow

Integer division overflow of scalars and arrays used to provide a RuntimeWarning and the return value was undefined leading to crashes at rare occasions:

>>> np.array([np.iinfo(np.int32).min]*10, dtype=np.int32) // np.int32(-1)
<stdin>:1: RuntimeWarning: divide by zero encountered in floor_divide
array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0], dtype=int32)

Integer division overflow now returns the input dtype's minimum value and raise the following RuntimeWarning:

>>> np.array([np.iinfo(np.int32).min]*10, dtype=np.int32) // np.int32(-1)
<stdin>:1: RuntimeWarning: overflow encountered in floor_divide
array([-2147483648, -2147483648, -2147483648, -2147483648, -2147483648,
       -2147483648, -2147483648, -2147483648, -2147483648, -2147483648],
      dtype=int32)

(gh-21506)

masked_invalid now modifies the mask in-place

When used with copy=False, numpy.ma.masked_invalid now modifies the input masked array in-place. This makes it behave identically to masked_where and better matches the documentation.

(gh-22046)

nditer/NpyIter allows all allocating all operands

The NumPy iterator available through np.nditer in Python and as NpyIter in C now supports allocating all arrays. The iterator shape defaults to () in this case. The operands dtype must be provided, since a "common dtype" cannot be inferred from the other inputs.

(gh-22457)

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SHA256

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NumPy 1.23.5 Release Notes

NumPy 1.23.5 is a maintenance release that fixes bugs discovered after the 1.23.4 release and keeps the build infrastructure current. The Python versions supported for this release are 3.8-3.11.

Contributors

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

• @DWesl
• Aayush Agrawal +
• Adam Knapp +
• Charles Harris
• Navpreet Singh +
• Sebastian Berg
• Tania Allard

Pull requests merged

A total of 10 pull requests were merged for this release.

• #22489: TST, MAINT: Replace most setup with setup_method (also teardown)
• #22490: MAINT, CI: Switch to cygwin/cygwin-install-action@v2
• #22494: TST: Make test_partial_iteration_cleanup robust but require leak...
• #22592: MAINT: Ensure graceful handling of large header sizes
• #22593: TYP: Spelling alignment for array flag literal
• #22594: BUG: Fix bounds checking for random.logseries
• #22595: DEV: Update GH actions and Dockerfile for Gitpod
• #22596: CI: Only fetch in actions/checkout
• #22597: BUG: Decrement ref count in gentype_reduce if allocated memory...
• #22625: BUG: Histogramdd breaks on big arrays in Windows

Checksums

MD5

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SHA256

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