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numpy

2.5.31.1B downloads/monumpy/numpy ↗New releases in your daily digest, changelog included.

Fundamental package for array computing in Python

Last release 27 days ago
06 Sep 2026
Ships fairly regularly
a new release about every 3 weeks
Most releases are documented
notes for 52 of the last 60 stable releases
1 version withdrawn
withdrawn after publishing
20 years old
151 releases · first in 2006

Release timeline

151 releases since 2006

20062008201020122014

Releases

2.0.016 Jun 2024Breaking
This major release includes breaking changes that could not happen in a regular minor (feature) release - including an ABI break, changes to type prom…
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.StringDType and a new numpy.strings namespace with performant ufuncs for string operations,

Support for float32 and longdouble in all numpy.fft functions,

Support for the array API standard in the main numpy namespace.

Performance improvements:

Sorting functions sort, argsort, partition, argpartition have 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.char fixed-length string operations have been accelerated by implementing ufuncs that also support numpy.dtypes.StringDType in 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.save now 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.

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.lib by ~80%.

Canonical dtype names and a new numpy.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_ImportNumPyAPI and PyUFunc_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 int64 rather than int32, 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 ruff mode 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 ImportError with 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.seterrobj and the related ufunc keyword argument extobj= have been removed. The preferred replacement for all of these is using the context manager with np.errstate():.

(gh-23922)

np.cast has been removed. The literal replacement for np.cast[dtype](arg) is np.asarray(arg, dtype=dtype).

np.source has been removed. The preferred replacement is inspect.getsource.

np.lookfor has been removed.

(gh-24144)

numpy.who has 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.oldnumeric and np.set_numeric_ops.

(gh-24316)

Replaced from ... import * in the numpy/__init__.py with 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_docstring and np.add_newdoc_ufunc.

(gh-24357)

Alias np.float_ has been removed. Use np.float64 instead.

Alias np.complex_ has been removed. Use np.complex128 instead.

Alias np.longfloat has been removed. Use np.longdouble instead.

Alias np.singlecomplex has been removed. Use np.complex64 instead.

Alias np.cfloat has been removed. Use np.complex128 instead.

Alias np.longcomplex has been removed. Use np.clongdouble instead.

Alias np.clongfloat has been removed. Use np.clongdouble instead.

Alias np.string_ has been removed. Use np.bytes_ instead.

Alias np.unicode_ has been removed. Use np.str_ instead.

Alias np.Inf has been removed. Use np.inf instead.

Alias np.Infinity has been removed. Use np.inf instead.

Alias np.NaN has been removed. Use np.nan instead.

Alias np.infty has been removed. Use np.inf instead.

Alias np.mat has been removed. Use np.asmatrix instead.

np.issubclass_ has been removed. Use the issubclass builtin instead.

np.asfarray has been removed. Use np.asarray with a proper dtype instead.

np.set_string_function has been removed. Use np.set_printoptions instead with a formatter for custom printing of NumPy objects.

np.tracemalloc_domain is now only available from np.lib.

np.recfromcsv and recfromtxt are now only available from np.lib.npyio.

np.issctype, np.maximum_sctype, np.obj2sctype, np.sctype2char, np.sctypes, np.issubsctype were all removed from the main namespace without replacement, as they where niche members.

Deprecated np.deprecate and np.deprecate_with_doc has been removed from the main namespace. Use DeprecationWarning instead.

Deprecated np.safe_eval has been removed from the main namespace. Use ast.literal_eval instead.

(gh-24376)

np.find_common_type has been removed. Use numpy.promote_types or numpy.result_type instead. To achieve semantics for the scalar_types argument, use numpy.result_type and pass 0, 0.0, or 0j as a Python scalar instead.

np.round_ has been removed. Use np.round instead.

np.nbytes has been removed. Use np.dtype(<dtype>).itemsize instead.

(gh-24477)

np.compare_chararrays has been removed from the main namespace. Use np.char.compare_chararrays instead.

The charrarray in the main namespace has been deprecated. It can be imported without a deprecation warning from np.char.chararray for now, but we are planning to fully deprecate and remove chararray in the future.

np.format_parser has been removed from the main namespace. Use np.rec.format_parser instead.

(gh-24587)

Support for seven data type string aliases has been removed from np.dtype: int0, uint0, void0, object0, str0, bytes0 and bool8.

(gh-24807)

The experimental numpy.array_api submodule has been removed. Use the main numpy namespace for regular usage instead, or the separate array-api-strict package for the compliance testing use case for which numpy.array_api was 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.compat has been deprecated, as Python 2 is no longer supported.

numpy.int8 and 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_eval has been deprecated. ast.literal_eval should be used instead.

(gh-23830)

np.recfromcsv, np.recfromtxt, np.disp, np.get_array_wrap, np.maximum_sctype, np.deprecate and np.deprecate_with_doc have been deprecated.

(gh-24154)

np.trapz has been deprecated. Use np.trapezoid or a scipy.integrate function instead.

np.in1d has been deprecated. Use np.isin instead.

Alias np.row_stack has been deprecated. Use np.vstack directly.

(gh-24445)

__array_wrap__ is now passed arr, context, return_scalar and 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.cross have been deprecated. Use arrays of 3-dimensional vectors instead.

(gh-24818)

np.dtype("a") alias for np.dtype(np.bytes_) was deprecated. Use np.dtype("S") alias instead.

(gh-24854)

Use of keyword arguments x and y with functions assert_array_equal and assert_array_almost_equal has 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_tests submodule has been removed from the public API. (Deprecated in NumPy 1.15)

(gh-23809)

The PyDataMem_SetEventHook deprecation has expired and it is removed. Use tracemalloc and the np.lib.tracemalloc_domain domain. (Deprecated in NumPy 1.23)

(gh-23921)

The deprecation of set_numeric_ops and the C functions PyArray_SetNumericOps and PyArray_GetNumericOps has been expired and the functions removed. (Deprecated in NumPy 1.16)

(gh-23998)

The fasttake, fastclip, and fastputmask ArrFuncs deprecation is now finalized.

The deprecated function fastCopyAndTranspose and its C counterpart are now removed.

The deprecation of PyArray_ScalarFromObject is now finalized.

(gh-24312)

np.msort has 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 .data attribute of an ndarray is disallowed and will raise.

np.binary_repr(a, width) will raise if width is too small.

Using NPY_CHAR in PyArray_DescrFromType() will raise, use NPY_STRING NPY_UNICODE, or NPY_VSTRING instead.

(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

f2py will no longer accept ambiguous -m and .pyf CLI combinations. When more than one .pyf file is passed, an error is raised. When both -m and a .pyf is passed, a warning is emitted and the -m provided 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 thin subprocess.run wrapper. It was also one of the test bottlenecks. See gh-25122 for the full rationale. It also used several np.distutils features which are too fragile to be ported to work with meson.

Users are urged to replace calls to f2py.compile with calls to subprocess.run("python", "-m", "numpy.f2py",... instead, and to use environment variables to interact with meson. 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_CNE macros have been removed.

PyArray_MIN and PyArray_MAX have been moved from ndarraytypes.h to npy_math.h.

(gh-24258)

A C API for working with numpy.dtypes.StringDType arrays 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_NTYPES has been renamed to NPY_NTYPES_LEGACY as 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_Pack to set an individual memory address. Unlike PyArray_SETITEM this function is equivalent to setting an individual array item and does not require a NumPy array input.

(gh-25954)

The ->f slot has been removed from PyArray_Descr. If you use this slot, replace accessing it with PyDataType_GetArrFuncs (see its documentation and the numpy-2-migration-guide). In some cases using other functions like PyArray_GETITEM may be an alternatives.

PyArray_GETITEM and PyArray_SETITEM now require the import of the NumPy API table to be used and are no longer defined in ndarraytypes.h.

(gh-25812)

Due to runtime dependencies, the definition for functionality accessing the dtype flags was moved from numpy/ndarraytypes.h and is only available after including numpy/ndarrayobject.h as it requires import_array(). This includes PyDataType_FLAGCHK, PyDataType_REFCHK and NPY_BEGIN_THREADS_DESCR.

The dtype flags on PyArray_Descr must now be accessed through the PyDataType_FLAGS inline function to be compatible with both 1.x and 2.x. This function is defined in npy_2_compat.h to allow backporting. Most or all users should use PyDataType_FLAGCHK which is available on 1.x and does not require backporting. Cython users should use Cython 3. Otherwise access will go through Python unless they use PyDataType_FLAGCHK instead.

(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 return statement.

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.h and the corresponding macros like NPY_SIGINT_ON have been removed. We recommend querying PyErr_CheckSignals() or PyOS_InterruptOccurred() periodically (these do currently require holding the GIL though).

The noprefix.h header has been removed. Replace missing symbols with their prefixed counterparts (usually an added NPY_ or npy_).

(gh-23919)

PyUFunc_GetPyVals, PyUFunc_handlefperr, and PyUFunc_checkfperr have 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 made with np.errstate() fixes much more difficult).

(gh-23922)

The numpy/old_defines.h which was part of the API deprecated since NumPy 1.7 has been removed. This removes macros of the form PyArray_CONSTANT. The replace_old_macros.sed script may be useful to convert them to the NPY_CONSTANT version.

(gh-24011)

The legacy_inner_loop_selector member 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_INTPLTR has been removed to avoid confusion (see intp redefinition).

(gh-24888)

The advanced indexing MapIter and 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 up ufunc.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_ELSIZE macro has been removed, as it only ever reflected builtin numeric types and served no internal purpose.

(gh-25149)

PyArray_REFCNT and NPY_REFCOUNT are removed. Use Py_REFCNT instead.

(gh-25156)

PyArrayFlags_Type and PyArray_NewFlagsObject as well as PyArrayFlagsObject are private now. There is no known use-case; use the Python API if needed.

PyArray_MoveInto, PyArray_CastTo, PyArray_CastAnyTo are removed use PyArray_CopyInto and if absolutely needed PyArray_CopyAnyInto (the latter does a flat copy).

PyArray_FillObjectArray is removed, its only true use was for implementing np.empty. Create a new empty array or use PyArray_FillWithScalar() (decrefs existing objects).

PyArray_CompareUCS4 and PyArray_CompareString are removed. Use the standard C string comparison functions.

PyArray_ISPYTHON is removed as it is misleading, has no known use-cases, and is easy to replace.

PyArray_FieldNames is removed, as it is unclear what it would be useful for. It also has incorrect semantics in some possible use-cases.

PyArray_TypestrConvert is 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_GetDatetimeMetaData is removed, it did not actually do anything since at least NumPy 1.7.

(gh-25802)

PyArray_GetCastFunc is 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 as PyArray_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 for numpy.vecdot.

numpy.linalg.matrix_transpose - An alias for numpy.matrix_transpose.

(gh-25155)

numpy.linalg.outer has been added. It computes the outer product of two vectors. It differs from numpy.outer by accepting one-dimensional arrays only. This function is compatible with the array API standard.

(gh-25101)

numpy.linalg.cross has been added. It computes the cross product of two (arrays of) 3-dimensional vectors. It differs from numpy.cross by 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:

rtol was added to numpy.linalg.matrix_rank.

stable was added to numpy.sort and numpy.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 PR 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.real or c.imag) is no longer an option. You can now use utilities provided in numpy/npy_math.h to 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-numbers for more info.

numpy/npy_common.h now includes complex.h, which means that complex is 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.errstate can only be entered once now.

np.setbufsize is now tied to np.errstate(): leaving an np.errstate context will also reset the bufsize.

(gh-23936)

A new public np.lib.array_utils submodule has been introduced and it currently contains three functions: byte_bounds (moved from np.lib.utils), normalize_axis_tuple and normalize_axis_index.

(gh-24540)

Introduce numpy.bool as the new canonical name for NumPy's boolean dtype, and make numpy.bool\_ an alias to it. Note that until NumPy 1.24, np.bool was an alias to Python's builtin bool. The new name helps with array API standard compatibility and is a more intuitive name.

(gh-25080)

The dtype.flags value 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 considered True since it is not empty. If you need the old behavior, you may use the above step (casting to integer first) or string_array == "0" (if the input is only ever 0 or 1). To get the new result on old NumPy versions use string_array != "".

np.nonzero(string_array) previously ignored whitespace so that a string only containing whitespace was considered False. Whitespace is now considered True.

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 intp typed array anymore. The p and P character codes can still be used, however.

Creating intptr_t or uintptr_t typed arrays in C remains possible in a cross-platform way via PyArray_DescrFromType('p').

The new character codes nN were introduced.

It is now correct to use the Python C-API functions when parsing to npy_intp typed 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 a ValueError is 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.

(gh-25553, gh-25570)

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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SHA256

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1.26.45 Feb 2024

NumPy 1.26.4 is a maintenance release that fixes bugs and regressions discovered after the 1.26.3 release. The Python versions supported by this relea

NumPy 1.26.4 Release Notes

NumPy 1.26.4 is a maintenance release that fixes bugs and regressions discovered after the 1.26.3 release. The Python versions supported by this release are 3.9-3.12. This is the last planned release in the 1.26.x series.

Contributors

A total of 13 people contributed to this release. People with a "+" by their names contributed a patch for the first time.

Charles Harris
Elliott Sales de Andrade
Lucas Colley +
Mark Ryan +
Matti Picus
Nathan Goldbaum
Ola x Nilsson +
Pieter Eendebak
Ralf Gommers
Sayed Adel
Sebastian Berg
Stefan van der Walt
Stefano Rivera

Pull requests merged

A total of 19 pull requests were merged for this release.

#25323: BUG: Restore missing asstr import
#25523: MAINT: prepare 1.26.x for further development
#25539: BUG: numpy.array_api: fix linalg.cholesky upper decomp...
#25584: CI: Bump azure pipeline timeout to 120 minutes
#25585: MAINT, BLD: Fix unused inline functions warnings on clang
#25599: BLD: include fix for MinGW platform detection
#25618: TST: Fix test_numeric on riscv64
#25619: BLD: fix building for windows ARM64
#25620: MAINT: add newaxis to __all__ in numpy.array_api
#25630: BUG: Use large file fallocate on 32 bit linux platforms
#25643: TST: Fix test_warning_calls on Python 3.12
#25645: TST: Bump pytz to 2023.3.post1
#25658: BUG: Fix AVX512 build flags on Intel Classic Compiler
#25670: BLD: fix potential issue with escape sequences in __config__.py
#25718: CI: pin cygwin python to 3.9.16-1 and fix typing tests [skip...
#25720: MAINT: Bump cibuildwheel to v2.16.4
#25748: BLD: unvendor meson-python on 1.26.x and upgrade to meson-python...
#25755: MAINT: Include header defining backtrace
#25756: BUG: Fix np.quantile([Fraction(2,1)], 0.5) (#24711)

Checksums

MD5

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SHA256

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Open source →

1.26.32 Jan 2024

NumPy 1.26.3 is a maintenance release that fixes bugs and regressions discovered after the 1.26.2 release. The most notable changes are the f2py bug f

NumPy 1.26.3 Release Notes

NumPy 1.26.3 is a maintenance release that fixes bugs and regressions discovered after the 1.26.2 release. The most notable changes are the f2py bug fixes. The Python versions supported by this release are 3.9-3.12.

Compatibility

f2py will no longer accept ambiguous -m and .pyf CLI combinations. When more than one .pyf file is passed, an error is raised. When both -m and a .pyf is passed, a warning is emitted and the -m provided name is ignored.

Improvements

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.

Contributors

A total of 18 people contributed to this release. People with a "+" by their names contributed a patch for the first time.

@DWesl
@Illviljan
Alexander Grund
Andrea Bianchi +
Charles Harris
Daniel Vanzo
Johann Rohwer +
Matti Picus
Nathan Goldbaum
Peter Hawkins
Raghuveer Devulapalli
Ralf Gommers
Rohit Goswami
Sayed Adel
Sebastian Berg
Stefano Rivera +
Thomas A Caswell
matoro

Pull requests merged

A total of 42 pull requests were merged for this release.

#25130: MAINT: prepare 1.26.x for further development
#25188: TYP: add None to __getitem__ in numpy.array_api
#25189: BLD,BUG: quadmath required where available [f2py]
#25190: BUG: alpha doesn't use REAL(10)
#25191: BUG: Fix FP overflow error in division when the divisor is scalar
#25192: MAINT: Pin scipy-openblas version.
#25201: BUG: Fix f2py to enable use of string optional inout argument
#25202: BUG: Fix -fsanitize=alignment issue in numpy/_core/src/multiarray/arraytypes.c.src
#25203: TST: Explicitly pass NumPy path to cython during tests (also...
#25204: BUG: fix issues with newaxis and linalg.solve in numpy.array_api
#25205: BUG: Disallow shadowed modulenames
#25217: BUG: Handle common blocks with kind specifications from modules
#25218: BUG: Fix moving compiled executable to root with f2py -c on Windows
#25219: BUG: Fix single to half-precision conversion on PPC64/VSX3
#25227: TST: f2py: fix issue in test skip condition
#25240: Revert "MAINT: Pin scipy-openblas version."
#25249: MAINT: do not use long type
#25377: TST: PyPy needs another gc.collect on latest versions
#25378: CI: Install Lapack runtime on Cygwin.
#25379: MAINT: Bump conda-incubator/setup-miniconda from 2.2.0 to 3.0.1
#25380: BLD: update vendored Meson for AIX shared library fix
#25419: MAINT: Init base in cpu_avx512_kn
#25420: BUG: Fix failing test_features on SapphireRapids
#25422: BUG: Fix non-contiguous memory load when ARM/Neon is enabled
#25428: MAINT,BUG: Never import distutils above 3.12 [f2py]
#25452: MAINT: make the import-time check for old Accelerate more specific
#25458: BUG: fix macOS version checks for Accelerate support
#25465: MAINT: Bump actions/setup-node and larsoner/circleci-artifacts-redirector-action
#25466: BUG: avoid seg fault from OOB access in RandomState.set_state()
#25467: BUG: Fix two errors related to not checking for failed allocations
#25468: BUG: Fix regression with f2py wrappers when modules and subroutines...
#25475: BUG: Fix build issues on SPR
#25478: BLD: fix uninitialized variable warnings from simd/neon/memory.h
#25480: BUG: Handle iso_c_type mappings more consistently
#25481: BUG: Fix module name bug in signature files [urgent] [f2py]
#25482: BUG: Handle .pyf.src and fix SciPy [urgent]
#25483: DOC: f2py rewrite with meson details
#25485: BUG: Add external library handling for meson [f2py]
#25486: MAINT: Run f2py's meson backend with the same python that ran...
#25489: MAINT: Update numpy/f2py/_backends from main.
#25490: MAINT: Easy updates of f2py/*.py from main.
#25491: MAINT: Update crackfortran.py and f2py2e.py from main

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Open source →

1.26.212 Nov 2023

NumPy 1.26.2 is a maintenance release that fixes bugs and regressions discovered after the 1.26.1 release. The 1.26.release series is the last planned

NumPy 1.26.2 Release Notes

NumPy 1.26.2 is a maintenance release that fixes bugs and regressions discovered after the 1.26.1 release. The 1.26.release series is the last planned minor release series before NumPy 2.0. 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.

@stefan6419846
@thalassemia +
Andrew Nelson
Charles Bousseau +
Charles Harris
Marcel Bargull +
Mark Mentovai +
Matti Picus
Nathan Goldbaum
Ralf Gommers
Sayed Adel
Sebastian Berg
William Ayd +

Pull requests merged

A total of 25 pull requests were merged for this release.

#24814: MAINT: align test_dispatcher s390x targets with _umath_tests_mtargets
#24929: MAINT: prepare 1.26.x for further development
#24955: ENH: Add Cython enumeration for NPY_FR_GENERIC
#24962: REL: Remove Python upper version from the release branch
#24971: BLD: Use the correct Python interpreter when running tempita.py
#24972: MAINT: Remove unhelpful error replacements from import_array()
#24977: BLD: use classic linker on macOS, the new one in XCode 15 has...
#25003: BLD: musllinux_aarch64 [wheel build]
#25043: MAINT: Update mailmap
#25049: MAINT: Update meson build infrastructure.
#25071: MAINT: Split up .github/workflows to match main
#25083: BUG: Backport fix build on ppc64 when the baseline set to Power9...
#25093: BLD: Fix features.h detection for Meson builds [1.26.x Backport]
#25095: BUG: Avoid intp conversion regression in Cython 3 (backport)
#25107: CI: remove obsolete jobs, and move macOS and conda Azure jobs...
#25108: CI: Add linux_qemu action and remove travis testing.
#25112: MAINT: Update .spin/cmds.py from main.
#25113: DOC: Visually divide main license and bundled licenses in wheels
#25115: MAINT: Add missing noexcept to shuffle helpers
#25116: DOC: Fix license identifier for OpenBLAS
#25117: BLD: improve detection of Netlib libblas/libcblas/liblapack
#25118: MAINT: Make bitfield integers unsigned
#25119: BUG: Make n a long int for np.random.multinomial
#25120: BLD: change default of the allow-noblas option to true.
#25121: BUG: ensure passing np.dtype to itself doesn't crash

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Open source →

1.26.114 Oct 2023

NumPy 1.26.1 is a maintenance release that fixes bugs and regressions discovered after the 1.26.0 release. In addition, it adds new functionality for

NumPy 1.26.1 Release Notes

NumPy 1.26.1 is a maintenance release that fixes bugs and regressions discovered after the 1.26.0 release. In addition, it adds new functionality for detecting BLAS and LAPACK when building from source. Highlights are:

Improved detection of BLAS and LAPACK libraries for meson builds
Pickle compatibility with the upcoming NumPy 2.0.

The 1.26.release series is the last planned minor release series before NumPy 2.0. The Python versions supported by this release are 3.9-3.12.

Build system changes

Improved BLAS/LAPACK detection and control

Auto-detection for a number of BLAS and LAPACK is now implemented for Meson. By default, the build system will try to detect MKL, Accelerate (on macOS >=13.3), OpenBLAS, FlexiBLAS, BLIS and reference BLAS/LAPACK. Support for MKL was significantly improved, and support for FlexiBLAS was added.

New command-line flags are available to further control the selection of the BLAS and LAPACK libraries to build against.

To select a specific library, use the config-settings interface via pip or pypa/build. E.g., to select libblas/liblapack, use:

$ pip install numpy -Csetup-args=-Dblas=blas -Csetup-args=-Dlapack=lapack
$ # OR
$ python -m build . -Csetup-args=-Dblas=blas -Csetup-args=-Dlapack=lapack

This works not only for the libraries named above, but for any library that Meson is able to detect with the given name through pkg-config or CMake.

Besides -Dblas and -Dlapack, a number of other new flags are available to control BLAS/LAPACK selection and behavior:

-Dblas-order and -Dlapack-order: a list of library names to search for in order, overriding the default search order.
-Duse-ilp64: if set to true, use ILP64 (64-bit integer) BLAS and LAPACK. Note that with this release, ILP64 support has been extended to include MKL and FlexiBLAS. OpenBLAS and Accelerate were supported in previous releases.
-Dallow-noblas: if set to true, allow NumPy to build with its internal (very slow) fallback routines instead of linking against an external BLAS/LAPACK library. The default for this flag may be changed to ``true`` in a future 1.26.x release, however for 1.26.1 we'd prefer to keep it as ``false`` because if failures to detect an installed library are happening, we'd like a bug report for that, so we can quickly assess whether the new auto-detection machinery needs further improvements.
-Dmkl-threading: to select the threading layer for MKL. There are four options: seq, iomp, gomp and tbb. The default is auto, which selects from those four as appropriate given the version of MKL selected.
-Dblas-symbol-suffix: manually select the symbol suffix to use for the library - should only be needed for linking against libraries built in a non-standard way.

New features

`numpy._core` submodule stubs

numpy._core submodule stubs were added to provide compatibility with pickled arrays created using NumPy 2.0 when running Numpy 1.26.

Contributors

A total of 13 people contributed to this release. People with a "+" by their names contributed a patch for the first time.

Andrew Nelson
Anton Prosekin +
Charles Harris
Chongyun Lee +
Ivan A. Melnikov +
Jake Lishman +
Mahder Gebremedhin +
Mateusz Sokół
Matti Picus
Munira Alduraibi +
Ralf Gommers
Rohit Goswami
Sayed Adel

Pull requests merged

A total of 20 pull requests were merged for this release.

#24742: MAINT: Update cibuildwheel version
#24748: MAINT: fix version string in wheels built with setup.py
#24771: BLD, BUG: Fix build failure for host flags e.g. -march=native...
#24773: DOC: Updated the f2py docs to remove a note on -fimplicit-none
#24776: BUG: Fix SIMD f32 trunc test on s390x when baseline is none
#24785: BLD: add libquadmath to licences and other tweaks (#24753)
#24786: MAINT: Activate use-compute-credits for Cirrus.
#24803: BLD: updated vendored-meson/meson for mips64 fix
#24804: MAINT: fix licence path win
#24813: BUG: Fix order of Windows OS detection macros.
#24831: BUG, SIMD: use scalar cmul on bad Apple clang x86_64 (#24828)
#24840: BUG: Fix DATA statements for f2py
#24870: API: Add NumpyUnpickler for backporting
#24872: MAINT: Xfail test failing on PyPy.
#24879: BLD: fix math func feature checks, fix FreeBSD build, add CI...
#24899: ENH: meson: implement BLAS/LAPACK auto-detection and many CI...
#24902: DOC: add a 1.26.1 release notes section for BLAS/LAPACK build...
#24906: MAINT: Backport numpy._core stubs. Remove NumpyUnpickler
#24911: MAINT: Bump pypa/cibuildwheel from 2.16.1 to 2.16.2
#24912: BUG: loongarch doesn't use REAL(10)

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c8c6c72d4a9f831f328efb1312642a1cafafaa88981d9ab76368d50d07d93cbe  numpy-1.26.1.tar.gz

Open source →

1.26.016 Sept 2023

The NumPy 1.26.0 release is a continuation of the 1.25.x release cycle with the addition of Python 3.12.0 support. Python 3.12 dropped distutils, cons

NumPy 1.26.0 Release Notes

The NumPy 1.26.0 release is a continuation of the 1.25.x release cycle with the addition of Python 3.12.0 support. Python 3.12 dropped distutils, consequently supporting it required finding a replacement for the setup.py/distutils based build system NumPy was using. We have chosen to use the Meson build system instead, and this is the first NumPy release supporting it. This is also the first release that supports Cython 3.0 in addition to retaining 0.29.X compatibility. Supporting those two upgrades was a large project, over 100 files have been touched in this release. The changelog doesn't capture the full extent of the work, special thanks to Ralf Gommers, Sayed Adel, Stéfan van der Walt, and Matti Picus who did much of the work in the main development branch.

The highlights of this release are:

Python 3.12.0 support.
Cython 3.0.0 compatibility.
Use of the Meson build system
Updated SIMD support
f2py fixes, meson and bind(x) support
Support for the updated Accelerate BLAS/LAPACK library

The Python versions supported in this release are 3.9-3.12.

New Features

Array API v2022.12 support in `numpy.array_api`

numpy.array_api now full supports the v2022.12 version of the array API standard. Note that this does not yet include the optional fft extension in the standard.

(gh-23789)

Support for the updated Accelerate BLAS/LAPACK library

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, the 13.3+ version will automatically be used if available.

(gh-24053)

`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 on-wards. Older versions 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)

Improvements

`iso_c_binding` support 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)

Build system changes

In this release, NumPy has switched to Meson as the build system and meson-python as the build backend. Installing NumPy or building a wheel can be done with standard tools like pip and pypa/build. The following are supported:

Regular installs: pip install numpy or (in a cloned repo) pip install .
Building a wheel: python -m build (preferred), or pip wheel .
Editable installs: pip install -e . --no-build-isolation
Development builds through the custom CLI implemented with spin: spin build.

All the regular pip and pypa/build flags (e.g., --no-build-isolation) should work as expected.

NumPy-specific build customization

Many of the NumPy-specific ways of customizing builds have changed. The NPY_* environment variables which control BLAS/LAPACK, SIMD, threading, and other such options are no longer supported, nor is a site.cfg file to select BLAS and LAPACK. Instead, there are command-line flags that can be passed to the build via pip/build's config-settings interface. These flags are all listed in the meson_options.txt file in the root of the repo. Detailed documented will be available before the final 1.26.0 release; for now please see the SciPy "building from source" docs since most build customization works in an almost identical way in SciPy as it does in NumPy.

Build dependencies

While the runtime dependencies of NumPy have not changed, the build dependencies have. Because we temporarily vendor Meson and meson-python, there are several new dependencies - please see the [build-system] section of pyproject.toml for details.

Troubleshooting

This build system change is quite large. In case of unexpected issues, it is still possible to use a setup.py-based build as a temporary workaround (on Python 3.9-3.11, not 3.12), by copying pyproject.toml.setuppy to pyproject.toml. However, please open an issue with details on the NumPy issue tracker. We aim to phase out setup.py builds as soon as possible, and therefore would like to see all potential blockers surfaced early on in the 1.26.0 release cycle.

Contributors

A total of 20 people contributed to this release. People with a "+" by their names contributed a patch for the first time.

@DWesl
Albert Steppi +
Bas van Beek
Charles Harris
Developer-Ecosystem-Engineering
Filipe Laíns +
Jake Vanderplas
Liang Yan +
Marten van Kerkwijk
Matti Picus
Melissa Weber Mendonça
Namami Shanker
Nathan Goldbaum
Ralf Gommers
Rohit Goswami
Sayed Adel
Sebastian Berg
Stefan van der Walt
Tyler Reddy
Warren Weckesser

Pull requests merged

A total of 59 pull requests were merged for this release.

#24305: MAINT: Prepare 1.26.x branch for development
#24308: MAINT: Massive update of files from main for numpy 1.26
#24322: CI: fix wheel builds on the 1.26.x branch
#24326: BLD: update openblas to newer version
#24327: TYP: Trim down the _NestedSequence.__getitem__ signature
#24328: BUG: fix choose refcount leak
#24337: TST: fix running the test suite in builds without BLAS/LAPACK
#24338: BUG: random: Fix generation of nan by dirichlet.
#24340: MAINT: Dependabot updates from main
#24342: MAINT: Add back NPY_RUN_MYPY_IN_TESTSUITE=1
#24353: MAINT: Update extbuild.py from main.
#24356: TST: fix distutils tests for deprecations in recent setuptools...
#24375: MAINT: Update cibuildwheel to version 2.15.0
#24381: MAINT: Fix codespaces setup.sh script
#24403: ENH: Vendor meson for multi-target build support
#24404: BLD: vendor meson-python to make the Windows builds with SIMD...
#24405: BLD, SIMD: The meson CPU dispatcher implementation
#24406: MAINT: Remove versioneer
#24409: REL: Prepare for the NumPy 1.26.0b1 release.
#24453: MAINT: Pin upper version of sphinx.
#24455: ENH: Add prefix to _ALIGN Macro
#24456: BUG: cleanup warnings
#24460: MAINT: Upgrade to spin 0.5
#24495: BUG: asv dev has been removed, use asv run.
#24496: BUG: Fix meson build failure due to unchanged inplace auto-generated...
#24521: BUG: fix issue with git-version script, needs a shebang to run
#24522: BUG: Use a default assignment for git_hash
#24524: BUG: fix NPY_cast_info error handling in choose
#24526: BUG: Fix common block handling in f2py
#24541: CI,TYP: Bump mypy to 1.4.1
#24542: BUG: Fix assumed length f2py regression
#24544: MAINT: Harmonize fortranobject
#24545: TYP: add kind argument to numpy.isin type specification
#24561: BUG: fix comparisons between masked and unmasked structured arrays
#24590: CI: Exclude import libraries from list of DLLs on Cygwin.
#24591: BLD: fix _umath_linalg dependencies
#24594: MAINT: Stop testing on ppc64le.
#24602: BLD: meson-cpu: fix SIMD support on platforms with no features
#24606: BUG: Change Cython binding directive to "False".
#24613: ENH: Adopt new macOS Accelerate BLAS/LAPACK Interfaces, including...
#24614: DOC: Update building docs to use Meson
#24615: TYP: Add the missing casting keyword to np.clip
#24616: TST: convert cython test from setup.py to meson
#24617: MAINT: Fixup fromnumeric.pyi
#24622: BUG, ENH: Fix iso_c_binding type maps and fix bind(c)...
#24629: TYP: Allow binary_repr to accept any object implementing...
#24630: TYP: Explicitly declare dtype and generic hashable
#24637: ENH: Refactor the typing "reveal" tests using typing.assert_type
#24638: MAINT: Bump actions/checkout from 3.6.0 to 4.0.0
#24647: ENH: meson backend for f2py
#24648: MAINT: Refactor partial load Workaround for Clang
#24653: REL: Prepare for the NumPy 1.26.0rc1 release.
#24659: BLD: allow specifying the long double format to avoid the runtime...
#24665: BLD: fix bug in random.mtrand extension, don't link libnpyrandom
#24675: BLD: build wheels for 32-bit Python on Windows, using MSVC
#24700: BLD: fix issue with compiler selection during cross compilation
#24701: BUG: Fix data stmt handling for complex values in f2py
#24707: TYP: Add annotations for the py3.12 buffer protocol
#24718: DOC: fix a few doc build issues on 1.26.x and update spin docs...

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b44e6a09afc12952a7d2a58ca0a2429ee0d49a4f89d83a0a11052da696440e49  numpy-1.26.0-cp39-cp39-musllinux_1_1_x86_64.whl
5671338034b820c8d58c81ad1dafc0ed5a00771a82fccc71d6438df00302094b  numpy-1.26.0-cp39-cp39-win32.whl
020cdbee66ed46b671429c7265cf00d8ac91c046901c55684954c3958525dab2  numpy-1.26.0-cp39-cp39-win_amd64.whl
0792824ce2f7ea0c82ed2e4fecc29bb86bee0567a080dacaf2e0a01fe7654369  numpy-1.26.0-pp39-pypy39_pp73-macosx_10_9_x86_64.whl
7d484292eaeb3e84a51432a94f53578689ffdea3f90e10c8b203a99be5af57d8  numpy-1.26.0-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
186ba67fad3c60dbe8a3abff3b67a91351100f2661c8e2a80364ae6279720299  numpy-1.26.0-pp39-pypy39_pp73-win_amd64.whl
f93fc78fe8bf15afe2b8d6b6499f1c73953169fad1e9a8dd086cdff3190e7fdf  numpy-1.26.0.tar.gz

Open source →

1.25.231 Jul 2023

NumPy 1.25.2 is a maintenance release that fixes bugs and regressions discovered after the 1.25.1 release. This is the last planned release in the 1.2

NumPy 1.25.2 Release Notes

NumPy 1.25.2 is a maintenance release that fixes bugs and regressions discovered after the 1.25.1 release. This is the last planned release in the 1.25.x series, the next release will be 1.26.0, which will use the meson build system and support Python 3.12. The Python versions supported by this release are 3.9-3.11.

Contributors

A total of 13 people contributed to this release. People with a "+" by their names contributed a patch for the first time.

Aaron Meurer
Andrew Nelson
Charles Harris
Kevin Sheppard
Matti Picus
Nathan Goldbaum
Peter Hawkins
Ralf Gommers
Randy Eckenrode +
Sam James +
Sebastian Berg
Tyler Reddy
dependabot[bot]

Pull requests merged

A total of 19 pull requests were merged for this release.

#24148: MAINT: prepare 1.25.x for further development
#24174: ENH: Improve clang-cl compliance
#24179: MAINT: Upgrade various build dependencies.
#24182: BLD: use -ftrapping-math with Clang on macOS
#24183: BUG: properly handle negative indexes in ufunc_at fast path
#24184: BUG: PyObject_IsTrue and PyObject_Not error handling in setflags
#24185: BUG: histogram small range robust
#24186: MAINT: Update meson.build files from main branch
#24234: MAINT: exclude min, max and round from np.__all__
#24241: MAINT: Dependabot updates
#24242: BUG: Fix the signature for np.array_api.take
#24243: BLD: update OpenBLAS to an intermeidate commit
#24244: BUG: Fix reference count leak in str(scalar).
#24245: BUG: fix invalid function pointer conversion error
#24255: BUG: Factor out slow getenv call used for memory policy warning
#24292: CI: correct URL in cirrus.star
#24293: BUG: Fix C types in scalartypes
#24294: BUG: do not modify the input to ufunc_at
#24295: BUG: Further fixes to indexing loop and added tests

Checksums

MD5

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SHA256

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1b9735c27cea5d995496f46a8b1cd7b408b3f34b6d50459d9ac8fe3a20cc17bf  numpy-1.25.2-pp39-pypy39_pp73-win_amd64.whl
fd608e19c8d7c55021dffd43bfe5492fab8cc105cc8986f813f8c3c048b38760  numpy-1.25.2.tar.gz

Open source →

1.25.18 Jul 2023

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 relea

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

Checksums

MD5

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SHA256

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0d3fe3dd0506a28493d82dc3cf254be8cd0d26f4008a417385cbf1ae95b54004  numpy-1.25.1-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
012097b5b0d00a11070e8f2e261128c44157a8689f7dedcf35576e525893f4fe  numpy-1.25.1-pp39-pypy39_pp73-win_amd64.whl
9a3a9f3a61480cc086117b426a8bd86869c213fc4072e606f01c4e4b66eb92bf  numpy-1.25.1.tar.gz

Open source →

1.25.017 Jun 2023Deprecated

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:

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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5177310ac2e63d6603f659fadc1e7bab33dd5a8db4e0596df34214eeab0fee3b  numpy-1.25.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
0ac6edfb35d2a99aaf102b509c8e9319c499ebd4978df4971b94419a116d0790  numpy-1.25.0-cp39-cp39-musllinux_1_1_x86_64.whl
7412125b4f18aeddca2ecd7219ea2d2708f697943e6f624be41aa5f8a9852cc4  numpy-1.25.0-cp39-cp39-win32.whl
26815c6c8498dc49d81faa76d61078c4f9f0859ce7817919021b9eba72b425e3  numpy-1.25.0-cp39-cp39-win_amd64.whl
5b1b90860bf7d8a8c313b372d4f27343a54f415b20fb69dd601b7efe1029c91e  numpy-1.25.0-pp39-pypy39_pp73-macosx_10_9_x86_64.whl
85cdae87d8c136fd4da4dad1e48064d700f63e923d5af6c8c782ac0df8044542  numpy-1.25.0-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
cc3fda2b36482891db1060f00f881c77f9423eead4c3579629940a3e12095fe8  numpy-1.25.0-pp39-pypy39_pp73-win_amd64.whl
f1accae9a28dc3cda46a91de86acf69de0d1b5f4edd44a9b0c3ceb8036dfff19  numpy-1.25.0.tar.gz

Open source →

1.24.426 Jun 2023

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. T

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

Checksums

MD5

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3f3995540a17854a29dc79f8eeecd832  numpy-1.24.4.tar.gz

SHA256

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befe2bf740fd8373cf56149a5c23a0f601e82869598d41f8e188a0e9869926f8  numpy-1.24.4-cp39-cp39-win_amd64.whl
31f13e25b4e304632a4619d0e0777662c2ffea99fcae2029556b17d8ff958aef  numpy-1.24.4-pp38-pypy38_pp73-macosx_10_9_x86_64.whl
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80f5e3a4e498641401868df4208b74581206afbee7cf7b8329daae82676d9463  numpy-1.24.4.tar.gz

Open source →

1.24.322 Apr 2023

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 relea

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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352ee00c7f8387b44d19f4cada524586f07379c0d49270f87233983bc5087ca0  numpy-1.24.3-pp38-pypy38_pp73-macosx_10_9_x86_64.whl
1a7d6acc2e7524c9955e5c903160aa4ea083736fde7e91276b0e5d98e6332812  numpy-1.24.3-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
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ab344f1bf21f140adab8e47fdbc7c35a477dc01408791f8ba00d018dd0bc5155  numpy-1.24.3.tar.gz

Open source →

1.24.25 Feb 2023

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 relea

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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Open source →

1.24.126 Dec 2022

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 relea

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.

Checksums

MD5

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SHA256

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Open source →

1.24.018 Dec 2022Deprecated

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. Hi…

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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Open source →

1.23.520 Nov 2022

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 versi

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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Open source →

1.23.412 Oct 2022

NumPy 1.23.4 is a maintenance release that fixes bugs discovered after the 1.23.3 release and keeps the build infrastructure current. The main improve

NumPy 1.23.4 Release Notes

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

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

Contributors

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

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

Pull requests merged

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

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

Checksums

MD5

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SHA256

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ed2cc92af0efad20198638c69bb0fc2870a58dabfba6eb722c933b48556c686c  numpy-1.23.4.tar.gz

Open source →

1.23.39 Sept 2022

NumPy 1.23.3 is a maintenance release that fixes bugs discovered after the 1.23.2 release. There is no major theme for this release, the main improvem

NumPy 1.23.3 Release Notes

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

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

Contributors

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

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

Pull requests merged

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

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

Checksums

MD5

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SHA256

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Open source →

1.23.214 Aug 2022

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

NumPy 1.23.2 Release Notes

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

Typing changes needed for Python 3.11
Wheels for Python 3.11.0rc1

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

Contributors

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

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

Pull requests merged

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

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

Checksums

MD5

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SHA256

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b78d00e48261fbbd04aa0d7427cf78d18401ee0abd89c7559bbf422e5b1c7d01  numpy-1.23.2.tar.gz

Open source →

1.23.19 Jul 2022

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

NumPy 1.23.1 Release Notes

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

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

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

Contributors

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

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

Pull requests merged

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

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

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Open source →

1.23.022 Jun 2022Deprecated

…speed, clarify the documentation, and expire old deprecations. The highlights are:

NumPy 1.23.0 Release Notes

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

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

See below for the details,

New functions

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

(gh-20020)
numpy.from_dlpack has been added to allow easy exchange of data using the DLPack protocol. It accepts Python objects that implement the __dlpack__ and __dlpack_device__ methods and returns a ndarray object which is generally the view of the data of the input object.

(gh-21145)

Deprecations

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

(gh-20766)
Using axis=32 (axis=np.MAXDIMS) in many cases had the same meaning as axis=None. This is deprecated and axis=None must be used instead.

(gh-20920)
The hook function PyDataMem_SetEventHook has been deprecated and the demonstration of its use in tool/allocation_tracking has been removed. The ability to track allocations is now built-in to python via tracemalloc.

(gh-20394)
numpy.distutils has been deprecated, as a result of distutils itself being deprecated. It will not be present in NumPy for Python >= 3.12, and will be removed completely 2 years after the release of Python 3.12 For more details, see distutils-status-migration{.interpreted-text role="ref"}.

(gh-20875)
numpy.loadtxt will now give a DeprecationWarning when an integer dtype is requested but the value is formatted as a floating point number.

(gh-21663)

Expired deprecations

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

(gh-16830)
The alen and asscalar functions have been removed.

(gh-20414)
The UPDATEIFCOPY array flag has been removed together with the enum NPY_ARRAY_UPDATEIFCOPY. The associated (and deprecated) PyArray_XDECREF_ERR was also removed. These were all deprecated in 1.14. They are replaced by WRITEBACKIFCOPY, that requires calling PyArray_ResoveWritebackIfCopy before the array is deallocated.

(gh-20589)
Exceptions will be raised during array-like creation. When an object raised an exception during access of the special attributes __array__ or __array_interface__, this exception was usually ignored. This behaviour was deprecated in 1.21, and the exception will now be raised.

(gh-20835)
Multidimensional indexing with non-tuple values is not allowed. Previously, code such as arr[ind] where ind = [[0, 1], [0, 1]] produced a FutureWarning and was interpreted as a multidimensional index (i.e., arr[tuple(ind)]). Now this example is treated like an array index over a single dimension (arr[array(ind)]). Multidimensional indexing with anything but a tuple was deprecated in NumPy 1.15.

(gh-21029)
Changing to a dtype of different size in F-contiguous arrays is no longer permitted. Deprecated since Numpy 1.11.0. See below for an extended explanation of the effects of this change.

(gh-20722)

New Features

crackfortran has support for operator and assignment overloading

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

(gh-15006)

f2py supports reading access type attributes from derived type statements

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

(gh-15844)

New parameter `ndmin` added to `genfromtxt`

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

(gh-20500)

`np.loadtxt` now supports quote character and single converter function

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

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

(gh-20580)

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

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

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

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

(gh-20722)

Deterministic output files for F2PY

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

(gh-21187)

`keepdims` parameter for `average`

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

(gh-21485)

New parameter `equal_nan` added to `np.unique`

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

(gh-21623)

Compatibility notes

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

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

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

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

(gh-17709)

Changes to structured (void) dtype promotion and comparisons

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

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

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

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

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

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

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

(gh-19226)

`NPY_RELAXED_STRIDES_CHECKING` has been removed

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

(gh-20220)

`np.loadtxt` has recieved several changes

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

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

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

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

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

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

(gh-20580)

Improvements

`ndarray.__array_finalize__` is now callable

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

(gh-20766)

Add support for VSX4/Power10

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

(gh-20821)

`np.fromiter` now accepts objects and subarrays

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

(gh-20993)

Math C library feature detection now uses correct signatures

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

(gh-21154)

`np.kron` now maintains subclass information

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

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

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

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

(gh-21262)

Performance improvements and changes

Faster `np.loadtxt`

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

(gh-20580)

Faster reduction operators

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

(gh-21001)

Faster `np.where`

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

(gh-21130)

Faster operations on NumPy scalars

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

(gh-21188)

Faster `np.kron`

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

(gh-21354)

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513e4241d06b8fae5732cd049cdf3b57  numpy-1.23.0.tar.gz

SHA256

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d6ca8dabe696c2785d0c8c9b0d8a9b6e5fdbe4f922bde70d57fa1a2848134f95  numpy-1.23.0-cp39-cp39-win32.whl
fc431493df245f3c627c0c05c2bd134535e7929dbe2e602b80e42bf52ff760bc  numpy-1.23.0-cp39-cp39-win_amd64.whl
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d0d2094e8f4d760500394d77b383a1b06d3663e8892cdf5df3c592f55f3bff66  numpy-1.23.0-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
94b170b4fa0168cd6be4becf37cb5b127bd12a795123984385b8cd4aca9857e5  numpy-1.23.0-pp38-pypy38_pp73-win_amd64.whl
bd3fa4fe2e38533d5336e1272fc4e765cabbbde144309ccee8675509d5cd7b05  numpy-1.23.0.tar.gz

Open source →

1.22.420 May 2022

NumPy 1.22.4 is a maintenance release that fixes bugs discovered after the 1.22.3 release. In addition, the wheels for this release are built using th

NumPy 1.22.4 Release Notes

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

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

Contributors

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

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

Pull requests merged

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

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

Checksums

MD5

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SHA256

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Open source →

1.22.37 Mar 2022

NumPy 1.22.3 is a maintenance release that fixes bugs discovered after the 1.22.2 release. The most noticeable fixes may be those for DLPack. One that

NumPy 1.22.3 Release Notes

NumPy 1.22.3 is a maintenance release that fixes bugs discovered after the 1.22.2 release. The most noticeable fixes may be those for DLPack. One that may cause some problems is disallowing strings as inputs to logical ufuncs. It is still undecided how strings should be treated in those functions and it was thought best to simply disallow them until a decision was reached. That should not cause problems with older code.

The Python versions supported for this release are 3.8-3.10. Note that the Mac wheels are now based on OS X 10.14 rather than 10.9 that was used in previous NumPy release cycles. 10.14 is the oldest release supported by Apple.

Contributors

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

@GalaxySnail +
Alexandre de Siqueira
Bas van Beek
Charles Harris
Melissa Weber Mendonça
Ross Barnowski
Sebastian Berg
Tirth Patel
Matthieu Darbois

Pull requests merged

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

#21048: MAINT: Use "3.10" instead of "3.10-dev" on travis.
#21106: TYP,MAINT: Explicitly allow sequences of array-likes in np.concatenate
#21137: BLD,DOC: skip broken ipython 8.1.0
#21138: BUG, ENH: np._from_dlpack: export correct device information
#21139: BUG: Fix numba DUFuncs added loops getting picked up
#21140: BUG: Fix unpickling an empty ndarray with a non-zero dimension...
#21141: BUG: use ThreadPoolExecutor instead of ThreadPool
#21142: API: Disallow strings in logical ufuncs
#21143: MAINT, DOC: Fix SciPy intersphinx link
#21148: BUG,ENH: np._from_dlpack: export arrays with any strided size-1...

Checksums

MD5

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SHA256

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a906c0b4301a3d62ccf66d058fe779a65c1c34f6719ef2058f96e1856f48bca5  numpy-1.22.3.tar.gz
dbc7601a3b7472d559dc7b933b18b4b66f9aa7452c120e87dfb33d02008c8a18  numpy-1.22.3.zip

Open source →

1.22.24 Feb 2022Security

Deal with CVE-2021-41495 complaint.

NumPy 1.22.2 Release Notes

The NumPy 1.22.2 is maintenance release that fixes bugs discovered after the 1.22.1 release. Notable fixes are:

Several build related fixes for downstream projects and other platforms.
Various Annotation fixes/additions.
Numpy wheels for Windows will use the 1.41 tool chain, fixing downstream link problems for projects using NumPy provided libraries on Windows.
Deal with CVE-2021-41495 complaint.

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

Contributors

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

Andrew J. Hesford +
Bas van Beek
Brénainn Woodsend +
Charles Harris
Hood Chatham
Janus Heide +
Leo Singer
Matti Picus
Mukulika Pahari
Niyas Sait
Pearu Peterson
Ralf Gommers
Sebastian Berg
Serge Guelton

Pull requests merged

A total of 21 pull requests were merged for this release.

#20842: BLD: Add NPY_DISABLE_SVML env var to opt out of SVML
#20843: BUG: Fix build of third party extensions with Py_LIMITED_API
#20844: TYP: Fix pyright being unable to infer the real and imag...
#20845: BUG: Fix comparator function signatures
#20906: BUG: Avoid importing numpy.distutils on import numpy.testing
#20907: MAINT: remove outdated mingw32 fseek support
#20908: TYP: Relax the return type of np.vectorize
#20909: BUG: fix f2py's define for threading when building with Mingw
#20910: BUG: distutils: fix building mixed C/Fortran extensions
#20912: DOC,TST: Fix Pandas code example as per new release
#20935: TYP, MAINT: Add annotations for flatiter.__setitem__
#20936: MAINT, TYP: Added missing where typehints in fromnumeric.pyi
#20937: BUG: Fix build_ext interaction with non numpy extensions
#20938: BUG: Fix missing intrinsics for windows/arm64 target
#20945: REL: Prepare for the NumPy 1.22.2 release.
#20982: MAINT: f2py: don't generate code that triggers -Wsometimes-uninitialized.
#20983: BUG: Fix incorrect return type in reduce without initial value
#20984: ENH: review return values for PyArray_DescrNew
#20985: MAINT: be more tolerant of setuptools >= 60
#20986: BUG: Fix misplaced return.
#20992: MAINT: Further small return value validation fixes

Checksums

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Open source →

1.22.114 Jan 2022

The NumPy 1.22.1 is maintenance release that fixes bugs discovered after the 1.22.0 release. Notable fixes are:

NumPy 1.22.1 Release Notes

The NumPy 1.22.1 is maintenance release that fixes bugs discovered after the 1.22.0 release. Notable fixes are:

Fix f2PY docstring problems (SciPy)
Fix reduction type problems (AstroPy)
Fix various typing bugs.

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

Contributors

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

Arryan Singh
Bas van Beek
Charles Harris
Denis Laxalde
Isuru Fernando
Kevin Sheppard
Matthew Barber
Matti Picus
Melissa Weber Mendonça
Mukulika Pahari
Omid Rajaei +
Pearu Peterson
Ralf Gommers
Sebastian Berg

Pull requests merged

A total of 20 pull requests were merged for this release.

#20702: MAINT, DOC: Post 1.22.0 release fixes.
#20703: DOC, BUG: Use pngs instead of svgs.
#20704: DOC: Fixed the link on user-guide landing page
#20714: BUG: Restore vc141 support
#20724: BUG: Fix array dimensions solver for multidimensional arguments...
#20725: TYP: change type annotation for __array_namespace__ to ModuleType
#20726: TYP, MAINT: Allow ndindex to accept integer tuples
#20757: BUG: Relax dtype identity check in reductions
#20763: TYP: Allow time manipulation functions to accept date and timedelta...
#20768: TYP: Relax the type of ndarray.__array_finalize__
#20795: MAINT: Raise RuntimeError if setuptools version is too recent.
#20796: BUG, DOC: Fixes SciPy docs build warnings
#20797: DOC: fix OpenBLAS version in release note
#20798: PERF: Optimize array check for bounded 0,1 values
#20805: BUG: Fix that reduce-likes honor out always (and live in the...
#20806: BUG: array_api.argsort(descending=True) respects relative...
#20807: BUG: Allow integer inputs for pow-related functions in array_api
#20814: DOC: Refer to NumPy, not pandas, in main page
#20815: DOC: Update Copyright to 2022 [License]
#20819: BUG: Return correctly shaped inverse indices in array_api set...

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Open source →

1.22.031 Dec 2021Deprecated

numpy.loads was deprecated in v1.15, with the recommendation that users use pickle.loads instead. ndfromtxt and mafromtxt were both deprecated in v1.1…

NumPy 1.22.0 Release Notes

NumPy 1.22.0 is a big release featuring the work of 153 contributors spread over 609 pull requests. There have been many improvements, highlights are:

Annotations of the main namespace are essentially complete. Upstream is a moving target, so there will likely be further improvements, but the major work is done. This is probably the most user visible enhancement in this release.
A preliminary version of the proposed Array-API is provided. This is a step in creating a standard collection of functions that can be used across application such as CuPy and JAX.
NumPy now has a DLPack backend. DLPack provides a common interchange format for array (tensor) data.
New methods for quantile, percentile, and related functions. The new methods provide a complete set of the methods commonly found in the literature.
A new configurable allocator for use by downstream projects.

These are in addition to the ongoing work to provide SIMD support for commonly used functions, improvements to F2PY, and better documentation.

The Python versions supported in this release are 3.8-3.10, Python 3.7 has been dropped. Note that 32 bit wheels are only provided for Python 3.8 and 3.9 on Windows, all other wheels are 64 bits on account of Ubuntu, Fedora, and other Linux distributions dropping 32 bit support. All 64 bit wheels are also linked with 64 bit integer OpenBLAS, which should fix the occasional problems encountered by folks using truly huge arrays.

Expired deprecations

Deprecated numeric style dtype strings have been removed

Using the strings "Bytes0", "Datetime64", "Str0", "Uint32", and "Uint64" as a dtype will now raise a TypeError.

(gh-19539)

Expired deprecations for `loads`, `ndfromtxt`, and `mafromtxt` in npyio

numpy.loads was deprecated in v1.15, with the recommendation that users use pickle.loads instead. ndfromtxt and mafromtxt were both deprecated in v1.17 - users should use numpy.genfromtxt instead with the appropriate value for the usemask parameter.

(gh-19615)

Deprecations

Use delimiter rather than delimitor as kwarg in mrecords

The misspelled keyword argument delimitor of numpy.ma.mrecords.fromtextfile() has been changed to delimiter, using it will emit a deprecation warning.

(gh-19921)

Passing boolean `kth` values to (arg-)partition has been deprecated

numpy.partition and numpy.argpartition would previously accept boolean values for the kth parameter, which would subsequently be converted into integers. This behavior has now been deprecated.

(gh-20000)

The `np.MachAr` class has been deprecated

The numpy.MachAr class and finfo.machar <numpy.finfo> attribute have been deprecated. Users are encouraged to access the property if interest directly from the corresponding numpy.finfo attribute.

(gh-20201)

Compatibility notes

Distutils forces strict floating point model on clang

NumPy now sets the -ftrapping-math option on clang to enforce correct floating point error handling for universal functions. Clang defaults to non-IEEE and C99 conform behaviour otherwise. This change (using the equivalent but newer -ffp-exception-behavior=strict) was attempted in NumPy 1.21, but was effectively never used.

(gh-19479)

Removed floor division support for complex types

Floor division of complex types will now result in a TypeError

>>> a = np.arange(10) + 1j* np.arange(10)
>>> a // 1
TypeError: ufunc 'floor_divide' not supported for the input types...

(gh-19135)

`numpy.vectorize` functions now produce the same output class as the base function

When a function that respects numpy.ndarray subclasses is vectorized using numpy.vectorize, the vectorized function will now be subclass-safe also for cases that a signature is given (i.e., when creating a gufunc): the output class will be the same as that returned by the first call to the underlying function.

(gh-19356)

Python 3.7 is no longer supported

Python support has been dropped. This is rather strict, there are changes that require Python >= 3.8.

(gh-19665)

str/repr of complex dtypes now include space after punctuation

The repr of np.dtype({"names": ["a"], "formats": [int], "offsets": [2]}) is now dtype({'names': ['a'], 'formats': ['<i8'], 'offsets': [2], 'itemsize': 10}), whereas spaces where previously omitted after colons and between fields.

The old behavior can be restored via np.set_printoptions(legacy="1.21").

(gh-19687)

Corrected `advance` in `PCG64DSXM` and `PCG64`

Fixed a bug in the advance method of PCG64DSXM and PCG64. The bug only affects results when the step was larger than $2^{64}$ on platforms that do not support 128-bit integers(e.g., Windows and 32-bit Linux).

(gh-20049)

Change in generation of random 32 bit floating point variates

There was bug in the generation of 32 bit floating point values from the uniform distribution that would result in the least significant bit of the random variate always being 0. This has been fixed.

This change affects the variates produced by the random.Generator methods random, standard_normal, standard_exponential, and standard_gamma, but only when the dtype is specified as numpy.float32.

(gh-20314)

C API changes

Masked inner-loops cannot be customized anymore

The masked inner-loop selector is now never used. A warning will be given in the unlikely event that it was customized.

We do not expect that any code uses this. If you do use it, you must unset the selector on newer NumPy version. Please also contact the NumPy developers, we do anticipate providing a new, more specific, mechanism.

The customization was part of a never-implemented feature to allow for faster masked operations.

(gh-19259)

New Features

NEP 49 configurable allocators

As detailed in NEP 49, the function used for allocation of the data segment of a ndarray can be changed. The policy can be set globally or in a context. For more information see the NEP and the data_memory{.interpreted-text role="ref"} reference docs. Also add a NUMPY_WARN_IF_NO_MEM_POLICY override to warn on dangerous use of transfering ownership by setting NPY_ARRAY_OWNDATA.

(gh-17582)

Implementation of the NEP 47 (adopting the array API standard)

An initial implementation of NEP47, adoption of the array API standard, has been added as numpy.array_api. The implementation is experimental and will issue a UserWarning on import, as the array API standard is still in draft state. numpy.array_api is a conforming implementation of the array API standard, which is also minimal, meaning that only those functions and behaviors that are required by the standard are implemented (see the NEP for more info). Libraries wishing to make use of the array API standard are encouraged to use numpy.array_api to check that they are only using functionality that is guaranteed to be present in standard conforming implementations.

(gh-18585)

Generate C/C++ API reference documentation from comments blocks is now possible

This feature depends on Doxygen in the generation process and on Breathe to integrate it with Sphinx.

(gh-18884)

Assign the platform-specific `c_intp` precision via a mypy plugin

The mypy plugin, introduced in numpy/numpy#17843, has again been expanded: the plugin now is now responsible for setting the platform-specific precision of numpy.ctypeslib.c_intp, the latter being used as data type for various numpy.ndarray.ctypes attributes.

Without the plugin, aforementioned type will default to ctypes.c_int64.

To enable the plugin, one must add it to their mypy configuration file:

[mypy]
plugins = numpy.typing.mypy_plugin

(gh-19062)

Add NEP 47-compatible dlpack support

Add a ndarray.__dlpack__() method which returns a dlpack C structure wrapped in a PyCapsule. Also add a np._from_dlpack(obj) function, where obj supports __dlpack__(), and returns an ndarray.

(gh-19083)

`keepdims` optional argument added to `numpy.argmin`, `numpy.argmax`

keepdims argument is added to numpy.argmin, numpy.argmax. If set to True, the axes which are reduced are left in the result as dimensions with size one. The resulting array has the same number of dimensions and will broadcast with the input array.

(gh-19211)

`bit_count` to compute the number of 1-bits in an integer

Computes the number of 1-bits in the absolute value of the input. This works on all the numpy integer types. Analogous to the builtin int.bit_count or popcount in C++.

>>> np.uint32(1023).bit_count()
10
>>> np.int32(-127).bit_count()
7

(gh-19355)

The `ndim` and `axis` attributes have been added to `numpy.AxisError`

The ndim and axis parameters are now also stored as attributes within each numpy.AxisError instance.

(gh-19459)

Preliminary support for `windows/arm64` target

numpy added support for windows/arm64 target. Please note OpenBLAS support is not yet available for windows/arm64 target.

(gh-19513)

Added support for LoongArch

LoongArch is a new instruction set, numpy compilation failure on LoongArch architecture, so add the commit.

(gh-19527)

A `.clang-format` file has been added

Clang-format is a C/C++ code formatter, together with the added .clang-format file, it produces code close enough to the NumPy C_STYLE_GUIDE for general use. Clang-format version 12+ is required due to the use of several new features, it is available in Fedora 34 and Ubuntu Focal among other distributions.

(gh-19754)

`is_integer` is now available to `numpy.floating` and `numpy.integer`

Based on its counterpart in Python float and int, the numpy floating point and integer types now support float.is_integer. Returns True if the number is finite with integral value, and False otherwise.

>>> np.float32(-2.0).is_integer()
True
>>> np.float64(3.2).is_integer()
False
>>> np.int32(-2).is_integer()
True

(gh-19803)

Symbolic parser for Fortran dimension specifications

A new symbolic parser has been added to f2py in order to correctly parse dimension specifications. The parser is the basis for future improvements and provides compatibility with Draft Fortran 202x.

(gh-19805)

`ndarray`, `dtype` and `number` are now runtime-subscriptable

Mimicking PEP-585, the numpy.ndarray, numpy.dtype and numpy.number classes are now subscriptable for python 3.9 and later. Consequently, expressions that were previously only allowed in .pyi stub files or with the help of from __future__ import annotations are now also legal during runtime.

>>> import numpy as np
>>> from typing import Any

>>> np.ndarray[Any, np.dtype[np.float64]]
numpy.ndarray[typing.Any, numpy.dtype[numpy.float64]]

(gh-19879)

Improvements

`ctypeslib.load_library` can now take any path-like object

All parameters in the can now take any python:path-like object{.interpreted-text role="term"}. This includes the likes of strings, bytes and objects implementing the __fspath__<os.PathLike.__fspath__>{.interpreted-text role="meth"} protocol.

(gh-17530)

Add `smallest_normal` and `smallest_subnormal` attributes to `finfo`

The attributes smallest_normal and smallest_subnormal are available as an extension of finfo class for any floating-point data type. To use these new attributes, write np.finfo(np.float64).smallest_normal or np.finfo(np.float64).smallest_subnormal.

(gh-18536)

`numpy.linalg.qr` accepts stacked matrices as inputs

numpy.linalg.qr is able to produce results for stacked matrices as inputs. Moreover, the implementation of QR decomposition has been shifted to C from Python.

(gh-19151)

`numpy.fromregex` now accepts `os.PathLike` implementations

numpy.fromregex now accepts objects implementing the __fspath__<os.PathLike> protocol, e.g. pathlib.Path.

(gh-19680)

Add new methods for `quantile` and `percentile`

quantile and percentile now have have a method= keyword argument supporting 13 different methods. This replaces the interpolation= keyword argument.

The methods are now aligned with nine methods which can be found in scientific literature and the R language. The remaining methods are the previous discontinuous variations of the default "linear" one.

Please see the documentation of numpy.percentile for more information.

(gh-19857)

Missing parameters have been added to the `nan<x>` functions

A number of the nan<x> functions previously lacked parameters that were present in their <x>-based counterpart, e.g. the where parameter was present in numpy.mean but absent from numpy.nanmean.

The following parameters have now been added to the nan<x> functions:

nanmin: initial & where
nanmax: initial & where
nanargmin: keepdims & out
nanargmax: keepdims & out
nansum: initial & where
nanprod: initial & where
nanmean: where
nanvar: where
nanstd: where

(gh-20027)

Annotating the main Numpy namespace

Starting from the 1.20 release, PEP 484 type annotations have been included for parts of the NumPy library; annotating the remaining functions being a work in progress. With the release of 1.22 this process has been completed for the main NumPy namespace, which is now fully annotated.

Besides the main namespace, a limited number of sub-packages contain annotations as well. This includes, among others, numpy.testing, numpy.linalg and numpy.random (available since 1.21).

(gh-20217)

Vectorize umath module using AVX-512

By leveraging Intel Short Vector Math Library (SVML), 18 umath functions (exp2, log2, log10, expm1, log1p, cbrt, sin, cos, tan, arcsin, arccos, arctan, sinh, cosh, tanh, arcsinh, arccosh, arctanh) are vectorized using AVX-512 instruction set for both single and double precision implementations. This change is currently enabled only for Linux users and on processors with AVX-512 instruction set. It provides an average speed up of 32x and 14x for single and double precision functions respectively.

(gh-19478)

OpenBLAS v0.3.18

Update the OpenBLAS used in testing and in wheels to v0.3.18

(gh-20058)

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Open source →

1.21.612 Apr 2022

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

NumPy 1.21.6 Release Notes

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

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

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

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7c4068a8c44014b2d55f3c3f574c376b2494ca9cc73d2f1bd692382b6dffe3db  numpy-1.21.6-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
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5c3c8def4230e1b959671eb959083661b4a0d2e9af93ee339c7dada6759a9470  numpy-1.21.6-cp38-cp38-win32.whl
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7f5ae4f304257569ef3b948810816bc87c9146e8c446053539947eedeaa32786  numpy-1.21.6-cp39-cp39-win32.whl
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dd1c8f6bd65d07d3810b90d02eba7997e32abbdf1277a481d698969e921a3be0  numpy-1.21.6-pp37-pypy37_pp73-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
d4efc6491a1cdc00f9eca9bf2c1aa13671776f6941c7321ddf75b45c862f0c2c  numpy-1.21.6.tar.gz
ecb55251139706669fdec2ff073c98ef8e9a84473e51e716211b41aa0f18e656  numpy-1.21.6.zip

Open source →

1.21.519 Dec 2021

NumPy 1.21.5 is a maintenance release that fixes a few bugs discovered after the 1.21.4 release and does some maintenance to extend the 1.21.x lifetim

NumPy 1.21.5 Release Notes

NumPy 1.21.5 is a maintenance release that fixes a few bugs discovered after the 1.21.4 release and does some maintenance to extend the 1.21.x lifetime. The Python versions supported in this release are 3.7-3.10. If you want to compile your own version using gcc-11, you will need to use gcc-11.2+ to avoid problems.

Contributors

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

Bas van Beek
Charles Harris
Matti Picus
Rohit Goswami
Ross Barnowski
Sayed Adel
Sebastian Berg

Pull requests merged

A total of 11 pull requests were merged for this release.

#20357: MAINT: Do not forward __(deep)copy__ calls of _GenericAlias...
#20462: BUG: Fix float16 einsum fastpaths using wrong tempvar
#20463: BUG, DIST: Print os error message when the executable not exist
#20464: BLD: Verify the ability to compile C++ sources before initiating...
#20465: BUG: Force npymath to respect npy_longdouble
#20466: BUG: Fix failure to create aligned, empty structured dtype
#20467: ENH: provide a convenience function to replace npy_load_module
#20495: MAINT: update wheel to version that supports python3.10
#20497: BUG: Clear errors correctly in F2PY conversions
#20613: DEV: add a warningfilter to fix pytest workflow.
#20618: MAINT: Help boost::python libraries at least not crash

Checksums

MD5

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88b5438ded7992fa2e6a810d43cd32a1  numpy-1.21.5.zip

SHA256

301e408a052fdcda5cdcf03021ebafc3c6ea093021bf9d1aa47c54d48bdad166  numpy-1.21.5-cp310-cp310-macosx_10_9_universal2.whl
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fc7a7d7b0ed72589fd8b8486b9b42a564f10b8762be8bd4d9df94b807af4a089  numpy-1.21.5-cp310-cp310-macosx_11_0_arm64.whl
58ca1d7c8aef6e996112d0ce873ac9dfa1eaf4a1196b4ff7ff73880a09923ba7  numpy-1.21.5-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
dc4b2fb01f1b4ddbe2453468ea0719f4dbb1f5caa712c8b21bb3dd1480cd30d9  numpy-1.21.5-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
cc1b30205d138d1005adb52087ff45708febbef0e420386f58664f984ef56954  numpy-1.21.5-cp310-cp310-win_amd64.whl
08de8472d9f7571f9d51b27b75e827f5296295fa78817032e84464be8bb905bc  numpy-1.21.5-cp37-cp37m-macosx_10_9_x86_64.whl
4fe6a006557b87b352c04596a6e3f12a57d6e5f401d804947bd3188e6b0e0e76  numpy-1.21.5-cp37-cp37m-manylinux_2_12_i686.manylinux2010_i686.whl
3d893b0871322eaa2f8c7072cdb552d8e2b27645b7875a70833c31e9274d4611  numpy-1.21.5-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
341dddcfe3b7b6427a28a27baa59af5ad51baa59bfec3264f1ab287aa3b30b13  numpy-1.21.5-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
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3a5098df115340fb17fc93867317a947e1dcd978c3888c5ddb118366095851f8  numpy-1.21.5-cp38-cp38-macosx_10_9_universal2.whl
311283acf880cfcc20369201bd75da907909afc4666966c7895cbed6f9d2c640  numpy-1.21.5-cp38-cp38-macosx_10_9_x86_64.whl
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00c9fa73a6989895b8815d98300a20ac993c49ac36c8277e8ffeaa3631c0dbbb  numpy-1.21.5-cp39-cp39-macosx_10_9_x86_64.whl
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3c978544be9e04ed12016dd295a74283773149b48f507d69b36f91aa90a643e5  numpy-1.21.5-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
2a9add27d7fc0fdb572abc3b2486eb3b1395da71e0254c5552b2aad2a18b5441  numpy-1.21.5-cp39-cp39-win32.whl
1964db2d4a00348b7a60ee9d013c8cb0c566644a589eaa80995126eac3b99ced  numpy-1.21.5-cp39-cp39-win_amd64.whl
a7c4b701ca418cd39e28ec3b496e6388fe06de83f5f0cb74794fa31cfa384c02  numpy-1.21.5-pp37-pypy37_pp73-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
1a7ee0ffb35dc7489aebe5185a483f4c43b0d2cf784c3c9940f975a7dde56506  numpy-1.21.5.tar.gz
6a5928bc6241264dce5ed509e66f33676fc97f464e7a919edc672fb5532221ee  numpy-1.21.5.zip

Open source →

1.21.45 Nov 2021

The NumPy 1.21.4 is a maintenance release that fixes a few bugs discovered after 1.21.3. The most important fix here is a fix for the NumPy header fil

NumPy 1.21.4 Release Notes

The NumPy 1.21.4 is a maintenance release that fixes a few bugs discovered after 1.21.3. The most important fix here is a fix for the NumPy header files to make them work for both x86_64 and M1 hardware when included in the Mac universal2 wheels. Previously, the header files only worked for M1 and this caused problems for folks building x86_64 extensions. This problem was not seen before Python 3.10 because there were thin wheels for x86_64 that had precedence. This release also provides thin x86_64 Mac wheels for Python 3.10.

The Python versions supported in this release are 3.7-3.10. If you want to compile your own version using gcc-11, you will need to use gcc-11.2+ to avoid problems.

Contributors

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

Bas van Beek
Charles Harris
Isuru Fernando
Matthew Brett
Sayed Adel
Sebastian Berg
傅立业（Chris Fu） +

Pull requests merged

A total of 9 pull requests were merged for this release.

#20278: BUG: Fix shadowed reference of dtype in type stub
#20293: BUG: Fix headers for universal2 builds
#20294: BUG: VOID_nonzero could sometimes mutate alignment flag
#20295: BUG: Do not use nonzero fastpath on unaligned arrays
#20296: BUG: Distutils patch to allow for 2 as a minor version (!)
#20297: BUG, SIMD: Fix 64-bit/8-bit integer division by a scalar
#20298: BUG, SIMD: Workaround broadcasting SIMD 64-bit integers on MSVC...
#20300: REL: Prepare for the NumPy 1.21.4 release.
#20302: TST: Fix a Arrayterator typing test failure

Checksums

MD5

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b3c4477a027d5b6fba5e1065064fd076  numpy-1.21.4.zip

SHA256

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b1e2312f5b8843a3e4e8224b2b48fe16119617b8fc0a54df8f50098721b5bed2  numpy-1.21.4-cp39-cp39-win32.whl
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5d412381aa489b8be82ac5c6a9e99c3eb3f754245ad3f90ab5c339d92f25fb47  numpy-1.21.4.tar.gz
e6c76a87633aa3fa16614b61ccedfae45b91df2767cf097aa9c933932a7ed1e0  numpy-1.21.4.zip

Open source →

1.21.320 Oct 2021

The NumPy 1.21.3 is a maintenance release the fixes a few bugs discovered after 1.21.2. It also provides 64 bit Python 3.10.0 wheels. Note a few oddit

NumPy 1.21.3 Release Notes

The NumPy 1.21.3 is a maintenance release the fixes a few bugs discovered after 1.21.2. It also provides 64 bit Python 3.10.0 wheels. Note a few oddities about Python 3.10:

There are no 32 bit wheels for Windows, Mac, or Linux.
The Mac Intel builds are only available in universal2 wheels.

The Python versions supported in this release are 3.7-3.10. If you want to compile your own version using gcc-11 you will need to use gcc-11.2+ to avoid problems.

Contributors

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

Aaron Meurer
Bas van Beek
Charles Harris
Developer-Ecosystem-Engineering +
Kevin Sheppard
Sebastian Berg
Warren Weckesser

Pull requests merged

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

#19745: ENH: Add dtype-support to 3 `generic/ndarray methods
#19955: BUG: Resolve Divide by Zero on Apple silicon + test failures...
#19958: MAINT: Mark type-check-only ufunc subclasses as ufunc aliases...
#19994: BUG: np.tan(np.inf) test failure
#20080: BUG: Correct incorrect advance in PCG with emulated int128
#20081: BUG: Fix NaT handling in the PyArray_CompareFunc for datetime...
#20082: DOC: Ensure that we add documentation also as to the dict for...
#20106: BUG: core: result_type(0, np.timedelta64(4)) would seg. fault.

Checksums

MD5

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SHA256

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d0bba24083c01ae43457514d875f10d9ce4c1125d55b1e2573277b2410f2d068  numpy-1.21.3.tar.gz
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Open source →

1.21.215 Aug 2021Deprecated

\#19662: BUG,DEP: Non-default UFunc signature/dtype usage should be deprecated

NumPy 1.21.2 Release Notes

The NumPy 1.21.2 is maintenance release that fixes bugs discovered after 1.21.1. It also provides 64 bit manylinux Python 3.10.0rc1 wheels for downstream testing. Note that Python 3.10 is not yet final. There is also preliminary support for Windows on ARM64 builds, but there is no OpenBLAS for that platform and no wheels are available.

The Python versions supported for this release are 3.7-3.9. The 1.21.x series is compatible with Python 3.10.0rc1 and Python 3.10 will be officially supported after it is released. The previous problems with gcc-11.1 have been fixed by gcc-11.2, check your version if you are using gcc-11.

Contributors

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

Bas van Beek
Carl Johnsen +
Charles Harris
Gwyn Ciesla +
Matthieu Dartiailh
Matti Picus
Niyas Sait +
Ralf Gommers
Sayed Adel
Sebastian Berg

Pull requests merged

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

#19497: MAINT: set Python version for 1.21.x to <3.11
#19533: BUG: Fix an issue wherein importing numpy.typing could raise
#19646: MAINT: Update Cython version for Python 3.10.
#19648: TST: Bump the python 3.10 test version from beta4 to rc1
#19651: TST: avoid distutils.sysconfig in runtests.py
#19652: MAINT: add missing dunder method to nditer type hints
#19656: BLD, SIMD: Fix testing extra checks when -Werror isn't applicable...
#19657: BUG: Remove logical object ufuncs with bool output
#19658: MAINT: Include .coveragerc in source distributions to support...
#19659: BUG: Fix bad write in masked iterator output copy paths
#19660: ENH: Add support for windows on arm targets
#19661: BUG: add base to templated arguments for platlib
#19662: BUG,DEP: Non-default UFunc signature/dtype usage should be deprecated
#19666: MAINT: Add Python 3.10 to supported versions.
#19668: TST,BUG: Sanitize path-separators when running runtest.py
#19671: BLD: load extra flags when checking for libflame
#19676: BLD: update circleCI docker image
#19677: REL: Prepare for 1.21.2 release.

Checksums

MD5

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SHA256

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Open source →

1.21.118 Jul 2021Deprecated

\#19391: BUG: Do not raise deprecation warning for all nans in unique\...

NumPy 1.21.1 Release Notes

The NumPy 1.21.1 is maintenance release that fixes bugs discovered after the 1.21.0 release and updates OpenBLAS to v0.3.17 to deal with problems on arm64.

The Python versions supported for this release are 3.7-3.9. The 1.21.x series is compatible with development Python 3.10. Python 3.10 will be officially supported after it is released.

:warning: There are unresolved problems compiling NumPy 1.20.0 with gcc-11.1.

Optimization level -O3 results in many incorrect warnings when running the tests.
On some hardware NumPY will hang in an infinite loop.

Contributors

A total of 11 people contributed to this release. People with a "+" by their names contributed a patch for the first time.

Bas van Beek
Charles Harris
Ganesh Kathiresan
Gregory R. Lee
Hugo Defois +
Kevin Sheppard
Matti Picus
Ralf Gommers
Sayed Adel
Sebastian Berg
Thomas J. Fan

Pull requests merged

A total of 26 pull requests were merged for this release.

#19311: REV,BUG: Replace NotImplemented with typing.Any
#19324: MAINT: Fixed the return-dtype of ndarray.real and imag
#19330: MAINT: Replace "dtype[Any]" with dtype in the definiton of...
#19342: DOC: Fix some docstrings that crash pdf generation.
#19343: MAINT: bump scipy-mathjax
#19347: BUG: Fix arr.flat.index for large arrays and big-endian machines
#19348: ENH: add numpy.f2py.get_include function
#19349: BUG: Fix reference count leak in ufunc dtype handling
#19350: MAINT: Annotate missing attributes of np.number subclasses
#19351: BUG: Fix cast safety and comparisons for zero sized voids
#19352: BUG: Correct Cython declaration in random
#19353: BUG: protect against accessing base attribute of a NULL subarray
#19365: BUG, SIMD: Fix detecting AVX512 features on Darwin
#19366: MAINT: remove print()'s in distutils template handling
#19390: ENH: SIMD architectures to show_config
#19391: BUG: Do not raise deprecation warning for all nans in unique...
#19392: BUG: Fix NULL special case in object-to-any cast code
#19430: MAINT: Use arm64-graviton2 for testing on travis
#19495: BUILD: update OpenBLAS to v0.3.17
#19496: MAINT: Avoid unicode characters in division SIMD code comments
#19499: BUG, SIMD: Fix infinite loop during count non-zero on GCC-11
#19500: BUG: fix a numpy.npiter leak in npyiter_multi_index_set
#19501: TST: Fix a GenericAlias test failure for python 3.9.0
#19502: MAINT: Start testing with Python 3.10.0b3.
#19503: MAINT: Add missing dtype overloads for object- and ctypes-based...
#19510: REL: Prepare for NumPy 1.21.1 release.

Checksums

MD5

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SHA256

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Open source →

1.21.022 Jun 2021Deprecated

The shape argument numpy.unravel_index cannot be passed as dims keyword argument anymore. (Was deprecated in NumPy 1.16.)

NumPy 1.21.0 Release Notes

The NumPy 1.21.0 release highlights are

continued SIMD work covering more functions and platforms,
initial work on the new dtype infrastructure and casting,
universal2 wheels for Python 3.8 and Python 3.9 on Mac,
improved documentation,
improved annotations,
new PCG64DXSM bitgenerator for random numbers.

In addition there are the usual large number of bug fixes and other improvements.

The Python versions supported for this release are 3.7-3.9. Official support for Python 3.10 will be added when it is released.

:warning: Warning: there are unresolved problems compiling NumPy 1.21.0 with gcc-11.1 .

Optimization level -O3 results in many wrong warnings when running the tests.
On some hardware NumPy will hang in an infinite loop.

New functions

Add PCG64DXSM BitGenerator

Uses of the PCG64 BitGenerator in a massively-parallel context have been shown to have statistical weaknesses that were not apparent at the first release in numpy 1.17. Most users will never observe this weakness and are safe to continue to use PCG64. We have introduced a new PCG64DXSM BitGenerator that will eventually become the new default BitGenerator implementation used by default_rng in future releases. PCG64DXSM solves the statistical weakness while preserving the performance and the features of PCG64.

See upgrading-pcg64 for more details.

(gh-18906)

Expired deprecations

The shape argument numpy.unravel_index cannot be passed as dims keyword argument anymore. (Was deprecated in NumPy 1.16.)

(gh-17900)
The function PyUFunc_GenericFunction has been disabled. It was deprecated in NumPy 1.19. Users should call the ufunc directly using the Python API.

(gh-18697)
The function PyUFunc_SetUsesArraysAsData has been disabled. It was deprecated in NumPy 1.19.

(gh-18697)
The class PolyBase has been removed (deprecated in numpy 1.9.0). Please use the abstract ABCPolyBase class instead.

(gh-18963)
The unused PolyError and PolyDomainError exceptions are removed.

(gh-18963)

Deprecations

The `.dtype` attribute must return a `dtype`

A DeprecationWarning is now given if the .dtype attribute of an object passed into np.dtype or as a dtype=obj argument is not a dtype. NumPy will stop attempting to recursively coerce the result of .dtype.

(gh-13578)

Inexact matches for `numpy.convolve` and `numpy.correlate` are deprecated

numpy.convolve and numpy.correlate now emit a warning when there are case insensitive and/or inexact matches found for mode argument in the functions. Pass full "same", "valid", "full" strings instead of "s", "v", "f" for the mode argument.

(gh-17492)

`np.typeDict` has been formally deprecated

np.typeDict is a deprecated alias for np.sctypeDict and has been so for over 14 years (6689502). A deprecation warning will now be issued whenever getting np.typeDict.

(gh-17586)

Exceptions will be raised during array-like creation

When an object raised an exception during access of the special attributes __array__ or __array_interface__, this exception was usually ignored. A warning is now given when the exception is anything but AttributeError. To silence the warning, the type raising the exception has to be adapted to raise an AttributeError.

(gh-19001)

Four `ndarray.ctypes` methods have been deprecated

Four methods of the ndarray.ctypes object have been deprecated, as they are (undocumentated) implementation artifacts of their respective properties.

The methods in question are:

_ctypes.get_data (use _ctypes.data instead)
_ctypes.get_shape (use _ctypes.shape instead)
_ctypes.get_strides (use _ctypes.strides instead)
_ctypes.get_as_parameter (use _ctypes._as_parameter_ instead)

(gh-19031)

Expired deprecations

The shape argument numpy.unravel_index] cannot be passed as dims keyword argument anymore. (Was deprecated in NumPy 1.16.)

(gh-17900)
The function PyUFunc_GenericFunction has been disabled. It was deprecated in NumPy 1.19. Users should call the ufunc directly using the Python API.

(gh-18697)
The function PyUFunc_SetUsesArraysAsData has been disabled. It was deprecated in NumPy 1.19.

(gh-18697)

Remove deprecated `PolyBase` and unused `PolyError` and `PolyDomainError`

The class PolyBase has been removed (deprecated in numpy 1.9.0). Please use the abstract ABCPolyBase class instead.

Furthermore, the unused PolyError and PolyDomainError exceptions are removed from the numpy.polynomial.

(gh-18963)

Compatibility notes

Error type changes in universal functions

The universal functions may now raise different errors on invalid input in some cases. The main changes should be that a RuntimeError was replaced with a more fitting TypeError. When multiple errors were present in the same call, NumPy may now raise a different one.

(gh-15271)

`__array_ufunc__` argument validation

NumPy will now partially validate arguments before calling __array_ufunc__. Previously, it was possible to pass on invalid arguments (such as a non-existing keyword argument) when dispatch was known to occur.

(gh-15271)

`__array_ufunc__` and additional positional arguments

Previously, all positionally passed arguments were checked for __array_ufunc__ support. In the case of reduce, accumulate, and reduceat all arguments may be passed by position. This means that when they were passed by position, they could previously have been asked to handle the ufunc call via __array_ufunc__. Since this depended on the way the arguments were passed (by position or by keyword), NumPy will now only dispatch on the input and output array. For example, NumPy will never dispatch on the where array in a reduction such as np.add.reduce.

(gh-15271)

Validate input values in `Generator.uniform`

Checked that high - low >= 0 in np.random.Generator.uniform. Raises ValueError if low > high. Previously out-of-order inputs were accepted and silently swapped, so that if low > high, the value generated was high + (low - high) * random().

(gh-17921)

`/usr/include` removed from default include paths

The default include paths when building a package with numpy.distutils no longer include /usr/include. This path is normally added by the compiler, and hardcoding it can be problematic. In case this causes a problem, please open an issue. A workaround is documented in PR 18658.

(gh-18658)

Changes to comparisons with `dtype=...`

When the dtype= (or signature) arguments to comparison ufuncs (equal, less, etc.) is used, this will denote the desired output dtype in the future. This means that:

np.equal(2, 3, dtype=object)

will give a FutureWarning that it will return an object array in the future, which currently happens for:

np.equal(None, None, dtype=object)

due to the fact that np.array(None) is already an object array. (This also happens for some other dtypes.)

Since comparisons normally only return boolean arrays, providing any other dtype will always raise an error in the future and give a DeprecationWarning now.

(gh-18718)

Changes to `dtype` and `signature` arguments in ufuncs

The universal function arguments dtype and signature which are also valid for reduction such as np.add.reduce (which is the implementation for np.sum) will now issue a warning when the dtype provided is not a "basic" dtype.

NumPy almost always ignored metadata, byteorder or time units on these inputs. NumPy will now always ignore it and raise an error if byteorder or time unit changed. The following are the most important examples of changes which will give the error. In some cases previously the information stored was not ignored, in all of these an error is now raised:

# Previously ignored the byte-order (affect if non-native)
np.add(3, 5, dtype=">i32")

# The biggest impact is for timedelta or datetimes:
arr = np.arange(10, dtype="m8[s]")

# The examples always ignored the time unit "ns":
np.add(arr, arr, dtype="m8[ns]")
np.maximum.reduce(arr, dtype="m8[ns]")

# The following previously did use "ns" (as opposed to `arr.dtype`)
np.add(3, 5, dtype="m8[ns]")  # Now return generic time units
np.maximum(arr, arr, dtype="m8[ns]")  # Now returns "s" (from `arr`)

The same applies for functions like np.sum which use these internally. This change is necessary to achieve consistent handling within NumPy.

If you run into these, in most cases pass for example dtype=np.timedelta64 which clearly denotes a general timedelta64 without any unit or byte-order defined. If you need to specify the output dtype precisely, you may do so by either casting the inputs or providing an output array using out=.

NumPy may choose to allow providing an exact output dtype here in the future, which would be preceded by a FutureWarning.

(gh-18718)

Ufunc `signature=...` and `dtype=` generalization and `casting`

The behaviour for np.ufunc(1.0, 1.0, signature=...) or np.ufunc(1.0, 1.0, dtype=...) can now yield different loops in 1.21 compared to 1.20 because of changes in promotion. When signature was previously used, the casting check on inputs was relaxed, which could lead to downcasting inputs unsafely especially if combined with casting="unsafe".

Casting is now guaranteed to be safe. If a signature is only partially provided, for example using signature=("float64", None, None), this could lead to no loop being found (an error). In that case, it is necessary to provide the complete signature to enforce casting the inputs. If dtype="float64" is used or only outputs are set (e.g. signature=(None, None, "float64") the is unchanged. We expect that very few users are affected by this change.

Further, the meaning of dtype="float64" has been slightly modified and now strictly enforces only the correct output (and not input) DTypes. This means it is now always equivalent to:

signature=(None, None, "float64")

(If the ufunc has two inputs and one output). Since this could lead to no loop being found in some cases, NumPy will normally also search for the loop:

signature=("float64", "float64", "float64")

if the first search failed. In the future, this behaviour may be customized to achieve the expected results for more complex ufuncs. (For some universal functions such as np.ldexp inputs can have different DTypes.)

(gh-18880)

Distutils forces strict floating point model on clang

NumPy distutils will now always add the -ffp-exception-behavior=strict compiler flag when compiling with clang. Clang defaults to a non-strict version, which allows the compiler to generate code that does not set floating point warnings/errors correctly.

(gh-19049)

C API changes

Use of `ufunc->type_resolver` and "type tuple"

NumPy now normalizes the "type tuple" argument to the type resolver functions before calling it. Note that in the use of this type resolver is legacy behaviour and NumPy will not do so when possible. Calling ufunc->type_resolver or PyUFunc_DefaultTypeResolver is strongly discouraged and will now enforce a normalized type tuple if done. Note that this does not affect providing a type resolver, which is expected to keep working in most circumstances. If you have an unexpected use-case for calling the type resolver, please inform the NumPy developers so that a solution can be found.

(gh-18718)

New Features

Added a mypy plugin for handling platform-specific `numpy.number` precisions

A mypy plugin is now available for automatically assigning the (platform-dependent) precisions of certain numpy.number subclasses, including the likes of numpy.int_, numpy.intp and numpy.longlong. See the documentation on scalar types <arrays.scalars.built-in> for a comprehensive overview of the affected classes.

Note that while usage of the plugin is completely optional, without it the precision of above-mentioned classes will be inferred as typing.Any.

To enable the plugin, one must add it to their mypy [configuration file] (https://mypy.readthedocs.io/en/stable/config_file.html):

[mypy]
plugins = numpy.typing.mypy_plugin

(gh-17843)

Let the mypy plugin manage extended-precision `numpy.number` subclasses

The mypy plugin, introduced in numpy/numpy#17843, has been expanded: the plugin now removes annotations for platform-specific extended-precision types that are not available to the platform in question. For example, it will remove numpy.float128 when not available.

Without the plugin all extended-precision types will, as far as mypy is concerned, be available on all platforms.

To enable the plugin, one must add it to their mypy configuration file:

[mypy]
plugins = numpy.typing.mypy_plugin
                                                                        cn

(gh-18322)

New `min_digits` argument for printing float values

A new min_digits argument has been added to the dragon4 float printing functions numpy.format_float_positional and numpy.format_float_scientific. This kwd guarantees that at least the given number of digits will be printed when printing in unique=True mode, even if the extra digits are unnecessary to uniquely specify the value. It is the counterpart to the precision argument which sets the maximum number of digits to be printed. When unique=False in fixed precision mode, it has no effect and the precision argument fixes the number of digits.

(gh-18629)

f2py now recognizes Fortran abstract interface blocks

numpy.f2py can now parse abstract interface blocks.

(gh-18695)

BLAS and LAPACK configuration via environment variables

Autodetection of installed BLAS and LAPACK libraries can be bypassed by using the NPY_BLAS_LIBS and NPY_LAPACK_LIBS environment variables. Instead, the link flags in these environment variables will be used directly, and the language is assumed to be F77. This is especially useful in automated builds where the BLAS and LAPACK that are installed are known exactly. A use case is replacing the actual implementation at runtime via stub library links.

If NPY_CBLAS_LIBS is set (optional in addition to NPY_BLAS_LIBS), this will be used as well, by defining HAVE_CBLAS and appending the environment variable content to the link flags.

(gh-18737)

A runtime-subcriptable alias has been added for `ndarray`

numpy.typing.NDArray has been added, a runtime-subscriptable alias for np.ndarray[Any, np.dtype[~Scalar]]. The new type alias can be used for annotating arrays with a given dtype and unspecified shape.

NumPy does not support the annotating of array shapes as of 1.21, this is expected to change in the future though (see 646{.interpreted-text role="pep"}).

Examples

>>> import numpy as np
>>> import numpy.typing as npt

>>> print(npt.NDArray)
numpy.ndarray[typing.Any, numpy.dtype[~ScalarType]]

>>> print(npt.NDArray[np.float64])
numpy.ndarray[typing.Any, numpy.dtype[numpy.float64]]

>>> NDArrayInt = npt.NDArray[np.int_]
>>> a: NDArrayInt = np.arange(10)

>>> def func(a: npt.ArrayLike) -> npt.NDArray[Any]:
...     return np.array(a)

(gh-18935)

Improvements

Arbitrary `period` option for `numpy.unwrap`

The size of the interval over which phases are unwrapped is no longer restricted to 2 * pi. This is especially useful for unwrapping degrees, but can also be used for other intervals.

>>> phase_deg = np.mod(np.linspace(0,720,19), 360) - 180
>>> phase_deg
array([-180., -140., -100.,  -60.,  -20.,   20.,   60.,  100.,  140.,
       -180., -140., -100.,  -60.,  -20.,   20.,   60.,  100.,  140.,
       -180.])

>>> unwrap(phase_deg, period=360)
array([-180., -140., -100.,  -60.,  -20.,   20.,   60.,  100.,  140.,
        180.,  220.,  260.,  300.,  340.,  380.,  420.,  460.,  500.,
        540.])

(gh-16987)

`np.unique` now returns single `NaN`

When np.unique operated on an array with multiple NaN entries, its return included a NaN for each entry that was NaN in the original array. This is now improved such that the returned array contains just one NaN as the last element.

Also for complex arrays all NaN values are considered equivalent (no matter whether the NaN is in the real or imaginary part). As the representant for the returned array the smallest one in the lexicographical order is chosen - see np.sort for how the lexicographical order is defined for complex arrays.

(gh-18070)

`Generator.rayleigh` and `Generator.geometric` performance improved

The performance of Rayleigh and geometric random variate generation in Generator has improved. These are both transformation of exponential random variables and the slow log-based inverse cdf transformation has been replaced with the Ziggurat-based exponential variate generator.

This change breaks the stream of variates generated when variates from either of these distributions are produced.

(gh-18666)

Placeholder annotations have been improved

All placeholder annotations, that were previously annotated as typing.Any, have been improved. Where appropiate they have been replaced with explicit function definitions, classes or other miscellaneous objects.

(gh-18934)

Performance improvements

Improved performance in integer division of NumPy arrays

Integer division of NumPy arrays now uses libdivide when the divisor is a constant. With the usage of libdivide and other minor optimizations, there is a large speedup. The // operator and np.floor_divide makes use of the new changes.

(gh-17727)

Improve performance of `np.save` and `np.load` for small arrays

np.save is now a lot faster for small arrays.

np.load is also faster for small arrays, but only when serializing with a version >= (3, 0).

Both are done by removing checks that are only relevant for Python 2, while still maintaining compatibility with arrays which might have been created by Python 2.

(gh-18657)

Changes

`numpy.piecewise` output class now matches the input class

When numpy.ndarray subclasses are used on input to numpy.piecewise, they are passed on to the functions. The output will now be of the same subclass as well.

(gh-18110)

Enable Accelerate Framework

With the release of macOS 11.3, several different issues that numpy was encountering when using Accelerate Framework's implementation of BLAS and LAPACK should be resolved. This change enables the Accelerate Framework as an option on macOS. If additional issues are found, please file a bug report against Accelerate using the developer feedback assistant tool (https://developer.apple.com/bug-reporting/). We intend to address issues promptly and plan to continue supporting and updating our BLAS and LAPACK libraries.

(gh-18874)

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b662c841b29848c04d9134f31dbaa7d4c8e673f45bb3a5f28d02f49c424d558a  numpy-1.21.0.tar.gz
e80fe25cba41c124d04c662f33f6364909b985f2eb5998aaa5ae4b9587242cce  numpy-1.21.0.zip

Open source →

1.20.310 May 2021

NumPy 1.20.3 is a bugfix release containing several fixes merged to the main branch after the NumPy 1.20.2 release.

NumPy 1.20.3 Release Notes

NumPy 1.20.3 is a bugfix release containing several fixes merged to the main branch after the NumPy 1.20.2 release.

Contributors

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

Anne Archibald
Bas van Beek
Charles Harris
Dong Keun Oh +
Kamil Choudhury +
Sayed Adel
Sebastian Berg

Pull requests merged

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

#18763: BUG: Correct datetime64 missing type overload for datetime.date...
#18764: MAINT: Remove __all__ in favor of explicit re-exports
#18768: BLD: Strip extra newline when dumping gfortran version on MacOS
#18769: BUG: fix segfault in object/longdouble operations
#18794: MAINT: Use towncrier build explicitly
#18887: MAINT: Relax certain integer-type constraints
#18915: MAINT: Remove unsafe unions and ABCs from return-annotations
#18921: MAINT: Allow more recursion depth for scalar tests.
#18922: BUG: Initialize the full nditer buffer in case of error
#18923: BLD: remove unnecessary flag -faltivec on macOS
#18924: MAINT, CI: treats _SIMD module build warnings as errors through...
#18925: BUG: for MINGW, threads.h existence test requires GLIBC > 2.12
#18941: BUG: Make changelog recognize gh- as a PR number prefix.
#18948: REL, DOC: Prepare for the NumPy 1.20.3 release.
#18953: BUG: Fix failing mypy test in 1.20.x.

Checksums

MD5

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SHA256

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Open source →

1.20.227 Mar 2021

NumPy 1,20.2 is a bugfix release containing several fixes merged to the main branch after the NumPy 1.20.1 release.

NumPy 1.20.2 Release Notes

NumPy 1,20.2 is a bugfix release containing several fixes merged to the main branch after the NumPy 1.20.1 release.

Contributors

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

Allan Haldane
Bas van Beek
Charles Harris
Christoph Gohlke
Mateusz Sokół +
Michael Lamparski
Sebastian Berg

Pull requests merged

A total of 20 pull requests were merged for this release.

#18382: MAINT: Update f2py from master.
#18459: BUG: diagflat could overflow on windows or 32-bit platforms
#18460: BUG: Fix refcount leak in f2py complex_double_from_pyobj.
#18461: BUG: Fix tiny memory leaks when like= overrides are used
#18462: BUG: Remove temporary change of descr/flags in VOID functions
#18469: BUG: Segfault in nditer buffer dealloc for Object arrays
#18485: BUG: Remove suspicious type casting
#18486: BUG: remove nonsensical comparison of pointer < 0
#18487: BUG: verify pointer against NULL before using it
#18488: BUG: check if PyArray_malloc succeeded
#18546: BUG: incorrect error fallthrough in nditer
#18559: CI: Backport CI fixes from main.
#18599: MAINT: Add annotations for __getitem__, __mul__ and...
#18611: BUG: NameError in numpy.distutils.fcompiler.compaq
#18612: BUG: Fixed where keyword for np.mean & np.var methods
#18617: CI: Update apt package list before Python install
#18636: MAINT: Ensure that re-exported sub-modules are properly annotated
#18638: BUG: Fix ma coercion list-of-ma-arrays if they do not cast to...
#18661: BUG: Fix small valgrind-found issues
#18671: BUG: Fix small issues found with pytest-leaks

Checksums

MD5

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SHA256

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Open source →

1.20.17 Feb 2021

NumPy 1.20.1 is a rapid bugfix release fixing several bugs and regressions reported after the 1.20.0 release.

NumPy 1.20.1 Release Notes

NumPy 1.20.1 is a rapid bugfix release fixing several bugs and regressions reported after the 1.20.0 release.

Highlights

The distutils bug that caused problems with downstream projects is fixed.
The random.shuffle regression is fixed.

Contributors

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

Bas van Beek
Charles Harris
Nicholas McKibben +
Pearu Peterson
Ralf Gommers
Sebastian Berg
Tyler Reddy
@Aerysv +

Pull requests merged

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

#18306: MAINT: Add missing placeholder annotations
#18310: BUG: Fix typo in numpy.__init__.py
#18326: BUG: don't mutate list of fake libraries while iterating over...
#18327: MAINT: gracefully shuffle memoryviews
#18328: BUG: Use C linkage for random distributions
#18336: CI: fix when GitHub Actions builds trigger, and allow ci skips
#18337: BUG: Allow unmodified use of isclose, allclose, etc. with timedelta
#18345: BUG: Allow pickling all relevant DType types/classes
#18351: BUG: Fix missing signed_char dependency. Closes #18335.
#18352: DOC: Change license date 2020 -> 2021
#18353: CI: CircleCI seems to occasionally time out, increase the limit
#18354: BUG: Fix f2py bugs when wrapping F90 subroutines.
#18356: MAINT: crackfortran regex simplify
#18357: BUG: threads.h existence test requires GLIBC > 2.12.
#18359: REL: Prepare for the NumPy 1.20.1 release.

Checksums

MD5

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Open source →

1.20.030 Jan 2021Deprecated

These aliases have been deprecated. The table below shows the full list of deprecated aliases, along with their exact meaning. Replacing uses of items…

NumPy 1.20.0 Release Notes

This NumPy release is the largest so made to date, some 684 PRs contributed by 184 people have been merged. See the list of highlights below for more details. The Python versions supported for this release are 3.7-3.9, support for Python 3.6 has been dropped. Highlights are

Annotations for NumPy functions. This work is ongoing and improvements can be expected pending feedback from users.
Wider use of SIMD to increase execution speed of ufuncs. Much work has been done in introducing universal functions that will ease use of modern features across different hardware platforms. This work is ongoing.
Preliminary work in changing the dtype and casting implementations in order to provide an easier path to extending dtypes. This work is ongoing but enough has been done to allow experimentation and feedback.
Extensive documentation improvements comprising some 185 PR merges. This work is ongoing and part of the larger project to improve NumPy's online presence and usefulness to new users.
Further cleanups related to removing Python 2.7. This improves code readability and removes technical debt.
Preliminary support for the upcoming Cython 3.0.

New functions

The random.Generator class has a new `permuted` function.

The new function differs from shuffle and permutation in that the subarrays indexed by an axis are permuted rather than the axis being treated as a separate 1-D array for every combination of the other indexes. For example, it is now possible to permute the rows or columns of a 2-D array.

(gh-15121)

`sliding_window_view` provides a sliding window view for numpy arrays

numpy.lib.stride\_tricks.sliding\_window\_view constructs views on numpy arrays that offer a sliding or moving window access to the array. This allows for the simple implementation of certain algorithms, such as running means.

(gh-17394)

[numpy.broadcast_shapes]{.title-ref} is a new user-facing function

numpy.broadcast\_shapes gets the resulting shape from broadcasting the given shape tuples against each other.

>>> np.broadcast_shapes((1, 2), (3, 1))
(3, 2)

>>> np.broadcast_shapes(2, (3, 1))
(3, 2)

>>> np.broadcast_shapes((6, 7), (5, 6, 1), (7,), (5, 1, 7))
(5, 6, 7)

(gh-17535)

Deprecations

Using the aliases of builtin types like `np.int` is deprecated

For a long time, np.int has been an alias of the builtin int. This is repeatedly a cause of confusion for newcomers, and existed mainly for historic reasons.

These aliases have been deprecated. The table below shows the full list of deprecated aliases, along with their exact meaning. Replacing uses of items in the first column with the contents of the second column will work identically and silence the deprecation warning.

The third column lists alternative NumPy names which may occasionally be preferential. See also basics.types{.interpreted-text role="ref"} for additional details.

Deprecated name	Identical to	NumPy scalar type names
`numpy.bool`	`bool`	`numpy.bool\_`
`numpy.int`	`int`	`numpy.int\_` (default), `numpy.int64`, or `numpy.int32`
`numpy.float`	`float`	`numpy.float64`, `numpy.float\_`, `numpy.double` (equivalent)
`numpy.complex`	`complex`	`numpy.complex128`, `numpy.complex\_`, `numpy.cdouble` (equivalent)
`numpy.object`	`object`	`numpy.object\_`
`numpy.str`	`str`	`numpy.str\_`
`numpy.long`	`int`	`numpy.int\_` (C `long`), `numpy.longlong` (largest integer type)
`numpy.unicode`	`str`	`numpy.unicode\_`

To give a clear guideline for the vast majority of cases, for the types bool, object, str (and unicode) using the plain version is shorter and clear, and generally a good replacement. For float and complex you can use float64 and complex128 if you wish to be more explicit about the precision.

For np.int a direct replacement with np.int_ or int is also good and will not change behavior, but the precision will continue to depend on the computer and operating system. If you want to be more explicit and review the current use, you have the following alternatives:

np.int64 or np.int32 to specify the precision exactly. This ensures that results cannot depend on the computer or operating system.
np.int_ or int (the default), but be aware that it depends on the computer and operating system.
The C types: np.cint (int), np.int_ (long), np.longlong.
np.intp which is 32bit on 32bit machines 64bit on 64bit machines. This can be the best type to use for indexing.

When used with np.dtype(...) or dtype=... changing it to the NumPy name as mentioned above will have no effect on the output. If used as a scalar with:

np.float(123)

changing it can subtly change the result. In this case, the Python version float(123) or int(12.) is normally preferable, although the NumPy version may be useful for consistency with NumPy arrays (for example, NumPy behaves differently for things like division by zero).

(gh-14882)

Passing `shape=None` to functions with a non-optional shape argument is deprecated

Previously, this was an alias for passing shape=(). This deprecation is emitted by PyArray\_IntpConverter in the C API. If your API is intended to support passing None, then you should check for None prior to invoking the converter, so as to be able to distinguish None and ().

(gh-15886)

Indexing errors will be reported even when index result is empty

In the future, NumPy will raise an IndexError when an integer array index contains out of bound values even if a non-indexed dimension is of length 0. This will now emit a DeprecationWarning. This can happen when the array is previously empty, or an empty slice is involved:

arr1 = np.zeros((5, 0))
arr1[[20]]
arr2 = np.zeros((5, 5))
arr2[[20], :0]

Previously the non-empty index [20] was not checked for correctness. It will now be checked causing a deprecation warning which will be turned into an error. This also applies to assignments.

(gh-15900)

Inexact matches for `mode` and `searchside` are deprecated

Inexact and case insensitive matches for mode and searchside were valid inputs earlier and will give a DeprecationWarning now. For example, below are some example usages which are now deprecated and will give a DeprecationWarning:

import numpy as np
arr = np.array([[3, 6, 6], [4, 5, 1]])
# mode: inexact match
np.ravel_multi_index(arr, (7, 6), mode="clap")  # should be "clip"
# searchside: inexact match
np.searchsorted(arr[0], 4, side='random')  # should be "right"

(gh-16056)

Deprecation of [numpy.dual]{.title-ref}

The module numpy.dual is deprecated. Instead of importing functions from numpy.dual, the functions should be imported directly from NumPy or SciPy.

(gh-16156)

`outer` and `ufunc.outer` deprecated for matrix

np.matrix use with \~numpy.outer or generic ufunc outer calls such as numpy.add.outer. Previously, matrix was converted to an array here. This will not be done in the future requiring a manual conversion to arrays.

(gh-16232)

Further Numeric Style types Deprecated

The remaining numeric-style type codes Bytes0, Str0, Uint32, Uint64, and Datetime64 have been deprecated. The lower-case variants should be used instead. For bytes and string "S" and "U" are further alternatives.

(gh-16554)

The `ndincr` method of `ndindex` is deprecated

The documentation has warned against using this function since NumPy 1.8. Use next(it) instead of it.ndincr().

(gh-17233)

ArrayLike objects which do not define `len` and `getitem`

Objects which define one of the protocols __array__, __array_interface__, or __array_struct__ but are not sequences (usually defined by having a __len__ and __getitem__) will behave differently during array-coercion in the future.

When nested inside sequences, such as np.array([array_like]), these were handled as a single Python object rather than an array. In the future they will behave identically to:

np.array([np.array(array_like)])

This change should only have an effect if np.array(array_like) is not 0-D. The solution to this warning may depend on the object:

Some array-likes may expect the new behaviour, and users can ignore the warning. The object can choose to expose the sequence protocol to opt-in to the new behaviour.
For example, shapely will allow conversion to an array-like using line.coords rather than np.asarray(line). Users may work around the warning, or use the new convention when it becomes available.

Unfortunately, using the new behaviour can only be achieved by calling np.array(array_like).

If you wish to ensure that the old behaviour remains unchanged, please create an object array and then fill it explicitly, for example:

arr = np.empty(3, dtype=object)
arr[:] = [array_like1, array_like2, array_like3]

This will ensure NumPy knows to not enter the array-like and use it as a object instead.

(gh-17973)

Future Changes

Arrays cannot be using subarray dtypes

Array creation and casting using np.array(arr, dtype) and arr.astype(dtype) will use different logic when dtype is a subarray dtype such as np.dtype("(2)i,").

For such a dtype the following behaviour is true:

res = np.array(arr, dtype)

res.dtype is not dtype
res.dtype is dtype.base
res.shape == arr.shape + dtype.shape

But res is filled using the logic:

res = np.empty(arr.shape + dtype.shape, dtype=dtype.base)
res[...] = arr

which uses incorrect broadcasting (and often leads to an error). In the future, this will instead cast each element individually, leading to the same result as:

res = np.array(arr, dtype=np.dtype(["f", dtype]))["f"]

Which can normally be used to opt-in to the new behaviour.

This change does not affect np.array(list, dtype="(2)i,") unless the list itself includes at least one array. In particular, the behaviour is unchanged for a list of tuples.

(gh-17596)

Expired deprecations

The deprecation of numeric style type-codes np.dtype("Complex64") (with upper case spelling), is expired. "Complex64" corresponded to "complex128" and "Complex32" corresponded to "complex64".
The deprecation of np.sctypeNA and np.typeNA is expired. Both have been removed from the public API. Use np.typeDict instead.

(gh-16554)
The 14-year deprecation of np.ctypeslib.ctypes_load_library is expired. Use ~numpy.ctypeslib.load_library{.interpreted-text role="func"} instead, which is identical.

(gh-17116)

Financial functions removed

In accordance with NEP 32, the financial functions are removed from NumPy 1.20. The functions that have been removed are fv, ipmt, irr, mirr, nper, npv, pmt, ppmt, pv, and rate. These functions are available in the numpy_financial library.

(gh-17067)

Compatibility notes

`isinstance(dtype, np.dtype)` and not `type(dtype) is not np.dtype`

NumPy dtypes are not direct instances of np.dtype anymore. Code that may have used type(dtype) is np.dtype will always return False and must be updated to use the correct version isinstance(dtype, np.dtype).

This change also affects the C-side macro PyArray_DescrCheck if compiled against a NumPy older than 1.16.6. If code uses this macro and wishes to compile against an older version of NumPy, it must replace the macro (see also C API changes section).

Same kind casting in concatenate with `axis=None`

When [~numpy.concatenate]{.title-ref} is called with axis=None, the flattened arrays were cast with unsafe. Any other axis choice uses "same kind". That different default has been deprecated and "same kind" casting will be used instead. The new casting keyword argument can be used to retain the old behaviour.

(gh-16134)

NumPy Scalars are cast when assigned to arrays

When creating or assigning to arrays, in all relevant cases NumPy scalars will now be cast identically to NumPy arrays. In particular this changes the behaviour in some cases which previously raised an error:

np.array([np.float64(np.nan)], dtype=np.int64)

will succeed and return an undefined result (usually the smallest possible integer). This also affects assignments:

arr[0] = np.float64(np.nan)

At this time, NumPy retains the behaviour for:

np.array(np.float64(np.nan), dtype=np.int64)

The above changes do not affect Python scalars:

np.array([float("NaN")], dtype=np.int64)

remains unaffected (np.nan is a Python float, not a NumPy one). Unlike signed integers, unsigned integers do not retain this special case, since they always behaved more like casting. The following code stops raising an error:

np.array([np.float64(np.nan)], dtype=np.uint64)

To avoid backward compatibility issues, at this time assignment from datetime64 scalar to strings of too short length remains supported. This means that np.asarray(np.datetime64("2020-10-10"), dtype="S5") succeeds now, when it failed before. In the long term this may be deprecated or the unsafe cast may be allowed generally to make assignment of arrays and scalars behave consistently.

Array coercion changes when Strings and other types are mixed

When strings and other types are mixed, such as:

np.array(["string", np.float64(3.)], dtype="S")

The results will change, which may lead to string dtypes with longer strings in some cases. In particularly, if dtype="S" is not provided any numerical value will lead to a string results long enough to hold all possible numerical values. (e.g. "S32" for floats). Note that you should always provide dtype="S" when converting non-strings to strings.

If dtype="S" is provided the results will be largely identical to before, but NumPy scalars (not a Python float like 1.0), will still enforce a uniform string length:

np.array([np.float64(3.)], dtype="S")  # gives "S32"
np.array([3.0], dtype="S")  # gives "S3"

Previously the first version gave the same result as the second.

Array coercion restructure

Array coercion has been restructured. In general, this should not affect users. In extremely rare corner cases where array-likes are nested:

np.array([array_like1])

Things will now be more consistent with:

np.array([np.array(array_like1)])

This can subtly change output for some badly defined array-likes. One example for this are array-like objects which are not also sequences of matching shape. In NumPy 1.20, a warning will be given when an array-like is not also a sequence (but behaviour remains identical, see deprecations). If an array like is also a sequence (defines __getitem__ and __len__) NumPy will now only use the result given by __array__, __array_interface__, or __array_struct__. This will result in differences when the (nested) sequence describes a different shape.

(gh-16200)

Writing to the result of `numpy.broadcast\_arrays` will export readonly buffers

In NumPy 1.17 numpy.broadcast\_arrays started warning when the resulting array was written to. This warning was skipped when the array was used through the buffer interface (e.g. memoryview(arr)). The same thing will now occur for the two protocols __array_interface__, and __array_struct__ returning read-only buffers instead of giving a warning.

(gh-16350)

Numeric-style type names have been removed from type dictionaries

To stay in sync with the deprecation for np.dtype("Complex64") and other numeric-style (capital case) types. These were removed from np.sctypeDict and np.typeDict. You should use the lower case versions instead. Note that "Complex64" corresponds to "complex128" and "Complex32" corresponds to "complex64". The numpy style (new) versions, denote the full size and not the size of the real/imaginary part.

(gh-16554)

The `operator.concat` function now raises TypeError for array arguments

The previous behavior was to fall back to addition and add the two arrays, which was thought to be unexpected behavior for a concatenation function.

(gh-16570)

`nickname` attribute removed from ABCPolyBase

An abstract property nickname has been removed from ABCPolyBase as it was no longer used in the derived convenience classes. This may affect users who have derived classes from ABCPolyBase and overridden the methods for representation and display, e.g. __str__, __repr__, _repr_latex, etc.

(gh-16589)

`float->timedelta` and `uint64->timedelta` promotion will raise a TypeError

Float and timedelta promotion consistently raises a TypeError. np.promote_types("float32", "m8") aligns with np.promote_types("m8", "float32") now and both raise a TypeError. Previously, np.promote_types("float32", "m8") returned "m8" which was considered a bug.

Uint64 and timedelta promotion consistently raises a TypeError. np.promote_types("uint64", "m8") aligns with np.promote_types("m8", "uint64") now and both raise a TypeError. Previously, np.promote_types("uint64", "m8") returned "m8" which was considered a bug.

(gh-16592)

`numpy.genfromtxt` now correctly unpacks structured arrays

Previously, numpy.genfromtxt failed to unpack if it was called with unpack=True and a structured datatype was passed to the dtype argument (or dtype=None was passed and a structured datatype was inferred). For example:

>>> data = StringIO("21 58.0\n35 72.0")
>>> np.genfromtxt(data, dtype=None, unpack=True)
array([(21, 58.), (35, 72.)], dtype=[('f0', '<i8'), ('f1', '<f8')])

Structured arrays will now correctly unpack into a list of arrays, one for each column:

>>> np.genfromtxt(data, dtype=None, unpack=True)
[array([21, 35]), array([58., 72.])]

(gh-16650)

`mgrid`, `r_`, etc. consistently return correct outputs for non-default precision input

Previously, np.mgrid[np.float32(0.1):np.float32(0.35):np.float32(0.1),] and np.r_[0:10:np.complex64(3j)] failed to return meaningful output. This bug potentially affects [~numpy.mgrid]{.title-ref}, numpy.ogrid, numpy.r\_, and numpy.c\_ when an input with dtype other than the default float64 and complex128 and equivalent Python types were used. The methods have been fixed to handle varying precision correctly.

(gh-16815)

Boolean array indices with mismatching shapes now properly give `IndexError`

Previously, if a boolean array index matched the size of the indexed array but not the shape, it was incorrectly allowed in some cases. In other cases, it gave an error, but the error was incorrectly a ValueError with a message about broadcasting instead of the correct IndexError.

For example, the following used to incorrectly give ValueError: operands could not be broadcast together with shapes (2,2) (1,4):

np.empty((2, 2))[np.array([[True, False, False, False]])]

And the following used to incorrectly return array([], dtype=float64):

np.empty((2, 2))[np.array([[False, False, False, False]])]

Both now correctly give IndexError: boolean index did not match indexed array along dimension 0; dimension is 2 but corresponding boolean dimension is 1.

(gh-17010)

Casting errors interrupt Iteration

When iterating while casting values, an error may stop the iteration earlier than before. In any case, a failed casting operation always returned undefined, partial results. Those may now be even more undefined and partial. For users of the NpyIter C-API such cast errors will now cause the [iternext()]{.title-ref} function to return 0 and thus abort iteration. Currently, there is no API to detect such an error directly. It is necessary to check PyErr_Occurred(), which may be problematic in combination with NpyIter_Reset. These issues always existed, but new API could be added if required by users.

(gh-17029)

f2py generated code may return unicode instead of byte strings

Some byte strings previously returned by f2py generated code may now be unicode strings. This results from the ongoing Python2 -> Python3 cleanup.

(gh-17068)

The first element of the `__array_interface__["data"]` tuple must be an integer

This has been the documented interface for many years, but there was still code that would accept a byte string representation of the pointer address. That code has been removed, passing the address as a byte string will now raise an error.

(gh-17241)

poly1d respects the dtype of all-zero argument

Previously, constructing an instance of poly1d with all-zero coefficients would cast the coefficients to np.float64. This affected the output dtype of methods which construct poly1d instances internally, such as np.polymul.

(gh-17577)

The numpy.i file for swig is Python 3 only.

Uses of Python 2.7 C-API functions have been updated to Python 3 only. Users who need the old version should take it from an older version of NumPy.

(gh-17580)

Void dtype discovery in `np.array`

In calls using np.array(..., dtype="V"), arr.astype("V"), and similar a TypeError will now be correctly raised unless all elements have the identical void length. An example for this is:

np.array([b"1", b"12"], dtype="V")

Which previously returned an array with dtype "V2" which cannot represent b"1" faithfully.

(gh-17706)

C API changes

The `PyArray_DescrCheck` macro is modified

The PyArray_DescrCheck macro has been updated since NumPy 1.16.6 to be:

#define PyArray_DescrCheck(op) PyObject_TypeCheck(op, &PyArrayDescr_Type)

Starting with NumPy 1.20 code that is compiled against an earlier version will be API incompatible with NumPy 1.20. The fix is to either compile against 1.16.6 (if the NumPy 1.16 release is the oldest release you wish to support), or manually inline the macro by replacing it with the new definition:

PyObject_TypeCheck(op, &PyArrayDescr_Type)

which is compatible with all NumPy versions.

Size of `np.ndarray` and `np.void_` changed

The size of the PyArrayObject and PyVoidScalarObject structures have changed. The following header definition has been removed:

#define NPY_SIZEOF_PYARRAYOBJECT (sizeof(PyArrayObject_fields))

since the size must not be considered a compile time constant: it will change for different runtime versions of NumPy.

The most likely relevant use are potential subclasses written in C which will have to be recompiled and should be updated. Please see the documentation for :cPyArrayObject{.interpreted-text role="type"} for more details and contact the NumPy developers if you are affected by this change.

NumPy will attempt to give a graceful error but a program expecting a fixed structure size may have undefined behaviour and likely crash.

(gh-16938)

New Features

`where` keyword argument for `numpy.all` and `numpy.any` functions

The keyword argument where is added and allows to only consider specified elements or subaxes from an array in the Boolean evaluation of all and any. This new keyword is available to the functions all and any both via numpy directly or in the methods of numpy.ndarray.

Any broadcastable Boolean array or a scalar can be set as where. It defaults to True to evaluate the functions for all elements in an array if where is not set by the user. Examples are given in the documentation of the functions.

`where` keyword argument for `numpy` functions `mean`, `std`, `var`

The keyword argument where is added and allows to limit the scope in the calculation of mean, std and var to only a subset of elements. It is available both via numpy directly or in the methods of numpy.ndarray.

(gh-15852)

`norm=backward`, `forward` keyword options for `numpy.fft` functions

The keyword argument option norm=backward is added as an alias for None and acts as the default option; using it has the direct transforms unscaled and the inverse transforms scaled by 1/n.

Using the new keyword argument option norm=forward has the direct transforms scaled by 1/n and the inverse transforms unscaled (i.e. exactly opposite to the default option norm=backward).

(gh-16476)

NumPy is now typed

Type annotations have been added for large parts of NumPy. There is also a new [numpy.typing]{.title-ref} module that contains useful types for end-users. The currently available types are

ArrayLike: for objects that can be coerced to an array
DtypeLike: for objects that can be coerced to a dtype

(gh-16515)

`numpy.typing` is accessible at runtime

The types in numpy.typing can now be imported at runtime. Code like the following will now work:

from numpy.typing import ArrayLike
x: ArrayLike = [1, 2, 3, 4]

(gh-16558)

New `__f2py_numpy_version__` attribute for f2py generated modules.

Because f2py is released together with NumPy, __f2py_numpy_version__ provides a way to track the version f2py used to generate the module.

(gh-16594)

`mypy` tests can be run via runtests.py

Currently running mypy with the NumPy stubs configured requires either:

Installing NumPy
Adding the source directory to MYPYPATH and linking to the mypy.ini

Both options are somewhat inconvenient, so add a --mypy option to runtests that handles setting things up for you. This will also be useful in the future for any typing codegen since it will ensure the project is built before type checking.

(gh-17123)

Negation of user defined BLAS/LAPACK detection order

[~numpy.distutils]{.title-ref} allows negation of libraries when determining BLAS/LAPACK libraries. This may be used to remove an item from the library resolution phase, i.e. to disallow NetLIB libraries one could do:

NPY_BLAS_ORDER='^blas' NPY_LAPACK_ORDER='^lapack' python setup.py build

That will use any of the accelerated libraries instead.

(gh-17219)

Allow passing optimizations arguments to asv build

It is now possible to pass -j, --cpu-baseline, --cpu-dispatch and --disable-optimization flags to ASV build when the --bench-compare argument is used.

(gh-17284)

The NVIDIA HPC SDK nvfortran compiler is now supported

Support for the nvfortran compiler, a version of pgfortran, has been added.

(gh-17344)

`dtype` option for `cov` and `corrcoef`

The dtype option is now available for [numpy.cov]{.title-ref} and [numpy.corrcoef]{.title-ref}. It specifies which data-type the returned result should have. By default the functions still return a [numpy.float64]{.title-ref} result.

(gh-17456)

Improvements

Improved string representation for polynomials (`str`)

The string representation (__str__) of all six polynomial types in [numpy.polynomial]{.title-ref} has been updated to give the polynomial as a mathematical expression instead of an array of coefficients. Two package-wide formats for the polynomial expressions are available - one using Unicode characters for superscripts and subscripts, and another using only ASCII characters.

(gh-15666)

Remove the Accelerate library as a candidate LAPACK library

Apple no longer supports Accelerate. Remove it.

(gh-15759)

Object arrays containing multi-line objects have a more readable `repr`

If elements of an object array have a repr containing new lines, then the wrapped lines will be aligned by column. Notably, this improves the repr of nested arrays:

>>> np.array([np.eye(2), np.eye(3)], dtype=object)
array([array([[1., 0.],
              [0., 1.]]),
       array([[1., 0., 0.],
              [0., 1., 0.],
              [0., 0., 1.]])], dtype=object)

(gh-15997)

Concatenate supports providing an output dtype

Support was added to [~numpy.concatenate]{.title-ref} to provide an output dtype and casting using keyword arguments. The dtype argument cannot be provided in conjunction with the out one.

(gh-16134)

Thread safe f2py callback functions

Callback functions in f2py are now thread safe.

(gh-16519)

[numpy.core.records.fromfile]{.title-ref} now supports file-like objects

[numpy.rec.fromfile]{.title-ref} can now use file-like objects, for instance :pyio.BytesIO{.interpreted-text role="class"}

(gh-16675)

RPATH support on AIX added to distutils

This allows SciPy to be built on AIX.

(gh-16710)

Use f90 compiler specified by the command line args

The compiler command selection for Fortran Portland Group Compiler is changed in [numpy.distutils.fcompiler]{.title-ref}. This only affects the linking command. This forces the use of the executable provided by the command line option (if provided) instead of the pgfortran executable. If no executable is provided to the command line option it defaults to the pgf90 executable, wich is an alias for pgfortran according to the PGI documentation.

(gh-16730)

Add NumPy declarations for Cython 3.0 and later

The pxd declarations for Cython 3.0 were improved to avoid using deprecated NumPy C-API features. Extension modules built with Cython 3.0+ that use NumPy can now set the C macro NPY_NO_DEPRECATED_API=NPY_1_7_API_VERSION to avoid C compiler warnings about deprecated API usage.

(gh-16986)

Make the window functions exactly symmetric

Make sure the window functions provided by NumPy are symmetric. There were previously small deviations from symmetry due to numerical precision that are now avoided by better arrangement of the computation.

(gh-17195)

Performance improvements and changes

Enable multi-platform SIMD compiler optimizations

A series of improvements for NumPy infrastructure to pave the way to NEP-38, that can be summarized as follow:

New Build Arguments
- --cpu-baseline to specify the minimal set of required optimizations, default value is min which provides the minimum CPU features that can safely run on a wide range of users platforms.
- --cpu-dispatch to specify the dispatched set of additional optimizations, default value is max -xop -fma4 which enables all CPU features, except for AMD legacy features.
- --disable-optimization to explicitly disable the whole new improvements, It also adds a new C compiler #definition called NPY_DISABLE_OPTIMIZATION which it can be used as guard for any SIMD code.
Advanced CPU dispatcher

A flexible cross-architecture CPU dispatcher built on the top of Python/Numpy distutils, support all common compilers with a wide range of CPU features.

The new dispatcher requires a special file extension *.dispatch.c to mark the dispatch-able C sources. These sources have the ability to be compiled multiple times so that each compilation process represents certain CPU features and provides different #definitions and flags that affect the code paths.
New auto-generated C header ``core/src/common/_cpu_dispatch.h``

This header is generated by the distutils module ccompiler_opt, and contains all the #definitions and headers of instruction sets, that had been configured through command arguments '--cpu-baseline' and '--cpu-dispatch'.
New C header ``core/src/common/npy_cpu_dispatch.h``

This header contains all utilities that required for the whole CPU dispatching process, it also can be considered as a bridge linking the new infrastructure work with NumPy CPU runtime detection.
Add new attributes to NumPy umath module(Python level)
- __cpu_baseline__ a list contains the minimal set of required optimizations that supported by the compiler and platform according to the specified values to command argument '--cpu-baseline'.
- __cpu_dispatch__ a list contains the dispatched set of additional optimizations that supported by the compiler and platform according to the specified values to command argument '--cpu-dispatch'.
Print the supported CPU features during the run of PytestTester

(gh-13516)

Changes

Changed behavior of `divmod(1., 0.)` and related functions

The changes also assure that different compiler versions have the same behavior for nan or inf usages in these operations. This was previously compiler dependent, we now force the invalid and divide by zero flags, making the results the same across compilers. For example, gcc-5, gcc-8, or gcc-9 now result in the same behavior. The changes are tabulated below:

Operator	Old Warning	New Warning	Old Result	New Result	Works on MacOS
np.divmod(1.0, 0.0)	Invalid	Invalid and Dividebyzero	nan, nan	inf, nan	Yes
np.fmod(1.0, 0.0)	Invalid	Invalid	nan	nan	No? Yes
np.floor_divide(1.0, 0.0)	Invalid	Dividebyzero	nan	inf	Yes
np.remainder(1.0, 0.0)	Invalid	Invalid	nan	nan	Yes

: Summary of New Behavior

(gh-16161)

`np.linspace` on integers now uses floor

When using a int dtype in [numpy.linspace]{.title-ref}, previously float values would be rounded towards zero. Now [numpy.floor]{.title-ref} is used instead, which rounds toward -inf. This changes the results for negative values. For example, the following would previously give:

>>> np.linspace(-3, 1, 8, dtype=int)
array([-3, -2, -1, -1,  0,  0,  0,  1])

and now results in:

>>> np.linspace(-3, 1, 8, dtype=int)
array([-3, -3, -2, -2, -1, -1,  0,  1])

The former result can still be obtained with:

>>> np.linspace(-3, 1, 8).astype(int)
array([-3, -2, -1, -1,  0,  0,  0,  1])

(gh-16841)

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Open source →

1.19.55 Jan 2021

NumPy 1.19.5 is a short bugfix release. Apart from fixing several bugs, the main improvement is the update to OpenBLAS 0.3.13 that works around the wi

NumPy 1.19.5 Release Notes

NumPy 1.19.5 is a short bugfix release. Apart from fixing several bugs, the main improvement is the update to OpenBLAS 0.3.13 that works around the windows 2004 bug while not breaking execution on other platforms. This release supports Python 3.6-3.9 and is planned to be the last release in the 1.19.x cycle.

Contributors

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

Charles Harris
Christoph Gohlke
Matti Picus
Raghuveer Devulapalli
Sebastian Berg
Simon Graham +
Veniamin Petrenko +
Bernie Gray +

Pull requests merged

A total of 11 pull requests were merged for this release.

#17756: BUG: Fix segfault due to out of bound pointer in floatstatus...
#17774: BUG: fix np.timedelta64('nat').__format__ throwing an exception
#17775: BUG: Fixed file handle leak in array_tofile.
#17786: BUG: Raise recursion error during dimension discovery
#17917: BUG: Fix subarray dtype used with too large count in fromfile
#17918: BUG: 'bool' object has no attribute 'ndim'
#17919: BUG: ensure _UFuncNoLoopError can be pickled
#17924: BLD: use BUFFERSIZE=20 in OpenBLAS
#18026: BLD: update to OpenBLAS 0.3.13
#18036: BUG: make a variable volatile to work around clang compiler bug
#18114: REL: Prepare for the NumPy 1.19.5 release.

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SHA256

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50a4a0ad0111cc1b71fa32dedd05fa239f7fb5a43a40663269bb5dc7877cfd28  numpy-1.19.5-cp37-cp37m-manylinux2014_aarch64.whl
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a76f502430dd98d7546e1ea2250a7360c065a5fdea52b2dffe8ae7180909b6f4  numpy-1.19.5.zip

Open source →

1.19.42 Nov 2020

NumPy 1.19.4 is a quick release to revert the OpenBLAS library version. It was hoped that the 0.3.12 OpenBLAS version used in 1.19.3 would work around

NumPy 1.19.4 Release Notes

NumPy 1.19.4 is a quick release to revert the OpenBLAS library version. It was hoped that the 0.3.12 OpenBLAS version used in 1.19.3 would work around the Microsoft fmod bug, but problems in some docker environments turned up. Instead, 1.19.4 will use the older library and run a sanity check on import, raising an error if the problem is detected. Microsoft is aware of the problem and has promised a fix, users should upgrade when it becomes available.

This release supports Python 3.6-3.9

Contributors

A total of 1 people contributed to this release. People with a "+" by their names contributed a patch for the first time.

Charles Harris

Pull requests merged

A total of 2 pull requests were merged for this release.

#17679: MAINT: Add check for Windows 10 version 2004 bug.
#17680: REV: Revert OpenBLAS to 1.19.2 version for 1.19.4

Checksums

MD5

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d40f6fcf611ab40eed4ff90606e05307  numpy-1.19.4.zip

SHA256

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c42c4b73121caf0ed6cd795512c9c09c52a7287b04d105d112068c1736d7c753  numpy-1.19.4-cp39-cp39-manylinux2014_aarch64.whl
8cac8790a6b1ddf88640a9267ee67b1aee7a57dfa2d2dd33999d080bc8ee3a0f  numpy-1.19.4-cp39-cp39-win32.whl
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2a2740aa9733d2e5b2dfb33639d98a64c3b0f24765fed86b0fd2aec07f6a0a08  numpy-1.19.4-pp36-pypy36_pp73-manylinux2010_x86_64.whl
fe836a685d6838dbb3f603caef01183ea98e88febf4ce956a2ea484a75378413  numpy-1.19.4.tar.gz
141ec3a3300ab89c7f2b0775289954d193cc8edb621ea05f99db9cb181530512  numpy-1.19.4.zip

Open source →

1.19.329 Oct 2020Deprecated

\#17336: BUG: Set deprecated fields to null in PyArray\_InitArrFuncs

NumPy 1.19.3 Release Notes

NumPy 1.19.3 is a small maintenace release with two major improvements:

Python 3.9 binary wheels on all supported platforms.
OpenBLAS fixes for Windows 10 version 2004 fmod bug.

This release supports Python 3.6-3.9 and is linked with OpenBLAS 3.7 to avoid some of the fmod problems on Windows 10 version 2004. Microsoft is aware of the problem and users should upgrade when the fix becomes available, the fix here is limited in scope.

Contributors

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

Charles Harris
Chris Brown +
Daniel Vanzo +
E. Madison Bray +
Hugo van Kemenade +
Ralf Gommers
Sebastian Berg
@danbeibei +

Pull requests merged

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

#17298: BLD: set upper versions for build dependencies
#17336: BUG: Set deprecated fields to null in PyArray_InitArrFuncs
#17446: ENH: Warn on unsupported Python 3.10+
#17450: MAINT: Update test_requirements.txt.
#17522: ENH: Support for the NVIDIA HPC SDK nvfortran compiler
#17568: BUG: Cygwin Workaround for #14787 on affected platforms
#17647: BUG: Fix memory leak of buffer-info cache due to relaxed strides
#17652: MAINT: Backport openblas_support from master.
#17653: TST: Add Python 3.9 to the CI testing on Windows, Mac.
#17660: TST: Simplify source path names in test_extending.

Checksums

MD5

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SHA256

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741d95eb2b505bb7a99fbf4be05fa69f466e240c2b4f2d3ddead4f1b5f82a5a5  numpy-1.19.3-cp39-cp39-manylinux2014_aarch64.whl
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Open source →

1.19.210 Sept 2020Deprecated

The pxd declarations for Cython 3.0 were improved to avoid using deprecated NumPy C-API features. Extension modules built with Cython 3.0+ that use Nu…

NumPy 1.19.2 Release Notes

NumPy 1.19.2 fixes several bugs, prepares for the upcoming Cython 3.x release. and pins setuptools to keep distutils working while upstream modifications are ongoing. The aarch64 wheels are built with the latest manylinux2014 release that fixes the problem of differing page sizes used by different linux distros.

This release supports Python 3.6-3.8. Cython >= 0.29.21 needs to be used when building with Python 3.9 for testing purposes.

There is a known problem with Windows 10 version=2004 and OpenBLAS svd that we are trying to debug. If you are running that Windows version you should use a NumPy version that links to the MKL library, earlier Windows versions are fine.

Improvements

Add NumPy declarations for Cython 3.0 and later

Contributors

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

Charles Harris
Matti Picus
Pauli Virtanen
Philippe Ombredanne +
Sebastian Berg
Stefan Behnel +
Stephan Loyd +
Zac Hatfield-Dodds

Pull requests merged

A total of 9 pull requests were merged for this release.

#16959: TST: Change aarch64 to arm64 in travis.yml.
#16998: MAINT: Configure hypothesis in np.test() for determinism,...
#17000: BLD: pin setuptools < 49.2.0
#17015: ENH: Add NumPy declarations to be used by Cython 3.0+
#17125: BUG: Remove non-threadsafe sigint handling from fft calculation
#17243: BUG: core: fix ilp64 blas dot/vdot/... for strides > int32 max
#17244: DOC: Use SPDX license expressions with correct license
#17245: DOC: Fix the link to the quick-start in the old API functions
#17272: BUG: fix pickling of arrays larger than 2GiB

Checksums

MD5

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SHA256

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Open source →

1.19.121 Jul 2020Deprecated

NumPy 1.19.1 fixes several bugs found in the 1.19.0 release, replaces several functions deprecated in the upcoming Python-3.9 release, has improved su…

NumPy 1.19.1 Release Notes

NumPy 1.19.1 fixes several bugs found in the 1.19.0 release, replaces several functions deprecated in the upcoming Python-3.9 release, has improved support for AIX, and has a number of development related updates to keep CI working with recent upstream changes.

This release supports Python 3.6-3.8. Cython >= 0.29.21 needs to be used when building with Python 3.9 for testing purposes.

Contributors

A total of 15 people contributed to this release. People with a "+" by their names contributed a patch for the first time.

Abhinav Reddy +
Anirudh Subramanian
Antonio Larrosa +
Charles Harris
Chunlin Fang
Eric Wieser
Etienne Guesnet +
Kevin Sheppard
Matti Picus
Raghuveer Devulapalli
Roman Yurchak
Ross Barnowski
Sayed Adel
Sebastian Berg
Tyler Reddy

Pull requests merged

A total of 25 pull requests were merged for this release.

#16649: MAINT, CI: disable Shippable cache
#16652: MAINT: Replace PyUString_GET_SIZE with PyUnicode_GetLength.
#16654: REL: Fix outdated docs link
#16656: BUG: raise IEEE exception on AIX
#16672: BUG: Fix bug in AVX complex absolute while processing array of...
#16693: TST: Add extra debugging information to CPU features detection
#16703: BLD: Add CPU entry for Emscripten / WebAssembly
#16705: TST: Disable Python 3.9-dev testing.
#16714: MAINT: Disable use_hugepages in case of ValueError
#16724: BUG: Fix PyArray_SearchSorted signature.
#16768: MAINT: Fixes for deprecated functions in scalartypes.c.src
#16772: MAINT: Remove unneeded call to PyUnicode_READY
#16776: MAINT: Fix deprecated functions in scalarapi.c
#16779: BLD, ENH: Add RPATH support for AIX
#16780: BUG: Fix default fallback in genfromtxt
#16784: BUG: Added missing return after raising error in methods.c
#16795: BLD: update cython to 0.29.21
#16832: MAINT: setuptools 49.2.0 emits a warning, avoid it
#16872: BUG: Validate output size in bin- and multinomial
#16875: BLD, MAINT: Pin setuptools
#16904: DOC: Reconstruct Testing Guideline.
#16905: TST, BUG: Re-raise MemoryError exception in test_large_zip's...
#16906: BUG, DOC: Fix bad MPL kwarg.
#16916: BUG: Fix string/bytes to complex assignment
#16922: REL: Prepare for NumPy 1.19.1 release

Checksums

MD5

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Open source →

1.19.020 Jun 2020Deprecated

This NumPy release is marked by the removal of much technical debt: support for Python 2 has been removed, many deprecations have been expired, and do…

NumPy 1.19.0 Release Notes

This NumPy release is marked by the removal of much technical debt: support for Python 2 has been removed, many deprecations have been expired, and documentation has been improved. The polishing of the random module continues apace with bug fixes and better usability from Cython.

The Python versions supported for this release are 3.6-3.8. Downstream developers should use Cython >= 0.29.16 for Python 3.8 support and OpenBLAS >= 3.7 to avoid problems on the Skylake architecture.

Highlights

Code compatibility with Python versions < 3.6 (including Python 2) was dropped from both the python and C code. The shims in numpy.compat will remain to support third-party packages, but they may be deprecated in a future release. Note that 1.19.x will not compile with earlier versions of Python due to the use of f-strings.

(gh-15233)

Expired deprecations

`numpy.insert` and `numpy.delete` can no longer be passed an axis on 0d arrays

This concludes a deprecation from 1.9, where when an axis argument was passed to a call to ~numpy.insert and ~numpy.delete on a 0d array, the axis and obj argument and indices would be completely ignored. In these cases, insert(arr, "nonsense", 42, axis=0) would actually overwrite the entire array, while delete(arr, "nonsense", axis=0) would be arr.copy()

Now passing axis on a 0d array raises ~numpy.AxisError.

(gh-15802)

`numpy.delete` no longer ignores out-of-bounds indices

This concludes deprecations from 1.8 and 1.9, where np.delete would ignore both negative and out-of-bounds items in a sequence of indices. This was at odds with its behavior when passed a single index.

Now out-of-bounds items throw IndexError, and negative items index from the end.

(gh-15804)

`numpy.insert` and `numpy.delete` no longer accept non-integral indices

This concludes a deprecation from 1.9, where sequences of non-integers indices were allowed and cast to integers. Now passing sequences of non-integral indices raises IndexError, just like it does when passing a single non-integral scalar.

(gh-15805)

`numpy.delete` no longer casts boolean indices to integers

This concludes a deprecation from 1.8, where np.delete would cast boolean arrays and scalars passed as an index argument into integer indices. The behavior now is to treat boolean arrays as a mask, and to raise an error on boolean scalars.

(gh-15815)

Compatibility notes

Changed random variate stream from `numpy.random.Generator.dirichlet`

A bug in the generation of random variates for the Dirichlet distribution with small 'alpha' values was fixed by using a different algorithm when max(alpha) < 0.1. Because of the change, the stream of variates generated by dirichlet in this case will be different from previous releases.

(gh-14924)

Scalar promotion in `PyArray_ConvertToCommonType`

The promotion of mixed scalars and arrays in PyArray_ConvertToCommonType has been changed to adhere to those used by np.result_type. This means that input such as (1000, np.array([1], dtype=np.uint8))) will now return uint16 dtypes. In most cases the behaviour is unchanged. Note that the use of this C-API function is generally discouraged. This also fixes np.choose to behave the same way as the rest of NumPy in this respect.

(gh-14933)

Fasttake and fastputmask slots are deprecated and NULL'ed

The fasttake and fastputmask slots are now never used and must always be set to NULL. This will result in no change in behaviour. However, if a user dtype should set one of these a DeprecationWarning will be given.

(gh-14942)

`np.ediff1d` casting behaviour with `to_end` and `to_begin`

np.ediff1d now uses the "same_kind" casting rule for its additional to_end and to_begin arguments. This ensures type safety except when the input array has a smaller integer type than to_begin or to_end. In rare cases, the behaviour will be more strict than it was previously in 1.16 and 1.17. This is necessary to solve issues with floating point NaN.

(gh-14981)

Converting of empty array-like objects to NumPy arrays

Objects with len(obj) == 0 which implement an "array-like" interface, meaning an object implementing obj.__array__(), obj.__array_interface__, obj.__array_struct__, or the python buffer interface and which are also sequences (i.e. Pandas objects) will now always retain there shape correctly when converted to an array. If such an object has a shape of (0, 1) previously, it could be converted into an array of shape (0,) (losing all dimensions after the first 0).

(gh-14995)

Removed `multiarray.int_asbuffer`

As part of the continued removal of Python 2 compatibility, multiarray.int_asbuffer was removed. On Python 3, it threw a NotImplementedError and was unused internally. It is expected that there are no downstream use cases for this method with Python 3.

(gh-15229)

`numpy.distutils.compat` has been removed

This module contained only the function get_exception(), which was used as:

try:
    ...
except Exception:
    e = get_exception()

Its purpose was to handle the change in syntax introduced in Python 2.6, from except Exception, e: to except Exception as e:, meaning it was only necessary for codebases supporting Python 2.5 and older.

(gh-15255)

`issubdtype` no longer interprets `float` as `np.floating`

numpy.issubdtype had a FutureWarning since NumPy 1.14 which has expired now. This means that certain input where the second argument was neither a datatype nor a NumPy scalar type (such as a string or a python type like int or float) will now be consistent with passing in np.dtype(arg2).type. This makes the result consistent with expectations and leads to a false result in some cases which previously returned true.

(gh-15773)

Change output of `round` on scalars to be consistent with Python

Output of the __round__ dunder method and consequently the Python built-in round has been changed to be a Python int to be consistent with calling it on Python float objects when called with no arguments. Previously, it would return a scalar of the np.dtype that was passed in.

(gh-15840)

The `numpy.ndarray` constructor no longer interprets `strides=()` as `strides=None`

The former has changed to have the expected meaning of setting numpy.ndarray.strides to (), while the latter continues to result in strides being chosen automatically.

(gh-15882)

C-Level string to datetime casts changed

The C-level casts from strings were simplified. This changed also fixes string to datetime and timedelta casts to behave correctly (i.e. like Python casts using string_arr.astype("M8") while previously the cast would behave like string_arr.astype(np.int_).astype("M8"). This only affects code using low-level C-API to do manual casts (not full array casts) of single scalar values or using e.g. PyArray_GetCastFunc, and should thus not affect the vast majority of users.

(gh-16068)

`SeedSequence` with small seeds no longer conflicts with spawning

Small seeds (less than 2**96) were previously implicitly 0-padded out to 128 bits, the size of the internal entropy pool. When spawned, the spawn key was concatenated before the 0-padding. Since the first spawn key is (0,), small seeds before the spawn created the same states as the first spawned SeedSequence. Now, the seed is explicitly 0-padded out to the internal pool size before concatenating the spawn key. Spawned SeedSequences will produce different results than in the previous release. Unspawned SeedSequences will still produce the same results.

(gh-16551)

Deprecations

Deprecate automatic `dtype=object` for ragged input

Calling np.array([[1, [1, 2, 3]]) will issue a DeprecationWarning as per NEP 34. Users should explicitly use dtype=object to avoid the warning.

(gh-15119)

Passing `shape=0` to factory functions in `numpy.rec` is deprecated

0 is treated as a special case and is aliased to None in the functions:

numpy.core.records.fromarrays
numpy.core.records.fromrecords
numpy.core.records.fromstring
numpy.core.records.fromfile

In future, 0 will not be special cased, and will be treated as an array length like any other integer.

(gh-15217)

Deprecation of probably unused C-API functions

The following C-API functions are probably unused and have been deprecated:

PyArray_GetArrayParamsFromObject
PyUFunc_GenericFunction
PyUFunc_SetUsesArraysAsData

In most cases PyArray_GetArrayParamsFromObject should be replaced by converting to an array, while PyUFunc_GenericFunction can be replaced with PyObject_Call (see documentation for details).

(gh-15427)

Converting certain types to dtypes is Deprecated

The super classes of scalar types, such as np.integer, np.generic, or np.inexact will now give a deprecation warning when converted to a dtype (or used in a dtype keyword argument). The reason for this is that np.integer is converted to np.int_, while it would be expected to represent any integer (e.g. also int8, int16, etc. For example, dtype=np.floating is currently identical to dtype=np.float64, even though also np.float32 is a subclass of np.floating.

(gh-15534)

Deprecation of `round` for `np.complexfloating` scalars

Output of the __round__ dunder method and consequently the Python built-in round has been deprecated on complex scalars. This does not affect np.round.

(gh-15840)

`numpy.ndarray.tostring()` is deprecated in favor of `tobytes()`

~numpy.ndarray.tobytes has existed since the 1.9 release, but until this release ~numpy.ndarray.tostring emitted no warning. The change to emit a warning brings NumPy in line with the builtin array.array methods of the same name.

(gh-15867)

C API changes

Better support for `const` dimensions in API functions

The following functions now accept a constant array of npy_intp:

PyArray_BroadcastToShape
PyArray_IntTupleFromIntp
PyArray_OverflowMultiplyList

Previously the caller would have to cast away the const-ness to call these functions.

(gh-15251)

Const qualify UFunc inner loops

UFuncGenericFunction now expects pointers to const dimension and strides as arguments. This means inner loops may no longer modify either dimension or strides. This change leads to an incompatible-pointer-types warning forcing users to either ignore the compiler warnings or to const qualify their own loop signatures.

(gh-15355)

New Features

`numpy.frompyfunc` now accepts an identity argument

This allows the `numpy.ufunc.identity{.interpreted-text role="attr"}[ attribute to be set on the resulting ufunc, meaning it can be used for empty and multi-dimensional calls to :meth:]{.title-ref}[numpy.ufunc.reduce]{.title-ref}`.

(gh-8255)

`np.str_` scalars now support the buffer protocol

np.str_ arrays are always stored as UCS4, so the corresponding scalars now expose this through the buffer interface, meaning memoryview(np.str_('test')) now works.

(gh-15385)

`subok` option for `numpy.copy`

A new kwarg, subok, was added to numpy.copy to allow users to toggle the behavior of numpy.copy with respect to array subclasses. The default value is False which is consistent with the behavior of numpy.copy for previous numpy versions. To create a copy that preserves an array subclass with numpy.copy, call np.copy(arr, subok=True). This addition better documents that the default behavior of numpy.copy differs from the numpy.ndarray.copy method which respects array subclasses by default.

(gh-15685)

`numpy.linalg.multi_dot` now accepts an `out` argument

out can be used to avoid creating unnecessary copies of the final product computed by numpy.linalg.multidot.

(gh-15715)

`keepdims` parameter for `numpy.count_nonzero`

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

(gh-15870)

`equal_nan` parameter for `numpy.array_equal`

The keyword argument equal_nan was added to numpy.array_equal. equal_nan is a boolean value that toggles whether or not nan values are considered equal in comparison (default is False). This matches API used in related functions such as numpy.isclose and numpy.allclose.

(gh-16128)

Improvements

Improve detection of CPU features

Replace npy_cpu_supports which was a gcc specific mechanism to test support of AVX with more general functions npy_cpu_init and npy_cpu_have, and expose the results via a NPY_CPU_HAVE c-macro as well as a python-level __cpu_features__ dictionary.

(gh-13421)

Use 64-bit integer size on 64-bit platforms in fallback lapack_lite

Use 64-bit integer size on 64-bit platforms in the fallback LAPACK library, which is used when the system has no LAPACK installed, allowing it to deal with linear algebra for large arrays.

(gh-15218)

Use AVX512 intrinsic to implement `np.exp` when input is `np.float64`

Use AVX512 intrinsic to implement np.exp when input is np.float64, which can improve the performance of np.exp with np.float64 input 5-7x faster than before. The _multiarray_umath.so module has grown about 63 KB on linux64.

(gh-15648)

Ability to disable madvise hugepages

On Linux NumPy has previously added support for madavise hugepages which can improve performance for very large arrays. Unfortunately, on older Kernel versions this led to peformance regressions, thus by default the support has been disabled on kernels before version 4.6. To override the default, you can use the environment variable:

NUMPY_MADVISE_HUGEPAGE=0

or set it to 1 to force enabling support. Note that this only makes a difference if the operating system is set up to use madvise transparent hugepage.

(gh-15769)

`numpy.einsum` accepts NumPy `int64` type in subscript list

There is no longer a type error thrown when numpy.einsum is passed a NumPy int64 array as its subscript list.

(gh-16080)

`np.logaddexp2.identity` changed to `-inf`

The ufunc ~numpy.logaddexp2 now has an identity of -inf, allowing it to be called on empty sequences. This matches the identity of ~numpy.logaddexp.

(gh-16102)

Changes

Remove handling of extra argument to `array`

A code path and test have been in the code since NumPy 0.4 for a two-argument variant of __array__(dtype=None, context=None). It was activated when calling ufunc(op) or ufunc.reduce(op) if op.__array__ existed. However that variant is not documented, and it is not clear what the intention was for its use. It has been removed.

(gh-15118)

`numpy.random._bit_generator` moved to `numpy.random.bit_generator`

In order to expose numpy.random.BitGenerator and numpy.random.SeedSequence to Cython, the _bitgenerator module is now public as numpy.random.bit_generator

Cython access to the random distributions is provided via a `pxd` file

c_distributions.pxd provides access to the c functions behind many of the random distributions from Cython, making it convenient to use and extend them.

(gh-15463)

Fixed `eigh` and `cholesky` methods in `numpy.random.multivariate_normal`

Previously, when passing method='eigh' or method='cholesky', numpy.random.multivariate_normal produced samples from the wrong distribution. This is now fixed.

(gh-15872)

Fixed the jumping implementation in `MT19937.jumped`

This fix changes the stream produced from jumped MT19937 generators. It does not affect the stream produced using RandomState or MT19937 that are directly seeded.

The translation of the jumping code for the MT19937 contained a reversed loop ordering. MT19937.jumped matches the Makoto Matsumoto's original implementation of the Horner and Sliding Window jump methods.

(gh-16153)

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Open source →

1.18.54 Jun 2020

This is a short release to allow pickle protocol=5 to be used in Python3.5. It is motivated by the recent backport of pickle5 to Python3.5.

NumPy 1.18.5 Release Notes

This is a short release to allow pickle protocol=5 to be used in Python3.5. It is motivated by the recent backport of pickle5 to Python3.5.

The Python versions supported in this release are 3.5-3.8. Downstream developers should use Cython >= 0.29.15 for Python 3.8 support and OpenBLAS >= 3.7 to avoid errors on the Skylake architecture.

Contributors

A total of 3 people contributed to this release. People with a "+" by their names contributed a patch for the first time.

Charles Harris
Matti Picus
Siyuan Zhuang +

Pull requests merged

A total of 2 pull requests were merged for this release.

#16439: ENH: enable pickle protocol 5 support for python3.5
#16441: BUG: relpath fails for different drives on windows

Checksums

MD5

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SHA256

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Open source →

1.18.43 May 2020

title: 'NumPy 1.18.4 Release Notes'

This is that last planned release in the 1.18.x series. It reverts the bool("0") behavior introduced in 1.18.3 and fixes a bug in Generator.integers. There is also improved help in the error message emitted when numpy import fails due to a link to a new troubleshooting section in the documentation that is now included.

The Python versions supported in this release are 3.5-3.8. Downstream developers should use Cython >= 0.29.15 for Python 3.8 support and OpenBLAS >= 3.7 to avoid errors on the Skylake architecture.

Contributors

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

Charles Harris
Matti Picus
Sebastian Berg
Warren Weckesser

Pull requests merged

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

#16055 BLD: add i686 for 1.18 builds
#16090 BUG: random: Generator.integers(2**32) always returned 0.
#16091 BLD: fix path to libgfortran on macOS
#16109 REV: Reverts side-effect changes to casting
#16114 BLD: put openblas library in local directory on windows
#16132 DOC: Change import error "howto" to link to new troubleshooting...

Checksums

MD5

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SHA256

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Open source →

1.18.319 Apr 2020

This release contains various bug/regression fixes.

NumPy 1.18.3 Release Notes

This release contains various bug/regression fixes.

The Python versions supported in this release are 3.5-3.8. Downstream developers should use Cython >= 0.29.15 for Python 3.8 support and OpenBLAS >= 3.7 to avoid errors on the Skylake architecture.

Highlights

Fix for the method='eigh' and method='cholesky' options in numpy.random.multivariate_normal. Those were producing samples from the wrong distribution.

Contributors

A total of 6 people contributed to this release. People with a "+" by their names contributed a patch for the first time.

Charles Harris
Max Balandat +
@Mibu287 +
Pan Jan +
Sebastian Berg
@panpiort8 +

Pull requests merged

A total of 5 pull requests were merged for this release.

#15916: BUG: Fix eigh and cholesky methods of numpy.random.multivariate_normal
#15929: BUG,MAINT: Remove incorrect special case in string to number...
#15930: BUG: Guarantee array is in valid state after memory error occurs...
#15954: BUG: Check that [pvals]{.title-ref} is 1D in _generator.multinomial.
#16017: BUG: Alpha parameter must be 1D in _generator.dirichlet

Checksums

MD5

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cd631c761f141d382b4e1b31c8232fc0  numpy-1.18.3.tar.gz
91314710fe9d29d80b6ccc9629e4532b  numpy-1.18.3.zip

SHA256

a6bc9432c2640b008d5f29bad737714eb3e14bb8854878eacf3d7955c4e91c36  numpy-1.18.3-cp35-cp35m-macosx_10_9_intel.whl
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1e37626bcb8895c4b3873fcfd54e9bfc5ffec8d0f525651d6985fcc5c6b6003c  numpy-1.18.3-cp35-cp35m-win32.whl
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4847f0c993298b82fad809ea2916d857d0073dc17b0510fbbced663b3265929d  numpy-1.18.3-cp37-cp37m-win32.whl
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264fd15590b3f02a1fbc095e7e1f37cdac698ff3829e12ffdcffdce3772f9d44  numpy-1.18.3-cp38-cp38-macosx_10_9_x86_64.whl
e94a39d5c40fffe7696009dbd11bc14a349b377e03a384ed011e03d698787dd3  numpy-1.18.3-cp38-cp38-manylinux1_i686.whl
a4305564e93f5c4584f6758149fd446df39fd1e0a8c89ca0deb3cce56106a027  numpy-1.18.3-cp38-cp38-manylinux1_x86_64.whl
99f0ba97e369f02a21bb95faa3a0de55991fd5f0ece2e30a9e2eaebeac238921  numpy-1.18.3-cp38-cp38-win32.whl
c60175d011a2e551a2f74c84e21e7c982489b96b6a5e4b030ecdeacf2914da68  numpy-1.18.3-cp38-cp38-win_amd64.whl
93ee59ec38f3bf8f9a42d5f4301f60e6825a4a6385a145f70badcd2bf2a11134  numpy-1.18.3.tar.gz
e46e2384209c91996d5ec16744234d1c906ab79a701ce1a26155c9ec890b8dc8  numpy-1.18.3.zip

Open source →

1.18.217 Mar 2020

This small elease contains a fix for a performance regression in numpy/random and several bug/maintenance updates.

NumPy 1.18.2 Release Notes

This small elease contains a fix for a performance regression in numpy/random and several bug/maintenance updates.

The Python versions supported in this release are 3.5-3.8. Downstream developers should use Cython >= 0.29.15 for Python 3.8 support and OpenBLAS >= 3.7 to avoid errors on the Skylake architecture.

Contributors

A total of 5 people contributed to this release. People with a "+" by their names contributed a patch for the first time.

Charles Harris
Ganesh Kathiresan +
Matti Picus
Sebastian Berg
przemb +

Pull requests merged

A total of 7 pull requests were merged for this release.

#15675: TST: move _no_tracing to testing._private
#15676: MAINT: Large overhead in some random functions
#15677: TST: Do not create gfortran link in azure Mac testing.
#15679: BUG: Added missing error check in ndarray.__contains__
#15722: MAINT: use list-based APIs to call subprocesses
#15729: REL: Prepare for 1.18.2 release.
#15734: BUG: fix logic error when nm fails on 32-bit

Checksums

MD5

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511010c9fbd2516fe5a24aabcb76a56d  numpy-1.18.2.zip

SHA256

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5e0feb76849ca3e83dd396254e47c7dba65b3fa9ed3df67c2556293ae3e16de3  numpy-1.18.2-cp38-cp38-win32.whl
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Open source →

1.18.16 Jan 2020

This release contains fixes for bugs reported against NumPy 1.18.0. Two bugs in particular that caused widespread problems downstream were:

NumPy 1.18.1 Release Notes

This release contains fixes for bugs reported against NumPy 1.18.0. Two bugs in particular that caused widespread problems downstream were:

The cython random extension test was not using a temporary directory for building, resulting in a permission violation. Fixed.
Numpy distutils was appending [-std=c99]{.title-ref} to all C compiler runs, leading to changed behavior and compile problems downstream. That flag is now only applied when building numpy C code.

The Python versions supported in this release are 3.5-3.8. Downstream developers should use Cython >= 0.29.14 for Python 3.8 support and OpenBLAS >= 3.7 to avoid errors on the Skylake architecture.

Contributors

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

Charles Harris
Matti Picus
Maxwell Aladago
Pauli Virtanen
Ralf Gommers
Tyler Reddy
Warren Weckesser

Pull requests merged

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

#15158: MAINT: Update pavement.py for towncrier.
#15159: DOC: add moved modules to 1.18 release note
#15161: MAINT, DOC: Minor backports and updates for 1.18.x
#15176: TST: Add assert_array_equal test for big integer arrays
#15184: BUG: use tmp dir and check version for cython test (#15170)
#15220: BUG: distutils: fix msvc+gfortran openblas handling corner case
#15221: BUG: remove -std=c99 for c++ compilation (#15194)
#15222: MAINT: unskip test on win32
#15223: TST: add BLAS ILP64 run in Travis & Azure
#15245: MAINT: only add --std=c99 where needed
#15246: BUG: lib: Fix handling of integer arrays by gradient.
#15247: MAINT: Do not use private Python function in testing
#15250: REL: Prepare for the NumPy 1.18.1 release.

Checksums

MD5

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18787d6482681c85a66629a781fb84c3  numpy-1.18.1.zip

SHA256

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Open source →

numpy

Release timeline

Releases

NumPy 2.0.0 Release Notes

Highlights

NumPy 2.0 Python API removals

__array_prepare__ is removed

Deprecations

numpy.fft deprecations for n-D transforms with None values in arguments

np.linalg.lstsq now defaults to a new rcond value

Expired deprecations

Compatibility notes

loadtxt and genfromtxt default encoding changed

f2py compatibility notes

Minor changes in behavior of sorting functions

Removed ambiguity when broadcasting in np.solve

Modified representation for Polynomial

C API changes

Datetime functionality exposed in the C API and Cython bindings

Const correctness for the generalized ufunc C API

Larger NPY_MAXDIMS and NPY_MAXARGS, NPY_RAVEL_AXIS introduced

NPY_MAXARGS not constant and PyArrayMultiIterObject size change

Required changes for custom legacy user dtypes

New Public DType API

New C-API import functions

Structured dtype information access through functions

Descriptor elsize and alignment access

NumPy 2.0 C API removals

New Features

np.add was extended to work with unicode and bytes dtypes.

A new bitwise_count function

macOS Accelerate support, including the ILP64

Option to use weights for quantile and percentile functions

Improved CPU optimization tracking

A new Meson backend for f2py

bind(c) support for f2py

A new strict option for several testing functions

Add np.core.umath.find and np.core.umath.rfind UFuncs

diagonal and trace for numpy.linalg

New long and ulong dtypes

svdvals for numpy.linalg

A new isdtype function

A new astype function

Array API compatible functions' aliases

New unique_* functions

Matrix transpose support for ndarrays

Array API compatible functions for numpy.linalg

A correction argument for var and std

ndarray.device and ndarray.to_device

StringDType has been added to NumPy

New keywords for cholesky and pinv

New keywords for sort, argsort and linalg.matrix_rank

New numpy.strings namespace for string ufuncs

numpy.fft support for different precisions and in-place calculations

configtool and pkg-config support

Array API standard support in the main namespace

Improvements

Strings are now supported by any, all, and the logical ufuncs.

Integer sequences as the shape argument for memmap

errstate is now faster and context safe

AArch64 quicksort speed improved by using Highway's VQSort

Complex types - underlying C type changes

iso_c_binding support and improved common blocks for f2py

Call str automatically on third argument to functions like assert_equal

Support for array-like atol/rtol in isclose, allclose

Consistent failure messages in test functions

n-D FFT transforms allow s[i] == -1

Guard PyArrayScalar_VAL and PyUnicodeScalarObject for the limited API

Changes

Representation of NumPy scalars changed

Truthiness of NumPy strings changed

A mean keyword was added to var and std function

Remove datetime64 deprecation warning when constructing with timezone

Default integer dtype is now 64-bit on 64-bit Windows

Renamed numpy.core to numpy._core

Redefinition of np.intp/np.uintp (almost never a change)

numpy.fft.helper made private

numpy.linalg.linalg made private

Out-of-bound axis not the same as axis=None

New copy keyword meaning for array and asarray constructors

`__array_prepare__` is removed

`numpy.fft` deprecations for n-D transforms with None values in arguments

`np.linalg.lstsq` now defaults to a new `rcond` value

`loadtxt` and `genfromtxt` default encoding changed

`f2py` compatibility notes

Removed ambiguity when broadcasting in `np.solve`

Modified representation for `Polynomial`

Larger `NPY_MAXDIMS` and `NPY_MAXARGS`, `NPY_RAVEL_AXIS` introduced

`NPY_MAXARGS` not constant and `PyArrayMultiIterObject` size change

Descriptor `elsize` and `alignment` access

`np.add` was extended to work with `unicode` and `bytes` dtypes.

A new `bitwise_count` function

A new Meson backend for `f2py`

`bind(c)` support for `f2py`

A new `strict` option for several testing functions

Add `np.core.umath.find` and `np.core.umath.rfind` UFuncs

`diagonal` and `trace` for `numpy.linalg`

New `long` and `ulong` dtypes

`svdvals` for `numpy.linalg`

A new `isdtype` function

A new `astype` function

New `unique_*` functions

Array API compatible functions for `numpy.linalg`

A `correction` argument for `var` and `std`

`ndarray.device` and `ndarray.to_device`

New keywords for `cholesky` and `pinv`

New keywords for `sort`, `argsort` and `linalg.matrix_rank`

New `numpy.strings` namespace for string ufuncs

`numpy.fft` support for different precisions and in-place calculations

Strings are now supported by `any`, `all`, and the logical ufuncs.

Integer sequences as the shape argument for `memmap`

`errstate` is now faster and context safe

`iso_c_binding` support and improved common blocks for `f2py`

Call `str` automatically on third argument to functions like `assert_equal`

Support for array-like `atol`/`rtol` in `isclose`, `allclose`

n-D FFT transforms allow `s[i] == -1`

A `mean` keyword was added to var and std function

Renamed `numpy.core` to `numpy._core`

Redefinition of `np.intp`/`np.uintp` (almost never a change)

`numpy.fft.helper` made private

`numpy.linalg.linalg` made private

Out-of-bound axis not the same as `axis=None`

New `copy` keyword meaning for `array` and `asarray` constructors

The `array` special method now takes a `copy` keyword argument.

Cleanup of initialization of `numpy.dtype` with strings with commas

`np.unique` `return_inverse` shape for multi-dimensional inputs

`any` and `all` return booleans for object arrays

`np.can_cast` cannot be called on Python int, float, or complex

`numpy._core` submodule stubs

Array API v2022.12 support in `numpy.array_api`

`meson` backend for `f2py`

`bind(c)` support for `f2py`

`iso_c_binding` support for `f2py`