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 12 comes out. That is
needed because distutils has been dropped by Python 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
innp.core
should generally be considered private.(gh-22638)
-
np.finfo(None)
is deprecated.(gh-23011)
-
np.round_
is deprecated. Usenp.round
instead.(gh-23302)
-
np.product
is deprecated. Usenp.prod
instead.(gh-23314)
-
np.cumproduct
is deprecated. Usenp.cumprod
instead.(gh-23314)
-
np.sometrue
is deprecated. Usenp.any
instead.(gh-23314)
-
np.alltrue
is deprecated. Usenp.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 eithernumpy.result_type
or
numpy.promote_types
. Most users leave the second
scalar_types
argument tofind_common_type
as[]
in which case
np.result_type
andnp.promote_types
are both faster and more
robust. When not usingscalar_types
the main difference is that
the replacement intentionally converts non-native byte-order to
native byte order. Further,find_common_type
returnsobject
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, usingnp.result_type
and passing the
Python values0
,0.0
, or0j
has the same result as using
int
,float
, orcomplex
inscalar_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 (whichnp.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
andnp.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
andcolumn_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 propagatenp.nan
values passed asmin
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 asasarray
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 allFalse
when values are
fundamentally not comparable (e.g. have different dtypes). An
example isnp.array(["a"]) == np.array([1])
.This mimics the Python behavior of returning
False
andTrue
when comparing incompatible types like"a" == 1
and
"a" != 1
. For a long time these gaveDeprecationWarning
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 theNUMPY_EXPERIMENTAL_ARRAY_FUNCTION
environment
variable has been removed. This variable disabled dispatching with
__array_function__
.(gh-23376)
-
Support for
y=
as an alias ofout=
has been removed. Thefix
,
isposinf
andisneginf
functions allowed usingy=
as a
(deprecated) alias forout=
. 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 theenddates
. 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 thedtype
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
callingnp.load
repeatedly, numpy previously raisedValueError
ifallow_pickle=False
, andOSError
ifallow_pickle=True
. Now
it raisesEOFError
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)
By default, the exported NumPy C API is now compatible with NumPy 1.19
Starting with NumPy 1.25 when including NumPy headers, NumPy now
defaults to exposing a backwards compatible API. This means that by
default binaries such as wheels build against NumPy 1.25 will also work
with NumPy 1.16 because it has the same API version as NumPy 1.19 which
is the oldest NumPy version compatible with Python 3.9.
You can customize this behavior using:
#define NPY_TARGET_VERSION NPY_1_22_API_VERSION
or the equivalent -D
option to the compiler. For more details please
see for-downstream-package-authors
{.interpreted-text role="ref"}. A
change should only be required in rare cases when a package relies on
newly added C-API.
(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.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)
Checksums
MD5
fbbc825509a7c07b510670d28ef8bbfa numpy-1.25.0rc1-cp310-cp310-macosx_10_9_x86_64.whl
4e180a777fb108729c5899f5d0dd809c numpy-1.25.0rc1-cp310-cp310-macosx_11_0_arm64.whl
d21d3e363c6e3ecef94a3cc9a014bd40 numpy-1.25.0rc1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
3d0d215ea9f1c0bcf2544133cfae4e60 numpy-1.25.0rc1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
fc7b40a9a38825e7065a3f68403870f1 numpy-1.25.0rc1-cp310-cp310-musllinux_1_1_x86_64.whl
b8277ceaf6893fa15cbfc550c23d0b87 numpy-1.25.0rc1-cp310-cp310-win32.whl
06018e514aea30f8bf533cdb05536e1f numpy-1.25.0rc1-cp310-cp310-win_amd64.whl
c74a294da0fbb44f3cf625527448ced7 numpy-1.25.0rc1-cp311-cp311-macosx_10_9_x86_64.whl
342eaeed6ba93fe58fcf0c70ed4df191 numpy-1.25.0rc1-cp311-cp311-macosx_11_0_arm64.whl
43c70b2d9fedd62053b9c5aac254eed2 numpy-1.25.0rc1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
eedfedfd3b08cce4969a5688bc11217d numpy-1.25.0rc1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
a97d41371f7a9b71be54091868c907ce numpy-1.25.0rc1-cp311-cp311-musllinux_1_1_x86_64.whl
9f23c161c940c2d5c88f6f2a96947edd numpy-1.25.0rc1-cp311-cp311-win32.whl
316c96a7a991af57a3956be3f070856b numpy-1.25.0rc1-cp311-cp311-win_amd64.whl
89e6d344c6b75c4795c4b44984fac20a numpy-1.25.0rc1-cp39-cp39-macosx_10_9_x86_64.whl
746d67157b23ab8c3f3cbeed1eb3ca6c numpy-1.25.0rc1-cp39-cp39-macosx_11_0_arm64.whl
e5155794ab1680a5e2b62fc8eae403d9 numpy-1.25.0rc1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
2777450b1791137614fde9505ad9371a numpy-1.25.0rc1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
10a1729d6126cc9ed75cb64589d916b8 numpy-1.25.0rc1-cp39-cp39-musllinux_1_1_x86_64.whl
e414a13a774f8034da9f8bcfda974653 numpy-1.25.0rc1-cp39-cp39-win32.whl
15ade485b3919f6839af2d30222b2cdd numpy-1.25.0rc1-cp39-cp39-win_amd64.whl
47aaca6df8579400191feac209f87589 numpy-1.25.0rc1-pp39-pypy39_pp73-macosx_10_9_x86_64.whl
8abee7d02cf0b89f15a8a769d31975c0 numpy-1.25.0rc1-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
7aa7729ceb639f1adadd2d1c493a1b9b numpy-1.25.0rc1-pp39-pypy39_pp73-win_amd64.whl
283924a7c0b1050e6b455d86e311c666 numpy-1.25.0rc1.tar.gz
SHA256
bd1de5d7ab75cdf56f2247aace7940dfd0a8fd048e07808358d8fca604f1d102 numpy-1.25.0rc1-cp310-cp310-macosx_10_9_x86_64.whl
a0dab69ef25ccabf6f066a4902e238767cbbe52bc5ff90aa99514f87812ba76a numpy-1.25.0rc1-cp310-cp310-macosx_11_0_arm64.whl
f64b730004e500f836f6405ad5cd36d309b6ac065366a0855860155f23eb2ad5 numpy-1.25.0rc1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
f8cbdb428d848f03a4f6f534284cf7fb168a6ec0e742357bf65ad268316906ea numpy-1.25.0rc1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
1365157813810cfda2be9518806bf32f6b5f56e5e501d8299e3b681d53e405e2 numpy-1.25.0rc1-cp310-cp310-musllinux_1_1_x86_64.whl
8aad2f86d2036622af1e1eb9db94e26618f42a571e02583fa72d5b1983782bf8 numpy-1.25.0rc1-cp310-cp310-win32.whl
a4a9f1eaa63b5e35e23e5465ed59746b0a680eb5b5da06f2d432f828d32b26c1 numpy-1.25.0rc1-cp310-cp310-win_amd64.whl
9a18d2c173a44e48e72614748df5624875439af2d352a416b9f3840583ad9efb numpy-1.25.0rc1-cp311-cp311-macosx_10_9_x86_64.whl
1bed69508b3b97dd3fb8c439352881c1bd232a0c8dd1e11d8df4e68046d434cf numpy-1.25.0rc1-cp311-cp311-macosx_11_0_arm64.whl
7cf92c2bfbaf7bd52df1a21e56e8d34cff711594498ecbd02a39df3aaada763b numpy-1.25.0rc1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
20dd2352806eb229bc79c4fa308431eaf1721c66f7928950ee0381df98a2d269 numpy-1.25.0rc1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
165b0fb4d5b6349eef7b909be2d61a673bc6e75e0eec43776eea3222385a9d11 numpy-1.25.0rc1-cp311-cp311-musllinux_1_1_x86_64.whl
b1a22ae597ee1d0e2336044854b33965fd92e731efe3c2ab965826e02cca2a8c numpy-1.25.0rc1-cp311-cp311-win32.whl
48e33b46b7db13de75dd0c1c919b8b297b5d7a4dc50b181066977ee17bed7cc3 numpy-1.25.0rc1-cp311-cp311-win_amd64.whl
416da35914d2fecc3afd31127b1eb1a283df33292cfcb453e1c8fb46d52611a1 numpy-1.25.0rc1-cp39-cp39-macosx_10_9_x86_64.whl
f59080829bbfe46660a201fc17315a4e8ec6e4499ee745bab3df61866f63e771 numpy-1.25.0rc1-cp39-cp39-macosx_11_0_arm64.whl
5e7ba92ad63ffded03400d5038af89f7788843794c77ad1a37522fa69762b06f numpy-1.25.0rc1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
04847257662eef90599a1beca30c757d8e562aa8c7d64e91ea465f299469075d numpy-1.25.0rc1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
dfe2e3845c3b630f6617f9e8a15c8a1cbaf452c9fa32c71ec0a77d09548cd662 numpy-1.25.0rc1-cp39-cp39-musllinux_1_1_x86_64.whl
7261d100c9bf722057fd5b9cd5b48f2973b17792b41e689eeaf9b55843cd1afd numpy-1.25.0rc1-cp39-cp39-win32.whl
763fca81a8d8beb6bf4b9a9bbf4045b0c134c15ea66c81d26e5b8683b1861293 numpy-1.25.0rc1-cp39-cp39-win_amd64.whl
06bae17a3629416eb5bae3a429655dc075561206b6d3c1ddfa38b51f273bae5c numpy-1.25.0rc1-pp39-pypy39_pp73-macosx_10_9_x86_64.whl
472bdc3ade289d3efa331738b1daa5a529eef0550650f5d5d2eadb936a2f83a5 numpy-1.25.0rc1-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
236c8ff573c02677b873e0934419c8e9873bd2b35aaba885170b7b43cb26d5da numpy-1.25.0rc1-pp39-pypy39_pp73-win_amd64.whl
224e8862a1cd357eede831b270b9e6c51d2cbc2bb5cc2e2b8d0c76d52cbd1edc numpy-1.25.0rc1.tar.gz