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 innp.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)
np.find_common_type
is deprecated. numpy.find_common_type is now deprecated and its use should be replaced with eithernumpy.result_type
ornumpy.promote_types
. Most users leave the secondscalar_types
argument tofind_common_type
as[]
in which casenp.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 usingint
,float
, orcomplex
in scalar_types.When
scalar_types
is constructed,np.result_type
is the correct replacement and it may be passed scalar values likenp.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
ormax
. 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 inastype
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
orFutureWarning
.
(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 thenumpy.testing.utils
shim has been deprecated since 2019, the shim has now been removed. All imports should be made directly fromnumpy.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 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 thebegindates
is later in time than theenddates
. Previously, theenddates
was included, even though the documentation states it is always excluded.(gh-23229)
When comparing datetimes and timedelta using
np.equal
ornp.not_equal
numpy previously allowed the comparison withcasting="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 invalid input 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
is a subclass of
numpy.ndarray
or is a duck type that defines
numpy.class.__array_ufunc__
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
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.
(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; 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)