NumPy 1.24 Release Notes
The NumPy 1.24.0 release continues the ongoing work to improve the
handling and promotion of dtypes, increase the execution speed, and
clarify the documentation. There are also a large number of new and
expired deprecations due to changes in promotion and cleanups. This
might be called a deprecation release. Highlights are
- Many new deprecations, check them out.
- Many expired deprecations,
- New F2PY features and fixes.
- New \"dtype\" and \"casting\" keywords for stacking functions.
See below for the details,
Deprecations
Deprecate fastCopyAndTranspose and PyArray_CopyAndTranspose
The numpy.fastCopyAndTranspose function has been deprecated. Use the
corresponding copy and transpose methods directly:
arr.T.copy()
The underlying C function PyArray_CopyAndTranspose has also been
deprecated from the NumPy C-API.
(gh-22313)
Conversion of out-of-bound Python integers
Attempting a conversion from a Python integer to a NumPy value will now
always check whether the result can be represented by NumPy. This means
the following examples will fail in the future and give a
DeprecationWarning now:
np.uint8(-1)
np.array([3000], dtype=np.int8)
Many of these did succeed before. Such code was mainly useful for
unsigned integers with negative values such as np.uint8(-1) giving
np.iinfo(np.uint8).max.
Note that conversion between NumPy integers is unaffected, so that
np.array(-1).astype(np.uint8) continues to work and use C integer
overflow logic.
(gh-22393)
Deprecate msort
The numpy.msort function is deprecated. Use np.sort(a, axis=0)
instead.
(gh-22456)
np.str0 and similar are now deprecated
The scalar type aliases ending in a 0 bit size: np.object0, np.str0,
np.bytes0, np.void0, np.int0, np.uint0 as well as np.bool8 are
now deprecated and will eventually be removed.
(gh-22607)
Expired deprecations
The normed keyword argument has been removed from
[np.histogram]{.title-ref}, [np.histogram2d]{.title-ref}, and
[np.histogramdd]{.title-ref}. Use density instead. If normed was
passed by position, density is now used.
(gh-21645)
Ragged array creation will now always raise a ValueError unless
dtype=object is passed. This includes very deeply nested
sequences.
(gh-22004)
Support for Visual Studio 2015 and earlier has been removed.
Support for the Windows Interix POSIX interop layer has been
removed.
(gh-22139)
Support for cygwin < 3.3 has been removed.
(gh-22159)
The mini() method of np.ma.MaskedArray has been removed. Use
either np.ma.MaskedArray.min() or np.ma.minimum.reduce().
The single-argument form of np.ma.minimum and np.ma.maximum has
been removed. Use np.ma.minimum.reduce() or
np.ma.maximum.reduce() instead.
(gh-22228)
Passing dtype instances other than the canonical (mainly native
byte-order) ones to dtype= or signature= in ufuncs will now
raise a TypeError. We recommend passing the strings "int8" or
scalar types np.int8 since the byte-order, datetime/timedelta
unit, etc. are never enforced. (Initially deprecated in NumPy 1.21.)
(gh-22540)
The dtype= argument to comparison ufuncs is now applied correctly.
That means that only bool and object are valid values and
dtype=object is enforced.
(gh-22541)
The deprecation for the aliases np.object, np.bool, np.float,
np.complex, np.str, and np.int is expired (introduces NumPy
1.20). Some of these will now give a FutureWarning in addition to
raising an error since they will be mapped to the NumPy scalars in
the future.
(gh-22607)
Compatibility notes
array.fill(scalar) may behave slightly different
numpy.ndarray.fill may in some cases behave slightly different now due
to the fact that the logic is aligned with item assignment:
arr = np.array([1]) # with any dtype/value
arr.fill(scalar)
# is now identical to:
arr[0] = scalar
Previously casting may have produced slightly different answers when
using values that could not be represented in the target dtype or when
the target had object dtype.
(gh-20924)
Subarray to object cast now copies
Casting a dtype that includes a subarray to an object will now ensure a
copy of the subarray. Previously an unsafe view was returned:
arr = np.ones(3, dtype=[("f", "i", 3)])
subarray_fields = arr.astype(object)[0]
subarray = subarray_fields[0] # "f" field
np.may_share_memory(subarray, arr)
Is now always false. While previously it was true for the specific cast.
(gh-21925)
Returned arrays respect uniqueness of dtype kwarg objects
When the dtype keyword argument is used with
:pynp.array(){.interpreted-text role="func"} or
:pyasarray(){.interpreted-text role="func"}, the dtype of the returned
array now always exactly matches the dtype provided by the caller.
In some cases this change means that a view rather than the input
array is returned. The following is an example for this on 64bit Linux
where long and longlong are the same precision but different
dtypes:
>>> arr = np.array([1, 2, 3], dtype="long")
>>> new_dtype = np.dtype("longlong")
>>> new = np.asarray(arr, dtype=new_dtype)
>>> new.dtype is new_dtype
True
>>> new is arr
False
Before the change, the dtype did not match because new is arr was
True.
(gh-21995)
DLPack export raises BufferError
When an array buffer cannot be exported via DLPack a BufferError is
now always raised where previously TypeError or RuntimeError was
raised. This allows falling back to the buffer protocol or
__array_interface__ when DLPack was tried first.
(gh-22542)
NumPy builds are no longer tested on GCC-6
Ubuntu 18.04 is deprecated for GitHub actions and GCC-6 is not available
on Ubuntu 20.04, so builds using that compiler are no longer tested. We
still test builds using GCC-7 and GCC-8.
(gh-22598)
New Features
New attribute symbol added to polynomial classes
The polynomial classes in the numpy.polynomial package have a new
symbol attribute which is used to represent the indeterminate of the
polynomial. This can be used to change the value of the variable when
printing:
>>> P_y = np.polynomial.Polynomial([1, 0, -1], symbol="y")
>>> print(P_y)
1.0 + 0.0Β·yΒΉ - 1.0Β·yΒ²
Note that the polynomial classes only support 1D polynomials, so
operations that involve polynomials with different symbols are
disallowed when the result would be multivariate:
>>> P = np.polynomial.Polynomial([1, -1]) # default symbol is "x"
>>> P_z = np.polynomial.Polynomial([1, 1], symbol="z")
>>> P * P_z
Traceback (most recent call last)
...
ValueError: Polynomial symbols differ
The symbol can be any valid Python identifier. The default is
symbol=x, consistent with existing behavior.
(gh-16154)
F2PY support for Fortran character strings
F2PY now supports wrapping Fortran functions with:
- character (e.g.
character x)
- character array (e.g.
character, dimension(n) :: x)
- character string (e.g.
character(len=10) x)
- and character string array (e.g.
character(len=10), dimension(n, m) :: x)
arguments, including passing Python unicode strings as Fortran character
string arguments.
(gh-19388)
New function np.show_runtime
A new function numpy.show_runtime has been added to display the
runtime information of the machine in addition to numpy.show_config
which displays the build-related information.
(gh-21468)
strict option for testing.assert_array_equal
The strict option is now available for testing.assert_array_equal.
Setting strict=True will disable the broadcasting behaviour for
scalars and ensure that input arrays have the same data type.
(gh-21595)
New parameter equal_nan added to np.unique
np.unique was changed in 1.21 to treat all NaN values as equal and
return a single NaN. Setting equal_nan=False will restore pre-1.21
behavior to treat NaNs as unique. Defaults to True.
(gh-21623)
casting and dtype keyword arguments for numpy.stack
The casting and dtype keyword arguments are now available for
numpy.stack. To use them, write
np.stack(..., dtype=None, casting='same_kind').
casting and dtype keyword arguments for numpy.vstack
The casting and dtype keyword arguments are now available for
numpy.vstack. To use them, write
np.vstack(..., dtype=None, casting='same_kind').
casting and dtype keyword arguments for numpy.hstack
The casting and dtype keyword arguments are now available for
numpy.hstack. To use them, write
np.hstack(..., dtype=None, casting='same_kind').
(gh-21627)
The bit generator underlying the singleton RandomState can be changed
The singleton RandomState instance exposed in the numpy.random
module is initialized at startup with the MT19937 bit generator. The
new function set_bit_generator allows the default bit generator to be
replaced with a user-provided bit generator. This function has been
introduced to provide a method allowing seamless integration of a
high-quality, modern bit generator in new code with existing code that
makes use of the singleton-provided random variate generating functions.
The companion function get_bit_generator returns the current bit
generator being used by the singleton RandomState. This is provided to
simplify restoring the original source of randomness if required.
The preferred method to generate reproducible random numbers is to use a
modern bit generator in an instance of Generator. The function
default_rng simplifies instantiation:
>>> rg = np.random.default_rng(3728973198)
>>> rg.random()
The same bit generator can then be shared with the singleton instance so
that calling functions in the random module will use the same bit
generator:
>>> orig_bit_gen = np.random.get_bit_generator()
>>> np.random.set_bit_generator(rg.bit_generator)
>>> np.random.normal()
The swap is permanent (until reversed) and so any call to functions in
the random module will use the new bit generator. The original can be
restored if required for code to run correctly:
>>> np.random.set_bit_generator(orig_bit_gen)
(gh-21976)
np.void now has a dtype argument
NumPy now allows constructing structured void scalars directly by
passing the dtype argument to np.void.
(gh-22316)
Improvements
F2PY Improvements
- The generated extension modules don\'t use the deprecated NumPy-C
API anymore
- Improved
f2py generated exception messages
- Numerous bug and
flake8 warning fixes
- various CPP macros that one can use within C-expressions of
signature files are prefixed with
f2py_. For example, one should
use f2py_len(x) instead of len(x)
- A new construct
character(f2py_len=...) is introduced to support
returning assumed length character strings (e.g. character(len=*))
from wrapper functions
A hook to support rewriting f2py internal data structures after
reading all its input files is introduced. This is required, for
instance, for BC of SciPy support where character arguments are treated
as character strings arguments in C expressions.
(gh-19388)
IBM zSystems Vector Extension Facility (SIMD)
Added support for SIMD extensions of zSystem (z13, z14, z15), through
the universal intrinsics interface. This support leads to performance
improvements for all SIMD kernels implemented using the universal
intrinsics, including the following operations: rint, floor, trunc,
ceil, sqrt, absolute, square, reciprocal, tanh, sin, cos, equal,
not_equal, greater, greater_equal, less, less_equal, maximum, minimum,
fmax, fmin, argmax, argmin, add, subtract, multiply, divide.
(gh-20913)
NumPy now gives floating point errors in casts
In most cases, NumPy previously did not give floating point warnings or
errors when these happened during casts. For examples, casts like:
np.array([2e300]).astype(np.float32) # overflow for float32
np.array([np.inf]).astype(np.int64)
Should now generally give floating point warnings. These warnings should
warn that floating point overflow occurred. For errors when converting
floating point values to integers users should expect invalid value
warnings.
Users can modify the behavior of these warnings using np.errstate.
Note that for float to int casts, the exact warnings that are given may
be platform dependent. For example:
arr = np.full(100, value=1000, dtype=np.float64)
arr.astype(np.int8)
May give a result equivalent to (the intermediate cast means no warning
is given):
arr.astype(np.int64).astype(np.int8)
May return an undefined result, with a warning set:
RuntimeWarning: invalid value encountered in cast
The precise behavior is subject to the C99 standard and its
implementation in both software and hardware.
(gh-21437)
F2PY supports the value attribute
The Fortran standard requires that variables declared with the value
attribute must be passed by value instead of reference. F2PY now
supports this use pattern correctly. So
integer, intent(in), value :: x in Fortran codes will have correct
wrappers generated.
(gh-21807)
Added pickle support for third-party BitGenerators
The pickle format for bit generators was extended to allow each bit
generator to supply its own constructor when during pickling. Previous
versions of NumPy only supported unpickling Generator instances
created with one of the core set of bit generators supplied with NumPy.
Attempting to unpickle a Generator that used a third-party bit
generators would fail since the constructor used during the unpickling
was only aware of the bit generators included in NumPy.
(gh-22014)
arange() now explicitly fails with dtype=str
Previously, the np.arange(n, dtype=str) function worked for n=1 and
n=2, but would raise a non-specific exception message for other values
of n. Now, it raises a [TypeError]{.title-ref} informing that arange
does not support string dtypes:
>>> np.arange(2, dtype=str)
Traceback (most recent call last)
...
TypeError: arange() not supported for inputs with DType <class 'numpy.dtype[str_]'>.
(gh-22055)
numpy.typing protocols are now runtime checkable
The protocols used in numpy.typing.ArrayLike and
numpy.typing.DTypeLike are now properly marked as runtime checkable,
making them easier to use for runtime type checkers.
(gh-22357)
Performance improvements and changes
Faster version of np.isin and np.in1d for integer arrays
np.in1d (used by np.isin) can now switch to a faster algorithm (up
to >10x faster) when it is passed two integer arrays. This is often
automatically used, but you can use kind="sort" or kind="table" to
force the old or new method, respectively.
(gh-12065)
Faster comparison operators
The comparison functions (numpy.equal, numpy.not_equal,
numpy.less, numpy.less_equal, numpy.greater and
numpy.greater_equal) are now much faster as they are now vectorized
with universal intrinsics. For a CPU with SIMD extension AVX512BW, the
performance gain is up to 2.57x, 1.65x and 19.15x for integer, float and
boolean data types, respectively (with N=50000).
(gh-21483)
Changes
Better reporting of integer division overflow
Integer division overflow of scalars and arrays used to provide a
RuntimeWarning and the return value was undefined leading to crashes
at rare occasions:
>>> np.array([np.iinfo(np.int32).min]*10, dtype=np.int32) // np.int32(-1)
<stdin>:1: RuntimeWarning: divide by zero encountered in floor_divide
array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0], dtype=int32)
Integer division overflow now returns the input dtype\'s minimum value
and raise the following RuntimeWarning:
>>> np.array([np.iinfo(np.int32).min]*10, dtype=np.int32) // np.int32(-1)
<stdin>:1: RuntimeWarning: overflow encountered in floor_divide
array([-2147483648, -2147483648, -2147483648, -2147483648, -2147483648,
-2147483648, -2147483648, -2147483648, -2147483648, -2147483648],
dtype=int32)
(gh-21506)
masked_invalid now modifies the mask in-place
When used with copy=False, numpy.ma.masked_invalid now modifies the
input masked array in-place. This makes it behave identically to
masked_where and better matches the documentation.
(gh-22046)
nditer/NpyIter allows all allocating all operands
The NumPy iterator available through np.nditer in Python and as
NpyIter in C now supports allocating all arrays. The iterator shape
defaults to () in this case. The operands dtype must be provided,
since a \"common dtype\" cannot be inferred from the other inputs.
(gh-22457)
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numpy/numpy
