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.


Deprecate fastCopyAndTranspose and PyArray_CopyAndTranspose#

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


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


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.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.


Deprecate msort#

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


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.


Expired deprecations#

  • The normed keyword argument has been removed from np.histogram, np.histogram2d, and np.histogramdd. Use density instead. If normed was passed by position, density is now used.


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


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

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


  • Support for Cygwin < 3.3 has been removed.


  • The mini() method of has been removed. Use either or

  • The single-argument form of and has been removed. Use or instead.


  • 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.)


  • 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.


  • The deprecation for the aliases np.object, np.bool, np.float, np.complex, np.str, and 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.


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
# 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.


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.


Returned arrays respect uniqueness of dtype kwarg objects#

When the dtype keyword argument is used with np.array or asarray, 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
>>> new is arr

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


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.


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.


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.


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.


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.


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.


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.


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').


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)


np.void now has a dtype argument#

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



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.


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.


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

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, fill_value=1000, dtype=np.float64)

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


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.


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.


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.


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 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_]'>.


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.


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.


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).



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],


masked_invalid now modifies the mask in-place#

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


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.