# 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*,*np.histogram2d*, and*np.histogramdd*. 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 `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
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 messagesNumerous bug and

`flake8`

warning fixesvarious 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* 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)