- `npy_interrupt.h` and the corresponding macros like `NPY_SIGINT_ON`
have been removed. We recommend querying `PyErr_CheckSignals()` or
`PyOS_InterruptOccurred()` periodically (these do currently require
holding the GIL though).
- The `noprefix.h` header has been removed. Replace missing symbols
with their prefixed counterparts (usually an added `NPY_` or
`npy_`).
([gh-23919](https://github.com/numpy/numpy/pull/23919))
- `PyUFunc_GetPyVals`, `PyUFunc_handlefperr`, and `PyUFunc_checkfperr`
have been removed. If needed, a new backwards compatible function to
raise floating point errors could be restored. Reason for removal:
there are no known users and the functions would have made
`with np.errstate()` fixes much more difficult).
([gh-23922](https://github.com/numpy/numpy/pull/23922))
- The `numpy/old_defines.h` which was part of the API deprecated since
NumPy 1.7 has been removed. This removes macros of the form
`PyArray_CONSTANT`. The
[replace_old_macros.sed](https://github.com/numpy/numpy/blob/main/tools/replace_old_macros.sed)
script may be useful to convert them to the `NPY_CONSTANT` version.
([gh-24011](https://github.com/numpy/numpy/pull/24011))
- The `legacy_inner_loop_selector` member of the ufunc struct is
removed to simplify improvements to the dispatching system. There
are no known users overriding or directly accessing this member.
([gh-24271](https://github.com/numpy/numpy/pull/24271))
- `NPY_INTPLTR` has been removed to avoid confusion (see `intp`
redefinition).
([gh-24888](https://github.com/numpy/numpy/pull/24888))
- The advanced indexing `MapIter` and related API has been removed.
The (truly) public part of it was not well tested and had only one
known user (Theano). Making it private will simplify improvements to
speed up `ufunc.at`, make advanced indexing more maintainable, and
was important for increasing the maximum number of dimensions of
arrays to 64. Please let us know if this API is important to you so
we can find a solution together.
([gh-25138](https://github.com/numpy/numpy/pull/25138))
- The `NPY_MAX_ELSIZE` macro has been removed, as it only ever
reflected builtin numeric types and served no internal purpose.
([gh-25149](https://github.com/numpy/numpy/pull/25149))
- `PyArray_REFCNT` and `NPY_REFCOUNT` are removed. Use `Py_REFCNT`
instead.
([gh-25156](https://github.com/numpy/numpy/pull/25156))
- `PyArrayFlags_Type` and `PyArray_NewFlagsObject` as well as
`PyArrayFlagsObject` are private now. There is no known use-case;
use the Python API if needed.
- `PyArray_MoveInto`, `PyArray_CastTo`, `PyArray_CastAnyTo` are
removed use `PyArray_CopyInto` and if absolutely needed
`PyArray_CopyAnyInto` (the latter does a flat copy).
- `PyArray_FillObjectArray` is removed, its only true use was for
implementing `np.empty`. Create a new empty array or use
`PyArray_FillWithScalar()` (decrefs existing objects).
- `PyArray_CompareUCS4` and `PyArray_CompareString` are removed. Use
the standard C string comparison functions.
- `PyArray_ISPYTHON` is removed as it is misleading, has no known
use-cases, and is easy to replace.
- `PyArray_FieldNames` is removed, as it is unclear what it would be
useful for. It also has incorrect semantics in some possible
use-cases.
- `PyArray_TypestrConvert` is removed, since it seems a misnomer and
unlikely to be used by anyone. If you know the size or are limited
to few types, just use it explicitly, otherwise go via Python
strings.
([gh-25292](https://github.com/numpy/numpy/pull/25292))
- `PyDataType_GetDatetimeMetaData` is removed, it did not actually do
anything since at least NumPy 1.7.
([gh-25802](https://github.com/numpy/numpy/pull/25802))
- `PyArray_GetCastFunc` is removed. Note that custom legacy user
dtypes can still provide a castfunc as their implementation, but any
access to them is now removed. The reason for this is that NumPy
never used these internally for many years. If you use simple
numeric types, please just use C casts directly. In case you require
an alternative, please let us know so we can create new API such as
`PyArray_CastBuffer()` which could use old or new cast functions
depending on the NumPy version.
([gh-25161](https://github.com/numpy/numpy/pull/25161))
New Features
`np.add` was extended to work with `unicode` and `bytes` dtypes.
> ([gh-24858](https://github.com/numpy/numpy/pull/24858))
A new `bitwise_count` function
This new function counts the number of 1-bits in a number.
`numpy.bitwise_count` works on all the numpy integer types
and integer-like objects.
python
>>> a = np.array([2**i - 1 for i in range(16)])
>>> np.bitwise_count(a)
array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15],
dtype=uint8)
([gh-19355](https://github.com/numpy/numpy/pull/19355))
macOS Accelerate support, including the ILP64
Support for the updated Accelerate BLAS/LAPACK library, including ILP64
(64-bit integer) support, in macOS 13.3 has been added. This brings
arm64 support, and significant performance improvements of up to 10x for
commonly used linear algebra operations. When Accelerate is selected at
build time, or if no explicit BLAS library selection is done, the 13.3+
version will automatically be used if available.
([gh-24053](https://github.com/numpy/numpy/pull/24053))
Binary wheels are also available. On macOS \>=14.0, users who install
NumPy from PyPI will get wheels built against Accelerate rather than
OpenBLAS.
([gh-25255](https://github.com/numpy/numpy/pull/25255))
Option to use weights for quantile and percentile functions
A `weights` keyword is now available for `numpy.quantile`, `numpy.percentile`,
`numpy.nanquantile` and `numpy.nanpercentile`. Only `method="inverted_cdf"`
supports weights.
([gh-24254](https://github.com/numpy/numpy/pull/24254))
Improved CPU optimization tracking
A new tracer mechanism is available which enables tracking of the
enabled targets for each optimized function (i.e., that uses
hardware-specific SIMD instructions) in the NumPy library. With this
enhancement, it becomes possible to precisely monitor the enabled CPU
dispatch targets for the dispatched functions.
A new function named `opt_func_info` has been added to the new namespace
`numpy.lib.introspect`, offering this tracing capability. This function allows
you to retrieve information about the enabled targets based on function names
and data type signatures.
([gh-24420](https://github.com/numpy/numpy/pull/24420))
A new Meson backend for `f2py`
`f2py` in compile mode (i.e. `f2py -c`) now accepts the
`--backend meson` option. This is the default option for Python \>=3.12.
For older Python versions, `f2py` will still default to
`--backend distutils`.
To support this in realistic use-cases, in compile mode `f2py` takes a
`--dep` flag one or many times which maps to `dependency()` calls in the
`meson` backend, and does nothing in the `distutils` backend.
There are no changes for users of `f2py` only as a code generator, i.e.
without `-c`.
([gh-24532](https://github.com/numpy/numpy/pull/24532))
`bind(c)` support for `f2py`
Both functions and subroutines can be annotated with `bind(c)`. `f2py`
will handle both the correct type mapping, and preserve the unique label
for other C interfaces.
**Note:** `bind(c, name = 'routine_name_other_than_fortran_routine')` is
not honored by the `f2py` bindings by design, since `bind(c)` with the
`name` is meant to guarantee only the same name in C and Fortran, not in
Python and Fortran.
([gh-24555](https://github.com/numpy/numpy/pull/24555))
A new `strict` option for several testing functions
The `strict` keyword is now available for `numpy.testing.assert_allclose`,
`numpy.testing.assert_equal`, and `numpy.testing.assert_array_less`. Setting
`strict=True` will disable the broadcasting behaviour for scalars and ensure
that input arrays have the same data type.
([gh-24680](https://github.com/numpy/numpy/pull/24680),
[gh-24770](https://github.com/numpy/numpy/pull/24770),
[gh-24775](https://github.com/numpy/numpy/pull/24775))
Add `np.core.umath.find` and `np.core.umath.rfind` UFuncs
Add two `find` and `rfind` UFuncs that operate on unicode or byte
strings and are used in `np.char`. They operate similar to `str.find`
and `str.rfind`.
([gh-24868](https://github.com/numpy/numpy/pull/24868))
`diagonal` and `trace` for `numpy.linalg`
`numpy.linalg.diagonal` and `numpy.linalg.trace` have been added, which are
array API standard-compatible variants of `numpy.diagonal` and `numpy.trace`.
They differ in the default axis selection which define 2-D sub-arrays.
([gh-24887](https://github.com/numpy/numpy/pull/24887))
New `long` and `ulong` dtypes
`numpy.long` and `numpy.ulong` have been added as NumPy integers mapping to
C\'s `long` and `unsigned long`. Prior to NumPy 1.24, `numpy.long` was an alias
to Python\'s `int`.
([gh-24922](https://github.com/numpy/numpy/pull/24922))
`svdvals` for `numpy.linalg`
`numpy.linalg.svdvals` has been added. It computes singular values for (a stack
of) matrices. Executing `np.svdvals(x)` is the same as calling `np.svd(x,
compute_uv=False, hermitian=False)`. This function is compatible with the array
API standard.
([gh-24940](https://github.com/numpy/numpy/pull/24940))
A new `isdtype` function
`numpy.isdtype` was added to provide a canonical way to classify NumPy\'s
dtypes in compliance with the array API standard.
([gh-25054](https://github.com/numpy/numpy/pull/25054))
A new `astype` function
`numpy.astype` was added to provide an array API standard-compatible
alternative to the `numpy.ndarray.astype` method.
([gh-25079](https://github.com/numpy/numpy/pull/25079))
Array API compatible functions\' aliases
13 aliases for existing functions were added to improve compatibility
with the array API standard:
- Trigonometry: `acos`, `acosh`, `asin`, `asinh`, `atan`, `atanh`,
`atan2`.
- Bitwise: `bitwise_left_shift`, `bitwise_invert`,
`bitwise_right_shift`.
- Misc: `concat`, `permute_dims`, `pow`.
- In `numpy.linalg`: `tensordot`, `matmul`.
([gh-25086](https://github.com/numpy/numpy/pull/25086))
New `unique_*` functions
The `numpy.unique_all`, `numpy.unique_counts`, `numpy.unique_inverse`, and
`numpy.unique_values` functions have been added. They provide functionality of
`numpy.unique` with different sets of flags. They are array API
standard-compatible, and because the number of arrays they return does not
depend on the values of input arguments, they are easier to target for JIT
compilation.
([gh-25088](https://github.com/numpy/numpy/pull/25088))
Matrix transpose support for ndarrays
NumPy now offers support for calculating the matrix transpose of an
array (or stack of arrays). The matrix transpose is equivalent to
swapping the last two axes of an array. Both `np.ndarray` and
`np.ma.MaskedArray` now expose a `.mT` attribute, and there is a
matching new `numpy.matrix_transpose` function.
([gh-23762](https://github.com/numpy/numpy/pull/23762))
Array API compatible functions for `numpy.linalg`
Six new functions and two aliases were added to improve compatibility
with the Array API standard for \`numpy.linalg\`:
- `numpy.linalg.matrix_norm` - Computes the matrix norm of
a matrix (or a stack of matrices).
- `numpy.linalg.vector_norm` - Computes the vector norm of
a vector (or batch of vectors).
- `numpy.vecdot` - Computes the (vector) dot product of
two arrays.
- `numpy.linalg.vecdot` - An alias for
`numpy.vecdot`.
- `numpy.linalg.matrix_transpose` - An alias for
`numpy.matrix_transpose`.
([gh-25155](https://github.com/numpy/numpy/pull/25155))
- `numpy.linalg.outer` has been added. It computes the
outer product of two vectors. It differs from
`numpy.outer` by accepting one-dimensional arrays only.
This function is compatible with the array API standard.
([gh-25101](https://github.com/numpy/numpy/pull/25101))
- `numpy.linalg.cross` has been added. It computes the
cross product of two (arrays of) 3-dimensional vectors. It differs
from `numpy.cross` by accepting three-dimensional
vectors only. This function is compatible with the array API
standard.
([gh-25145](https://github.com/numpy/numpy/pull/25145))
A `correction` argument for `var` and `std`
A `correction` argument was added to `numpy.var` and `numpy.std`, which is an
array API standard compatible alternative to `ddof`. As both arguments serve a
similar purpose, only one of them can be provided at the same time.
([gh-25169](https://github.com/numpy/numpy/pull/25169))
`ndarray.device` and `ndarray.to_device`
An `ndarray.device` attribute and `ndarray.to_device` method were added
to `numpy.ndarray` for array API standard compatibility.
Additionally, `device` keyword-only arguments were added to:
`numpy.asarray`, `numpy.arange`, `numpy.empty`, `numpy.empty_like`,
`numpy.eye`, `numpy.full`, `numpy.full_like`, `numpy.linspace`, `numpy.ones`,
`numpy.ones_like`, `numpy.zeros`, and `numpy.zeros_like`.
For all these new arguments, only `device="cpu"` is supported.
([gh-25233](https://github.com/numpy/numpy/pull/25233))
StringDType has been added to NumPy
We have added a new variable-width UTF-8 encoded string data type, implementing
a \"NumPy array of Python strings\", including support for a user-provided
missing data sentinel. It is intended as a drop-in replacement for arrays of
Python strings and missing data sentinels using the object dtype. See
[NEP 55](https://numpy.org/neps/nep-0055-string_dtype.html) and the documentation
of stringdtype for more details.
([gh-25347](https://github.com/numpy/numpy/pull/25347))
New keywords for `cholesky` and `pinv`
The `upper` and `rtol` keywords were added to
`numpy.linalg.cholesky` and `numpy.linalg.pinv`,
respectively, to improve array API standard compatibility.
For `numpy.linalg.pinv`, if neither `rcond` nor `rtol` is
specified, the `rcond`\'s default is used. We plan to deprecate and
remove `rcond` in the future.
([gh-25388](https://github.com/numpy/numpy/pull/25388))
New keywords for `sort`, `argsort` and `linalg.matrix_rank`
New keyword parameters were added to improve array API standard
compatibility:
- `rtol` was added to `numpy.linalg.matrix_rank`.
- `stable` was added to `numpy.sort` and
`numpy.argsort`.
([gh-25437](https://github.com/numpy/numpy/pull/25437))
New `numpy.strings` namespace for string ufuncs
NumPy now implements some string operations as ufuncs. The old `np.char`
namespace is still available, and where possible the string manipulation
functions in that namespace have been updated to use the new ufuncs,
substantially improving their performance.
Where possible, we suggest updating code to use functions in
`np.strings` instead of `np.char`. In the future we may deprecate
`np.char` in favor of `np.strings`.
([gh-25463](https://github.com/numpy/numpy/pull/25463))
`numpy.fft` support for different precisions and in-place calculations
The various FFT routines in `numpy.fft` now do their
calculations natively in float, double, or long double precision,
depending on the input precision, instead of always calculating in
double precision. Hence, the calculation will now be less precise for
single and more precise for long double precision. The data type of the
output array will now be adjusted accordingly.
Furthermore, all FFT routines have gained an `out` argument that can be
used for in-place calculations.
([gh-25536](https://github.com/numpy/numpy/pull/25536))
configtool and pkg-config support
A new `numpy-config` CLI script is available that can be queried for the
NumPy version and for compile flags needed to use the NumPy C API. This
will allow build systems to better support the use of NumPy as a
dependency. Also, a `numpy.pc` pkg-config file is now included with
Numpy. In order to find its location for use with `PKG_CONFIG_PATH`, use
`numpy-config --pkgconfigdir`.
([gh-25730](https://github.com/numpy/numpy/pull/25730))
Array API standard support in the main namespace
The main `numpy` namespace now supports the array API standard. See
`array-api-standard-compatibility` for
details.
([gh-25911](https://github.com/numpy/numpy/pull/25911))
Improvements
Strings are now supported by `any`, `all`, and the logical ufuncs.
> ([gh-25651](https://github.com/numpy/numpy/pull/25651))
Integer sequences as the shape argument for `memmap`
`numpy.memmap` can now be created with any integer sequence
as the `shape` argument, such as a list or numpy array of integers.
Previously, only the types of tuple and int could be used without
raising an error.
([gh-23729](https://github.com/numpy/numpy/pull/23729))
`errstate` is now faster and context safe
The `numpy.errstate` context manager/decorator is now faster
and safer. Previously, it was not context safe and had (rare) issues
with thread-safety.
([gh-23936](https://github.com/numpy/numpy/pull/23936))
AArch64 quicksort speed improved by using Highway\'s VQSort
The first introduction of the Google Highway library, using VQSort on
AArch64. Execution time is improved by up to 16x in some cases, see the
PR for benchmark results. Extensions to other platforms will be done in
the future.
([gh-24018](https://github.com/numpy/numpy/pull/24018))
Complex types - underlying C type changes
- The underlying C types for all of NumPy\'s complex types have been
changed to use C99 complex types.
- While this change does not affect the memory layout of complex
types, it changes the API to be used to directly retrieve or write
the real or complex part of the complex number, since direct field
access (as in `c.real` or `c.imag`) is no longer an option. You can
now use utilities provided in `numpy/npy_math.h` to do these
operations, like this:
c
npy_cdouble c;
npy_csetreal(&c, 1.0);
npy_csetimag(&c, 0.0);
printf("%d + %di\n", npy_creal(c), npy_cimag(c));
- To ease cross-version compatibility, equivalent macros and a
compatibility layer have been added which can be used by downstream
packages to continue to support both NumPy 1.x and 2.x. See
`complex-numbers` for more info.
- `numpy/npy_common.h` now includes `complex.h`, which means that
`complex` is now a reserved keyword.
([gh-24085](https://github.com/numpy/numpy/pull/24085))
`iso_c_binding` support and improved common blocks for `f2py`
Previously, users would have to define their own custom `f2cmap` file to
use type mappings defined by the Fortran2003 `iso_c_binding` intrinsic
module. These type maps are now natively supported by `f2py`
([gh-24555](https://github.com/numpy/numpy/pull/24555))
`f2py` now handles `common` blocks which have `kind` specifications from
modules. This further expands the usability of intrinsics like
`iso_fortran_env` and `iso_c_binding`.
([gh-25186](https://github.com/numpy/numpy/pull/25186))
Call `str` automatically on third argument to functions like `assert_equal`
The third argument to functions like
`numpy.testing.assert_equal` now has `str` called on it
automatically. This way it mimics the built-in `assert` statement, where
`assert_equal(a, b, obj)` works like `assert a == b, obj`.
([gh-24877](https://github.com/numpy/numpy/pull/24877))
Support for array-like `atol`/`rtol` in `isclose`, `allclose`
The keywords `atol` and `rtol` in `numpy.isclose` and
`numpy.allclose` now accept both scalars and arrays. An
array, if given, must broadcast to the shapes of the first two array
arguments.
([gh-24878](https://github.com/numpy/numpy/pull/24878))
Consistent failure messages in test functions
Previously, some `numpy.testing` assertions printed messages
that referred to the actual and desired results as `x` and `y`. Now,
these values are consistently referred to as `ACTUAL` and `DESIRED`.
([gh-24931](https://github.com/numpy/numpy/pull/24931))
n-D FFT transforms allow `s[i] == -1`
The `numpy.fft.fftn`, `numpy.fft.ifftn`,
`numpy.fft.rfftn`, `numpy.fft.irfftn`,
`numpy.fft.fft2`, `numpy.fft.ifft2`,
`numpy.fft.rfft2` and `numpy.fft.irfft2`
functions now use the whole input array along the axis `i` if
`s[i] == -1`, in line with the array API standard.
([gh-25495](https://github.com/numpy/numpy/pull/25495))
Guard PyArrayScalar_VAL and PyUnicodeScalarObject for the limited API
`PyUnicodeScalarObject` holds a `PyUnicodeObject`, which is not
available when using `Py_LIMITED_API`. Add guards to hide it and
consequently also make the `PyArrayScalar_VAL` macro hidden.
([gh-25531](https://github.com/numpy/numpy/pull/25531))
Changes
- `np.gradient()` now returns a tuple rather than a list making the
return value immutable.
([gh-23861](https://github.com/numpy/numpy/pull/23861))
- Being fully context and thread-safe, `np.errstate` can only be
entered once now.
- `np.setbufsize` is now tied to `np.errstate()`: leaving an
`np.errstate` context will also reset the `bufsize`.
([gh-23936](https://github.com/numpy/numpy/pull/23936))
- A new public `np.lib.array_utils` submodule has been introduced and
it currently contains three functions: `byte_bounds` (moved from
`np.lib.utils`), `normalize_axis_tuple` and `normalize_axis_index`.
([gh-24540](https://github.com/numpy/numpy/pull/24540))
- Introduce `numpy.bool` as the new canonical name for
NumPy\'s boolean dtype, and make `numpy.bool\_` an alias
to it. Note that until NumPy 1.24, `np.bool` was an alias to
Python\'s builtin `bool`. The new name helps with array API standard
compatibility and is a more intuitive name.
([gh-25080](https://github.com/numpy/numpy/pull/25080))
- The `dtype.flags` value was previously stored as a signed integer.
This means that the aligned dtype struct flag lead to negative flags
being set (-128 rather than 128). This flag is now stored unsigned
(positive). Code which checks flags manually may need to adapt. This
may include code compiled with Cython 0.29.x.
([gh-25816](https://github.com/numpy/numpy/pull/25816))
Representation of NumPy scalars changed
As per NEP 51, the scalar representation has been updated to include the type
information to avoid confusion with Python scalars.
Scalars are now printed as `np.float64(3.0)` rather than just `3.0`.
This may disrupt workflows that store representations of numbers (e.g.,
to files) making it harder to read them. They should be stored as
explicit strings, for example by using `str()` or `f"{scalar!s}"`. For
the time being, affected users can use
`np.set_printoptions(legacy="1.25")` to get the old behavior (with
possibly a few exceptions). Documentation of downstream projects may
require larger updates, if code snippets are tested. We are working on
tooling for
[doctest-plus](https://github.com/scientific-python/pytest-doctestplus/issues/107)
to facilitate updates.
([gh-22449](https://github.com/numpy/numpy/pull/22449))
Truthiness of NumPy strings changed
NumPy strings previously were inconsistent about how they defined if the
string is `True` or `False` and the definition did not match the one
used by Python. Strings are now considered `True` when they are
non-empty and `False` when they are empty. This changes the following
distinct cases:
- Casts from string to boolean were previously roughly equivalent to
`string_array.astype(np.int64).astype(bool)`, meaning that only
valid integers could be cast. Now a string of `"0"` will be
considered `True` since it is not empty. If you need the old
behavior, you may use the above step (casting to integer first) or
`string_array == "0"` (if the input is only ever `0` or `1`). To get
the new result on old NumPy versions use `string_array != ""`.
- `np.nonzero(string_array)` previously ignored whitespace so that a
string only containing whitespace was considered `False`. Whitespace
is now considered `True`.
This change does not affect `np.loadtxt`, `np.fromstring`, or
`np.genfromtxt`. The first two still use the integer definition, while
`genfromtxt` continues to match for `"true"` (ignoring case). However,
if `np.bool_` is used as a converter the result will change.
The change does affect `np.fromregex` as it uses direct assignments.
([gh-23871](https://github.com/numpy/numpy/pull/23871))
A `mean` keyword was added to var and std function
Often when the standard deviation is needed the mean is also needed. The
same holds for the variance and the mean. Until now the mean is then
calculated twice, the change introduced here for the `numpy.var` and
`numpy.std` functions allows for passing in a precalculated mean as an keyword
argument. See the docstrings for details and an example illustrating the
speed-up.
([gh-24126](https://github.com/numpy/numpy/pull/24126))
Remove datetime64 deprecation warning when constructing with timezone
The `numpy.datetime64` method now issues a UserWarning rather than a
DeprecationWarning whenever a timezone is included in the datetime string that
is provided.
([gh-24193](https://github.com/numpy/numpy/pull/24193))
Default integer dtype is now 64-bit on 64-bit Windows
The default NumPy integer is now 64-bit on all 64-bit systems as the
historic 32-bit default on Windows was a common source of issues. Most
users should not notice this. The main issues may occur with code
interfacing with libraries written in a compiled language like C. For
more information see `migration_windows_int64`.
([gh-24224](https://github.com/numpy/numpy/pull/24224))
Renamed `numpy.core` to `numpy._core`
Accessing `numpy.core` now emits a DeprecationWarning. In practice we
have found that most downstream usage of `numpy.core` was to access
functionality that is available in the main `numpy` namespace. If for
some reason you are using functionality in `numpy.core` that is not
available in the main `numpy` namespace, this means you are likely using
private NumPy internals. You can still access these internals via
`numpy._core` without a deprecation warning but we do not provide any
backward compatibility guarantees for NumPy internals. Please open an
issue if you think a mistake was made and something needs to be made
public.
([gh-24634](https://github.com/numpy/numpy/pull/24634))
The \"relaxed strides\" debug build option, which was previously enabled
through the `NPY_RELAXED_STRIDES_DEBUG` environment variable or the
`-Drelaxed-strides-debug` config-settings flag has been removed.
([gh-24717](https://github.com/numpy/numpy/pull/24717))
Redefinition of `np.intp`/`np.uintp` (almost never a change)
Due to the actual use of these types almost always matching the use of
`size_t`/`Py_ssize_t` this is now the definition in C. Previously, it
matched `intptr_t` and `uintptr_t` which would often have been subtly
incorrect. This has no effect on the vast majority of machines since the
size of these types only differ on extremely niche platforms.
However, it means that:
- Pointers may not necessarily fit into an `intp` typed array anymore.
The `p` and `P` character codes can still be used, however.
- Creating `intptr_t` or `uintptr_t` typed arrays in C remains
possible in a cross-platform way via `PyArray_DescrFromType('p')`.
- The new character codes `nN` were introduced.
- It is now correct to use the Python C-API functions when parsing to
`npy_intp` typed arguments.
([gh-24888](https://github.com/numpy/numpy/pull/24888))
`numpy.fft.helper` made private
`numpy.fft.helper` was renamed to `numpy.fft._helper` to indicate that
it is a private submodule. All public functions exported by it should be
accessed from `numpy.fft`.
([gh-24945](https://github.com/numpy/numpy/pull/24945))
`numpy.linalg.linalg` made private
`numpy.linalg.linalg` was renamed to `numpy.linalg._linalg` to indicate
that it is a private submodule. All public functions exported by it
should be accessed from `numpy.linalg`.
([gh-24946](https://github.com/numpy/numpy/pull/24946))
Out-of-bound axis not the same as `axis=None`
In some cases `axis=32` or for concatenate any large value was the same
as `axis=None`. Except for `concatenate` this was deprecate. Any out of
bound axis value will now error, make sure to use `axis=None`.
([gh-25149](https://github.com/numpy/numpy/pull/25149))
New `copy` keyword meaning for `array` and `asarray` constructors
Now `numpy.array` and `numpy.asarray` support
three values for `copy` parameter:
- `None` - A copy will only be made if it is necessary.
- `True` - Always make a copy.
- `False` - Never make a copy. If a copy is required a `ValueError` is
raised.
The meaning of `False` changed as it now raises an exception if a copy
is needed.
([gh-25168](https://github.com/numpy/numpy/pull/25168))
The `__array__` special method now takes a `copy` keyword argument.
NumPy will pass `copy` to the `__array__` special method in situations
where it would be set to a non-default value (e.g. in a call to
`np.asarray(some_object, copy=False)`). Currently, if an unexpected
keyword argument error is raised after this, NumPy will print a warning
and re-try without the `copy` keyword argument. Implementations of
objects implementing the `__array__` protocol should accept a `copy`
keyword argument with the same meaning as when passed to
`numpy.array` or `numpy.asarray`.
([gh-25168](https://github.com/numpy/numpy/pull/25168))
Cleanup of initialization of `numpy.dtype` with strings with commas
The interpretation of strings with commas is changed slightly, in that a
trailing comma will now always create a structured dtype. E.g., where
previously `np.dtype("i")` and `np.dtype("i,")` were treated as
identical, now `np.dtype("i,")` will create a structured dtype, with a
single field. This is analogous to `np.dtype("i,i")` creating a
structured dtype with two fields, and makes the behaviour consistent
with that expected of tuples.
At the same time, the use of single number surrounded by parenthesis to
indicate a sub-array shape, like in `np.dtype("(2)i,")`, is deprecated.
Instead; one should use `np.dtype("(2,)i")` or `np.dtype("2i")`.
Eventually, using a number in parentheses will raise an exception, like
is the case for initializations without a comma, like
`np.dtype("(2)i")`.
([gh-25434](https://github.com/numpy/numpy/pull/25434))
Change in how complex sign is calculated
Following the array API standard, the complex sign is now calculated as
`z / |z|` (instead of the rather less logical case where the sign of the
real part was taken, unless the real part was zero, in which case the
sign of the imaginary part was returned). Like for real numbers, zero is
returned if `z==0`.
([gh-25441](https://github.com/numpy/numpy/pull/25441))
Return types of functions that returned a list of arrays
Functions that returned a list of ndarrays have been changed to return a
tuple of ndarrays instead. Returning tuples consistently whenever a
sequence of arrays is returned makes it easier for JIT compilers like
Numba, as well as for static type checkers in some cases, to support
these functions. Changed functions are: `numpy.atleast_1d`, `numpy.atleast_2d`,
`numpy.atleast_3d`, `numpy.broadcast_arrays`, `numpy.meshgrid`,
`numpy.ogrid`, `numpy.histogramdd`.
`np.unique` `return_inverse` shape for multi-dimensional inputs
When multi-dimensional inputs are passed to `np.unique` with
`return_inverse=True`, the `unique_inverse` output is now shaped such
that the input can be reconstructed directly using
`np.take(unique, unique_inverse)` when `axis=None`, and
`np.take_along_axis(unique, unique_inverse, axis=axis)` otherwise.
([gh-25553](https://github.com/numpy/numpy/pull/24126),
[gh-25570](https://github.com/numpy/numpy/pull/25570))
`any` and `all` return booleans for object arrays
The `any` and `all` functions and methods now return booleans also for
object arrays. Previously, they did a reduction which behaved like the
Python `or` and `and` operators which evaluates to one of the arguments.
You can use `np.logical_or.reduce` and `np.logical_and.reduce` to
achieve the previous behavior.
([gh-25712](https://github.com/numpy/numpy/pull/25712))
**Content from release note snippets in doc/release/upcoming_changes:**
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