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# NumPy 2.3.0 Release Notes

The NumPy 2.3.0 release continues the work to improve free threaded Python support and annotations together with the usual set of bug fixes. It is unusual in the number of expired deprecations, code modernizations, and style cleanups. The latter may not be visible to users, but is important for code maintenance over the long term. Note that we have also upgraded from manylinux2014 to manylinux_2_28.

Users running on a Mac having an M4 cpu might see various warnings about invalid values and such. The warnings are a known problem with Accelerate. They are annoying, but otherwise harmless. Apple promises to fix them.

This release supports Python versions 3.11-3.13, Python 3.14 will be supported when it is released.

## Highlights

- Interactive examples in the NumPy documentation.

- Building NumPy with OpenMP Parallelization.

- Preliminary support for Windows on ARM.

- Improved support for free threaded Python.

- Improved annotations.

## New functions

### New function `numpy.strings.slice`

The new function `numpy.strings.slice` was added, which implements fast native slicing of string arrays. It supports the full slicing API including negative slice offsets and steps.

(gh-27789)

## Deprecations

- The `numpy.typing.mypy_plugin` has been deprecated in favor of platform-agnostic static type inference. Please remove `numpy.typing.mypy_plugin` from the `plugins` section of your mypy configuration. If this change results in new errors being reported, kindly open an issue.

  (gh-28129)

- The `numpy.typing.NBitBase` type has been deprecated and will be removed in a future version.

  This type was previously intended to be used as a generic upper bound for type-parameters, for example:

      import numpy as np
      import numpy.typing as npt

      def f[NT: npt.NBitBase](x: np.complexfloating[NT]) -> np.floating[NT]: ...

  But in NumPy 2.2.0, `float64` and `complex128` were changed to concrete subtypes, causing static type-checkers to reject `x: np.float64 = f(np.complex128(42j))`.

  So instead, the better approach is to use `typing.overload`:

      import numpy as np
      from typing import overload

      @overload
      def f(x: np.complex64) -> np.float32: ...
      @overload
      def f(x: np.complex128) -> np.float64: ...
      @overload
      def f(x: np.clongdouble) -> np.longdouble: ...

  (gh-28884)

## Expired deprecations

- Remove deprecated macros like `NPY_OWNDATA` from Cython interfaces in favor of `NPY_ARRAY_OWNDATA` (deprecated since 1.7)

  (gh-28254)

- Remove `numpy/npy_1_7_deprecated_api.h` and C macros like `NPY_OWNDATA` in favor of `NPY_ARRAY_OWNDATA` (deprecated since 1.7)

  (gh-28254)

- Remove alias `generate_divbyzero_error` to `npy_set_floatstatus_divbyzero` and `generate_overflow_error` to `npy_set_floatstatus_overflow` (deprecated since 1.10)

  (gh-28254)

- Remove `np.tostring` (deprecated since 1.19)

  (gh-28254)

- Raise on `np.conjugate` of non-numeric types (deprecated since 1.13)

  (gh-28254)

- Raise when using `np.bincount(...minlength=None)`, use 0 instead (deprecated since 1.14)

  (gh-28254)

- Passing `shape=None` to functions with a non-optional shape argument errors, use `()` instead (deprecated since 1.20)

  (gh-28254)

- Inexact matches for `mode` and `searchside` raise (deprecated since 1.20)

  (gh-28254)

- Setting `__array_finalize__ = None` errors (deprecated since 1.23)

  (gh-28254)

- `np.fromfile` and `np.fromstring` error on bad data, previously they would guess (deprecated since 1.18)

  (gh-28254)

- `datetime64` and `timedelta64` construction with a tuple no longer accepts an `event` value, either use a two-tuple of (unit, num) or a 4-tuple of (unit, num, den, 1) (deprecated since 1.14)

  (gh-28254)

- When constructing a `dtype` from a class with a `dtype` attribute, that attribute must be a dtype-instance rather than a thing that can be parsed as a dtype instance (deprecated in 1.19). At some point the whole construct of using a dtype attribute will be deprecated (see #25306)

  (gh-28254)

- Passing booleans as partition index errors (deprecated since 1.23)

  (gh-28254)

- Out-of-bounds indexes error even on empty arrays (deprecated since 1.20)

  (gh-28254)

- `np.tostring` has been removed, use `tobytes` instead (deprecated since 1.19)

  (gh-28254)

- Disallow make a non-writeable array writeable for arrays with a base that do not own their data (deprecated since 1.17)

  (gh-28254)

- `concatenate()` with `axis=None` uses `same-kind` casting by default, not `unsafe` (deprecated since 1.20)

  (gh-28254)

- Unpickling a scalar with object dtype errors (deprecated since 1.20)

  (gh-28254)

- The binary mode of `fromstring` now errors, use `frombuffer` instead (deprecated since 1.14)

  (gh-28254)

- Converting `np.inexact` or `np.floating` to a dtype errors (deprecated since 1.19)

  (gh-28254)

- Converting `np.complex`, `np.integer`, `np.signedinteger`, `np.unsignedinteger`, `np.generic` to a dtype errors (deprecated since 1.19)

  (gh-28254)

- The Python built-in `round` errors for complex scalars. Use `np.round` or `scalar.round` instead (deprecated since 1.19)

  (gh-28254)

- ‘np.bool’ scalars can no longer be interpreted as an index (deprecated since 1.19)

  (gh-28254)

- Parsing an integer via a float string is no longer supported. (deprecated since 1.23) To avoid this error you can * make sure the original data is stored as integers. * use the `converters=float` keyword argument. * Use `np.loadtxt(...).astype(np.int64)`

  (gh-28254)

- The use of a length 1 tuple for the ufunc `signature` errors. Use `dtype` or fill the tuple with `None` (deprecated since 1.19)

  (gh-28254)

- Special handling of matrix is in np.outer is removed. Convert to a ndarray via `matrix.A` (deprecated since 1.20)

  (gh-28254)

- Removed the `np.compat` package source code (removed in 2.0)

  (gh-28961)

## C API changes

- `NpyIter_GetTransferFlags` is now available to check if the iterator needs the Python API or if casts may cause floating point errors (FPE). FPEs can for example be set when casting `float64(1e300)` to `float32` (overflow to infinity) or a NaN to an integer (invalid value).

  (gh-27883)

- `NpyIter` now has no limit on the number of operands it supports.

  (gh-28080)

### New `NpyIter_GetTransferFlags` and `NpyIter_IterationNeedsAPI` change

NumPy now has the new `NpyIter_GetTransferFlags` function as a more precise way checking of iterator/buffering needs. I.e. whether the Python API/GIL is required or floating point errors may occur. This function is also faster if you already know your needs without buffering.

The `NpyIter_IterationNeedsAPI` function now performs all the checks that were previously performed at setup time. While it was never necessary to call it multiple times, doing so will now have a larger cost.

(gh-27998)

## New Features

- The type parameter of `np.dtype` now defaults to `typing.Any`. This way, static type-checkers will infer `dtype: np.dtype` as `dtype: np.dtype[Any]`, without reporting an error.

  (gh-28669)

- Static type-checkers now interpret:

  - `_: np.ndarray` as `_: npt.NDArray[typing.Any]`.

  - `_: np.flatiter` as `_: np.flatiter[np.ndarray]`.

  This is because their type parameters now have default values.

  (gh-28940)

### NumPy now registers its pkg-config paths with the pkgconf PyPI package

The pkgconf PyPI package provides an interface for projects like NumPy to register their own paths to be added to the pkg-config search path. This means that when using pkgconf from PyPI, NumPy will be discoverable without needing for any custom environment configuration.

Attention

Attention

This only applies when using the pkgconf package from PyPI, or put another way, this only applies when installing pkgconf via a Python package manager.

If you are using `pkg-config` or `pkgconf` provided by your system, or any other source that does not use the pkgconf-pypi project, the NumPy pkg-config directory will not be automatically added to the search path. In these situations, you might want to use `numpy-config`.

(gh-28214)

### Allow `out=...` in ufuncs to ensure array result

NumPy has the sometimes difficult behavior that it currently usually returns scalars rather than 0-D arrays (even if the inputs were 0-D arrays). This is especially problematic for non-numerical dtypes (e.g. `object`).

For ufuncs (i.e. most simple math functions) it is now possible to use `out=...` (literally `…`, e.g. `out=Ellipsis`) which is identical in behavior to `out` not being passed, but will ensure a non-scalar return. This spelling is borrowed from `arr1d[0, ...]` where the `...` also ensures a non-scalar return.

Other functions with an `out=` kwarg should gain support eventually. Downstream libraries that interoperate via `__array_ufunc__` or `__array_function__` may need to adapt to support this.

(gh-28576)

### Building NumPy with OpenMP Parallelization

NumPy now supports OpenMP parallel processing capabilities when built with the `-Denable_openmp=true` Meson build flag. This feature is disabled by default. When enabled, `np.sort` and `np.argsort` functions can utilize OpenMP for parallel thread execution, improving performance for these operations.

(gh-28619)

### Interactive examples in the NumPy documentation

The NumPy documentation includes a number of examples that can now be run interactively in your browser using WebAssembly and Pyodide.

Please note that the examples are currently experimental in nature and may not work as expected for all methods in the public API.

(gh-26745)

## Improvements

- Scalar comparisons between non-comparable dtypes such as `np.array(1) == np.array('s')` now return a NumPy bool instead of a Python bool.

  (gh-27288)

- `np.nditer` now has no limit on the number of supported operands (C-integer).

  (gh-28080)

- No-copy pickling is now supported for any array that can be transposed to a C-contiguous array.

  (gh-28105)

- The `__repr__` for user-defined dtypes now prefers the `__name__` of the custom dtype over a more generic name constructed from its `kind` and `itemsize`.

  (gh-28250)

- `np.dot` now reports floating point exceptions.

  (gh-28442)

- `np.dtypes.StringDType` is now a generic type which accepts a type argument for `na_object` that defaults to `typing.Never`. For example, `StringDType(na_object=None)` returns a `StringDType[None]`, and `StringDType()` returns a `StringDType[typing.Never]`.

  (gh-28856)

### Added warnings to `np.isclose`

Added warning messages if at least one of atol or rtol are either `np.nan` or `np.inf` within `np.isclose`.

- Warnings follow the user’s `np.seterr` settings

(gh-28205)

## Performance improvements and changes

### Performance improvements to `np.unique`

`np.unique` now tries to use a hash table to find unique values instead of sorting values before finding unique values. This is limited to certain dtypes for now, and the function is now faster for those dtypes. The function now also exposes a `sorted` parameter to allow returning unique values as they were found, instead of sorting them afterwards.

(gh-26018)

### Performance improvements to `np.sort` and `np.argsort`

`np.sort` and `np.argsort` functions now can leverage OpenMP for parallel thread execution, resulting in up to 3.5x speedups on x86 architectures with AVX2 or AVX-512 instructions. This opt-in feature requires NumPy to be built with the -Denable_openmp Meson flag. Users can control the number of threads used by setting the OMP_NUM_THREADS environment variable.

(gh-28619)

### Performance improvements for `np.float16` casts

Earlier, floating point casts to and from `np.float16` types were emulated in software on all platforms.

Now, on ARM devices that support Neon float16 intrinsics (such as recent Apple Silicon), the native float16 path is used to achieve the best performance.

(gh-28769)

### Performance improvements for `np.matmul`

Enable using BLAS for `matmul` even when operands are non-contiguous by copying if needed.

(gh-23752)

## Changes

- The vector norm `ord=inf` and the matrix norms `ord={1, 2, inf, 'nuc'}` now always returns zero for empty arrays. Empty arrays have at least one axis of size zero. This affects `np.linalg.norm`, `np.linalg.vector_norm`, and `np.linalg.matrix_norm`. Previously, NumPy would raises errors or return zero depending on the shape of the array.

  (gh-28343)

- A spelling error in the error message returned when converting a string to a float with the method `np.format_float_positional` has been fixed.

  (gh-28569)

- NumPy’s `__array_api_version__` was upgraded from `2023.12` to `2024.12`.

- `numpy.count_nonzero` for `axis=None` (default) now returns a NumPy scalar instead of a Python integer.

- The parameter `axis` in `numpy.take_along_axis` function has now a default value of `-1`.

  (gh-28615)

- Printing of `np.float16` and `np.float32` scalars and arrays have been improved by adjusting the transition to scientific notation based on the floating point precision. A new legacy `np.printoptions` mode `'2.2'` has been added for backwards compatibility.

  (gh-28703)

- Multiplication between a string and integer now raises OverflowError instead of MemoryError if the result of the multiplication would create a string that is too large to be represented. This follows Python’s behavior.

  (gh-29060)

### `unique_values` may return unsorted data

The relatively new function (added in NumPy 2.0) `unique_values` may now return unsorted results. Just as `unique_counts` and `unique_all` these never guaranteed a sorted result, however, the result was sorted until now. In cases where these do return a sorted result, this may change in future releases to improve performance.

(gh-26018)

### Changes to the main iterator and potential numerical changes

The main iterator, used in math functions and via `np.nditer` from Python and `NpyIter` in C, now behaves differently for some buffered iterations. This means that:

- The buffer size used will often be smaller than the maximum buffer sized allowed by the `buffersize` parameter.

- The “growinner” flag is now honored with buffered reductions when no operand requires buffering.

For `np.sum()` such changes in buffersize may slightly change numerical results of floating point operations. Users who use “growinner” for custom reductions could notice changes in precision (for example, in NumPy we removed it from `einsum` to avoid most precision changes and improve precision for some 64bit floating point inputs).

(gh-27883)

### The minimum supported GCC version is now 9.3.0

The minimum supported version was updated from 8.4.0 to 9.3.0, primarily in order to reduce the chance of platform-specific bugs in old GCC versions from causing issues.

(gh-28102)

### Changes to automatic bin selection in numpy.histogram

The automatic bin selection algorithm in `numpy.histogram` has been modified to avoid out-of-memory errors for samples with low variation. For full control over the selected bins the user can use set the `bin` or `range` parameters of `numpy.histogram`.

(gh-28426)

### Build manylinux_2_28 wheels

Wheels for linux systems will use the `manylinux_2_28` tag (instead of the `manylinux2014` tag), which means dropping support for redhat7/centos7, amazonlinux2, debian9, ubuntu18.04, and other pre-glibc2.28 operating system versions, as per the PEP 600 support table.

(gh-28436)

### Remove use of -Wl,-ld_classic on macOS

Remove use of -Wl,-ld_classic on macOS. This hack is no longer needed by Spack, and results in libraries that cannot link to other libraries built with ld (new).

(gh-28713)

### Re-enable overriding functions in the `numpy.strings`

Re-enable overriding functions in the `numpy.strings` module.

(gh-28741)