NumPy 2.1.0 Release Notes#

Highlights#

We’ll choose highlights for this release near the end of the release cycle.

New functions#

New function `numpy.unstack`#

A new function np.unstack(array, axis=...) was added, which splits an array into a tuple of arrays along an axis. It serves as the inverse of numpy.stack.

(gh-26579)

Deprecations#

The fix_imports keyword argument in numpy.save is deprecated. Since NumPy 1.17, numpy.save uses a pickle protocol that no longer supports Python 2, and ignored fix_imports keyword. This keyword is kept only for backward compatibility. It is now deprecated.

(gh-26452)

Expired deprecations#

Scalars and 0D arrays are disallowed for numpy.nonzero and numpy.ndarray.nonzero.

(gh-26268)
set_string_function internal function was removed and PyArray_SetStringFunction was stubbed out.

(gh-26611)

C API changes#

API symbols now hidden but customizable#

NumPy now defaults to hide the API symbols it adds to allow all NumPy API usage. This means that by default you cannot dynamically fetch the NumPy API from another library (this was never possible on windows).

If you are experiencing linking errors related to PyArray_API or PyArray_RUNTIME_VERSION, you can define the NPY_API_SYMBOL_ATTRIBUTE to opt-out of this change.

If you are experiencing problems due to an upstream header including NumPy, the solution is to make sure you #include "numpy/ndarrayobject.h" before their header and import NumPy yourself based on Including and importing the C API.

(gh-26103)

Many shims removed from npy_3kcompat.h#

Many of the old shims and helper functions were removed from npy_3kcompat.h. If you find yourself in need of these, vendor the previous version of the file into your codebase.

(gh-26842)

New `PyUFuncObject` field `process_core_dims_func`#

The field process_core_dims_func was added to the structure PyUFuncObject. For generalized ufuncs, this field can be set to a function of type PyUFunc_ProcessCoreDimsFunc that will be called when the ufunc is called. It allows the ufunc author to check that core dimensions satisfy additional constraints, and to set output core dimension sizes if they have not been provided.

(gh-26908)

New Features#

numpy.reshape and numpy.ndarray.reshape now support shape and copy arguments.

(gh-26292)
NumPy now supports DLPack v1, support for older versions will be deprecated in the future.

(gh-26501)
numpy.asanyarray now supports copy and device arguments, matching numpy.asarray.

(gh-26580)
numpy.printoptions, numpy.get_printoptions, and numpy.set_printoptions now support a new option, override_repr, for defining custom repr(array) behavior.

(gh-26611)
numpy.cumulative_sum and numpy.cumulative_prod were added as Array API compatible alternatives for numpy.cumsum and numpy.cumprod. The new functions can include a fixed initial (zeros for sum and ones for prod) in the result.
numpy.clip now supports max and min keyword arguments which are meant to replace a_min and a_max. Also, for np.clip(a) or np.clip(a, None, None) a copy of the input array will be returned instead of raising an error.
numpy.astype now supports device argument.

(gh-26724)

`f2py` can generate freethreading-compatible C extensions#

Pass --freethreading-compatible to the f2py CLI tool to produce a C extension marked as compatible with the free threading CPython interpreter. Doing so prevents the interpreter from re-enabling the GIL at runtime when it imports the C extension. Note that f2py does not analyze fortran code for thread safety, so you must verify that the wrapped fortran code is thread safe before marking the extension as compatible.

(gh-26981)

Improvements#

`histogram` auto-binning now returns bin sizes >=1 for integer input data#

For integer input data, bin sizes smaller than 1 result in spurious empty bins. This is now avoided when the number of bins is computed using one of the algorithms provided by histogram_bin_edges.

(gh-12150)

np.quantile with method `closest_observation` chooses nearest even order statistic#

This changes the definition of nearest for border cases from the nearest odd order statistic to nearest even order statistic. The numpy implementation now matches other reference implementations.

(gh-26656)

lapack_lite is now thread safe#

NumPy provides a minimal low-performance version of LAPACK named lapack_lite that can be used if no BLAS/LAPACK system is detected at build time.

Until now, lapack_lite was not thread safe. Single-threaded use cases did not hit any issues, but running linear algebra operations in multiple threads could lead to errors, incorrect results, or seg faults due to data races.

We have added a global lock, serializing access to lapack_lite in multiple threads.

(gh-26750)

The `numpy.printoptions` context manager is now thread and async-safe#

In prior versions of NumPy, the printoptions were defined using a combination of Python and C global variables. We have refactored so the state is stored in a python ContextVar, making the context manager thread and async-safe.

(gh-26846)

Performance improvements and changes#

`ma.cov` and `ma.corrcoef` are now significantly faster#

The private function has been refactored along with ma.cov and ma.corrcoef. They are now significantly faster, particularly on large, masked arrays.

(gh-26285)

numpy.save now uses pickle protocol version 4 for saving arrays with object dtype, which allows for pickle objects larger than 4GB and improves saving speed by about 5% for large arrays.

(gh-26388)

Changes#

As numpy.vecdot is now a ufunc it has a less precise signature. This is due to the limitations of ufunc’s typing stub.

(gh-26313)
numpy.floor, numpy.ceil, and numpy.trunc now won’t perform casting to a floating dtype for integer and boolean dtype input arrays.

(gh-26766)

`ma.corrcoef` may return a slightly different result#

A pairwise observation approach is currently used in ma.corrcoef to calculate the standard deviations for each pair of variables. This has been changed as it is being used to normalise the covariance, estimated using ma.cov, which does not consider the observations for each variable in a pairwise manner, rendering it unnecessary. The normalisation has been replaced by the more appropriate standard deviation for each variable, which significantly reduces the wall time, but will return slightly different estimates of the correlation coefficients in cases where the observations between a pair of variables are not aligned. However, it will return the same estimates in all other cases, including returning the same correlation matrix as corrcoef when using a masked array with no masked values.