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NumPy 1.23.0 Release Notes

The NumPy 1.23.0 release continues the ongoing work to improve the
handling and promotion of dtypes, increase the execution speed, clarify
the documentation, and expire old deprecations. The highlights are:

  • Implementation of loadtxt in C, greatly improving its performance.
  • Exposing DLPack at the Python level for easy data exchange.
  • Changes to the promotion and comparisons of structured dtypes.
  • Improvements to f2py.

See below for the details,

New functions

  • A masked array specialization of ndenumerate is now available as
    numpy.ma.ndenumerate. It provides an alternative to
    numpy.ndenumerate and skips masked values by default.

    (gh-20020)

  • numpy.from_dlpack has been added to allow easy exchange of data
    using the DLPack protocol. It accepts Python objects that implement
    the __dlpack__ and __dlpack_device__ methods and returns a
    ndarray object which is generally the view of the data of the input
    object.

    (gh-21145)

Deprecations

  • Setting __array_finalize__ to None is deprecated. It must now be
    a method and may wish to call super().__array_finalize__(obj)
    after checking for None or if the NumPy version is sufficiently
    new.

    (gh-20766)

  • Using axis=32 (axis=np.MAXDIMS) in many cases had the same
    meaning as axis=None. This is deprecated and axis=None must be
    used instead.

    (gh-20920)

  • The hook function PyDataMem_SetEventHook has been deprecated and
    the demonstration of its use in tool/allocation_tracking has been
    removed. The ability to track allocations is now built-in to python
    via tracemalloc.

    (gh-20394)

  • numpy.distutils has been deprecated, as a result of distutils
    itself being deprecated. It will not be present in NumPy for
    Python >= 3.12, and will be removed completely 2 years after the
    release of Python 3.12 For more details, see
    distutils-status-migration{.interpreted-text role="ref"}.

    (gh-20875)

Expired deprecations

  • The NpzFile.iteritems() and NpzFile.iterkeys() methods have been
    removed as part of the continued removal of Python 2 compatibility.
    This concludes the deprecation from 1.15.

    (gh-16830)

  • The alen and asscalar functions have been removed.

    (gh-20414)

  • The UPDATEIFCOPY array flag has been removed together with the
    enum NPY_ARRAY_UPDATEIFCOPY. The associated (and deprecated)
    PyArray_XDECREF_ERR was also removed. These were all deprecated in
    1.14. They are replaced by WRITEBACKIFCOPY, that requires calling
    PyArray_ResoveWritebackIfCopy before the array is deallocated.

    (gh-20589)

  • Exceptions will be raised during array-like creation. When an object
    raised an exception during access of the special attributes
    __array__ or __array_interface__, this exception was usually
    ignored. This behaviour was deprecated in 1.21, and the exception
    will now be raised.

    (gh-20835)

  • Multidimensional indexing with non-tuple values is not allowed.
    Previously, code such as arr[ind] where ind = [[0, 1], [0, 1]]
    produced a FutureWarning and was interpreted as a multidimensional
    index (i.e., arr[tuple(ind)]). Now this example is treated like an
    array index over a single dimension (arr[array(ind)]).
    Multidimensional indexing with anything but a tuple was deprecated
    in NumPy 1.15.

    (gh-21029)

  • Changing to a dtype of different size in F-contiguous arrays is no
    longer permitted. Deprecated since Numpy 1.11.0. See below for an
    extended explanation of the effects of this change.

    (gh-20722)

New Features

crackfortran has support for operator and assignment overloading

crackfortran parser now understands operator and assignment
definitions in a module. They are added in the body list of the module
which contains a new key implementedby listing the names of the
subroutines or functions implementing the operator or assignment.

(gh-15006)

f2py supports reading access type attributes from derived type statements

As a result, one does not need to use public or private statements
to specify derived type access properties.

(gh-15844)

New parameter ndmin added to genfromtxt

This parameter behaves the same as ndmin from numpy.loadtxt.

(gh-20500)

np.loadtxt now supports quote character and single converter function

numpy.loadtxt now supports an additional quotechar keyword argument
which is not set by default. Using quotechar='"' will read quoted
fields as used by the Excel CSV dialect.

Further, it is now possible to pass a single callable rather than a
dictionary for the converters argument.

(gh-20580)

Changing to dtype of a different size now requires contiguity of only the last axis

Previously, viewing an array with a dtype of a different item size
required that the entire array be C-contiguous. This limitation would
unnecessarily force the user to make contiguous copies of non-contiguous
arrays before being able to change the dtype.

This change affects not only ndarray.view, but other construction
mechanisms, including the discouraged direct assignment to
ndarray.dtype.

This change expires the deprecation regarding the viewing of
F-contiguous arrays, described elsewhere in the release notes.

(gh-20722)

Deterministic output files for F2PY

For F77 inputs, f2py will generate modname-f2pywrappers.f
unconditionally, though these may be empty. For free-form inputs,
modname-f2pywrappers.f, modname-f2pywrappers2.f90 will both be
generated unconditionally, and may be empty. This allows writing generic
output rules in cmake or meson and other build systems. Older
behavior can be restored by passing --skip-empty-wrappers to f2py.
f2py-meson{.interpreted-text role="ref"} details usage.

(gh-21187)

keepdims parameter for average

The parameter keepdims was added to the functions numpy.average and
numpy.ma.average. The parameter has the same meaning as it does in
reduction functions such as numpy.sum or numpy.mean.

(gh-21485)

Compatibility notes

1D np.linalg.norm preserves float input types, even for scalar results

Previously, this would promote to float64 when the ord argument was
not one of the explicitly listed values, e.g. ord=3:

>>> f32 = np.float32([1, 2])
>>> np.linalg.norm(f32, 2).dtype
dtype('float32')
>>> np.linalg.norm(f32, 3)
dtype('float64')  # numpy 1.22
dtype('float32')  # numpy 1.23

This change affects only float32 and float16 vectors with ord
other than -Inf, 0, 1, 2, and Inf.

(gh-17709)

Changes to structured (void) dtype promotion and comparisons

In general, NumPy now defines correct, but slightly limited, promotion
for structured dtypes by promoting the subtypes of each field instead of
raising an exception:

>>> np.result_type(np.dtype("i,i"), np.dtype("i,d"))
dtype([('f0', '<i4'), ('f1', '<f8')])

For promotion matching field names, order, and titles are enforced,
however padding is ignored. Promotion involving structured dtypes now
always ensures native byte-order for all fields (which may change the
result of np.concatenate) and ensures that the result will be
"packed", i.e. all fields are ordered contiguously and padding is
removed. See
structured_dtype_comparison_and_promotion{.interpreted-text
role="ref"} for further details.

The repr of aligned structures will now never print the long form
including offsets and itemsize unless the structure includes padding
not guaranteed by align=True.

In alignment with the above changes to the promotion logic, the casting
safety has been updated:

  • "equiv" enforces matching names and titles. The itemsize is
    allowed to differ due to padding.
  • "safe" allows mismatching field names and titles
  • The cast safety is limited by the cast safety of each included
    field.
  • The order of fields is used to decide cast safety of each individual
    field. Previously, the field names were used and only unsafe casts
    were possible when names mismatched.

The main important change here is that name mismatches are now
considered "safe" casts.

(gh-19226)

NPY_RELAXED_STRIDES_CHECKING has been removed

NumPy cannot be compiled with NPY_RELAXED_STRIDES_CHECKING=0 anymore.
Relaxed strides have been the default for many years and the option was
initially introduced to allow a smoother transition.

(gh-20220)

np.loadtxt has recieved several changes

The row counting of numpy.loadtxt was fixed. loadtxt ignores fully
empty lines in the file, but counted them towards max_rows. When
max_rows is used and the file contains empty lines, these will now not
be counted. Previously, it was possible that the result contained fewer
than max_rows rows even though more data was available to be read. If
the old behaviour is required, itertools.islice may be used:

import itertools
lines = itertools.islice(open("file"), 0, max_rows)
result = np.loadtxt(lines, ...)

While generally much faster and improved, numpy.loadtxt may now fail
to converter certain strings to numbers that were previously
successfully read. The most important cases for this are:

  • Parsing floating point values such as 1.0 into integers will now
    fail
  • Parsing hexadecimal floats such as 0x3p3 will fail
  • An _ was previously accepted as a thousands delimiter 100_000.
    This will now result in an error.

If you experience these limitations, they can all be worked around by
passing appropriate converters=. NumPy now supports passing a single
converter to be used for all columns to make this more convenient. For
example, converters=float.fromhex can read hexadecimal float numbers
and converters=int will be able to read 100_000.

Further, the error messages have been generally improved. However, this
means that error types may differ. In particularly, a ValueError is
now always raised when parsing of a single entry fails.

(gh-20580)

Improvements

ndarray.__array_finalize__ is now callable

This means subclasses can now use super().__array_finalize__(obj)
without worrying whether ndarray is their superclass or not. The
actual call remains a no-op.

(gh-20766)

Add support for VSX4/Power10

With VSX4/Power10 enablement, the new instructions available in Power
ISA 3.1 can be used to accelerate some NumPy operations, e.g.,
floor_divide, modulo, etc.

(gh-20821)

np.fromiter now accepts objects and subarrays

The numpy.fromiter function now supports object and subarray dtypes.
Please see he function documentation for examples.

(gh-20993)

Math C library feature detection now uses correct signatures

Compiling is preceded by a detection phase to determine whether the
underlying libc supports certain math operations. Previously this code
did not respect the proper signatures. Fixing this enables compilation
for the wasm-ld backend (compilation for web assembly) and reduces the
number of warnings.

(gh-21154)

np.kron now maintains subclass information

np.kron maintains subclass information now such as masked arrays while
computing the Kronecker product of the inputs

>>> x = ma.array([[1, 2], [3, 4]], mask=[[0, 1], [1, 0]])
>>> np.kron(x,x)
masked_array(
  data=[[1, --, --, --],
        [--, 4, --, --],
        [--, --, 4, --],
        [--, --, --, 16]],
  mask=[[False,  True,  True,  True],
        [ True, False,  True,  True],
        [ True,  True, False,  True],
        [ True,  True,  True, False]],
  fill_value=999999)

⚠️ Warning, np.kron output now follows ufunc ordering (multiply) to determine
the output class type

>>> class myarr(np.ndarray):
>>>    __array_priority__ = -1
>>> a = np.ones([2, 2])
>>> ma = myarray(a.shape, a.dtype, a.data)
>>> type(np.kron(a, ma)) == np.ndarray
False # Before it was True
>>> type(np.kron(a, ma)) == myarr
True

(gh-21262)

Performance improvements and changes

Faster np.loadtxt

numpy.loadtxt is now generally much faster than previously as most of
it is now implemented in C.

(gh-20580)

Faster reduction operators

Reduction operations like numpy.sum, numpy.prod, numpy.add.reduce,
numpy.logical_and.reduce on contiguous integer-based arrays are now
much faster.

(gh-21001)

Faster np.where

numpy.where is now much faster than previously on unpredictable/random
input data.

(gh-21130)

Faster operations on NumPy scalars

Many operations on NumPy scalars are now significantly faster, although
rare operations (e.g. with 0-D arrays rather than scalars) may be slower
in some cases. However, even with these improvements users who want the
best performance for their scalars, may want to convert a known NumPy
scalar into a Python one using scalar.item().

(gh-21188)

Faster np.kron

numpy.kron is about 80% faster as the product is now computed using
broadcasting.

(gh-21354)

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