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__
toNone
is deprecated. It must now be
a method and may wish to callsuper().__array_finalize__(obj)
after checking forNone
or if the NumPy version is sufficiently
new.(gh-20766)
-
Using
axis=32
(axis=np.MAXDIMS
) in many cases had the same
meaning asaxis=None
. This is deprecated andaxis=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
viatracemalloc
.(gh-20394)
-
numpy.distutils
has been deprecated, as a result ofdistutils
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)
-
numpy.loadtxt
will now give aDeprecationWarning
when an integer
dtype
is requested but the value is formatted as a floating point number.(gh-21663)
Expired deprecations
-
The
NpzFile.iteritems()
andNpzFile.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
andasscalar
functions have been removed.(gh-20414)
-
The
UPDATEIFCOPY
array flag has been removed together with the
enumNPY_ARRAY_UPDATEIFCOPY
. The associated (and deprecated)
PyArray_XDECREF_ERR
was also removed. These were all deprecated in
1.14. They are replaced byWRITEBACKIFCOPY
, 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 asarr[ind]
whereind = [[0, 1], [0, 1]]
produced aFutureWarning
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)
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)
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 is now
deprecated. - Parsing hexadecimal floats such as
0x3p3
will fail - An
_
was previously accepted as a thousands delimiter100_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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