NumPy 1.24.0 Release Notes#
Expired deprecations#
The
normed
keyword argument has been removed from np.histogram, np.histogram2d, and np.histogramdd. Usedensity
instead. Ifnormed
was passed by position,density
is now used.(gh-21645)
Compatibility notes#
array.fill(scalar)
may behave slightly different#
fill
may in some cases behave slightly different
now due to the fact that the logic is aligned with item assignment:
arr = np.array([1]) # with any dtype/value
arr.fill(scalar)
# is now identical to:
arr[0] = scalar
Previously casting may have produced slightly different answers when using
values that could not be represented in the target dtype
or when the
target had object
dtype.
(gh-20924)
New Features#
New attribute symbol
added to polynomial classes#
The polynomial classes in the numpy.polynomial
package have a new
symbol
attribute which is used to represent the indeterminate
of the polynomial.
This can be used to change the value of the variable when printing:
>>> P_y = np.polynomial.Polynomial([1, 0, -1], symbol="y")
>>> print(P_y)
1.0 + 0.0·y¹ - 1.0·y²
Note that the polynomial classes only support 1D polynomials, so operations that involve polynomials with different symbols are disallowed when the result would be multivariate:
>>> P = np.polynomial.Polynomial([1, -1]) # default symbol is "x"
>>> P_z = np.polynomial.Polynomial([1, 1], symbol="z")
>>> P * P_z
Traceback (most recent call last)
...
ValueError: Polynomial symbols differ
The symbol can be any valid Python identifier. The default is symbol=x
,
consistent with existing behavior.
(gh-16154)
F2PY support for Fortran character
strings#
F2PY now supports wrapping Fortran functions with:
character (e.g.
character x
)character array (e.g.
character, dimension(n) :: x
)character string (e.g.
character(len=10) x
)and character string array (e.g.
character(len=10), dimension(n, m) :: x
)
arguments, including passing Python unicode strings as Fortran character string arguments.
(gh-19388)
strict
option for testing.assert_array_equal#
The strict
option is now available for testing.assert_array_equal.
Setting strict=True
will disable the broadcasting behaviour for scalars and
ensure that input arrays have the same data type.
(gh-21595)
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)
casting
and dtype
keyword arguments for numpy.stack
#
The casting
and dtype
keyword arguments are now available for numpy.stack
.
To use them, write np.stack(..., dtype=None, casting='same_kind')
.
casting
and dtype
keyword arguments for numpy.vstack
#
The casting
and dtype
keyword arguments are now available for numpy.vstack
.
To use them, write np.vstack(..., dtype=None, casting='same_kind')
.
casting
and dtype
keyword arguments for numpy.hstack
#
The casting
and dtype
keyword arguments are now available for numpy.hstack
.
To use them, write np.hstack(..., dtype=None, casting='same_kind')
.
(gh-21627)
Improvements#
F2PY Improvements#
The generated extension modules don’t use the deprecated NumPy-C API anymore
Improved
f2py
generated exception messagesNumerous bug and
flake8
warning fixesvarious CPP macros that one can use within C-expressions of signature files are prefixed with
f2py_
. For example, one should usef2py_len(x)
instead oflen(x)
A new construct
character(f2py_len=...)
is introduced to support returning assumed length character strings (e.g.character(len=*)
) from wrapper functions
A hook to support rewriting f2py
internal data structures after reading all its input files is introduced. This is required, for instance, for BC of SciPy support where character arguments are treated as character strings arguments in C
expressions.
(gh-19388)
IBM zSystems Vector Extension Facility (SIMD)#
Added support for SIMD extensions of zSystem (z13, z14, z15), through the universal intrinsics interface. This support leads to performance improvements for all SIMD kernels implemented using the universal intrinsics, including the following operations:
rint, floor, trunc, ceil, sqrt, absolute, square, reciprocal, tanh, sin, cos, equal, not_equal, greater, greater_equal, less, less_equal, maximum, minimum, fmax, fmin, argmax, argmin, add, subtract, multiply, divide.
(gh-20913)
NumPy now gives floating point errors in casts#
In most cases, NumPy previously did not give floating point warnings or errors when these happened during casts. For examples, casts like:
np.array([2e300]).astype(np.float32) # overflow for float32
np.array([np.inf]).astype(np.int64)
Should now generally give floating point warnings. These warnings should warn that floating point overflow occurred. For errors when converting floating point values to integers users should expect invalid value warnings.
Users can modify the behavior of these warnings using np.errstate.
Note that for float to int casts, the exact warnings that are given may be platform dependend. For example:
arr = np.full(100, value=1000, dtype=np.float64)
arr.astype(np.int8)
May give a result equivalent to (the intermediat means no warning is given):
arr.astype(np.int64).astype(np.int8)
May may return an undefined result, with a warning set:
RuntimeWarning: invalid value encountered in cast
The precise behavior if subject to the C99 standard and its implementation in both software and hardware.
(gh-21437)
F2PY supports the value attribute#
The Fortran standard requires that variables declared with the value
attribute must be passed by value instead of reference. F2PY now supports this
use pattern correctly. So integer, intent(in), value :: x
in Fortran codes
will have correct wrappers generated.
(gh-21807)
Performance improvements and changes#
Faster version of np.isin
and np.in1d
for integer arrays#
np.in1d
(used by np.isin
) can now switch to a faster algorithm
(up to >10x faster) when it is passed two integer arrays.
This is often automatically used, but you can use kind="sort"
or
kind="table"
to force the old or new method, respectively.
(gh-12065)
Faster comparison operators#
The comparison functions (numpy.equal
, numpy.not_equal
, numpy.less
,
numpy.less_equal
, numpy.greater
and numpy.greater_equal
) are now
much faster as they are now vectorized with universal intrinsics. For a CPU
with SIMD extension AVX512BW, the performance gain is up to 2.57x, 1.65x and
19.15x for integer, float and boolean data types, respectively (with N=50000).
(gh-21483)