NumPy 1.20.0 Release Notes
This NumPy release is the largest so made to date, some 654 PRs
contributed by 182 people have been merged. See the list of highlights
below for more details. The Python versions supported for this release
are 3.73.9, support for Python 3.6 has been dropped. Highlights are
 Annotations for NumPy functions. This work is ongoing and
improvements can be expected pending feedback from users.  Wider use of SIMD to increase execution speed of ufuncs. Much work
has been done in introducing universal functions that will ease use
of modern features across different hardware platforms. This work is
ongoing.  Preliminary work in changing the dtype and casting implementations
in order to provide an easier path to extending dtypes. This work is
ongoing but enough has been done to allow experimentation and
feedback.  Extensive documentation improvements comprising some 185 PR merges.
This work is ongoing and part of the larger project to improve
NumPy's online presence and usefulness to new users.  Further cleanups related to removing Python 2.7. This improves code
readability and removes technical debt.  Preliminary support for the upcoming Cython 3.0.
New functions
The random.Generator class has a new permuted
function.
The new function differs from shuffle
and permutation
in that the
subarrays indexed by an axis are permuted rather than the axis being
treated as a separate 1D array for every combination of the other
indexes. For example, it is now possible to permute the rows or columns
of a 2D array.
(gh15121)
sliding_window_view
provides a sliding window view for numpy arrays
[numpy.lib.stride_tricks.sliding_window_view]{.titleref} constructs
views on numpy arrays that offer a sliding or moving window access to
the array. This allows for the simple implementation of certain
algorithms, such as running means.
(gh17394)
[numpy.broadcast_shapes]{.titleref} is a new userfacing function
[~numpy.broadcast_shapes]{.titleref} gets the resulting shape from
broadcasting the given shape tuples against each other.
>>> np.broadcast_shapes((1, 2), (3, 1))
(3, 2)
>>> np.broadcast_shapes(2, (3, 1))
(3, 2)
>>> np.broadcast_shapes((6, 7), (5, 6, 1), (7,), (5, 1, 7))
(5, 6, 7)
(gh17535)
Deprecations
Using the aliases of builtin types like np.int
is deprecated
For a long time, np.int
has been an alias of the builtin int
. This
is repeatedly a cause of confusion for newcomers, and is also simply not
useful.
These aliases have been deprecated. The table below shows the full list
of deprecated aliases, along with their exact meaning. Replacing uses of
items in the first column with the contents of the second column will
work identically and silence the deprecation warning.
In many cases, it may have been intended to use the types from the third
column. Be aware that use of these types may result in subtle but
desirable behavior changes.
Deprecated name Identical to Possibly intended numpy type
numpy.bool
bool
[numpy.bool_]{.titleref}
numpy.int
int
[numpy.int_]{.titleref} (default int dtype), [numpy.cint]{.titleref} (C int
)
numpy.float
float
[numpy.float_]{.titleref}, [numpy.double]{.titleref} (equivalent)
numpy.complex
complex
[numpy.complex_]{.titleref}, [numpy.cdouble]{.titleref} (equivalent)
numpy.object
object
[numpy.object_]{.titleref}
numpy.str
str
[numpy.str_]{.titleref}
numpy.long
int
(long
on Python 2) [numpy.int_]{.titleref} (C long
), [numpy.longlong]{.titleref} (largest integer type)
numpy.unicode
str
(unicode
on Python 2) [numpy.unicode_]{.titleref}
Note that for technical reasons these deprecation warnings will only be
emitted on Python 3.7 and above.
(gh14882)
Passing shape=None
to functions with a nonoptional shape argument is deprecated
Previously, this was an alias for passing shape=()
. This deprecation
is emitted by [PyArray_IntpConverter]{.titleref} in the C API. If your
API is intended to support passing None
, then you should check for
None
prior to invoking the converter, so as to be able to distinguish
None
and ()
.
(gh15886)
Indexing errors will be reported even when index result is empty
In the future, NumPy will raise an IndexError when an integer array
index contains out of bound values even if a nonindexed dimension is of
length 0. This will now emit a DeprecationWarning. This can happen when
the array is previously empty, or an empty slice is involved:
arr1 = np.zeros((5, 0))
arr1[[20]]
arr2 = np.zeros((5, 5))
arr2[[20], :0]
Previously the nonempty index [20]
was not checked for correctness.
It will now be checked causing a deprecation warning which will be
turned into an error. This also applies to assignments.
(gh15900)
Inexact matches for mode
and searchside
are deprecated
Inexact and case insensitive matches for mode
and searchside
were
valid inputs earlier and will give a DeprecationWarning now. For
example, below are some example usages which are now deprecated and will
give a DeprecationWarning:
import numpy as np
arr = np.array([[3, 6, 6], [4, 5, 1]])
# mode: inexact match
np.ravel_multi_index(arr, (7, 6), mode="clap") # should be "clip"
# searchside: inexact match
np.searchsorted(arr[0], 4, side='random') # should be "right"
(gh16056)
Deprecation of [numpy.dual]{.titleref}
The module [numpy.dual]{.titleref} is deprecated. Instead of importing
functions from [numpy.dual]{.titleref}, the functions should be
imported directly from NumPy or SciPy.
(gh16156)
outer
and ufunc.outer
deprecated for matrix
np.matrix
use with [~numpy.outer]{.titleref} or generic ufunc outer
calls such as numpy.add.outer
. Previously, matrix was converted to an
array here. This will not be done in the future requiring a manual
conversion to arrays.
(gh16232)
Further Numeric Style types Deprecated
The remaining numericstyle type codes Bytes0
, Str0
, Uint32
,
Uint64
, and Datetime64
have been deprecated. The lowercase variants
should be used instead. For bytes and string "S"
and "U"
are further
alternatives.
(gh16554)
The ndincr
method of ndindex
is deprecated
The documentation has warned against using this function since NumPy
1.8. Use next(it)
instead of it.ndincr()
.
(gh17233)
Future Changes
Arrays cannot be using subarray dtypes
Array creation and casting using np.array(arr, dtype)
and
arr.astype(dtype)
will use different logic when dtype
is a subarray
dtype such as np.dtype("(2)i,")
.
For such a dtype
the following behaviour is true:
res = np.array(arr, dtype)
res.dtype is not dtype
res.dtype is dtype.base
res.shape == arr.shape + dtype.shape
But res
is filled using the logic:
res = np.empty(arr.shape + dtype.shape, dtype=dtype.base)
res[...] = arr
which uses incorrect broadcasting (and often leads to an error). In the
future, this will instead cast each element individually, leading to the
same result as:
res = np.array(arr, dtype=np.dtype(["f", dtype]))["f"]
Which can normally be used to optin to the new behaviour.
This change does not affect np.array(list, dtype="(2)i,")
unless the
list
itself includes at least one array. In particular, the behaviour
is unchanged for a list of tuples.
(gh17596)
Expired deprecations

The deprecation of numeric style typecodes
np.dtype("Complex64")
(with upper case spelling), is expired."Complex64"
corresponded
to"complex128"
and"Complex32"
corresponded to"complex64"
. 
The deprecation of
np.sctypeNA
andnp.typeNA
is expired. Both
have been removed from the public API. Usenp.typeDict
instead.(gh16554)

The 14year deprecation of
np.ctypeslib.ctypes_load_library
is
expired. Use~numpy.ctypeslib.load_library
{.interpretedtext
role="func"} instead, which is identical.(gh17116)
Financial functions removed
In accordance with NEP 32, the financial functions are removed from
NumPy 1.20. The functions that have been removed are fv
, ipmt
,
irr
, mirr
, nper
, npv
, pmt
, ppmt
, pv
, and rate
. These
functions are available in the
numpy_financial library.
(gh17067)
Compatibility notes
Same kind casting in concatenate with axis=None
When [~numpy.concatenate]{.titleref} is called with axis=None
, the
flattened arrays were cast with unsafe
. Any other axis choice uses
"same kind". That different default has been deprecated and "same
kind" casting will be used instead. The new casting
keyword argument
can be used to retain the old behaviour.
(gh16134)
NumPy Scalars are cast when assigned to arrays
When creating or assigning to arrays, in all relevant cases NumPy
scalars will now be cast identically to NumPy arrays. In particular this
changes the behaviour in some cases which previously raised an error:
np.array([np.float64(np.nan)], dtype=np.int64)
will succeed and return an undefined result (usually the smallest
possible integer). This also affects assignments:
arr[0] = np.float64(np.nan)
At this time, NumPy retains the behaviour for:
np.array(np.float64(np.nan), dtype=np.int64)
The above changes do not affect Python scalars:
np.array([float("NaN")], dtype=np.int64)
remains unaffected (np.nan
is a Python float
, not a NumPy one).
Unlike signed integers, unsigned integers do not retain this special
case, since they always behaved more like casting. The following code
stops raising an error:
np.array([np.float64(np.nan)], dtype=np.uint64)
To avoid backward compatibility issues, at this time assignment from
datetime64
scalar to strings of too short length remains supported.
This means that np.asarray(np.datetime64("20201010"), dtype="S5")
succeeds now, when it failed before. In the long term this may be
deprecated or the unsafe cast may be allowed generally to make
assignment of arrays and scalars behave consistently.
Array coercion changes when Strings and other types are mixed
When strings and other types are mixed, such as:
np.array(["string", np.float64(3.)], dtype="S")
The results will change, which may lead to string dtypes with longer
strings in some cases. In particularly, if dtype="S"
is not provided
any numerical value will lead to a string results long enough to hold
all possible numerical values. (e.g. "S32" for floats). Note that you
should always provide dtype="S"
when converting nonstrings to
strings.
If dtype="S"
is provided the results will be largely identical to
before, but NumPy scalars (not a Python float like 1.0
), will still
enforce a uniform string length:
np.array([np.float64(3.)], dtype="S") # gives "S32"
np.array([3.0], dtype="S") # gives "S3"
Previously the first version gave the same result as the second.
Array coercion restructure
Array coercion has been restructured. In general, this should not affect
users. In extremely rare corner cases where arraylikes are nested:
np.array([array_like1])
Things will now be more consistent with:
np.array([np.array(array_like1)])
This could potentially subtly change output for badly defined
arraylikes. We are not aware of any such case where the results were
not clearly incorrect previously.
(gh16200)
Writing to the result of [numpy.broadcast_arrays]{.titleref} will export readonly buffers
In NumPy 1.17 [numpy.broadcast_arrays]{.titleref} started warning when
the resulting array was written to. This warning was skipped when the
array was used through the buffer interface (e.g. memoryview(arr)
).
The same thing will now occur for the two protocols
__array_interface__
, and __array_struct__
returning readonly
buffers instead of giving a warning.
(gh16350)
Numericstyle type names have been removed from type dictionaries
To stay in sync with the deprecation for np.dtype("Complex64")
and
other numericstyle (capital case) types. These were removed from
np.sctypeDict
and np.typeDict
. You should use the lower case
versions instead. Note that "Complex64"
corresponds to "complex128"
and "Complex32"
corresponds to "complex64"
. The numpy style (new)
versions, denote the full size and not the size of the real/imaginary
part.
(gh16554)
The operator.concat
function now raises TypeError for array arguments
The previous behavior was to fall back to addition and add the two
arrays, which was thought to be unexpected behavior for a concatenation
function.
(gh16570)
nickname
attribute removed from ABCPolyBase
An abstract property nickname
has been removed from ABCPolyBase
as
it was no longer used in the derived convenience classes. This may
affect users who have derived classes from ABCPolyBase
and overridden
the methods for representation and display, e.g. __str__
, __repr__
,
_repr_latex
, etc.
(gh16589)
float>timedelta
and uint64>timedelta
promotion will raise a TypeError
Float and timedelta promotion consistently raises a TypeError.
np.promote_types("float32", "m8")
aligns with
np.promote_types("m8", "float32")
now and both raise a TypeError.
Previously, np.promote_types("float32", "m8")
returned "m8"
which
was considered a bug.
Uint64 and timedelta promotion consistently raises a TypeError.
np.promote_types("uint64", "m8")
aligns with
np.promote_types("m8", "uint64")
now and both raise a TypeError.
Previously, np.promote_types("uint64", "m8")
returned "m8"
which was
considered a bug.
(gh16592)
numpy.genfromtxt
now correctly unpacks structured arrays
Previously, [numpy.genfromtxt]{.titleref} failed to unpack if it was
called with unpack=True
and a structured datatype was passed to the
dtype
argument (or dtype=None
was passed and a structured datatype
was inferred). For example:
>>> data = StringIO("21 58.0\n35 72.0")
>>> np.genfromtxt(data, dtype=None, unpack=True)
array([(21, 58.), (35, 72.)], dtype=[('f0', '<i8'), ('f1', '<f8')])
Structured arrays will now correctly unpack into a list of arrays, one
for each column:
>>> np.genfromtxt(data, dtype=None, unpack=True)
[array([21, 35]), array([58., 72.])]
(gh16650)
mgrid
, r_
, etc. consistently return correct outputs for nondefault precision input
Previously,
np.mgrid[np.float32(0.1):np.float32(0.35):np.float32(0.1),]
and
np.r_[0:10:np.complex64(3j)]
failed to return meaningful output. This
bug potentially affects [~numpy.mgrid]{.titleref},
[~numpy.ogrid]{.titleref}, [~numpy.r_]{.titleref}, and
[~numpy.c_]{.titleref} when an input with dtype other than the
default float64
and complex128
and equivalent Python types were
used. The methods have been fixed to handle varying precision correctly.
(gh16815)
Boolean array indices with mismatching shapes now properly give IndexError
Previously, if a boolean array index matched the size of the indexed
array but not the shape, it was incorrectly allowed in some cases. In
other cases, it gave an error, but the error was incorrectly a
ValueError
with a message about broadcasting instead of the correct
IndexError
.
For example, the following used to incorrectly give
ValueError: operands could not be broadcast together with shapes (2,2) (1,4)
:
np.empty((2, 2))[np.array([[True, False, False, False]])]
And the following used to incorrectly return array([], dtype=float64)
:
np.empty((2, 2))[np.array([[False, False, False, False]])]
Both now correctly give
IndexError: boolean index did not match indexed array along dimension 0; dimension is 2 but corresponding boolean dimension is 1
.
(gh17010)
Casting errors interrupt Iteration
When iterating while casting values, an error may stop the iteration
earlier than before. In any case, a failed casting operation always
returned undefined, partial results. Those may now be even more
undefined and partial. For users of the NpyIter
CAPI such cast errors
will now cause the [iternext()]{.titleref} function to return 0 and
thus abort iteration. Currently, there is no API to detect such an error
directly. It is necessary to check PyErr_Occurred()
, which may be
problematic in combination with NpyIter_Reset
. These issues always
existed, but new API could be added if required by users.
(gh17029)
f2py generated code may return unicode instead of byte strings
Some byte strings previously returned by f2py generated code may now be
unicode strings. This results from the ongoing Python2 > Python3
cleanup.
(gh17068)
The first element of the __array_interface__["data"]
tuple must be an integer
This has been the documented interface for many years, but there was
still code that would accept a byte string representation of the pointer
address. That code has been removed, passing the address as a byte
string will now raise an error.
(gh17241)
poly1d respects the dtype of allzero argument
Previously, constructing an instance of poly1d
with allzero
coefficients would cast the coefficients to np.float64
. This affected
the output dtype of methods which construct poly1d
instances
internally, such as np.polymul
.
(gh17577)
The numpy.i file for swig is Python 3 only.
Uses of Python 2.7 CAPI functions have been updated to Python 3 only.
Users who need the old version should take it from an older version of
NumPy.
(gh17580)
Void dtype discovery in np.array
In calls using np.array(..., dtype="V")
, arr.astype("V")
, and
similar a TypeError will now be correctly raised unless all elements
have the identical void length. An example for this is:
np.array([b"1", b"12"], dtype="V")
Which previously returned an array with dtype "V2"
which cannot
represent b"1"
faithfully.
(gh17706)
C API changes
Size of np.ndarray
and np.void_
changed
The size of the PyArrayObject
and PyVoidScalarObject
structures have
changed. The following header definition has been removed:
#define NPY_SIZEOF_PYARRAYOBJECT (sizeof(PyArrayObject_fields))
since the size must not be considered a compile time constant: it will
change for different runtime versions of NumPy.
The most likely relevant use are potential subclasses written in C which
will have to be recompiled and should be updated. Please see the
documentation for :cPyArrayObject
{.interpretedtext role="type"} for
more details and contact the NumPy developers if you are affected by
this change.
NumPy will attempt to give a graceful error but a program expecting a
fixed structure size may have undefined behaviour and likely crash.
(gh16938)
New Features
where
keyword argument for numpy.all
and numpy.any
functions
The keyword argument where
is added and allows to only consider
specified elements or subaxes from an array in the Boolean evaluation of
all
and any
. This new keyword is available to the functions all
and any
both via numpy
directly or in the methods of
numpy.ndarray
.
Any broadcastable Boolean array or a scalar can be set as where
. It
defaults to True
to evaluate the functions for all elements in an
array if where
is not set by the user. Examples are given in the
documentation of the functions.
where
keyword argument for numpy
functions mean
, std
, var
The keyword argument where
is added and allows to limit the scope in
the calculation of mean
, std
and var
to only a subset of elements.
It is available both via numpy
directly or in the methods of
numpy.ndarray
.
Any broadcastable Boolean array or a scalar can be set as where
. It
defaults to True
to evaluate the functions for all elements in an
array if where
is not set by the user. Examples are given in the
documentation of the functions.
(gh15852)
norm=backward
, forward
keyword options for numpy.fft
functions
The keyword argument option norm=backward
is added as an alias for
None
and acts as the default option; using it has the direct
transforms unscaled and the inverse transforms scaled by 1/n
.
Using the new keyword argument option norm=forward
has the direct
transforms scaled by 1/n
and the inverse transforms unscaled (i.e.
exactly opposite to the default option norm=backward
).
(gh16476)
NumPy is now typed
Type annotations have been added for large parts of NumPy. There is also
a new [numpy.typing]{.titleref} module that contains useful types for
endusers. The currently available types are
ArrayLike
: for objects that can be coerced to an arrayDtypeLike
: for objects that can be coerced to a dtype
(gh16515)
numpy.typing
is accessible at runtime
The types in numpy.typing
can now be imported at runtime. Code like
the following will now work:
from numpy.typing import ArrayLike
x: ArrayLike = [1, 2, 3, 4]
(gh16558)
New __f2py_numpy_version__
attribute for f2py generated modules.
Because f2py is released together with NumPy, __f2py_numpy_version__
provides a way to track the version f2py used to generate the module.
(gh16594)
mypy
tests can be run via runtests.py
Currently running mypy with the NumPy stubs configured requires either:
 Installing NumPy
 Adding the source directory to MYPYPATH and linking to the
mypy.ini
Both options are somewhat inconvenient, so add a mypy
option to
runtests that handles setting things up for you. This will also be
useful in the future for any typing codegen since it will ensure the
project is built before type checking.
(gh17123)
Negation of user defined BLAS/LAPACK detection order
[~numpy.distutils]{.titleref} allows negation of libraries when
determining BLAS/LAPACK libraries. This may be used to remove an item
from the library resolution phase, i.e. to disallow NetLIB libraries one
could do:
NPY_BLAS_ORDER='^blas' NPY_LAPACK_ORDER='^lapack' python setup.py build
That will use any of the accelerated libraries instead.
(gh17219)
Allow passing optimizations arguments to asv build
It is now possible to pass j
, cpubaseline
, cpudispatch
and
disableoptimization
flags to ASV build when the benchcompare
argument is used.
(gh17284)
The NVIDIA HPC SDK nvfortran compiler is now supported
Support for the nvfortran compiler, a version of pgfortran, has been
added.
(gh17344)
dtype
option for cov
and corrcoef
The dtype
option is now available for [numpy.cov]{.titleref} and
[numpy.corrcoef]{.titleref}. It specifies which datatype the returned
result should have. By default the functions still return a
[numpy.float64]{.titleref} result.
(gh17456)
Improvements
Improved string representation for polynomials (__str__
)
The string representation (__str__
) of all six polynomial types in
[numpy.polynomial]{.titleref} has been updated to give the polynomial
as a mathematical expression instead of an array of coefficients. Two
packagewide formats for the polynomial expressions are available  one
using Unicode characters for superscripts and subscripts, and another
using only ASCII characters.
(gh15666)
Remove the Accelerate library as a candidate LAPACK library
Apple no longer supports Accelerate. Remove it.
(gh15759)
Object arrays containing multiline objects have a more readable repr
If elements of an object array have a repr
containing new lines, then
the wrapped lines will be aligned by column. Notably, this improves the
repr
of nested arrays:
>>> np.array([np.eye(2), np.eye(3)], dtype=object)
array([array([[1., 0.],
[0., 1.]]),
array([[1., 0., 0.],
[0., 1., 0.],
[0., 0., 1.]])], dtype=object)
(gh15997)
Concatenate supports providing an output dtype
Support was added to [~numpy.concatenate]{.titleref} to provide an
output dtype
and casting
using keyword arguments. The dtype
argument cannot be provided in conjunction with the out
one.
(gh16134)
Thread safe f2py callback functions
Callback functions in f2py are now thread safe.
(gh16519)
[numpy.core.records.fromfile]{.titleref} now supports filelike objects
[numpy.rec.fromfile]{.titleref} can now use filelike objects, for
instance :pyio.BytesIO
{.interpretedtext role="class"}
(gh16675)
RPATH support on AIX added to distutils
This allows SciPy to be built on AIX.
(gh16710)
Use f90 compiler specified by the command line args
The compiler command selection for Fortran Portland Group Compiler is
changed in [numpy.distutils.fcompiler]{.titleref}. This only affects
the linking command. This forces the use of the executable provided by
the command line option (if provided) instead of the pgfortran
executable. If no executable is provided to the command line option it
defaults to the pgf90 executable, wich is an alias for pgfortran
according to the PGI documentation.
(gh16730)
Add NumPy declarations for Cython 3.0 and later
The pxd declarations for Cython 3.0 were improved to avoid using
deprecated NumPy CAPI features. Extension modules built with Cython
3.0+ that use NumPy can now set the C macro
NPY_NO_DEPRECATED_API=NPY_1_7_API_VERSION
to avoid C compiler warnings
about deprecated API usage.
(gh16986)
Make the window functions exactly symmetric
Make sure the window functions provided by NumPy are symmetric. There
were previously small deviations from symmetry due to numerical
precision that are now avoided by better arrangement of the computation.
(gh17195)
Performance improvements and changes
Enable multiplatform SIMD compiler optimizations
A series of improvements for NumPy infrastructure to pave the way to
NEP38, that can be summarized as follow:

New Build Arguments
cpubaseline
to specify the minimal set of required
optimizations, default value ismin
which provides the minimum
CPU features that can safely run on a wide range of users
platforms.cpudispatch
to specify the dispatched set of additional
optimizations, default value ismax xop fma4
which enables
all CPU features, except for AMD legacy features.disableoptimization
to explicitly disable the whole new
improvements, It also adds a new C compiler #definition
calledNPY_DISABLE_OPTIMIZATION
which it can be used as guard
for any SIMD code.

Advanced CPU dispatcher
A flexible crossarchitecture CPU dispatcher built on the top of
Python/Numpy distutils, support all common compilers with a wide
range of CPU features.The new dispatcher requires a special file extension
*.dispatch.c
to mark the dispatchable C sources. These sources have the
ability to be compiled multiple times so that each compilation
process represents certain CPU features and provides different
#definitions and flags that affect the code paths. 
New autogenerated C header
``core/src/common/_cpu_dispatch.h``This header is generated by the distutils module
ccompiler_opt
,
and contains all the #definitions and headers of instruction sets,
that had been configured through command arguments
'cpubaseline' and 'cpudispatch'. 
New C header ``core/src/common/npy_cpu_dispatch.h``
This header contains all utilities that required for the whole CPU
dispatching process, it also can be considered as a bridge linking
the new infrastructure work with NumPy CPU runtime detection. 
Add new attributes to NumPy umath module(Python level)
__cpu_baseline__
a list contains the minimal set of required
optimizations that supported by the compiler and platform
according to the specified values to command argument
'cpubaseline'.__cpu_dispatch__
a list contains the dispatched set of
additional optimizations that supported by the compiler and
platform according to the specified values to command argument
'cpudispatch'.

Print the supported CPU features during the run of PytestTester
(gh13516)
Changes
Changed behavior of divmod(1., 0.)
and related functions
The changes also assure that different compiler versions have the same
behavior for nan or inf usages in these operations. This was previously
compiler dependent, we now force the invalid and divide by zero flags,
making the results the same across compilers. For example, gcc5, gcc8,
or gcc9 now result in the same behavior. The changes are tabulated
below:
Operator Old Warning New Warning Old Result New Result Works on MacOS
np.divmod(1.0, 0.0) Invalid Invalid and Dividebyzero nan, nan inf, nan Yes
np.fmod(1.0, 0.0) Invalid Invalid nan nan No? Yes
np.floor_divide(1.0, 0.0) Invalid Dividebyzero nan inf Yes
np.remainder(1.0, 0.0) Invalid Invalid nan nan Yes
: Summary of New Behavior
(gh16161)
np.linspace
on integers now uses floor
When using a int
dtype in [numpy.linspace]{.titleref}, previously
float values would be rounded towards zero. Now
[numpy.floor]{.titleref} is used instead, which rounds toward inf
.
This changes the results for negative values. For example, the following
would previously give:
>>> np.linspace(3, 1, 8, dtype=int)
array([3, 2, 1, 1, 0, 0, 0, 1])
and now results in:
>>> np.linspace(3, 1, 8, dtype=int)
array([3, 3, 2, 2, 1, 1, 0, 1])
The former result can still be obtained with:
>>> np.linspace(3, 1, 8).astype(int)
array([3, 2, 1, 1, 0, 0, 0, 1])
(gh16841)
Checksums
MD5
c182567139ec82a140d5fbf363ed1697 numpy1.20.0rc1cp37cp37mmacosx_10_9_x86_64.whl
06de963440e3dcf0dfd619a2b4936e59 numpy1.20.0rc1cp37cp37mmanylinux1_i686.whl
436a34182e234dce16bc597929a61313 numpy1.20.0rc1cp37cp37mmanylinux1_x86_64.whl
73aa4c274c2aee36f5f4d1c58b74a4a8 numpy1.20.0rc1cp37cp37mmanylinux2010_i686.whl
cc0bbd29cca3f80dbb09a3177df6b677 numpy1.20.0rc1cp37cp37mmanylinux2010_x86_64.whl
be993784ee6c9e9e95f949071f30a853 numpy1.20.0rc1cp37cp37mmanylinux2014_aarch64.whl
971e01facbe869f95dda518ecc0b4c09 numpy1.20.0rc1cp37cp37mwin32.whl
951e744fb554af874a0ba4bdbeedc882 numpy1.20.0rc1cp37cp37mwin_amd64.whl
197de0d040463b4d6026e83284f272ac numpy1.20.0rc1cp38cp38macosx_10_9_x86_64.whl
27f146dbee25d7058def106f5c15eca0 numpy1.20.0rc1cp38cp38manylinux1_i686.whl
b0f5ec0b31566270f8546a79d73e3424 numpy1.20.0rc1cp38cp38manylinux1_x86_64.whl
2e9c06be7d826451b9227b11b3e6cd69 numpy1.20.0rc1cp38cp38manylinux2010_i686.whl
f9fb7537b1e8197824f47650e883c63d numpy1.20.0rc1cp38cp38manylinux2010_x86_64.whl
83ad71e9a7a46947e2fe203e3f822ad3 numpy1.20.0rc1cp38cp38manylinux2014_aarch64.whl
318da96660e8c8ce5bac22e851969d15 numpy1.20.0rc1cp38cp38win32.whl
051419fe996b984eced3a6e28320a45a numpy1.20.0rc1cp38cp38win_amd64.whl
bca434dd07cd58b2436e592efc72c10b numpy1.20.0rc1cp39cp39macosx_10_9_x86_64.whl
91beb7602bc10db4a912b9de8d082efe numpy1.20.0rc1cp39cp39manylinux2010_i686.whl
d213368ea0f28697597041ab175242f1 numpy1.20.0rc1cp39cp39manylinux2010_x86_64.whl
c1f3936e707014e1683eaeae30b71649 numpy1.20.0rc1cp39cp39manylinux2014_aarch64.whl
d4881ad35eb820a5e3a27bb47adcbb2b numpy1.20.0rc1cp39cp39win32.whl
ae4a01a84de51a0957452611dbad1199 numpy1.20.0rc1cp39cp39win_amd64.whl
c3d85a1bada3081b917ee9498ec4fb08 numpy1.20.0rc1pp37pypy37_pp73manylinux2010_x86_64.whl
94bb7d8f42e03c0c1cd37c230fcdfc14 numpy1.20.0rc1.tar.gz
1ed93be9e6bfb1de153af93d20c4e443 numpy1.20.0rc1.zip
SHA256
01e9029472857f8dd9868e1f83f3ff9df0b477e9e7554bc4455eb5293b8ae335 numpy1.20.0rc1cp37cp37mmacosx_10_9_x86_64.whl
c6545bc46b3e4accaff4c542b4b4b95993ef0e4d74bb86fe19189b630740f76e numpy1.20.0rc1cp37cp37mmanylinux1_i686.whl
b028d1104eb8f7c5f0bc5bdc4c35768efc755b459f3a67b38a79eb9b86e354c2 numpy1.20.0rc1cp37cp37mmanylinux1_x86_64.whl
4789ad6cc531c9a24ce8cff59ac3c669e307599fba2bd40c2bc700d5b3013105 numpy1.20.0rc1cp37cp37mmanylinux2010_i686.whl
e9b0138142f72a3c143f262fd435b331e9807a2eb1c0e2ff6904f2cdc9b9b1a5 numpy1.20.0rc1cp37cp37mmanylinux2010_x86_64.whl
a2a4d00b119c71ba83fc1dd0f4dc71e2dd0fd61acd5cbd40541da4f9172427d3 numpy1.20.0rc1cp37cp37mmanylinux2014_aarch64.whl
dcee4823291188e213d681b05a2749ff36a87d4933a91b9654fcf0e2bf02ce4a numpy1.20.0rc1cp37cp37mwin32.whl
b1cf3925dda0920ee469c95260d2313f1f4a8d6381d42cfdd607e6fd991c4256 numpy1.20.0rc1cp37cp37mwin_amd64.whl
10baa94959bcbea0070e82e7ed3db9090fbaf267811a74701bdc0a0697dfe2ae numpy1.20.0rc1cp38cp38macosx_10_9_x86_64.whl
7f7f08cc1a3415a61382a2c60bd71d5bd3efa33ee90ebc14ce4754dfa0a138c9 numpy1.20.0rc1cp38cp38manylinux1_i686.whl
1a544c8d7928c85fcfb3af1649aad7d60376e89f9bc5be89f6da6d5eff1ed107 numpy1.20.0rc1cp38cp38manylinux1_x86_64.whl
c9a21184a9b40793bc17a1456c3713a98c9466af2d1f849354cef0a756e2f7c6 numpy1.20.0rc1cp38cp38manylinux2010_i686.whl
09fcbaef175786b99287039ada5dcf2c9131b65ceab1807fa9a61e5b062091a8 numpy1.20.0rc1cp38cp38manylinux2010_x86_64.whl
ce8c2a2fbfdaf14fd7ed85e9a10bea9247f9f884bf504ed773aee8c0adcee220 numpy1.20.0rc1cp38cp38manylinux2014_aarch64.whl
dd298a8efe8c62acb94797932d39ad3b74d1c4c1f496fa259b28a677e8f4793e numpy1.20.0rc1cp38cp38win32.whl
55dcf4a830d1198a72b6afb72cb02879c3abb95024c250402851de66db94a30d numpy1.20.0rc1cp38cp38win_amd64.whl
76cda96f70435bf75cac28dd081bb9e47e8eaf2badd1d9ad5ab19723d74e5921 numpy1.20.0rc1cp39cp39macosx_10_9_x86_64.whl
ed955ab39527f7500b31dae6075b00f284a0c2eb23e7fd1a5a40a5066cab309f numpy1.20.0rc1cp39cp39manylinux2010_i686.whl
b38a2caf64429bab2c06b87eea0d2b24f736894f805f3d7210ecafb5573cb0de numpy1.20.0rc1cp39cp39manylinux2010_x86_64.whl
14aa454e438290d1ce464c80530157dc1ece86e34dd79a8077ca0e05ebab3665 numpy1.20.0rc1cp39cp39manylinux2014_aarch64.whl
3c27251c07f8e3cd727cab8cc275c1294be006ed725496090a6081f6d8d1d811 numpy1.20.0rc1cp39cp39win32.whl
4925c540f1bde557987c2d0b258b9c57cb6da020957ffa4ac355f13d6819b121 numpy1.20.0rc1cp39cp39win_amd64.whl
e9a5652afbe2128cb1734546608a0a0d5dbd21160738a40521464d7cb4cc22b5 numpy1.20.0rc1pp37pypy37_pp73manylinux2010_x86_64.whl
b4993844022e98fe363467ce42404ffd6f975239d294b6e4ea44a6e797891fac numpy1.20.0rc1.tar.gz
98f4e754f1c3db7ca53e53b1ef6474703b167af75f3784f99b1fe4dd936ea77f numpy1.20.0rc1.zip