Releases: numpy/numpy
v1.19.5
NumPy 1.19.5 Release Notes
NumPy 1.19.5 is a short bugfix release. Apart from fixing several bugs,
the main improvement is the update to OpenBLAS 0.3.13 that works around
the windows 2004 bug while not breaking execution on other platforms.
This release supports Python 3.6-3.9 and is planned to be the last
release in the 1.19.x cycle.
Contributors
A total of 8 people contributed to this release. People with a "+" by
their names contributed a patch for the first time.
- Charles Harris
- Christoph Gohlke
- Matti Picus
- Raghuveer Devulapalli
- Sebastian Berg
- Simon Graham +
- Veniamin Petrenko +
- Bernie Gray +
Pull requests merged
A total of 11 pull requests were merged for this release.
- #17756: BUG: Fix segfault due to out of bound pointer in floatstatus...
- #17774: BUG: fix np.timedelta64('nat').__format__ throwing an exception
- #17775: BUG: Fixed file handle leak in array_tofile.
- #17786: BUG: Raise recursion error during dimension discovery
- #17917: BUG: Fix subarray dtype used with too large count in fromfile
- #17918: BUG: 'bool' object has no attribute 'ndim'
- #17919: BUG: ensure _UFuncNoLoopError can be pickled
- #17924: BLD: use BUFFERSIZE=20 in OpenBLAS
- #18026: BLD: update to OpenBLAS 0.3.13
- #18036: BUG: make a variable volatile to work around clang compiler bug
- #18114: REL: Prepare for the NumPy 1.19.5 release.
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v1.20.0rc2
charris
Charles Harris
NumPy 1.20.0 Release Notes
This NumPy release is the largest so made to date, some 670 PRs
contributed by 184 people have been merged. See the list of highlights
below for more details. The Python versions supported for this release
are 3.7-3.9, 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 1-D array for every combination of the other
indexes. For example, it is now possible to permute the rows or columns
of a 2-D array.
(gh-15121)
sliding_window_view provides a sliding window view for numpy arrays
[numpy.lib.stride_tricks.sliding_window_view]{.title-ref} 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.
(gh-17394)
[numpy.broadcast_shapes]{.title-ref} is a new user-facing function
[~numpy.broadcast_shapes]{.title-ref} 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)
(gh-17535)
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_]{.title-ref}
numpy.int int [numpy.int_]{.title-ref} (default int dtype), [numpy.cint]{.title-ref} (C int)
numpy.float float [numpy.float_]{.title-ref}, [numpy.double]{.title-ref} (equivalent)
numpy.complex complex [numpy.complex_]{.title-ref}, [numpy.cdouble]{.title-ref} (equivalent)
numpy.object object [numpy.object_]{.title-ref}
numpy.str str [numpy.str_]{.title-ref}
numpy.long int (long on Python 2) [numpy.int_]{.title-ref} (C long), [numpy.longlong]{.title-ref} (largest integer type)
numpy.unicode str (unicode on Python 2) [numpy.unicode_]{.title-ref}
Note that for technical reasons these deprecation warnings will only be
emitted on Python 3.7 and above.
(gh-14882)
Passing shape=None to functions with a non-optional shape argument is deprecated
Previously, this was an alias for passing shape=(). This deprecation
is emitted by [PyArray_IntpConverter]{.title-ref} 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 ().
(gh-15886)
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 non-indexed 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 non-empty 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.
(gh-15900)
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"
(gh-16056)
Deprecation of [numpy.dual]{.title-ref}
The module [numpy.dual]{.title-ref} is deprecated. Instead of importing
functions from [numpy.dual]{.title-ref}, the functions should be
imported directly from NumPy or SciPy.
(gh-16156)
outer and ufunc.outer deprecated for matrix
np.matrix use with [~numpy.outer]{.title-ref} 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.
(gh-16232)
Further Numeric Style types Deprecated
The remaining numeric-style type codes Bytes0, Str0, Uint32,
Uint64, and Datetime64 have been deprecated. The lower-case variants
should be used instead. For bytes and string "S" and "U" are further
alternatives.
(gh-16554)
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().
(gh-17233)
ArrayLike objects which do not define __len__ and __getitem__
Objects which define one of the protocols __array__,
__array_interface__, or __array_struct__ but are not sequences
(usually defined by having a __len__ and __getitem__) will behave
differently during array-coercion in the future.
When nested inside sequences, such as np.array([array_like]), these
were handled as a single Python object rather than an array. In the
future they will behave identically to:
np.array([np.array(array_like)])
This change should only have an effect if np.array(array_like) is not
0-D. The solution to this warning may depend on the object:
- Some array-likes may expect the new behaviour, and users can ignore
the warning. The object can choose to expose the sequence protocol
to opt-in to the new behaviour. - For example,
shapelywill allow conversion to an array-like using
line.coordsrather thannp.asarray(line). Users may work around
the warning, or use the new convention when it becomes available.
Unfortunately, using the new behaviour can only be achieved by calling
np.array(array_like).
If you wish to ensure that the old behaviour remains unchanged, please
create an object array and then fill it explicitly, for example:
arr = np.empty(3, dtype=object)
arr[:] = [array_like1, array_like2, array_like3]
This will ensure NumPy knows to not enter the array-like and use it as a
object instead.
(gh-17973)
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 opt-in to the new behaviour.
This change does not...
v1.20.0rc1
charris
Charles Harris
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 3.7-3.9, 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 1-D array for every combination of the other
indexes. For example, it is now possible to permute the rows or columns
of a 2-D array.
(gh-15121)
sliding_window_view provides a sliding window view for numpy arrays
[numpy.lib.stride_tricks.sliding_window_view]{.title-ref} 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.
(gh-17394)
[numpy.broadcast_shapes]{.title-ref} is a new user-facing function
[~numpy.broadcast_shapes]{.title-ref} 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)
(gh-17535)
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_]{.title-ref}
numpy.int int [numpy.int_]{.title-ref} (default int dtype), [numpy.cint]{.title-ref} (C int)
numpy.float float [numpy.float_]{.title-ref}, [numpy.double]{.title-ref} (equivalent)
numpy.complex complex [numpy.complex_]{.title-ref}, [numpy.cdouble]{.title-ref} (equivalent)
numpy.object object [numpy.object_]{.title-ref}
numpy.str str [numpy.str_]{.title-ref}
numpy.long int (long on Python 2) [numpy.int_]{.title-ref} (C long), [numpy.longlong]{.title-ref} (largest integer type)
numpy.unicode str (unicode on Python 2) [numpy.unicode_]{.title-ref}
Note that for technical reasons these deprecation warnings will only be
emitted on Python 3.7 and above.
(gh-14882)
Passing shape=None to functions with a non-optional shape argument is deprecated
Previously, this was an alias for passing shape=(). This deprecation
is emitted by [PyArray_IntpConverter]{.title-ref} 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 ().
(gh-15886)
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 non-indexed 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 non-empty 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.
(gh-15900)
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"
(gh-16056)
Deprecation of [numpy.dual]{.title-ref}
The module [numpy.dual]{.title-ref} is deprecated. Instead of importing
functions from [numpy.dual]{.title-ref}, the functions should be
imported directly from NumPy or SciPy.
(gh-16156)
outer and ufunc.outer deprecated for matrix
np.matrix use with [~numpy.outer]{.title-ref} 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.
(gh-16232)
Further Numeric Style types Deprecated
The remaining numeric-style type codes Bytes0, Str0, Uint32,
Uint64, and Datetime64 have been deprecated. The lower-case variants
should be used instead. For bytes and string "S" and "U" are further
alternatives.
(gh-16554)
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().
(gh-17233)
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 opt-in 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.
(gh-17596)
Expired deprecations
-
The deprecation of numeric style type-codes
np.dtype("Complex64")
(with upper case spelling), is expired."Complex64"corresponded
to"complex128"and"Complex32"corresponded to"complex64". -
The deprecation of
np.sctypeNAandnp.typeNAis expired. Both
have been removed from the public API. Usenp.typeDictinstead.(gh-16554)
-
The 14-year deprecation of
np.ctypeslib.ctypes_load_libraryis
expired. Use~numpy.ctypeslib.load_library{.interpreted-text
role="func"} instead, which is identical.(gh-17116)
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.
(gh-17067)
Compatibility notes
Same kind casting in concatenate with axis=None
When [~numpy.concatenate]{.title-ref} is called with axis=None, the
flattened arrays were cast with unsafe....
v1.19.4
NumPy 1.19.4 Release Notes
NumPy 1.19.4 is a quick release to revert the OpenBLAS library version.
It was hoped that the 0.3.12 OpenBLAS version used in 1.19.3 would work
around the Microsoft fmod bug, but problems in some docker environments
turned up. Instead, 1.19.4 will use the older library and run a sanity
check on import, raising an error if the problem is detected. Microsoft
is aware of the problem and has promised a fix, users should upgrade
when it becomes available.
This release supports Python 3.6-3.9
Contributors
A total of 1 people contributed to this release. People with a "+" by
their names contributed a patch for the first time.
- Charles Harris
Pull requests merged
A total of 2 pull requests were merged for this release.
- #17679: MAINT: Add check for Windows 10 version 2004 bug.
- #17680: REV: Revert OpenBLAS to 1.19.2 version for 1.19.4
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v1.19.3
NumPy 1.19.3 Release Notes
NumPy 1.19.3 is a small maintenace release with two major improvements:
- Python 3.9 binary wheels on all supported platforms.
- OpenBLAS fixes for Windows 10 version 2004 fmod bug.
This release supports Python 3.6-3.9 and is linked with OpenBLAS 3.7 to
avoid some of the fmod problems on Windows 10 version 2004. Microsoft is
aware of the problem and users should upgrade when the fix becomes
available, the fix here is limited in scope.
Contributors
A total of 8 people contributed to this release. People with a "+" by
their names contributed a patch for the first time.
- Charles Harris
- Chris Brown +
- Daniel Vanzo +
- E. Madison Bray +
- Hugo van Kemenade +
- Ralf Gommers
- Sebastian Berg
- @danbeibei +
Pull requests merged
A total of 10 pull requests were merged for this release.
- #17298: BLD: set upper versions for build dependencies
- #17336: BUG: Set deprecated fields to null in PyArray_InitArrFuncs
- #17446: ENH: Warn on unsupported Python 3.10+
- #17450: MAINT: Update test_requirements.txt.
- #17522: ENH: Support for the NVIDIA HPC SDK nvfortran compiler
- #17568: BUG: Cygwin Workaround for #14787 on affected platforms
- #17647: BUG: Fix memory leak of buffer-info cache due to relaxed strides
- #17652: MAINT: Backport openblas_support from master.
- #17653: TST: Add Python 3.9 to the CI testing on Windows, Mac.
- #17660: TST: Simplify source path names in test_extending.
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v1.19.2
NumPy 1.19.2 Release Notes
NumPy 1.19.2 fixes several bugs, prepares for the upcoming Cython 3.x
release. and pins setuptools to keep distutils working while upstream
modifications are ongoing. The aarch64 wheels are built with the latest
manylinux2014 release that fixes the problem of differing page sizes
used by different linux distros.
This release supports Python 3.6-3.8. Cython >= 0.29.21 needs to be
used when building with Python 3.9 for testing purposes.
There is a known problem with Windows 10 version=2004 and OpenBLAS svd
that we are trying to debug. If you are running that Windows version you
should use a NumPy version that links to the MKL library, earlier
Windows versions are fine.
Improvements
Add NumPy declarations for Cython 3.0 and later
The pxd declarations for Cython 3.0 were improved to avoid using
deprecated NumPy C-API 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.
Contributors
A total of 8 people contributed to this release. People with a "+" by
their names contributed a patch for the first time.
- Charles Harris
- Matti Picus
- Pauli Virtanen
- Philippe Ombredanne +
- Sebastian Berg
- Stefan Behnel +
- Stephan Loyd +
- Zac Hatfield-Dodds
Pull requests merged
A total of 9 pull requests were merged for this release.
- #16959: TST: Change aarch64 to arm64 in travis.yml.
- #16998: MAINT: Configure hypothesis in
np.test()for determinism,... - #17000: BLD: pin setuptools < 49.2.0
- #17015: ENH: Add NumPy declarations to be used by Cython 3.0+
- #17125: BUG: Remove non-threadsafe sigint handling from fft calculation
- #17243: BUG: core: fix ilp64 blas dot/vdot/... for strides > int32 max
- #17244: DOC: Use SPDX license expressions with correct license
- #17245: DOC: Fix the link to the quick-start in the old API functions
- #17272: BUG: fix pickling of arrays larger than 2GiB
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v1.19.1
NumPy 1.19.1 Release Notes
NumPy 1.19.1 fixes several bugs found in the 1.19.0 release, replaces
several functions deprecated in the upcoming Python-3.9 release, has
improved support for AIX, and has a number of development related
updates to keep CI working with recent upstream changes.
This release supports Python 3.6-3.8. Cython >= 0.29.21 needs to be
used when building with Python 3.9 for testing purposes.
Contributors
A total of 15 people contributed to this release. People with a "+" by
their names contributed a patch for the first time.
- Abhinav Reddy +
- Anirudh Subramanian
- Antonio Larrosa +
- Charles Harris
- Chunlin Fang
- Eric Wieser
- Etienne Guesnet +
- Kevin Sheppard
- Matti Picus
- Raghuveer Devulapalli
- Roman Yurchak
- Ross Barnowski
- Sayed Adel
- Sebastian Berg
- Tyler Reddy
Pull requests merged
A total of 25 pull requests were merged for this release.
- #16649: MAINT, CI: disable Shippable cache
- #16652: MAINT: Replace PyUString_GET_SIZE with PyUnicode_GetLength.
- #16654: REL: Fix outdated docs link
- #16656: BUG: raise IEEE exception on AIX
- #16672: BUG: Fix bug in AVX complex absolute while processing array of...
- #16693: TST: Add extra debugging information to CPU features detection
- #16703: BLD: Add CPU entry for Emscripten / WebAssembly
- #16705: TST: Disable Python 3.9-dev testing.
- #16714: MAINT: Disable use_hugepages in case of ValueError
- #16724: BUG: Fix PyArray_SearchSorted signature.
- #16768: MAINT: Fixes for deprecated functions in scalartypes.c.src
- #16772: MAINT: Remove unneeded call to PyUnicode_READY
- #16776: MAINT: Fix deprecated functions in scalarapi.c
- #16779: BLD, ENH: Add RPATH support for AIX
- #16780: BUG: Fix default fallback in genfromtxt
- #16784: BUG: Added missing return after raising error in methods.c
- #16795: BLD: update cython to 0.29.21
- #16832: MAINT: setuptools 49.2.0 emits a warning, avoid it
- #16872: BUG: Validate output size in bin- and multinomial
- #16875: BLD, MAINT: Pin setuptools
- #16904: DOC: Reconstruct Testing Guideline.
- #16905: TST, BUG: Re-raise MemoryError exception in test_large_zip's...
- #16906: BUG, DOC: Fix bad MPL kwarg.
- #16916: BUG: Fix string/bytes to complex assignment
- #16922: REL: Prepare for NumPy 1.19.1 release
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v1.19.0
NumPy 1.19.0 Release Notes
This NumPy release is marked by the removal of much technical debt:
support for Python 2 has been removed, many deprecations have been
expired, and documentation has been improved. The polishing of the
random module continues apace with bug fixes and better usability from
Cython.
The Python versions supported for this release are 3.6-3.8. Downstream
developers should use Cython >= 0.29.16 for Python 3.8 support and
OpenBLAS >= 3.7 to avoid problems on the Skylake architecture.
Highlights
-
Code compatibility with Python versions < 3.6 (including Python 2)
was dropped from both the python and C code. The shims in
numpy.compatwill remain to support third-party packages, but they
may be deprecated in a future release. Note that 1.19.x will not
compile with earlier versions of Python due to the use of f-strings.(gh-15233)
Expired deprecations
numpy.insert and numpy.delete can no longer be passed an axis on 0d arrays
This concludes a deprecation from 1.9, where when an axis argument was
passed to a call to ~numpy.insert and ~numpy.delete on a 0d array,
the axis and obj argument and indices would be completely ignored.
In these cases, insert(arr, "nonsense", 42, axis=0) would actually
overwrite the entire array, while delete(arr, "nonsense", axis=0)
would be arr.copy()
Now passing axis on a 0d array raises ~numpy.AxisError.
(gh-15802)
numpy.delete no longer ignores out-of-bounds indices
This concludes deprecations from 1.8 and 1.9, where np.delete would
ignore both negative and out-of-bounds items in a sequence of indices.
This was at odds with its behavior when passed a single index.
Now out-of-bounds items throw IndexError, and negative items index
from the end.
(gh-15804)
numpy.insert and numpy.delete no longer accept non-integral indices
This concludes a deprecation from 1.9, where sequences of non-integers
indices were allowed and cast to integers. Now passing sequences of
non-integral indices raises IndexError, just like it does when passing
a single non-integral scalar.
(gh-15805)
numpy.delete no longer casts boolean indices to integers
This concludes a deprecation from 1.8, where np.delete would cast
boolean arrays and scalars passed as an index argument into integer
indices. The behavior now is to treat boolean arrays as a mask, and to
raise an error on boolean scalars.
(gh-15815)
Compatibility notes
Changed random variate stream from numpy.random.Generator.dirichlet
A bug in the generation of random variates for the Dirichlet
distribution with small 'alpha' values was fixed by using a different
algorithm when max(alpha) < 0.1. Because of the change, the stream of
variates generated by dirichlet in this case will be different from
previous releases.
(gh-14924)
Scalar promotion in PyArray_ConvertToCommonType
The promotion of mixed scalars and arrays in
PyArray_ConvertToCommonType has been changed to adhere to those used
by np.result_type. This means that input such as
(1000, np.array([1], dtype=np.uint8))) will now return uint16
dtypes. In most cases the behaviour is unchanged. Note that the use of
this C-API function is generally discouraged. This also fixes
np.choose to behave the same way as the rest of NumPy in this respect.
(gh-14933)
Fasttake and fastputmask slots are deprecated and NULL'ed
The fasttake and fastputmask slots are now never used and must always be
set to NULL. This will result in no change in behaviour. However, if a
user dtype should set one of these a DeprecationWarning will be given.
(gh-14942)
np.ediff1d casting behaviour with to_end and to_begin
np.ediff1d now uses the "same_kind" casting rule for its additional
to_end and to_begin arguments. This ensures type safety except when
the input array has a smaller integer type than to_begin or to_end.
In rare cases, the behaviour will be more strict than it was previously
in 1.16 and 1.17. This is necessary to solve issues with floating point
NaN.
(gh-14981)
Converting of empty array-like objects to NumPy arrays
Objects with len(obj) == 0 which implement an "array-like"
interface, meaning an object implementing obj.__array__(),
obj.__array_interface__, obj.__array_struct__, or the python buffer
interface and which are also sequences (i.e. Pandas objects) will now
always retain there shape correctly when converted to an array. If such
an object has a shape of (0, 1) previously, it could be converted into
an array of shape (0,) (losing all dimensions after the first 0).
(gh-14995)
Removed multiarray.int_asbuffer
As part of the continued removal of Python 2 compatibility,
multiarray.int_asbuffer was removed. On Python 3, it threw a
NotImplementedError and was unused internally. It is expected that
there are no downstream use cases for this method with Python 3.
(gh-15229)
numpy.distutils.compat has been removed
This module contained only the function get_exception(), which was
used as:
try:
...
except Exception:
e = get_exception()
Its purpose was to handle the change in syntax introduced in Python 2.6,
from except Exception, e: to except Exception as e:, meaning it was
only necessary for codebases supporting Python 2.5 and older.
(gh-15255)
issubdtype no longer interprets float as np.floating
numpy.issubdtype had a FutureWarning since NumPy 1.14 which has
expired now. This means that certain input where the second argument was
neither a datatype nor a NumPy scalar type (such as a string or a python
type like int or float) will now be consistent with passing in
np.dtype(arg2).type. This makes the result consistent with
expectations and leads to a false result in some cases which previously
returned true.
(gh-15773)
Change output of round on scalars to be consistent with Python
Output of the __round__ dunder method and consequently the Python
built-in round has been changed to be a Python int to be consistent
with calling it on Python float objects when called with no arguments.
Previously, it would return a scalar of the np.dtype that was passed
in.
(gh-15840)
The numpy.ndarray constructor no longer interprets strides=() as strides=None
The former has changed to have the expected meaning of setting
numpy.ndarray.strides to (), while the latter continues to result in
strides being chosen automatically.
(gh-15882)
C-Level string to datetime casts changed
The C-level casts from strings were simplified. This changed also fixes
string to datetime and timedelta casts to behave correctly (i.e. like
Python casts using string_arr.astype("M8") while previously the cast
would behave like string_arr.astype(np.int_).astype("M8"). This only
affects code using low-level C-API to do manual casts (not full array
casts) of single scalar values or using e.g. PyArray_GetCastFunc, and
should thus not affect the vast majority of users.
(gh-16068)
SeedSequence with small seeds no longer conflicts with spawning
Small seeds (less than 2**96) were previously implicitly 0-padded out
to 128 bits, the size of the internal entropy pool. When spawned, the
spawn key was concatenated before the 0-padding. Since the first spawn
key is (0,), small seeds before the spawn created the same states as
the first spawned SeedSequence. Now, the seed is explicitly 0-padded
out to the internal pool size before concatenating the spawn key.
Spawned SeedSequences will produce different results than in the
previous release. Unspawned SeedSequences will still produce the same
results.
(gh-16551)
Deprecations
Deprecate automatic dtype=object for ragged input
Calling np.array([[1, [1, 2, 3]]) will issue a DeprecationWarning as
per NEP 34. Users should
explicitly use dtype=object to avoid the warning.
(gh-15119)
Passing shape=0 to factory functions in numpy.rec is deprecated
0 is treated as a special case and is aliased to None in the
functions:
numpy.core.records.fromarraysnumpy.core.records.fromrecordsnumpy.core.records.fromstringnumpy.core.records.fromfile
In future, 0 will not be special cased, and will be treated as an
array length like any other integer.
(gh-15217)
Deprecation of probably unused C-API functions
The following C-API functions are probably unused and have been
deprecated:
PyArray_GetArrayParamsFromObjectPyUFunc_GenericFunctionPyUFunc_SetUsesArraysAsData
In most cases PyArray_GetArrayParamsFromObject should be replaced by
converting to an array, while PyUFunc_GenericFunction can be replaced
with PyObject_Call (s...
v1.18.5
NumPy 1.18.5 Release Notes
This is a short release to allow pickle protocol=5 to be used in
Python3.5. It is motivated by the recent backport of pickle5 to
Python3.5.
The Python versions supported in this release are 3.5-3.8. Downstream
developers should use Cython >= 0.29.15 for Python 3.8 support and
OpenBLAS >= 3.7 to avoid errors on the Skylake architecture.
Contributors
A total of 3 people contributed to this release. People with a "+" by
their names contributed a patch for the first time.
- Charles Harris
- Matti Picus
- Siyuan Zhuang +
Pull requests merged
A total of 2 pull requests were merged for this release.
- #16439: ENH: enable pickle protocol 5 support for python3.5
- #16441: BUG: relpath fails for different drives on windows
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v1.19.0rc2
charris
Charles Harris
NumPy 1.19.0 Release Notes
This NumPy release is marked by the removal of much technical debt:
support for Python 2 has been removed, many deprecations have been
expired, and documentation has been improved. The polishing of the
random module continues apace with bug fixes and better usability from
Cython.
The Python versions supported for this release are 3.6-3.8. Downstream
developers should use Cython >= 0.29.16 for Python 3.8 support and
OpenBLAS >= 3.7 to avoid problems on the Skylake architecture.
Highlights
-
Code compatibility with Python versions < 3.6 (including Python 2)
was dropped from both the python and C code. The shims in
numpy.compatwill remain to support third-party packages, but they
may be deprecated in a future release. Note that 1.19.x will not
compile with earlier versions of Python due to the use of f-strings.(gh-15233)
Expired deprecations
numpy.insert and numpy.delete can no longer be passed an axis on 0d arrays
This concludes a deprecation from 1.9, where when an axis argument was
passed to a call to ~numpy.insert and ~numpy.delete on a 0d array,
the axis and obj argument and indices would be completely ignored.
In these cases, insert(arr, "nonsense", 42, axis=0) would actually
overwrite the entire array, while delete(arr, "nonsense", axis=0)
would be arr.copy()
Now passing axis on a 0d array raises ~numpy.AxisError.
(gh-15802)
numpy.delete no longer ignores out-of-bounds indices
This concludes deprecations from 1.8 and 1.9, where np.delete would
ignore both negative and out-of-bounds items in a sequence of indices.
This was at odds with its behavior when passed a single index.
Now out-of-bounds items throw IndexError, and negative items index
from the end.
(gh-15804)
numpy.insert and numpy.delete no longer accept non-integral indices
This concludes a deprecation from 1.9, where sequences of non-integers
indices were allowed and cast to integers. Now passing sequences of
non-integral indices raises IndexError, just like it does when passing
a single non-integral scalar.
(gh-15805)
numpy.delete no longer casts boolean indices to integers
This concludes a deprecation from 1.8, where np.delete would cast
boolean arrays and scalars passed as an index argument into integer
indices. The behavior now is to treat boolean arrays as a mask, and to
raise an error on boolean scalars.
(gh-15815)
Compatibility notes
Changed random variate stream from numpy.random.Generator.dirichlet
A bug in the generation of random variates for the Dirichlet
distribution with small 'alpha' values was fixed by using a different
algorithm when max(alpha) < 0.1. Because of the change, the stream of
variates generated by dirichlet in this case will be different from
previous releases.
(gh-14924)
Scalar promotion in PyArray_ConvertToCommonType
The promotion of mixed scalars and arrays in
PyArray_ConvertToCommonType has been changed to adhere to those used
by np.result_type. This means that input such as
(1000, np.array([1], dtype=np.uint8))) will now return uint16
dtypes. In most cases the behaviour is unchanged. Note that the use of
this C-API function is generally discouraged. This also fixes
np.choose to behave the same way as the rest of NumPy in this respect.
(gh-14933)
Fasttake and fastputmask slots are deprecated and NULL'ed
The fasttake and fastputmask slots are now never used and must always be
set to NULL. This will result in no change in behaviour. However, if a
user dtype should set one of these a DeprecationWarning will be given.
(gh-14942)
np.ediff1d casting behaviour with to_end and to_begin
np.ediff1d now uses the "same_kind" casting rule for its additional
to_end and to_begin arguments. This ensures type safety except when
the input array has a smaller integer type than to_begin or to_end.
In rare cases, the behaviour will be more strict than it was previously
in 1.16 and 1.17. This is necessary to solve issues with floating point
NaN.
(gh-14981)
Converting of empty array-like objects to NumPy arrays
Objects with len(obj) == 0 which implement an "array-like"
interface, meaning an object implementing obj.__array__(),
obj.__array_interface__, obj.__array_struct__, or the python buffer
interface and which are also sequences (i.e. Pandas objects) will now
always retain there shape correctly when converted to an array. If such
an object has a shape of (0, 1) previously, it could be converted into
an array of shape (0,) (losing all dimensions after the first 0).
(gh-14995)
Removed multiarray.int_asbuffer
As part of the continued removal of Python 2 compatibility,
multiarray.int_asbuffer was removed. On Python 3, it threw a
NotImplementedError and was unused internally. It is expected that
there are no downstream use cases for this method with Python 3.
(gh-15229)
numpy.distutils.compat has been removed
This module contained only the function get_exception(), which was
used as:
try:
...
except Exception:
e = get_exception()
Its purpose was to handle the change in syntax introduced in Python 2.6,
from except Exception, e: to except Exception as e:, meaning it was
only necessary for codebases supporting Python 2.5 and older.
(gh-15255)
issubdtype no longer interprets float as np.floating
numpy.issubdtype had a FutureWarning since NumPy 1.14 which has
expired now. This means that certain input where the second argument was
neither a datatype nor a NumPy scalar type (such as a string or a python
type like int or float) will now be consistent with passing in
np.dtype(arg2).type. This makes the result consistent with
expectations and leads to a false result in some cases which previously
returned true.
(gh-15773)
Change output of round on scalars to be consistent with Python
Output of the __round__ dunder method and consequently the Python
built-in round has been changed to be a Python int to be consistent
with calling it on Python float objects when called with no arguments.
Previously, it would return a scalar of the np.dtype that was passed
in.
(gh-15840)
The numpy.ndarray constructor no longer interprets strides=() as strides=None
The former has changed to have the expected meaning of setting
numpy.ndarray.strides to (), while the latter continues to result in
strides being chosen automatically.
(gh-15882)
C-Level string to datetime casts changed
The C-level casts from strings were simplified. This changed also fixes
string to datetime and timedelta casts to behave correctly (i.e. like
Python casts using string_arr.astype("M8") while previously the cast
would behave like string_arr.astype(np.int_).astype("M8"). This only
affects code using low-level C-API to do manual casts (not full array
casts) of single scalar values or using e.g. PyArray_GetCastFunc, and
should thus not affect the vast majority of users.
(gh-16068)
Deprecations
Deprecate automatic dtype=object for ragged input
Calling np.array([[1, [1, 2, 3]]) will issue a DeprecationWarning as
per NEP 34. Users should
explicitly use dtype=object to avoid the warning.
(gh-15119)
Passing shape=0 to factory functions in numpy.rec is deprecated
0 is treated as a special case and is aliased to None in the
functions:
numpy.core.records.fromarraysnumpy.core.records.fromrecordsnumpy.core.records.fromstringnumpy.core.records.fromfile
In future, 0 will not be special cased, and will be treated as an
array length like any other integer.
(gh-15217)
Deprecation of probably unused C-API functions
The following C-API functions are probably unused and have been
deprecated:
PyArray_GetArrayParamsFromObjectPyUFunc_GenericFunctionPyUFunc_SetUsesArraysAsData
In most cases PyArray_GetArrayParamsFromObject should be replaced by
converting to an array, while PyUFunc_GenericFunction can be replaced
with PyObject_Call (see documentation for details).
(gh-15427)
Converting certain types to dtypes is Deprecated
The super classes of scalar types, such as np.integer, np.generic,
or np.inexact will now give a deprecation warning when converted to a
dtype (or used in a dtype keyword argument). The reason for this is that
np.integer is converted to np.int_, while it would be expected to
represent any integer (e.g. also int8, int16, etc. For example,
dtype=np.floating is currently identical to dtype=np.float64, even
though also np.float32 is a subclass of np.floating.
(gh-15534)