NumPy 1.9.0 Release Notes — NumPy v2.1 Manual (2024)

The diagonal and diag functions return readonly views.#

In NumPy 1.8, the diagonal and diag functions returned readonly copies, inNumPy 1.9 they return readonly views, and in 1.10 they will return writeableviews.

Special scalar float values don’t cause upcast to double anymore#

In previous numpy versions operations involving floating point scalarscontaining special values NaN, Inf and -Inf caused the resulttype to be at least float64. As the special values can be representedin the smallest available floating point type, the upcast is not performedanymore.

For example the dtype of:

np.array([1.], dtype=np.float32) * float('nan')

now remains float32 instead of being cast to float64.Operations involving non-special values have not been changed.

Percentile output changes#

If given more than one percentile to compute numpy.percentile returns anarray instead of a list. A single percentile still returns a scalar. Thearray is equivalent to converting the list returned in older versionsto an array via np.array.

If the overwrite_input option is used the input is only partiallyinstead of fully sorted.

ndarray.tofile exception type#

All tofile exceptions are now IOError, some were previouslyValueError.

Invalid fill value exceptions#

Two changes to numpy.ma.core._check_fill_value:

• When the fill value is a string and the array type is not one of‘OSUV’, TypeError is raised instead of the default fill value being used.

  See Also

  NumPy 1.14.0 Release Notes — NumPy v2.1 ManualCommand Reference, Cisco IOS XE 17.15.x (Catalyst 9600 Switches) - System Management Commands [Cisco Catalyst 9600 Series Switches]NumPy 1.12.0 Release Notes — NumPy v2.1 ManualBoost mailing page: Re: [boost] [AFIO] Formal review

• When the fill value overflows the array type, TypeError is raised insteadof OverflowError.

Polynomial Classes no longer derived from PolyBase#

This may cause problems with folks who depended on the polynomial classesbeing derived from PolyBase. They are now all derived from the abstractbase class ABCPolyBase. Strictly speaking, there should be a deprecationinvolved, but no external code making use of the old baseclass could befound.

Using numpy.random.binomial may change the RNG state vs. numpy < 1.9#

A bug in one of the algorithms to generate a binomial random variate hasbeen fixed. This change will likely alter the number of random drawsperformed, and hence the sequence location will be different after acall to distribution.c::rk_binomial_btpe. Any tests which rely on the RNGbeing in a known state should be checked and/or updated as a result.

Random seed enforced to be a 32 bit unsigned integer#

np.random.seed and np.random.RandomState now throw a ValueErrorif the seed cannot safely be converted to 32 bit unsigned integers.Applications that now fail can be fixed by masking the higher 32 bit values tozero: seed = seed & 0xFFFFFFFF. This is what is done silently in olderversions so the random stream remains the same.

Argmin and argmax out argument#

The out argument to np.argmin and np.argmax and theirequivalent C-API functions is now checked to match the desired output shapeexactly. If the check fails a ValueError instead of TypeError israised.

Einsum#

Remove unnecessary broadcasting notation restrictions.np.einsum('ijk,j->ijk', A, B) can also be written asnp.einsum('ij...,j->ij...', A, B) (ellipsis is no longer required on ‘j’)

Indexing#

The NumPy indexing has seen a complete rewrite in this version. This makesmost advanced integer indexing operations much faster and should have noother implications. However some subtle changes and deprecations wereintroduced in advanced indexing operations:

• Boolean indexing into scalar arrays will always return a new 1-d array.This means that array(1)[array(True)] gives array([1]) andnot the original array.

• Advanced indexing into one dimensional arrays used to have(undocumented) special handling regarding repeating the value array inassignments when the shape of the value array was too small or did notmatch. Code using this will raise an error. For compatibility you canuse arr.flat[index] = values, which uses the old code branch. (forexample a = np.ones(10); a[np.arange(10)] = [1, 2, 3])

• The iteration order over advanced indexes used to be always C-order.In NumPy 1.9. the iteration order adapts to the inputs and is notguaranteed (with the exception of a single advanced index which isnever reversed for compatibility reasons). This means that the resultis undefined if multiple values are assigned to the same element. Anexample for this is arr[[0, 0], [1, 1]] = [1, 2], which may setarr[0, 1] to either 1 or 2.

• Equivalent to the iteration order, the memory layout of the advancedindexing result is adapted for faster indexing and cannot be predicted.

• All indexing operations return a view or a copy. No indexing operationwill return the original array object. (For example arr[...])

• In the future Boolean array-likes (such as lists of python bools) willalways be treated as Boolean indexes and Boolean scalars (includingpython True) will be a legal boolean index. At this time, this isalready the case for scalar arrays to allow the generalpositive = a[a > 0] to work when a is zero dimensional.

• In NumPy 1.8 it was possible to use array(True) andarray(False) equivalent to 1 and 0 if the result of the operationwas a scalar. This will raise an error in NumPy 1.9 and, as notedabove, treated as a boolean index in the future.

• All non-integer array-likes are deprecated, object arrays of custominteger like objects may have to be cast explicitly.

• The error reporting for advanced indexing is more informative, howeverthe error type has changed in some cases. (Broadcasting errors ofindexing arrays are reported as IndexError)

• Indexing with more then one ellipsis (...) is deprecated.

Non-integer reduction axis indexes are deprecated#

Non-integer axis indexes to reduction ufuncs like add.reduce or sum aredeprecated.

promote_types and string dtype#

promote_types function now returns a valid string length when given aninteger or float dtype as one argument and a string dtype as anotherargument. Previously it always returned the input string dtype, even if itwasn’t long enough to store the max integer/float value converted to astring.

can_cast and string dtype#

can_cast function now returns False in “safe” casting mode forinteger/float dtype and string dtype if the string dtype length is not longenough to store the max integer/float value converted to a string.Previously can_cast in “safe” mode returned True for integer/floatdtype and a string dtype of any length.

astype and string dtype#

The astype method now returns an error if the string dtype to cast tois not long enough in “safe” casting mode to hold the max value ofinteger/float array that is being casted. Previously the casting wasallowed even if the result was truncated.

npyio.recfromcsv keyword arguments change#

npyio.recfromcsv no longer accepts the undocumented update keyword,which used to override the dtype keyword.

The doc/swig directory moved#

The doc/swig directory has been moved to tools/swig.

The npy_3kcompat.h header changed#

The unused simple_capsule_dtor function has been removed fromnpy_3kcompat.h. Note that this header is not meant to be used outsideof numpy; other projects should be using their own copy of this file whenneeded.

Negative indices in C-Api sq_item and sq_ass_item sequence methods#

When directly accessing the sq_item or sq_ass_item PyObject slotsfor item getting, negative indices will not be supported anymore.PySequence_GetItem and PySequence_SetItem however fix negativeindices so that they can be used there.

NDIter#

When NpyIter_RemoveAxis is now called, the iterator range will be reset.

When a multi index is being tracked and an iterator is not buffered, it ispossible to use NpyIter_RemoveAxis. In this case an iterator can shrinkin size. Because the total size of an iterator is limited, the iteratormay be too large before these calls. In this case its size will be set to -1and an error issued not at construction time but when removing the multiindex, setting the iterator range, or getting the next function.

This has no effect on currently working code, but highlights the necessityof checking for an error return if these conditions can occur. In mostcases the arrays being iterated are as large as the iterator so that sucha problem cannot occur.

This change was already applied to the 1.8.1 release.

zeros_like for string dtypes now returns empty strings#

To match the zeros function zeros_like now returns an array initializedwith empty strings instead of an array filled with ‘0’.

Percentile supports more interpolation options#

np.percentile now has the interpolation keyword argument to specify inwhich way points should be interpolated if the percentiles fall between twovalues. See the documentation for the available options.

Generalized axis support for median and percentile#

np.median and np.percentile now support generalized axis arguments likeufunc reductions do since 1.7. One can now say axis=(index, index) to pick alist of axes for the reduction. The keepdims keyword argument was alsoadded to allow convenient broadcasting to arrays of the original shape.

Dtype parameter added to np.linspace and np.logspace#

The returned data type from the linspace and logspace functions cannow be specified using the dtype parameter.

More general np.triu and np.tril broadcasting#

For arrays with ndim exceeding 2, these functions will now apply to thefinal two axes instead of raising an exception.

tobytes alias for tostring method#

ndarray.tobytes and MaskedArray.tobytes have been added as aliasesfor tostring which exports arrays as bytes. This is more consistentin Python 3 where str and bytes are not the same.

Build system#

Added experimental support for the ppc64le and OpenRISC architecture.

Compatibility to python numbers module#

All numerical numpy types are now registered with the type hierarchy inthe python numbers module.

increasing parameter added to np.vander#

The ordering of the columns of the Vandermonde matrix can be specified withthis new boolean argument.

unique_counts parameter added to np.unique#

The number of times each unique item comes up in the input can now beobtained as an optional return value.

Support for median and percentile in nanfunctions#

The np.nanmedian and np.nanpercentile functions behave likethe median and percentile functions except that NaNs are ignored.

NumpyVersion class added#

The class may be imported from numpy.lib and can be used for versioncomparison when the numpy version goes to 1.10.devel. For example:

>>> from numpy.lib import NumpyVersion>>> if NumpyVersion(np.__version__) < '1.10.0'):...  print('Wow, that is an old NumPy version!')

Allow saving arrays with large number of named columns#

The numpy storage format 1.0 only allowed the array header to have a total sizeof 65535 bytes. This can be exceeded by structured arrays with a large numberof columns. A new format 2.0 has been added which extends the header size to 4GiB. np.save will automatically save in 2.0 format if the data requires it,else it will always use the more compatible 1.0 format.

Full broadcasting support for np.cross#

np.cross now properly broadcasts its two input arrays, even if theyhave different number of dimensions. In earlier versions this would resultin either an error being raised, or wrong results computed.

Better numerical stability for sum in some cases#

Pairwise summation is now used in the sum method, but only along the fastaxis and for groups of the values <= 8192 in length. This should alsoimprove the accuracy of var and std in some common cases.

Percentile implemented in terms of np.partition#

np.percentile has been implemented in terms of np.partition whichonly partially sorts the data via a selection algorithm. This improves thetime complexity from O(nlog(n)) to O(n).

Performance improvement for np.array#

The performance of converting lists containing arrays to arrays usingnp.array has been improved. It is now equivalent in speed tonp.vstack(list).

Performance improvement for np.searchsorted#

For the built-in numeric types, np.searchsorted no longer relies on thedata type’s compare function to perform the search, but is nowimplemented by type specific functions. Depending on the size of theinputs, this can result in performance improvements over 2x.

Optional reduced verbosity for np.distutils#

Set numpy.distutils.system_info.system_info.verbosity = 0 and thencalls to numpy.distutils.system_info.get_info('blas_opt') will notprint anything on the output. This is mostly for other packages usingnumpy.distutils.

Covariance check in np.random.multivariate_normal#

A RuntimeWarning warning is raised when the covariance matrix is notpositive-semidefinite.

Polynomial Classes no longer template based#

The polynomial classes have been refactored to use an abstract base classrather than a template in order to implement a common interface. This makesimporting the polynomial package faster as the classes do not need to becompiled on import.

More GIL releases#

Several more functions now release the Global Interpreter Lock allowing moreefficient parallelization using the threading module. Most notably the GIL isnow released for fancy indexing, np.where and the random module nowuses a per-state lock instead of the GIL.

MaskedArray support for more complicated base classes#

Built-in assumptions that the baseclass behaved like a plain array are beingremoved. In particular, repr and str should now work more reliably.

C-API#

Non-integer scalars for sequence repetition#

Using non-integer numpy scalars to repeat python sequences is deprecated.For example np.float_(2) * [1] will be an error in the future.

select input deprecations#

The integer and empty input to select is deprecated. In the future onlyboolean arrays will be valid conditions and an empty condlist will beconsidered an input error instead of returning the default.

rank function#

The rank function has been deprecated to avoid confusion withnumpy.linalg.matrix_rank.

Object array equality comparisons#

In the future object array comparisons both == and np.equal will notmake use of identity checks anymore. For example:

>>> a = np.array([np.array([1, 2, 3]), 1])>>> b = np.array([np.array([1, 2, 3]), 1])>>> a == b

will consistently return False (and in the future an error) even if the arrayin a and b was the same object.

The equality operator == will in the future raise errors like np.equalif broadcasting or element comparisons, etc. fails.

Comparison with arr == None will in the future do an elementwise comparisoninstead of just returning False. Code should be using arr is None.

All of these changes will give Deprecation- or FutureWarnings at this time.

C-API#

The utility function npy_PyFile_Dup and npy_PyFile_DupClose are broken by theinternal buffering python 3 applies to its file objects.To fix this two new functions npy_PyFile_Dup2 and npy_PyFile_DupClose2 aredeclared in npy_3kcompat.h and the old functions are deprecated.Due to the fragile nature of these functions it is recommended to instead usethe python API when possible.

This change was already applied to the 1.8.1 release.