NumPy 1.9.0 Release Notes#
This release supports Python 2.6 - 2.7 and 3.2 - 3.4.
Numerous performance improvements in various areas, most notably indexing and operations on small arrays are significantly faster. Indexing operations now also release the GIL.
Addition of nanmedian and nanpercentile rounds out the nanfunction set.
The oldnumeric and numarray modules have been removed.
The doc/pyrex and doc/cython directories have been removed.
The doc/numpybook directory has been removed.
The numpy/testing/numpytest.py file has been removed together with the importall function it contained.
The numpy/polynomial/polytemplate.py file will be removed in NumPy 1.10.0.
Default casting for inplace operations will change to ‘same_kind’ in Numpy 1.10.0. This will certainly break some code that is currently ignoring the warning.
Relaxed stride checking will be the default in 1.10.0
String version checks will break because, e.g., ‘1.9’ > ‘1.10’ is True. A NumpyVersion class has been added that can be used for such comparisons.
The diagonal and diag functions will return writeable views in 1.10.0
The S and/or a dtypes may be changed to represent Python strings instead of bytes, in Python 3 these two types are very different.
The diagonal and diag functions return readonly views.#
In NumPy 1.8, the diagonal and diag functions returned readonly copies, in NumPy 1.9 they return readonly views, and in 1.10 they will return writeable views.
Special scalar float values don’t cause upcast to double anymore#
In previous numpy versions operations involving floating point scalars
containing special values
-Inf caused the result
type to be at least
float64. As the special values can be represented
in the smallest available floating point type, the upcast is not performed
For example the dtype of:
np.array([1.], dtype=np.float32) * float('nan')
float32 instead of being cast to
Operations involving non-special values have not been changed.
Percentile output changes#
If given more than one percentile to compute numpy.percentile returns an
array instead of a list. A single percentile still returns a scalar. The
array is equivalent to converting the list returned in older versions
to an array via
overwrite_input option is used the input is only partially
instead of fully sorted.
ndarray.tofile exception type#
tofile exceptions are now
IOError, some were previously
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.
When the fill value overflows the array type, TypeError is raised instead of OverflowError.
Polynomial Classes no longer derived from PolyBase#
This may cause problems with folks who depended on the polynomial classes being derived from PolyBase. They are now all derived from the abstract base class ABCPolyBase. Strictly speaking, there should be a deprecation involved, but no external code making use of the old baseclass could be found.
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 has been fixed. This change will likely alter the number of random draws performed, and hence the sequence location will be different after a call to distribution.c::rk_binomial_btpe. Any tests which rely on the RNG being 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.RandomState now throw a
if 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 to
seed = seed & 0xFFFFFFFF. This is what is done silently in older
versions so the random stream remains the same.
Argmin and argmax out argument#
out argument to
np.argmax and their
equivalent C-API functions is now checked to match the desired output shape
exactly. If the check fails a
ValueError instead of
Remove unnecessary broadcasting notation restrictions.
np.einsum('ijk,j->ijk', A, B) can also be written as
np.einsum('ij...,j->ij...', A, B) (ellipsis is no longer required on ‘j’)
The NumPy indexing has seen a complete rewrite in this version. This makes most advanced integer indexing operations much faster and should have no other implications. However some subtle changes and deprecations were introduced in advanced indexing operations:
Boolean indexing into scalar arrays will always return a new 1-d array. This means that
array()and not the original array.
Advanced indexing into one dimensional arrays used to have (undocumented) special handling regarding repeating the value array in assignments when the shape of the value array was too small or did not match. Code using this will raise an error. For compatibility you can use
arr.flat[index] = values, which uses the old code branch. (for example
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 not guaranteed (with the exception of a single advanced index which is never reversed for compatibility reasons). This means that the result is undefined if multiple values are assigned to the same element. An example for this is
arr[[0, 0], [1, 1]] = [1, 2], which may set
arr[0, 1]to either 1 or 2.
Equivalent to the iteration order, the memory layout of the advanced indexing result is adapted for faster indexing and cannot be predicted.
All indexing operations return a view or a copy. No indexing operation will return the original array object. (For example
In the future Boolean array-likes (such as lists of python bools) will always be treated as Boolean indexes and Boolean scalars (including python
True) will be a legal boolean index. At this time, this is already the case for scalar arrays to allow the general
positive = a[a > 0]to work when
ais zero dimensional.
In NumPy 1.8 it was possible to use
array(False)equivalent to 1 and 0 if the result of the operation was a scalar. This will raise an error in NumPy 1.9 and, as noted above, treated as a boolean index in the future.
All non-integer array-likes are deprecated, object arrays of custom integer like objects may have to be cast explicitly.
The error reporting for advanced indexing is more informative, however the error type has changed in some cases. (Broadcasting errors of indexing arrays are reported as
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 are deprecated.
promote_types and string dtype#
promote_types function now returns a valid string length when given an
integer or float dtype as one argument and a string dtype as another
argument. Previously it always returned the input string dtype, even if it
wasn’t long enough to store the max integer/float value converted to a
can_cast and string dtype#
can_cast function now returns False in “safe” casting mode for
integer/float dtype and string dtype if the string dtype length is not long
enough to store the max integer/float value converted to a string.
can_cast in “safe” mode returned True for integer/float
dtype and a string dtype of any length.
astype and string dtype#
astype method now returns an error if the string dtype to cast to
is not long enough in “safe” casting mode to hold the max value of
integer/float array that is being casted. Previously the casting was
allowed 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.
doc/swig directory moved#
doc/swig directory has been moved to
npy_3kcompat.h header changed#
simple_capsule_dtor function has been removed from
npy_3kcompat.h. Note that this header is not meant to be used outside
of numpy; other projects should be using their own copy of this file when
Negative indices in C-Api
sq_ass_item sequence methods#
When directly accessing the
sq_ass_item PyObject slots
for item getting, negative indices will not be supported anymore.
PySequence_SetItem however fix negative
indices so that they can be used there.
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 is
possible to use
NpyIter_RemoveAxis. In this case an iterator can shrink
in size. Because the total size of an iterator is limited, the iterator
may be too large before these calls. In this case its size will be set to
and an error issued not at construction time but when removing the multi
index, setting the iterator range, or getting the next function.
This has no effect on currently working code, but highlights the necessity of checking for an error return if these conditions can occur. In most cases the arrays being iterated are as large as the iterator so that such a 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 initialized with empty strings instead of an array filled with ‘0’.
Percentile supports more interpolation options#
np.percentile now has the interpolation keyword argument to specify in
which way points should be interpolated if the percentiles fall between two
values. See the documentation for the available options.
Generalized axis support for median and percentile#
np.percentile now support generalized axis arguments like
ufunc reductions do since 1.7. One can now say axis=(index, index) to pick a
list of axes for the reduction. The
keepdims keyword argument was also
added to allow convenient broadcasting to arrays of the original shape.
Dtype parameter added to
The returned data type from the
logspace functions can
now be specified using the dtype parameter.
For arrays with
ndim exceeding 2, these functions will now apply to the
final two axes instead of raising an exception.
tobytes alias for
MaskedArray.tobytes have been added as aliases
tostring which exports arrays as
bytes. This is more consistent
in Python 3 where
bytes are not the same.
Added experimental support for the ppc64le and OpenRISC architecture.
Compatibility to python
All numerical numpy types are now registered with the type hierarchy in
increasing parameter added to
The ordering of the columns of the Vandermonde matrix can be specified with this new boolean argument.
unique_counts parameter added to
The number of times each unique item comes up in the input can now be obtained as an optional return value.
Support for median and percentile in nanfunctions#
np.nanpercentile functions behave like
the 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 version comparison 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 size of 65535 bytes. This can be exceeded by structured arrays with a large number of columns. A new format 2.0 has been added which extends the header size to 4 GiB. 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 now properly broadcasts its two input arrays, even if they
have different number of dimensions. In earlier versions this would result
in 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 fast axis and for groups of the values <= 8192 in length. This should also improve the accuracy of var and std in some common cases.
Percentile implemented in terms of
np.percentile has been implemented in terms of
only partially sorts the data via a selection algorithm. This improves the
time complexity from
Performance improvement for
The performance of converting lists containing arrays to arrays using
np.array has been improved. It is now equivalent in speed to
Performance improvement for
For the built-in numeric types,
np.searchsorted no longer relies on the
compare function to perform the search, but is now
implemented by type specific functions. Depending on the size of the
inputs, this can result in performance improvements over 2x.
Optional reduced verbosity for np.distutils#
numpy.distutils.system_info.system_info.verbosity = 0 and then
numpy.distutils.system_info.get_info('blas_opt') will not
print anything on the output. This is mostly for other packages using
Covariance check in
RuntimeWarning warning is raised when the covariance matrix is not
Polynomial Classes no longer template based#
The polynomial classes have been refactored to use an abstract base class rather than a template in order to implement a common interface. This makes importing the polynomial package faster as the classes do not need to be compiled on import.
More GIL releases#
Several more functions now release the Global Interpreter Lock allowing more
efficient parallelization using the
threading module. Most notably the GIL is
now released for fancy indexing,
np.where and the
random module now
uses 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 being
removed. In particular,
str should now work more reliably.
Non-integer scalars for sequence repetition#
Using non-integer numpy scalars to repeat python sequences is deprecated.
np.float_(2) *  will be an error in the future.
select input deprecations#
The integer and empty input to
select is deprecated. In the future only
boolean arrays will be valid conditions and an empty
condlist will be
considered an input error instead of returning the default.
rank function has been deprecated to avoid confusion with
Object array equality comparisons#
In the future object array comparisons both == and np.equal will not make 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 array in a and b was the same object.
The equality operator == will in the future raise errors like np.equal if broadcasting or element comparisons, etc. fails.
Comparison with arr == None will in the future do an elementwise comparison instead of just returning False. Code should be using arr is None.
All of these changes will give Deprecation- or FutureWarnings at this time.
The utility function npy_PyFile_Dup and npy_PyFile_DupClose are broken by the internal buffering python 3 applies to its file objects. To fix this two new functions npy_PyFile_Dup2 and npy_PyFile_DupClose2 are declared in npy_3kcompat.h and the old functions are deprecated. Due to the fragile nature of these functions it is recommended to instead use the python API when possible.
This change was already applied to the 1.8.1 release.