numpy.split(ary, indices_or_sections, axis=0) [source] Split an array into multiple sub-arrays. Parameters: ary : ndarray Array to be divided into sub-arrays. indices_or_sections : int or 1-D array If indices_or_sections is an integer, N, the array will be divided into N equal arrays along axis. If such a split is not possible, an error is raised. If indices_or_sections is a 1-D array of sorted integers, the entries indicate where along axis the array is split. For example, [2, 3] would

ndarray.__idiv__ x.__idiv__(y) <==> x/=y

MaskedArray.mask Mask

masked_array.mask Mask

record.searchsorted() Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also The

ndarray.__le__ x.__le__(y) <==> x<=y

MaskedArray.dumps() Returns the pickle of the array as a string. pickle.loads or numpy.loads will convert the string back to an array. Parameters: None

Chebyshev.roots() [source] Return the roots of the series polynomial. Compute the roots for the series. Note that the accuracy of the roots decrease the further outside the domain they lie. Returns: roots : ndarray Array containing the roots of the series.

numpy.ma.mask_or(m1, m2, copy=False, shrink=True) [source] Combine two masks with the logical_or operator. The result may be a view on m1 or m2 if the other is nomask (i.e. False). Parameters: m1, m2 : array_like Input masks. copy : bool, optional If copy is False and one of the inputs is nomask, return a view of the other input mask. Defaults to False. shrink : bool, optional Whether to shrink the output to nomask if all its values are False. Defaults to True. Returns: mask : outp

record.sum() Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also The