static ndarray.__new__(S, ...) ? a new object with type S, a subtype of T

static MaskedArray.__new__(data=None, mask=False, dtype=None, copy=False, subok=True, ndmin=0, fill_value=None, keep_mask=True, hard_mask=None, shrink=True, order=None, **options) [source] Create a new masked array from scratch. Notes A masked array can also be created by taking a .view(MaskedArray).

Sorting sort(a[, axis, kind, order]) Return a sorted copy of an array. lexsort(keys[, axis]) Perform an indirect sort using a sequence of keys. argsort(a[, axis, kind, order]) Returns the indices that would sort an array. ndarray.sort([axis, kind, order]) Sort an array, in-place. msort(a) Return a copy of an array sorted along the first axis. sort_complex(a) Sort a complex array using the real part first, then the imaginary part. partition(a, kth[, axis, kind, order]) Return a partitio

Making proper sets unique(ar[, return_index, return_inverse, ...]) Find the unique elements of an array. Boolean operations in1d(ar1, ar2[, assume_unique, invert]) Test whether each element of a 1-D array is also present in a second array. intersect1d(ar1, ar2[, assume_unique]) Find the intersection of two arrays. setdiff1d(ar1, ar2[, assume_unique]) Find the set difference of two arrays. setxor1d(ar1, ar2[, assume_unique]) Find the set exclusive-or of two arrays. union1d(ar1, ar2) Fi

Introduction Writing tests for the numpy.i SWIG interface file is a combinatorial headache. At present, 12 different data types are supported, each with 74 different argument signatures, for a total of 888 typemaps supported ?out of the box?. Each of these typemaps, in turn, might require several unit tests in order to verify expected behavior for both proper and improper inputs. Currently, this results in more than 1,000 individual unit tests executed when make test is run in the numpy/tools

Python defines only one type of a particular data class (there is only one integer type, one floating-point type, etc.). This can be convenient in applications that don?t need to be concerned with all the ways data can be represented in a computer. For scientific computing, however, more control is often needed. In NumPy, there are 24 new fundamental Python types to describe different types of scalars. These type descriptors are mostly based on the types available in the C language that CPytho

record.view() 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

record.var() 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

record.transpose() 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

record.trace() 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