numpy.digitize(x, bins, right=False) Return the indices of the bins to which each value in input array belongs. Each index i returned is such that bins[i-1] <= x < bins[i] if bins is monotonically increasing, or bins[i-1] > x >= bins[i] if bins is monotonically decreasing. If values in x are beyond the bounds of bins, 0 or len(bins) is returned as appropriate. If right is True, then the right bin is closed so that the index i is such that bins[i-1] < x <= bins[i] or bins[i

numpy.argsort(a, axis=-1, kind='quicksort', order=None) [source] Returns the indices that would sort an array. Perform an indirect sort along the given axis using the algorithm specified by the kind keyword. It returns an array of indices of the same shape as a that index data along the given axis in sorted order. Parameters: a : array_like Array to sort. axis : int or None, optional Axis along which to sort. The default is -1 (the last axis). If None, the flattened array is used. kind

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

Rationale Masked arrays are arrays that may have missing or invalid entries. The numpy.ma module provides a nearly work-alike replacement for numpy that supports data arrays with masks. What is a masked array? In many circumstances, datasets can be incomplete or tainted by the presence of invalid data. For example, a sensor may have failed to record a data, or recorded an invalid value. The numpy.ma module provides a convenient way to address this issue, by introducing masked arrays. A mask

generic.tostring() 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.cumsum() 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

numpy.linalg.matrix_rank(M, tol=None) [source] Return matrix rank of array using SVD method Rank of the array is the number of SVD singular values of the array that are greater than tol. Parameters: M : {(M,), (M, N)} array_like array of <=2 dimensions tol : {None, float}, optional threshold below which SVD values are considered zero. If tol is None, and S is an array with singular values for M, and eps is the epsilon value for datatype of S, then tol is set to S.max() * max(M.shape)

recarray.min(axis=None, out=None, keepdims=False) Return the minimum along a given axis. Refer to numpy.amin for full documentation. See also numpy.amin equivalent function

MaskedArray.view(dtype=None, type=None) [source] New view of array with the same data. Parameters: dtype : data-type or ndarray sub-class, optional Data-type descriptor of the returned view, e.g., float32 or int16. The default, None, results in the view having the same data-type as a. This argument can also be specified as an ndarray sub-class, which then specifies the type of the returned object (this is equivalent to setting the type parameter). type : Python type, optional Type of th

numpy.polynomial.legendre.legx = array([0, 1])