flatiter.copy() Get a copy of the iterator as a 1-D array. Examples >>> x = np.arange(6).reshape(2, 3) >>> x array([[0, 1, 2], [3, 4, 5]]) >>> fl = x.flat >>> fl.copy() array([0, 1, 2, 3, 4, 5])

generic.round() 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.nanstd(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False) [source] Compute the standard deviation along the specified axis, while ignoring NaNs. Returns the standard deviation, a measure of the spread of a distribution, of the non-NaN array elements. The standard deviation is computed for the flattened array by default, otherwise over the specified axis. For all-NaN slices or slices with zero degrees of freedom, NaN is returned and a RuntimeWarning is raised. New in version

MaskedArray.__gt__ x.__gt__(y) <==> x>y

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

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

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)

MaskedArray.__xor__ x.__xor__(y) <==> x^y

dtype.metadata

MaskedArray.__array_wrap__(obj, context=None) [source] Special hook for ufuncs. Wraps the numpy array and sets the mask according to context.