ndarray.__contains__ x.__contains__(y) <==> y in x

chararray.upper() [source] Return an array with the elements of self converted to uppercase. See also char.upper

numpy.polynomial.laguerre.laggrid3d(x, y, z, c) [source] Evaluate a 3-D Laguerre series on the Cartesian product of x, y, and z. This function returns the values: where the points (a, b, c) consist of all triples formed by taking a from x, b from y, and c from z. The resulting points form a grid with x in the first dimension, y in the second, and z in the third. The parameters x, y, and z are converted to arrays only if they are tuples or a lists, otherwise they are treated as a scalars.

numpy.piecewise(x, condlist, funclist, *args, **kw) [source] Evaluate a piecewise-defined function. Given a set of conditions and corresponding functions, evaluate each function on the input data wherever its condition is true. Parameters: x : ndarray The input domain. condlist : list of bool arrays Each boolean array corresponds to a function in funclist. Wherever condlist[i] is True, funclist[i](x) is used as the output value. Each boolean array in condlist selects a piece of x, and s

ndarray.__ne__ x.__ne__(y) <==> x!=y

generic.take() 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

MaskedArray.flat Flat version of the array.

ndindex.next() [source] Standard iterator method, updates the index and returns the index tuple. Returns: val : tuple of ints Returns a tuple containing the indices of the current iteration.

recarray.view(dtype=None, type=None) 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 the returned v

numpy.frombuffer(buffer, dtype=float, count=-1, offset=0) Interpret a buffer as a 1-dimensional array. Parameters: buffer : buffer_like An object that exposes the buffer interface. dtype : data-type, optional Data-type of the returned array; default: float. count : int, optional Number of items to read. -1 means all data in the buffer. offset : int, optional Start reading the buffer from this offset; default: 0. Notes If the buffer has data that is not in machine byte-order, this