record.all() 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.around(a, decimals=0, out=None) [source] Evenly round to the given number of decimals. Parameters: a : array_like Input data. decimals : int, optional Number of decimal places to round to (default: 0). If decimals is negative, it specifies the number of positions to the left of the decimal point. out : ndarray, optional Alternative output array in which to place the result. It must have the same shape as the expected output, but the type of the output values will be cast if nece

chararray.title() [source] For each element in self, return a titlecased version of the string: words start with uppercase characters, all remaining cased characters are lowercase. See also char.title

numpy.ma.dot(a, b, strict=False, out=None) [source] Return the dot product of two arrays. This function is the equivalent of numpy.dot that takes masked values into account. Note that strict and out are in different position than in the method version. In order to maintain compatibility with the corresponding method, it is recommended that the optional arguments be treated as keyword only. At some point that may be mandatory. Note Works only with 2-D arrays at the moment. Parameters: a,

numpy.asmatrix(data, dtype=None) [source] Interpret the input as a matrix. Unlike matrix, asmatrix does not make a copy if the input is already a matrix or an ndarray. Equivalent to matrix(data, copy=False). Parameters: data : array_like Input data. dtype : data-type Data-type of the output matrix. Returns: mat : matrix data interpreted as a matrix. Examples >>> x = np.array([[1, 2], [3, 4]]) >>> m = np.asmatrix(x) >>> x[0,0] = 5 >>> m matrix

MaskedArray.flat Flat version of the array.

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

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