numpy.core.defchararray.encode(a, encoding=None, errors=None) [source] Calls str.encode element-wise. The set of available codecs comes from the Python standard library, and may be extended at runtime. For more information, see the codecs module. Parameters: a : array_like of str or unicode encoding : str, optional The name of an encoding errors : str, optional Specifies how to handle encoding errors Returns: out : ndarray See also str.encode Notes The type of the result will depe

numpy.polynomial.hermite.hermadd(c1, c2) [source] Add one Hermite series to another. Returns the sum of two Hermite series c1 + c2. The arguments are sequences of coefficients ordered from lowest order term to highest, i.e., [1,2,3] represents the series P_0 + 2*P_1 + 3*P_2. Parameters: c1, c2 : array_like 1-D arrays of Hermite series coefficients ordered from low to high. Returns: out : ndarray Array representing the Hermite series of their sum. See also hermsub, hermmul, hermdiv,

numpy.polynomial.hermite.hermsub(c1, c2) [source] Subtract one Hermite series from another. Returns the difference of two Hermite series c1 - c2. The sequences of coefficients are from lowest order term to highest, i.e., [1,2,3] represents the series P_0 + 2*P_1 + 3*P_2. Parameters: c1, c2 : array_like 1-D arrays of Hermite series coefficients ordered from low to high. Returns: out : ndarray Of Hermite series coefficients representing their difference. See also hermadd, hermmul, he

chararray.dump(file) Dump a pickle of the array to the specified file. The array can be read back with pickle.load or numpy.load. Parameters: file : str A string naming the dump file.

numpy.asfortranarray(a, dtype=None) [source] Return an array laid out in Fortran order in memory. Parameters: a : array_like Input array. dtype : str or dtype object, optional By default, the data-type is inferred from the input data. Returns: out : ndarray The input a in Fortran, or column-major, order. See also ascontiguousarray Convert input to a contiguous (C order) array. asanyarray Convert input to an ndarray with either row or column-major memory order. require Re

MaskedArray.cumprod(axis=None, dtype=None, out=None) [source] Return the cumulative product of the elements along the given axis. The cumulative product is taken over the flattened array by default, otherwise over the specified axis. Masked values are set to 1 internally during the computation. However, their position is saved, and the result will be masked at the same locations. Parameters: axis : {None, -1, int}, optional Axis along which the product is computed. The default (axis = Non

numpy.rollaxis(a, axis, start=0) [source] Roll the specified axis backwards, until it lies in a given position. Parameters: a : ndarray Input array. axis : int The axis to roll backwards. The positions of the other axes do not change relative to one another. start : int, optional The axis is rolled until it lies before this position. The default, 0, results in a ?complete? roll. Returns: res : ndarray For Numpy >= 1.10 a view of a is always returned. For earlier Numpy versions

numpy.core.defchararray.isalpha(a) [source] Returns true for each element if all characters in the string are alphabetic and there is at least one character, false otherwise. Calls str.isalpha element-wise. For 8-bit strings, this method is locale-dependent. Parameters: a : array_like of str or unicode Returns: out : ndarray Output array of bools See also str.isalpha

RandomState.beta(a, b, size=None) Draw samples from a Beta distribution. The Beta distribution is a special case of the Dirichlet distribution, and is related to the Gamma distribution. It has the probability distribution function where the normalisation, B, is the beta function, It is often seen in Bayesian inference and order statistics. Parameters: a : float Alpha, non-negative. b : float Beta, non-negative. size : int or tuple of ints, optional Output shape. If the given sha

chararray.transpose(*axes) Returns a view of the array with axes transposed. For a 1-D array, this has no effect. (To change between column and row vectors, first cast the 1-D array into a matrix object.) For a 2-D array, this is the usual matrix transpose. For an n-D array, if axes are given, their order indicates how the axes are permuted (see Examples). If axes are not provided and a.shape = (i[0], i[1], ... i[n-2], i[n-1]), then a.transpose().shape = (i[n-1], i[n-2], ... i[1], i[0]). Pa