generic.mean() 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

Laguerre.degree() [source] The degree of the series. New in version 1.5.0. Returns: degree : int Degree of the series, one less than the number of coefficients.

Polynomial.deriv(m=1) [source] Differentiate. Return a series instance of that is the derivative of the current series. Parameters: m : non-negative int Find the derivative of order m. Returns: new_series : series A new series representing the derivative. The domain is the same as the domain of the differentiated series.

numpy.polynomial.hermite.hermgrid3d(x, y, z, c) [source] Evaluate a 3-D Hermite 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.polynomial.chebyshev.chebval3d(x, y, z, c) [source] Evaluate a 3-D Chebyshev series at points (x, y, z). This function returns the values: 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 and they must have the same shape after conversion. In either case, either x, y, and z or their elements must support multiplication and addition both with themselves and with the elements of c. If c has fewer than 3 dim

numpy.diff(a, n=1, axis=-1) [source] Calculate the n-th discrete difference along given axis. The first difference is given by out[n] = a[n+1] - a[n] along the given axis, higher differences are calculated by using diff recursively. Parameters: a : array_like Input array n : int, optional The number of times values are differenced. axis : int, optional The axis along which the difference is taken, default is the last axis. Returns: diff : ndarray The n-th differences. The sh

MaskedArray.searchsorted(v, side='left', sorter=None) Find indices where elements of v should be inserted in a to maintain order. For full documentation, see numpy.searchsorted See also numpy.searchsorted equivalent function

chararray.isupper() [source] Returns true for each element if all cased characters in the string are uppercase and there is at least one character, false otherwise. See also char.isupper

numpy.repeat(a, repeats, axis=None) [source] Repeat elements of an array. Parameters: a : array_like Input array. repeats : int or array of ints The number of repetitions for each element. repeats is broadcasted to fit the shape of the given axis. axis : int, optional The axis along which to repeat values. By default, use the flattened input array, and return a flat output array. Returns: repeated_array : ndarray Output array which has the same shape as a, except along the given a

matrix.getH() [source] Returns the (complex) conjugate transpose of self. Equivalent to np.transpose(self) if self is real-valued. Parameters: None Returns: ret : matrix object complex conjugate transpose of self Examples >>> x = np.matrix(np.arange(12).reshape((3,4))) >>> z = x - 1j*x; z matrix([[ 0. +0.j, 1. -1.j, 2. -2.j, 3. -3.j], [ 4. -4.j, 5. -5.j, 6. -6.j, 7. -7.j], [ 8. -8.j, 9. -9.j, 10.-10.j, 11.-11.j]]) >>> z.get