ndarray.__ifloordiv__ x.__ifloordiv__(y) <==> x//y

recarray.put(indices, values, mode='raise') Set a.flat[n] = values[n] for all n in indices. Refer to numpy.put for full documentation. See also numpy.put equivalent function

numpy.logical_and(x1, x2[, out]) = Compute the truth value of x1 AND x2 element-wise. Parameters: x1, x2 : array_like Input arrays. x1 and x2 must be of the same shape. Returns: y : ndarray or bool Boolean result with the same shape as x1 and x2 of the logical AND operation on corresponding elements of x1 and x2. See also logical_or, logical_not, logical_xor, bitwise_and Examples >>> np.logical_and(True, False) False >>> np.logical_and([True, False], [False, Fal

numpy.polynomial.hermite.hermval2d(x, y, c) [source] Evaluate a 2-D Hermite series at points (x, y). This function returns the values: The parameters x and y 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 and y or their elements must support multiplication and addition both with themselves and with the elements of c. If c is a 1-D array a one is implicitly app

numpy.polynomial.legendre.legval2d(x, y, c) [source] Evaluate a 2-D Legendre series at points (x, y). This function returns the values: The parameters x and y 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 and y or their elements must support multiplication and addition both with themselves and with the elements of c. If c is a 1-D array a one is implicitly ap

record.searchsorted() 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.chebyshev.chebfit(x, y, deg, rcond=None, full=False, w=None) [source] Least squares fit of Chebyshev series to data. Return the coefficients of a Legendre series of degree deg that is the least squares fit to the data values y given at points x. If y is 1-D the returned coefficients will also be 1-D. If y is 2-D multiple fits are done, one for each column of y, and the resulting coefficients are stored in the corresponding columns of a 2-D return. The fitted polynomial(s) a

ndarray.__oct__() <==> oct(x)

numpy.polynomial.laguerre.lagfit(x, y, deg, rcond=None, full=False, w=None) [source] Least squares fit of Laguerre series to data. Return the coefficients of a Laguerre series of degree deg that is the least squares fit to the data values y given at points x. If y is 1-D the returned coefficients will also be 1-D. If y is 2-D multiple fits are done, one for each column of y, and the resulting coefficients are stored in the corresponding columns of a 2-D return. The fitted polynomial(s) are

numpy.random.standard_gamma(shape, size=None) Draw samples from a standard Gamma distribution. Samples are drawn from a Gamma distribution with specified parameters, shape (sometimes designated ?k?) and scale=1. Parameters: shape : float Parameter, should be > 0. size : int or tuple of ints, optional Output shape. If the given shape is, e.g., (m, n, k), then m * n * k samples are drawn. Default is None, in which case a single value is returned. Returns: samples : ndarray or scalar