MaskedArray.__le__ x.__le__(y) <==> x<=y

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

ndarray.itemsize Length of one array element in bytes. Examples >>> x = np.array([1,2,3], dtype=np.float64) >>> x.itemsize 8 >>> x = np.array([1,2,3], dtype=np.complex128) >>> x.itemsize 16

classmethod Chebyshev.fit(x, y, deg, domain=None, rcond=None, full=False, w=None, window=None) [source] Least squares fit to data. Return a series instance that is the least squares fit to the data y sampled at x. The domain of the returned instance can be specified and this will often result in a superior fit with less chance of ill conditioning. Parameters: x : array_like, shape (M,) x-coordinates of the M sample points (x[i], y[i]). y : array_like, shape (M,) or (M, K) y-coordinates

chararray.expandtabs(tabsize=8) [source] Return a copy of each string element where all tab characters are replaced by one or more spaces. See also char.expandtabs