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

generic.argmin() 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

HermiteE.integ(m=1, k=[], lbnd=None) [source] Integrate. Return a series instance that is the definite integral of the current series. Parameters: m : non-negative int The number of integrations to perform. k : array_like Integration constants. The first constant is applied to the first integration, the second to the second, and so on. The list of values must less than or equal to m in length and any missing values are set to zero. lbnd : Scalar The lower bound of the definite integra

Chebyshev.copy() [source] Return a copy. Returns: new_series : series Copy of self.

MaskedArray.__hex__() <==> hex(x)

numpy.polynomial.hermite_e.hermeone = array([1])

numpy.ma.polyfit(x, y, deg, rcond=None, full=False, w=None, cov=False) [source] Least squares polynomial fit. Fit a polynomial p(x) = p[0] * x**deg + ... + p[deg] of degree deg to points (x, y). Returns a vector of coefficients p that minimises the squared error. 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 of the sample points. Several data sets of sample points sharing the same x-coordinate

numpy.triu(m, k=0) [source] Upper triangle of an array. Return a copy of a matrix with the elements below the k-th diagonal zeroed. Please refer to the documentation for tril for further details. See also tril lower triangle of an array Examples >>> np.triu([[1,2,3],[4,5,6],[7,8,9],[10,11,12]], -1) array([[ 1, 2, 3], [ 4, 5, 6], [ 0, 8, 9], [ 0, 0, 12]])

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

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