recarray.var(axis=None, dtype=None, out=None, ddof=0, keepdims=False) Returns the variance of the array elements, along given axis. Refer to numpy.var for full documentation. See also numpy.var equivalent function

recarray.argmin(axis=None, out=None) Return indices of the minimum values along the given axis of a. Refer to numpy.argmin for detailed documentation. See also numpy.argmin equivalent function

numpy.core.defchararray.istitle(a) [source] Returns true for each element if the element is a titlecased string and there is at least one character, false otherwise. Call str.istitle 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.istitle

numpy.ptp(a, axis=None, out=None) [source] Range of values (maximum - minimum) along an axis. The name of the function comes from the acronym for ?peak to peak?. Parameters: a : array_like Input values. axis : int, optional Axis along which to find the peaks. By default, flatten the array. out : array_like Alternative output array in which to place the result. It must have the same shape and buffer length as the expected output, but the type of the output values will be cast if necess

numpy.ma.soften_mask(self) = Force the mask to soft. Whether the mask of a masked array is hard or soft is determined by its hardmask property. soften_mask sets hardmask to False. See also hardmask

numpy.deg2rad(x[, out]) = Convert angles from degrees to radians. Parameters: x : array_like Angles in degrees. Returns: y : ndarray The corresponding angle in radians. See also rad2deg Convert angles from radians to degrees. unwrap Remove large jumps in angle by wrapping. Notes New in version 1.3.0. deg2rad(x) is x * pi / 180. Examples >>> np.deg2rad(180) 3.1415926535897931

This module contains all functions in the numpy namespace, with the following replacement functions that return matrices instead of ndarrays. Functions that are also in the numpy namespace and return matrices mat(data[, dtype]) Interpret the input as a matrix. matrix Returns a matrix from an array-like object, or from a string of data. asmatrix(data[, dtype]) Interpret the input as a matrix. bmat(obj[, ldict, gdict]) Build a matrix object from a string, nested sequence, or array. Replacem

numpy.polynomial.laguerre.lagroots(c) [source] Compute the roots of a Laguerre series. Return the roots (a.k.a. ?zeros?) of the polynomial Parameters: c : 1-D array_like 1-D array of coefficients. Returns: out : ndarray Array of the roots of the series. If all the roots are real, then out is also real, otherwise it is complex. See also polyroots, legroots, chebroots, hermroots, hermeroots Notes The root estimates are obtained as the eigenvalues of the companion matrix, Roots fa

numpy.sort_complex(a) [source] Sort a complex array using the real part first, then the imaginary part. Parameters: a : array_like Input array Returns: out : complex ndarray Always returns a sorted complex array. Examples >>> np.sort_complex([5, 3, 6, 2, 1]) array([ 1.+0.j, 2.+0.j, 3.+0.j, 5.+0.j, 6.+0.j]) >>> np.sort_complex([1 + 2j, 2 - 1j, 3 - 2j, 3 - 3j, 3 + 5j]) array([ 1.+2.j, 2.-1.j, 3.-3.j, 3.-2.j, 3.+5.j])

RandomState.standard_normal(size=None) Draw samples from a standard Normal distribution (mean=0, stdev=1). Parameters: 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: out : float or ndarray Drawn samples. Examples >>> s = np.random.standard_normal(8000) >>> s array([ 0.6888893 , 0.78096262, -0.89086505, ..., 0.49876311,