chararray.swapcase() [source] For each element in self, return a copy of the string with uppercase characters converted to lowercase and vice versa. See also char.swapcase

numpy.ma.masked_not_equal(x, value, copy=True) [source] Mask an array where not equal to a given value. This function is a shortcut to masked_where, with condition = (x != value). See also masked_where Mask where a condition is met. Examples >>> import numpy.ma as ma >>> a = np.arange(4) >>> a array([0, 1, 2, 3]) >>> ma.masked_not_equal(a, 2) masked_array(data = [-- -- 2 --], mask = [ True True False True], fill_value=999999)

numpy.core.defchararray.isupper(a) [source] Returns true for each element if all cased characters in the string are uppercase and there is at least one character, false otherwise. Call str.isupper 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.isupper

numpy.polynomial.laguerre.lagtrim(c, tol=0) [source] Remove ?small? ?trailing? coefficients from a polynomial. ?Small? means ?small in absolute value? and is controlled by the parameter tol; ?trailing? means highest order coefficient(s), e.g., in [0, 1, 1, 0, 0] (which represents 0 + x + x**2 + 0*x**3 + 0*x**4) both the 3-rd and 4-th order coefficients would be ?trimmed.? Parameters: c : array_like 1-d array of coefficients, ordered from lowest order to highest. tol : number, optional T

numpy.matlib.zeros(shape, dtype=None, order='C') [source] Return a matrix of given shape and type, filled with zeros. Parameters: shape : int or sequence of ints Shape of the matrix dtype : data-type, optional The desired data-type for the matrix, default is float. order : {?C?, ?F?}, optional Whether to store the result in C- or Fortran-contiguous order, default is ?C?. Returns: out : matrix Zero matrix of given shape, dtype, and order. See also numpy.zeros Equivalent array

Making proper sets unique(ar[, return_index, return_inverse, ...]) Find the unique elements of an array. Boolean operations in1d(ar1, ar2[, assume_unique, invert]) Test whether each element of a 1-D array is also present in a second array. intersect1d(ar1, ar2[, assume_unique]) Find the intersection of two arrays. setdiff1d(ar1, ar2[, assume_unique]) Find the set difference of two arrays. setxor1d(ar1, ar2[, assume_unique]) Find the set exclusive-or of two arrays. union1d(ar1, ar2) Fi

numpy.polynomial.laguerre.lagvander2d(x, y, deg) [source] Pseudo-Vandermonde matrix of given degrees. Returns the pseudo-Vandermonde matrix of degrees deg and sample points (x, y). The pseudo-Vandermonde matrix is defined by where 0 <= i <= deg[0] and 0 <= j <= deg[1]. The leading indices of V index the points (x, y) and the last index encodes the degrees of the Laguerre polynomials. If V = lagvander2d(x, y, [xdeg, ydeg]), then the columns of V correspond to the elements of a

Masked arrays are arrays that may have missing or invalid entries. The numpy.ma module provides a nearly work-alike replacement for numpy that supports data arrays with masks. The numpy.ma moduleRationale What is a masked array? The numpy.ma module Using numpy.maConstructing masked arrays Accessing the data Accessing the mask Accessing only the valid entries Modifying the mask Indexing and slicing Operations on masked arrays ExamplesData with a given value representing missing data Filli

matrix.I Returns the (multiplicative) inverse of invertible self. Parameters: None Returns: ret : matrix object If self is non-singular, ret is such that ret * self == self * ret == np.matrix(np.eye(self[0,:].size) all return True. Raises: numpy.linalg.LinAlgError: Singular matrix If self is singular. See also linalg.inv Examples >>> m = np.matrix('[1, 2; 3, 4]'); m matrix([[1, 2], [3, 4]]) >>> m.getI() matrix([[-2. , 1. ], [ 1.5, -0.5]]) >&

matrix.getA1() [source] Return self as a flattened ndarray. Equivalent to np.asarray(x).ravel() Parameters: None Returns: ret : ndarray self, 1-D, as an ndarray Examples >>> x = np.matrix(np.arange(12).reshape((3,4))); x matrix([[ 0, 1, 2, 3], [ 4, 5, 6, 7], [ 8, 9, 10, 11]]) >>> x.getA1() array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11])