ndarray.swapaxes(axis1, axis2) Return a view of the array with axis1 and axis2 interchanged. Refer to numpy.swapaxes for full documentation. See also numpy.swapaxes equivalent function

ndarray.__iand__ x.__iand__(y) <==> x&=y

numpy.absolute(x[, out]) = Calculate the absolute value element-wise. Parameters: x : array_like Input array. Returns: absolute : ndarray An ndarray containing the absolute value of each element in x. For complex input, a + ib, the absolute value is . Examples >>> x = np.array([-1.2, 1.2]) >>> np.absolute(x) array([ 1.2, 1.2]) >>> np.absolute(1.2 + 1j) 1.5620499351813308 Plot the function over [-10, 10]: >>> import matplotlib.pyplot as plt >

dtype.flags Bit-flags describing how this data type is to be interpreted. Bit-masks are in numpy.core.multiarray as the constants ITEM_HASOBJECT, LIST_PICKLE, ITEM_IS_POINTER, NEEDS_INIT, NEEDS_PYAPI, USE_GETITEM, USE_SETITEM. A full explanation of these flags is in C-API documentation; they are largely useful for user-defined data-types.

numpy.polynomial.hermite_e.hermeint(c, m=1, k=[], lbnd=0, scl=1, axis=0) [source] Integrate a Hermite_e series. Returns the Hermite_e series coefficients c integrated m times from lbnd along axis. At each iteration the resulting series is multiplied by scl and an integration constant, k, is added. The scaling factor is for use in a linear change of variable. (?Buyer beware?: note that, depending on what one is doing, one may want scl to be the reciprocal of what one might expect; for more i

class numpy.flatiter [source] Flat iterator object to iterate over arrays. A flatiter iterator is returned by x.flat for any array x. It allows iterating over the array as if it were a 1-D array, either in a for-loop or by calling its next method. Iteration is done in row-major, C-style order (the last index varying the fastest). The iterator can also be indexed using basic slicing or advanced indexing. See also ndarray.flat Return a flat iterator over an array. ndarray.flatten Retur

numpy.ma.column_stack(tup) = Stack 1-D arrays as columns into a 2-D array. Take a sequence of 1-D arrays and stack them as columns to make a single 2-D array. 2-D arrays are stacked as-is, just like with hstack. 1-D arrays are turned into 2-D columns first. Parameters: tup : sequence of 1-D or 2-D arrays. Arrays to stack. All of them must have the same first dimension. Returns: stacked : 2-D array The array formed by stacking the given arrays. Notes The function is applied to both

ndarray.nonzero() Return the indices of the elements that are non-zero. Refer to numpy.nonzero for full documentation. See also numpy.nonzero equivalent function

numpy.isneginf(x, y=None) [source] Test element-wise for negative infinity, return result as bool array. Parameters: x : array_like The input array. y : array_like, optional A boolean array with the same shape and type as x to store the result. Returns: y : ndarray A boolean array with the same dimensions as the input. If second argument is not supplied then a numpy boolean array is returned with values True where the corresponding element of the input is negative infinity and value

MaskedArray.tobytes(fill_value=None, order='C') [source] Return the array data as a string containing the raw bytes in the array. The array is filled with a fill value before the string conversion. New in version 1.9.0. Parameters: fill_value : scalar, optional Value used to fill in the masked values. Deafult is None, in which case MaskedArray.fill_value is used. order : {?C?,?F?,?A?}, optional Order of the data item in the copy. Default is ?C?. ?C? ? C order (row major). ?F? ? Fortra