chararray.T Same as self.transpose(), except that self is returned if self.ndim < 2. Examples >>> x = np.array([[1.,2.],[3.,4.]]) >>> x array([[ 1., 2.], [ 3., 4.]]) >>> x.T array([[ 1., 3.], [ 2., 4.]]) >>> x = np.array([1.,2.,3.,4.]) >>> x array([ 1., 2., 3., 4.]) >>> x.T array([ 1., 2., 3., 4.])

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

chararray.strip(chars=None) [source] For each element in self, return a copy with the leading and trailing characters removed. See also char.strip

chararray.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

chararray.strides Tuple of bytes to step in each dimension when traversing an array. The byte offset of element (i[0], i[1], ..., i[n]) in an array a is: offset = sum(np.array(i) * a.strides) A more detailed explanation of strides can be found in the ?ndarray.rst? file in the NumPy reference guide. See also numpy.lib.stride_tricks.as_strided Notes Imagine an array of 32-bit integers (each 4 bytes): x = np.array([[0, 1, 2, 3, 4], [5, 6, 7, 8, 9]], dtype=np.int32) This array

chararray.startswith(prefix, start=0, end=None) [source] Returns a boolean array which is True where the string element in self starts with prefix, otherwise False. See also char.startswith

chararray.splitlines(keepends=None) [source] For each element in self, return a list of the lines in the element, breaking at line boundaries. See also char.splitlines

chararray.split(sep=None, maxsplit=None) [source] For each element in self, return a list of the words in the string, using sep as the delimiter string. See also char.split

chararray.squeeze(axis=None) Remove single-dimensional entries from the shape of a. Refer to numpy.squeeze for full documentation. See also numpy.squeeze equivalent function

chararray.size Number of elements in the array. Equivalent to np.prod(a.shape), i.e., the product of the array?s dimensions. Examples >>> x = np.zeros((3, 5, 2), dtype=np.complex128) >>> x.size 30 >>> np.prod(x.shape) 30