chararray.data Python buffer object pointing to the start of the array?s data.

chararray.decode(encoding=None, errors=None) [source] Calls str.decode element-wise. See also char.decode

chararray.count(sub, start=0, end=None) [source] Returns an array with the number of non-overlapping occurrences of substring sub in the range [start, end]. See also char.count

chararray.copy(order='C') Return a copy of the array. Parameters: order : {?C?, ?F?, ?A?, ?K?}, optional Controls the memory layout of the copy. ?C? means C-order, ?F? means F-order, ?A? means ?F? if a is Fortran contiguous, ?C? otherwise. ?K? means match the layout of a as closely as possible. (Note that this function and :func:numpy.copy are very similar, but have different default values for their order= arguments.) See also numpy.copy, numpy.copyto Examples >>> x = np.arr

chararray.base Base object if memory is from some other object. Examples The base of an array that owns its memory is None: >>> x = np.array([1,2,3,4]) >>> x.base is None True Slicing creates a view, whose memory is shared with x: >>> y = x[2:] >>> y.base is x True

Background The API exposed by NumPy for third-party extensions has grown over years of releases, and has allowed programmers to directly access NumPy functionality from C. This API can be best described as ?organic?. It has emerged from multiple competing desires and from multiple points of view over the years, strongly influenced by the desire to make it easy for users to move to NumPy from Numeric and Numarray. The core API originated with Numeric in 1995 and there are patterns such as the

busdaycalendar.weekmask A copy of the seven-element boolean mask indicating valid days.

chararray.astype(dtype, order='K', casting='unsafe', subok=True, copy=True) Copy of the array, cast to a specified type. Parameters: dtype : str or dtype Typecode or data-type to which the array is cast. order : {?C?, ?F?, ?A?, ?K?}, optional Controls the memory layout order of the result. ?C? means C order, ?F? means Fortran order, ?A? means ?F? order if all the arrays are Fortran contiguous, ?C? order otherwise, and ?K? means as close to the order the array elements appear in memory a

broadcast.size Total size of broadcasted result. Examples >>> x = np.array([1, 2, 3]) >>> y = np.array([[4], [5], [6]]) >>> b = np.broadcast(x, y) >>> b.size 9

broadcast.shape Shape of broadcasted result. Examples >>> x = np.array([1, 2, 3]) >>> y = np.array([[4], [5], [6]]) >>> b = np.broadcast(x, y) >>> b.shape (3, 3)