chararray.resize(new_shape, refcheck=True) Change shape and size of array in-place. Parameters: new_shape : tuple of ints, or n ints Shape of resized array. refcheck : bool, optional If False, reference count will not be checked. Default is True. Returns: None Raises: ValueError If a does not own its own data or references or views to it exist, and the data memory must be changed. SystemError If the order keyword argument is specified. This behaviour is a bug in NumPy. See al

chararray.reshape(shape, order='C') Returns an array containing the same data with a new shape. Refer to numpy.reshape for full documentation. See also numpy.reshape equivalent function

chararray.replace(old, new, count=None) [source] For each element in self, return a copy of the string with all occurrences of substring old replaced by new. See also char.replace

chararray.repeat(repeats, axis=None) Repeat elements of an array. Refer to numpy.repeat for full documentation. See also numpy.repeat equivalent function

chararray.real The real part of the array. See also numpy.real equivalent function Examples >>> x = np.sqrt([1+0j, 0+1j]) >>> x.real array([ 1. , 0.70710678]) >>> x.real.dtype dtype('float64')

chararray.ravel([order]) Return a flattened array. Refer to numpy.ravel for full documentation. See also numpy.ravel equivalent function ndarray.flat a flat iterator on the array.

chararray.put(indices, values, mode='raise') Set a.flat[n] = values[n] for all n in indices. Refer to numpy.put for full documentation. See also numpy.put equivalent function

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

chararray.ndim Number of array dimensions. Examples >>> x = np.array([1, 2, 3]) >>> x.ndim 1 >>> y = np.zeros((2, 3, 4)) >>> y.ndim 3

chararray.nbytes Total bytes consumed by the elements of the array. Notes Does not include memory consumed by non-element attributes of the array object. Examples >>> x = np.zeros((3,5,2), dtype=np.complex128) >>> x.nbytes 480 >>> np.prod(x.shape) * x.itemsize 480