numpy.broadcast_arrays#

numpy.broadcast_arrays(*args, subok=False)[source]#

Broadcast any number of arrays against each other.

Parameters:

*argsarray_likes: The arrays to broadcast.
subokbool, optional: If True, then sub-classes will be passed-through, otherwise the returned arrays will be forced to be a base-class array (default).

Returns:

broadcastedtuple of arrays: These arrays are views on the original arrays. They are typically not contiguous. Furthermore, more than one element of a broadcasted array may refer to a single memory location. If you need to write to the arrays, make copies first. While you can set the writable flag True, writing to a single output value may end up changing more than one location in the output array.

Deprecated since version 1.17: The output is currently marked so that if written to, a deprecation warning will be emitted. A future version will set the writable flag False so writing to it will raise an error.

See also

broadcast
broadcast_to
broadcast_shapes

Examples

>>> import numpy as np
>>> x = np.array([[1,2,3]])
>>> y = np.array([[4],[5]])
>>> np.broadcast_arrays(x, y)
(array([[1, 2, 3],
        [1, 2, 3]]),
 array([[4, 4, 4],
        [5, 5, 5]]))

Here is a useful idiom for getting contiguous copies instead of non-contiguous views.

>>> [np.array(a) for a in np.broadcast_arrays(x, y)]
[array([[1, 2, 3],
        [1, 2, 3]]),
 array([[4, 4, 4],
        [5, 5, 5]])]