numpy.broadcast_arrays — NumPy v2.2 Manual (original) (raw)

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 thewritable 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 thewritable flag False so writing to it will raise an error.

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]])]