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, otherwisethe 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 typicallynot contiguous. Furthermore, more than one element of abroadcasted array may refer to a single memory location. If you needto write to the arrays, make copies first. While you can set thewritable flag True, writing to a single output value may end upchanging more than one location in the output array.

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

Examples

>>>importnumpyasnp>>>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 ofnon-contiguous views.

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