numpy.broadcast_arrays¶

numpy.broadcast_arrays(*args,**kwargs)[source]¶

Broadcast any number of arrays against each other.

Parameters:	`*args`:array_likes The arrays to broadcast. subok:bool, optional If True, then sub-classes will be passed-through, otherwisethe returned arrays will be forced to be a base-class array (default).
Returns:	broadcasted:list 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 youneed to write to the arrays, make copies first.

Examples

>>>x=np.array([[1,2,3]])>>>y=np.array([[1],[2],[3]])>>>np.broadcast_arrays(x,y)[array([[1, 2, 3],       [1, 2, 3],       [1, 2, 3]]), array([[1, 1, 1],       [2, 2, 2],       [3, 3, 3]])]

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],       [1, 2, 3]]), array([[1, 1, 1],       [2, 2, 2],       [3, 3, 3]])]