The numpy.broadcast_to() function is used to produce an object that mimics broadcasting.

This function is useful when we want to broadcast an array to a larger shape or for arithmetic operations on arrays with different shapes.

Syntax:

`numpy.broadcast_to(array, shape, subok=False)`

Parameters:

Name Description Required /
Optional
array The array to broadcast. Required
shape The shape of the desired array. Required
subok If True, then sub-classes will be passed-through, otherwise the returned array will be forced to be a base-class array (default). Optional

Return value:

If True, then sub-classes will be passed-through, otherwise the returned array will be forced to be a base-class array (default).

Raises: ValueError - If the array is not compatible with the new shape according to NumPy’s broadcasting rules.

``````>>> import numpy as np
>>> a = np.array([2,3,4])
array([[2, 3, 4],
[2, 3, 4],
[2, 3, 4]])
``````

In the above code, an array 'a' is created with the values [2, 3, 4]. The numpy.broadcast_to() function is then used to create a new array with shape (3, 3) by repeating the values of 'a'. The resulting array has the values [[2, 3, 4], [2, 3, 4], [2, 3, 4]].

Pictorial Presentation:

``````>>> import numpy as np
>>> x = np.ma.array([2, 3, 4], mask=[False, True, False])
>>> y = np.broadcast_to(x, (3, 3), subok=True)
False
>>> y
[[2 -- 4]
[2 -- 4]
[2 -- 4]],
[[False  True False]
[False  True False]
[False  True False]],
fill_value = 999999)
``````

In the above code, first a masked array 'x' is created using the numpy.ma.array() function. The mask parameter specifies which elements of the array are masked (in this case, the second element is masked).
Then, numpy.broadcast_to() is used to create a new array y with the same shape as a 3x3 matrix. The subok parameter is set to True to allow the output to be a masked array.
Next, the mask of y is set to a broadcasted version of the mask of x, which ensures that the masked elements are propagated to the new array.

Python - NumPy Code Editor: