numpy.resize

numpy.resize(a, new_shape)

Return a new array with the specified shape.

If the new array is larger than the original array, then the new array is filled with repeated copied of a. Note that this behavior is different from a.resize(new_shape) which fills with zeros instead of repeated copies of a.

Parameters:

a : array_like

Array to be resized.

new_shape : {tuple, int}

Shape of resized array.

Returns:

reshaped_array : ndarray

The new array is formed from the data in the old array, repeated if necessary to fill out the required number of elements. The data are repeated in the order that the data are stored in memory.

See also

ndarray.resize

resize an array in-place.

Examples

>>> a=np.array([[0,1],[2,3]])
>>> np.resize(a,(1,4))
array([[0, 1, 2, 3]])
>>> np.resize(a,(2,4))
array([[0, 1, 2, 3],
       [0, 1, 2, 3]])