With Numpy numpy.resize () we can resize the array. The array can be of any shape, but to resize it we just need the size, i.e. (2, 2) , (2, 3) and many others. When resizing, zero is added, if there are no values in a specific location.
new_shape: strong> [tuple of ints, or n ints] Shape of resized array
refcheck: [bool, optional] This parameter is used to check the reference counter. By Default it is True.
Most of you are now wondering what is the difference between reshaping strong> and resizing . When we talk about resizing, the array changes its shape as temporary, but when we talk about resizing, changes are made all the time.
Example # 1:
In this In the example, we see that using the
.resize() method we changed the shape of the array from 1 × 6 to 2 × 3 strong>.
[[1 2 3] [4 5 6 ]]
Example # 2:
In this example, we can see that we are trying to resize an array of a shape that is a type outside of bound values. But NumPy handles this situation by adding zeros when the values do not exist in the array.
[[1 2 3 4] [5 6 0 0] [ 0 0 0 0]]