 # Python | Numpy numpy.resize ()

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.

Parameters:
new_shape: [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.

Returns: None

Most of you are now wondering what is the difference between reshaping 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 .

 ` # import python numpy module ` ` import ` ` numpy as np `   ` # Generate a random array ` ` gfg ` ` = ` ` np.array ( [` ` 1 ` `, ` ` 2 ` `, ` ` 3 ` `, ` 4 `, ` ` 5 ` `, ` ` 6 ` `]) `   ` # Change the array forever ` ` gfg.resize (` ` 2 ` `, ` ` 3 ` `) `   ` print ` ` (gfg) `

Exit:

` [[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.

 ` # import python numpy module ```` import numpy as np   # Generate a random array gfg = np.array ([ 1 , 2 , 3 , 4 , 5 , 6 ])   # Mandatory values ​​12, existing values ​​6 gfg.resize ( 3 , 4 )    print (gfg) ```

Exit:

` [[1 2 3 4] [5 6 0 0] [ 0 0 0 0]] `