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# Python | Numpy numpy.resize ()

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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]] `

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