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# NumPy | resize method

schedule Aug 11, 2023
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Numpy's `resize(~)` method returns a new Numpy array with the desired shape. If the reshaped array contains more values than the original array, then numbers will be repeated.

This is equivalent to Numpy's `reshape(~)` method, just without the `order` parameter.

# Parameters

1. `a` | `array-like`

The input array.

2. `new_shape` | `int` or `tuple` of `int`

The desired shape of the array.

# Return value

A new Numpy array with the desired shape.

WARNING

The behaviour of a.resize(~) is different

This documentation covers the method `np.resize(~)`, which has a different behaviour from `a.resize(~)` where `a` is the source array.

• Firstly, the `a.resize(~)` method performs the resizing in-place, that is, the original array is directly modified without the creation of a new array.

• Secondly, instead of numbers being repeated in cases where the reshaped array contains more values than the original array, zeros are added.

# Examples

## Going from 1D to 2D

``` a = np.array([4,5,6,7])np.resize(a, (2,2)) array([[4, 5], [6, 7]]) ```

### Case when values are repeated

Values are repeated when resized array contains more values than the original array:

``` np.resize([4,5], (2,2)) array([[4, 5], [4, 5]]) ```

Notice how the numbers are simply repeated.

## Going from 2D to 1D

Consider the following:

``` a = np.array([[1,2],[3,4]])a array([[1, 2], [3, 4]]) ```

To obtain the 1D representation:

``` np.resize(a, 4) array([1, 2, 3, 4]) ```
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