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# NumPy | squeeze method

schedule Aug 11, 2023
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Numpy's `squeeze(~)` method returns a Numpy array with redundant axes removed. Check the examples below for clarification.

# Parameters

1. `a` | `array-like`

The input array.

2. `axis`link | `int` | `optional`

The axis you want to remove if redundant. By default, all redundant axises are removed.

# Return value

A new Numpy array with redundant axises removed.

# Examples

## Basic usage

Suppose we have the following 2D array:

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

Can you see how `a` can be expressed simply as an 1D array `[4,5,6]` in this case? The `squeeze(~)` method allows us to do this:

``` a.squeeze() array([4, 5, 6]) ```

The same principle applies for the following case:

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

Here, we have a 3D array, yet it's clear that this can be reduced to a 2D array, like so:

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

## Squeezing only a subset of axis

Suppose we have the following 3D array:

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

It's clear that this can be reduced to either a 2D array or even more compactly to a 1D array. We can specify which dimension to reduce to using the `axis` parameter.

Reducing to a 2D array:

``` np.squeeze(a, axis=0) array([[4],       [5],       [6]]) ```

Note that, by default, all redundant axises are removed:

``` np.squeeze(a) array([4, 5, 6]) ```
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