# numpy.squeeze() Method | Why do we need numpy.squeeze()?

Learn about the numpy.squeeze() method and why do we need this method?
By Pranit Sharma Last updated : December 22, 2023

NumPy is an abbreviated form of Numerical Python. It is used for different types of scientific operations in python. Numpy is a vast library in python which is used for almost every kind of scientific or mathematical operation. It is itself an array which is a collection of various methods and functions for processing the arrays.

## Python numpy.squeeze() method

The numpy.squeeze() is used to remove axes of length one from an array. It takes an input array (arr) and an axis parameter. It selects a subset of the entries of length one in the shape. If an axis is selected with a shape entry greater than one, an error is raised.

## Syntax of numpy.squeeze() method

Below is the syntax of numpy.squeeze() method:

```numpy.squeeze(a, axis=None)
```

## Parameters of numpy.squeeze() method

Here are the list of parameters of numpy.squeeze() method:

• a: An array-like input data.
• axis: It is an optional parameter, selects a subset of the entries of length one in the shape.

## Return value of numpy.squeeze() method

It returns the same input array as an output but with all or a subset of the dimensions of length 1 removed. This is always arr itself or a view into arr. An important point is that if all axes are squeezed, the result is a 0d array and not a scalar.

Let us understand with the help of an example,

## Example of numpy.squeeze() method

### Python code to demonstrate that why do we need numpy.squeeze()

```# Import numpy
import numpy as np

# Creating a numpy array
arr = np.array([[[0], [1], [2]]])

# Display original array
print("Original Array:\n",arr,"\n")

# First look at the shape of the array
print("Shape of array:\n",arr.shape,"\n")

# Removing an axes of length one using squeeze
res = np.squeeze(arr, axis=0).shape

# Display result
print("Squeezed array shape:\n",res,"\n")
```