# numpy.squeeze() in Python

In this tutorial, we are going to learn one of the important functions i.e squeeze() of the numpy module in Python.

## numpy.squeeze() function in Python

numpy.squeeze() function is used when we want to remove one dimension in the multidimensional array.

For example, if the shape of the array is 3-dimension and we want the 2-dimension array, then we use squeeze() function to remove one dimension in array.

#### Syntax: numpy.squeeze

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

#### Parameter:

array = Like input array

axis = ( None or int or tuple of ints, optional). Axis parameter is to select the subset of the single dimension in the shape or multi-dimension.

### Let’s see the example of numpy.squeeze

Step 1: Import the numpy module as np

Step 2: Creating the one-dimensional array. np.arange(0,12) start from 0 to 12.

```import numpy as np

#one dimensional array
one_dimen = np.arange(0,12)
print("\nOne dimensional array:\n", one_dimen)```

Output:

```One dimensional array:
[ 0 1 2 3 4 5 6 7 8 9 10 11]```

Step 3: Now we convert the one-dimensional array into the two-dimensional array and three-dimensional array using np.reshape(3,4) which represents 3 rows and 4 columns and np.reshape(1,3,4) which represent 1 block, 3 rows and 4 columns.

```import numpy as np

#one dimensional array
one_dimen = np.arange(0,12)
print("\nOne dimensional array:\n", one_dimen)

#Two Dimensional Array
two_dimen = one_dimen.reshape(3,4)
print("\ntwo dimensional array:\n", two_dimen)
print("Shape of the two_dimen:", two_dimen.shape)

# Three Dimensional Array
three_dimen = one_dimen.reshape(1,3,4)
print("\nThree dimensional array:\n", three_dimen)
print("Shape of three_dimen:", three_dimen.shape)```

Output:

```One dimensional array:
[ 0 1 2 3 4 5 6 7 8 9 10 11]

two dimensional array:
[[ 0 1 2 3]
[ 4 5 6 7]
[ 8 9 10 11]]
Shape of the two_dimen: (3, 4)

Three dimensional array:
[[[ 0 1 2 3]
[ 4 5 6 7]
[ 8 9 10 11]]]
Shape of three_dimen: (1, 3, 4)```

Step 4: If some situation came where we want the 2-dimensional array from a 3-dimensional array. So now squeeze function come into existence.
By using squeeze function we remove one-dimension in three-dimensional array i.e from three_dimen in program.

```import numpy as np

#one dimensional array
one_dimen = np.arange(0,12)
print("\nOne dimensional array:\n", one_dimen)

#Two Dimensional Array
two_dimen = one_dimen.reshape(3,4)
print("\ntwo dimensional array:\n", two_dimen)
print("Shape of the two_dimen:", two_dimen.shape)

# Three Dimensional Array
three_dimen = one_dimen.reshape(1,3,4)
print("\nThree dimensional array:\n", three_dimen)
print("Shape of three_dimen:", three_dimen.shape)

# Removing the one dimension in the array
squeeze_three_dimen = np.squeeze(three_dimen, axis=0)
print("\nNew Squeezed Array of three_dimen:\n",squeeze_three_dimen)
print("Squeeze Shape:", squeeze_three_dimen.shape)```

Output:

```One dimensional array:
[ 0 1 2 3 4 5 6 7 8 9 10 11]

two dimensional array:
[[ 0  1  2  3]
[ 4  5  6  7]
[ 8  9 10 11]]
Shape of the two_dimen: (3, 4)

Three dimensional array:
[[[ 0 1 2 3]
[ 4 5 6 7]
[ 8 9 10 11]]]
Shape of three_dimen: (1, 3, 4)

New Squeezed Array of three_dimen:
[[ 0 1 2 3]
[ 4 5 6 7]
[ 8 9 10 11]]
Squeeze Shape: (3, 4)```

Here, in program line no. 17 show the shape of the three_dimen is  (1, 3, 4) which is three dimensional. But after using squeeze function the new dimension is ( 3, 4) that is two dimensional which shown in line no 21.

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