The expand_dims() function in NumPy allows us to expand the dimensions of a given input array. In simple terms, the function enables you to expand the shape of a given array.

It works by adding a new axis into the array that will appear at the axis position resulting in an expanded shape.

Let us explore how this function works.

## Function Syntax

The function syntax is as shown below:

1 | numpy.expand_dims(a, axis) |

The function has a relatively simple syntax. It accepts the parameters as shown:

**a**– refers to the input array.**axis**– specifies the position in the output array where the axis is positioned.

## Return Value

The function returns a view of the input array with the dimensions expanded according to the specified parameters.

## Example

Consider the example code shown below:

1 2 3 4 5 6 | # import numpy import numpy as np arr = np.array([1,2,3,4]) print(f"before: {arr.shape}") arr = np.expand_dims(arr, axis=1) print(f"after: {arr.shape}") |

The example above uses the expand_dims() function to expand the shape of a one-dimensional array.

An example output is as shown:

1 2 | before: (4,) after: (4, 1) |

## Example 2

We can also perform the same operation on a two-dimensional array. An example is as shown:

1 2 3 4 | arr = np.array([[1,2,3], [4,5,6]]) print(f"before: {arr.shape}") arr = np.expand_dims(arr, axis=0) print(f"after: {arr.shape}") |

The resulting output are as shown:

1 2 | before: (2, 3) after: (1, 2, 3) |

## Conclusion

This tutorial illustrates how to use the expand_dims() function in NumPy to alter the shape of an input array.

Happy coding, my friends!!