Let us explore how this routine works and how we can use it.

## Syntax

The syntax of the numpy c_ routine is as shown below:

## Return Value

The routine does not take any parameters except the arrays that you need to concatenate.

It will then return the concatenated array along the second axis.

## Example Illustration

The example below illustrates how to use the np.c_ to concatenate two arrays.

# import numpy

import numpy as np

# create an array

arr1 = np.array([1,2,3])

arr2 = np.array([7,8,9])

print(np.c_[arr1, arr2])

In this example, the np.c_ routine takes the arrays and concatenates them along the second axis.

NOTE: When talking about the second axis, we refer to the axis=1 or the column axis.

The code above should return an array as:

In this case, the np.c_ takes two one-dimensional arrays and concatenates them to form a two-dimensional array.

## Example #2

Let us see what happens when we apply the routine in 2d arrays.

arr1 = np.array([[1,2,3,4], [5,6,7,8]])

arr2 = np.array([[9,10,11,12], [13,14,15,16]])

print(np.c_[arr1, arr2])

The code snippet above should return:

[[ 1 2 3 4 9 10 11 12]

[ 5 6 7 8 13 14 15 16]]

## Closing

This article aims to help you understand NumPy’s indexing routine np.c_ and how to use it.

Thanks for reading!!!