numpy.concatenate in detail, including its axis

When processing images and rejoining them after some operations, there are cases where you need to use the numpy concatenate function with different settings for the axis. Today, I intend to write about this.

numpy.concatenate is a function used in NumPy to combine (concatenate) arrays. This function allows you to specify the axis along which you want to concatenate arrays. The axis parameter is an integer that indicates the axis of the arrays to be concatenated. Here are some examples to illustrate the usage of the numpy.concatenate function:

First, let’s create two NumPy arrays as follows:

import numpy as np

arr1 = np.array([[1, 2], [3, 4]])
arr2 = np.array([[5, 6]])

1. Basic Concatenation (axis=0):
   • When you use axis=0, the arrays are concatenated along the vertical direction.

result = np.concatenate((arr1, arr2), axis=0)
print(result)

Result:

[[1 2]
 [3 4]
 [5 6]]

1. Concatenation with axis=1 (Horizontal):
   • Using axis=1, the arrays are concatenated horizontally.

result = np.concatenate((arr1, arr2.T), axis=1)  # Transpose arr2 to concatenate horizontally
print(result)

Result:

[[1 2 5]
 [3 4 6]]

1. Concatenating Multiple Arrays:
   • You can concatenate multiple arrays.

arr3 = np.array([[7, 8]])
result = np.concatenate((arr1, arr2, arr3), axis=0)
print(result)

Result:

[[1 2]
 [3 4]
 [5 6]
 [7 8]]

1. Concatenation without specifying axis:
   • If you don’t specify the axis parameter, it defaults to axis=0.

result = np.concatenate((arr1, arr2))  # Axis is not specified (default is axis=0)
print(result)

The result is the same as in the first example.

By using the numpy.concatenate function with the appropriate axis parameter, you can concatenate arrays in the desired direction.