Let’s learn how to stack arrays in Numpy Python library.

Arrays are stackable. I’ll show how to stack array vertically and horizontally in Python Numpy.

To stack arrays vertically use Numpy vstack function.

import numpy as np my_array = np.array([[0, 1], [2, 4], [5, 6]]) my_second_array = np.array([[1, 4], [3, 7], [5, 8]]) stacked_array = np.vstack((my_array, my_second_array)) print(f"Arrays stacked vertically: \n {stacked_array}")

Similarlly you can stack array horizontally with Numpy hstack function.

import numpy as np my_array = np.array([[0, 1], [2, 4], [5, 6]]) my_second_array = np.array([[1, 4], [3, 7], [5, 8]]) stacked_array = np.hstack((my_array, my_second_array)) print(f"Arrays stacked horizontally: \n {stacked_array}")

There is also a possible to stack array based on dimensions configured by Numpy stack axis parameter.

Let’s see above example with two different axis parameters based on -1 and 1 dimension of stacking.

import numpy as np my_array = np.array([[0, 1], [2, 4], [5, 6]]) my_second_array = np.array([[1, 4], [3, 7], [5, 8]]) stacked_array = np.stack((my_array, my_second_array), axis=-1) print(f"Arrays stacked using axis=-1: \n {stacked_array}") stacked_array = np.stack((my_array, my_second_array), axis=1) print(f"Arrays stacked using axis=1: \n {stacked_array}")

As you can see there are difference in outputs depends on axis parameter chosen.

For 3-dimension axes you can set axis parameter between -3 and 3 (including 0). For every other value you will get different outputs.

The other stacking possibility is also third axis stack. There is Numpy dstack function for that.

import numpy as np my_array = np.array([[0, 1], [2, 4], [5, 6]]) my_second_array = np.array([[1, 4], [3, 7], [5, 8]]) stacked_array = np.dstack((my_array, my_second_array)) print(f"Third axis stack: \n {stacked_array}")

Now you know so many ways to stack arrays in Python Numpy.