How to use NumPy concatenate() in Python?

Numpy concatenate() is not a database join. It is basically stacking Numpy arrays either vertically or horizontally.

Syntax for Numpy concatenate()

np.concatenate((a1, a2, ...), axis=0)

(a1, a2, ...) parameter is used to pass more than one Numpy arrays. Here you pass arrays in the form of Python tuple or Python list.

axis parameter is used to specify the axis along which you want to perform concatenation. Its default value is 1.

Python program to concatenate 1-Dimensional Numpy Array

import numpy as np

a = np.array([1, 2, 3, 4, 5])
b = np.array([16, 17, 18, 19, 20])
print('Concatenate 1-D array:\n', np.concatenate([a, b]))

Output

Concatenate 1-D array:
 [ 1  2  3  4  5 16 17 18 19 20]

As you can see in the above output, np.concatenate() has concatenated two Numpy arrays. In 1-dimensional array, it is optional to provide axis parameter to concatenate() function.

Python program to concatenate 2-Dimensional Numpy Array

import numpy as np

a = np.array([[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]])
b = np.array([[11, 12, 13, 14, 15], [16, 17, 18, 19, 20]])
print('Concatenate along axis=0:\n', np.concatenate((a,b), axis=0))
print('Concatenate along axis=1:\n', np.concatenate([a,b], axis=1))

Output

Concatenate along axis=0:
 [[ 1  2  3  4  5]
 [ 6  7  8  9 10]
 [11 12 13 14 15]
 [16 17 18 19 20]]
Concatenate along axis=1:
 [[ 1  2  3  4  5 11 12 13 14 15]
 [ 6  7  8  9 10 16 17 18 19 20]]

When axis=0 is passed to concatenate() function then it performs vertical stacking. In other words, it is called concatenating Numpy arrays vertically.

When axis=1 is passed to concatenate() function then it performs horizontal stacking. In other words, it is called concatenating Numpy arrays horizontally.

Recommended Posts

• What is NumPy Horizontal Stacking?
• What is NumPy Vertical Stacking?
• What is NumPy Depth Stacking?
• What is NumPy Column Stacking?
• What is NumPy Row Stacking?