Axes basically tell you the direction along rows and columns. The number of axes is called dimension of Numpy array.

The numbering of the axis starts from 0. For 1-D Numpy array, there is only one axis which is axis0. For 2-D Numpy array, there are two axes which are- axis0 and axis1. For 3-D Numpy array, there are three axes which are- axis0, axis1, and axis2.

In 1-Dimensional array, axis0 goes horizontally across the columns.

In 2-Dimensional array, axis0 goes vertically across the rows and axis1 goes horizontally across the columns.

You know that 3-Dimensional array is simply a collection of 2-Dimensional array. axis0 goes from one element of 2-Dimensional array to another element present just opposite to another 2-Dimensional array and so on, axis1 goes vertically across the rows and axis2 goes horizontally across the columns.

Axis is generally used in aggregate functions like `sum()`

, `max()`

, `min()`

, `mean()`

, etc. where we specify the axis along which we have to perform aggregation.

Let's see an example of how we can use `sum()`

function to perform addition-

import numpy as np a = np.array([1, 2, 3, 4, 5]) print('Numpy array a: \n', a) print('Sum of all the elements: ', np.sum(a, axis=0)) b = np.array([[1, 2, 3, 4], [5, 6, 7, 8]]) print('Numpy array b: \n', b) print('Sum of elements along axis 0: ', np.sum(b, axis=0)) print('Sum of elements along axis 1: ', np.sum(b, axis=1))

Output of the above program

Numpy array a: [1 2 3 4 5] Sum of all the elements: 15 Numpy array b: [[1 2 3 4] [5 6 7 8]] Sum of elements along axis 0: [ 6 8 10 12] Sum of elements along axis 1: [10 26]

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