NumPy mean()

Summary: in this tutorial, you’ll learn how to use the numpy mean() function to calculate the average of elements of an array.

Introduction to the NumPy mean() function #

The mean() function returns the average of elements in an array. Here’s the syntax of the mean() function:

numpy.mean(a, axis=None, dtype=None, out=None, keepdims=<no value>, *, where=<no value>)Copy

In this syntax:

• a is an array that you want to calculate the average of elements.
• axis is the axis if specified will return the average of elements on that axis.

To understand more about other parameters and their usages, check out the numpy mean() function documentation.

NumPy mean() function examples #

Let’s take some examples of using the mean() function.

1) Using NumPy mean() function on 1-D array example #

The following example uses the mean() function to calculate the average of numbers in an array:

import numpy as np

a = np.array([1, 2, 3])
average = np.mean(a)
print(average)Copy

Output:

2.0Copy

How it works.

First, create an array that has three numbers:

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

Second, calculate the average of elements in the array a using the mean() function:

average = np.mean(a)Copy

Third, display the average:

print(average)Copy

The output is 2.0 because (1 + 2 + 3) / 3 = 2.0

2) Using NumPy mean() function on 2-D array example #

The following example uses the mean() function to calculate the average of elements on axis-0:

import numpy as np

a = np.array([
    [1, 2, 3],
    [4, 5, 6]
])
average = np.mean(a, axis=0)
print(average)Copy

Output:

[2.5 3.5 4.5]Copy

Summary #

• Use the numpy mean() function to calculate the average of elements in an array.