# 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>)`

Code language: Python (python)

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)`

Code language: Python (python)

Output:

`2.0`

Code language: Python (python)

How it works.

First, create an array that has three numbers:

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

Code language: Python (python)

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

`average = np.mean(a)`

Code language: Python (python)

Third, display the average:

`print(average)`

Code language: Python (python)

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)`

Code language: Python (python)

Output:

`[2.5 3.5 4.5]`

Code language: Python (python)

## Summary

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