The Numpy library in Python comes with a number of useful built-in functions for computing common descriptive statistics like mean, median, standard deviation, etc. In this tutorial, we will look at how to get the mean value of a Numpy array containing one or more NaN values.

## Can you use the `numpy.mean()`

function on an array with NaN values?

We use the `numpy.mean()`

function to get the mean (or the average) value of an array in Numpy. But what happens if the array contains one or more NaN values?

Let’s find out.

import numpy as np # create array ar = np.array([1, 2, np.nan, 3]) # get array mean print(np.mean(ar))

Output:

nan

Here, we created a one-dimensional Numpy array containing some numbers and a NaN value. We then applied the `numpy.mean()`

function which resulted in `nan`

. This happened because the `numpy.mean()`

function wasn’t able to handle the `nan`

value present in the array when computing the mean.

Thus, you cannot use the `numpy.mean()`

function to calculate the mean of an array with NaN values.

## How to ignore NaN values when calculating the mean of a Numpy array?

You can use the `numpy.nanmean()`

function to calculate the mean of a Numpy array containing NaN values. Pass the array as an argument.

The following is the syntax –

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# mean of array with nan values numpy.nanmean(ar)

It returns the mean value in the array ignoring all the NaN values.

Let’s look at some examples of using the `numpy.nanmean()`

function.

### Example 1 – Mean of one-dimensional array with NaN values

Let’s apply the `numpy.nanmean()`

function on the same array used in the example above.

# create array ar = np.array([1, 2, np.nan, 3]) # get array mean print(np.nanmean(ar))

Output:

2.0

We get the mean in the above array as 2.0. The `numpy.nanmean()`

function ignores the NaN values when computing the mean ((1+2+3)/3 = 2).

### Example 2 – Mean of multi-dimensional array with NaN values

The `numpy.nanmean()`

function is very similar to the `numpy.mean()`

function in its arguments. For example, use the `axis`

parameter to specify the axis along which to compute the mean.

First, let’s create a 2-D Numpy array.

# create 2-D numpy array ar = np.array([[1, np.nan, 3], [np.nan, 5, np.nan]]) # display the array print(ar)

Output:

[[ 1. nan 3.] [nan 5. nan]]

Here, we used the `numpy.array()`

function to create a Numpy array with two rows and three columns. You can see that there are some NaN values present in the array.

If you use the Numpy `nanmean()`

function on an array without specifying the axis, it will return the mean of all the values inside the array.

# mean of array print(np.nanmean(ar))

Output:

3.0

We get the mean of all the values inside the 2-D array.

Use the `numpy.nanmean()`

function with `axis=1`

to get the mean value for each row in the array.

# mean of each row in array print(np.nanmean(ar, axis=1))

Output:

[2. 5.]

We get the mean of each row in the above 2-D array. The mean of values in the first row is (1+3)/2 = 2 and the mean of values in the second row is 5/1 = 5.

Use the `numpy.nanmean()`

function with `axis=0`

to get the mean of each column in the array.

# mean of each column in array print(np.nanmean(ar, axis=0))

Output:

[1. 5. 3.]

We get the mean of each column in the above 2-D array. In this example, each column has one NaN value and one non-NaN value (which naturally becomes the mean since it’s the only value in the column).

## Summary – Mean of Numpy array with NaN values

The following is a short summary of the important points mentioned in this tutorial.

- Using the
`numpy.mean()`

function on an array with NaN values results in NaN. - Use the
`numpy.nanmean()`

function to get the mean value in an array containing one or more NaN values. It computes the mean by taking into account only the non-NaN values in the array. - Similar to the
`numpy.mean()`

function, you can specify the axis along which you want to compute the mean with the`numpy.nanmean()`

function.

You might also be interested in –

- Get the Mean of Numpy Array – (With Examples)
- Numpy – Get Standard Deviation of Array Values
- Extract the First N Elements of Numpy Array

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