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 standard deviation of values in a Numpy array containing one or more NaN values.

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

function on an array with NaN values?

We use the `numpy.std()`

function to get the standard deviation of values in a Numpy array. 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 std dev print(np.std(ar))

Output:

nan

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

function which resulted in `nan`

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

function wasn’t able to handle the `nan`

value present in the array when computing the standard deviation.

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

function to calculate the standard deviation of an array with NaN values.

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

You can use the `numpy.nanstd()`

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

The following is the syntax –

# std dev of array with nan values numpy.nanstd(ar)

It returns the standard deviation among all the values in the array ignoring all the NaN values.

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

function.

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

Let’s apply the `numpy.nanstd()`

function on the same array used in the example above.

# create array ar = np.array([1, 2, np.nan, 3]) # get array std dev print(np.nanstd(ar))

Output:

0.816496580927726

We get the standard deviation in the above array as approximately 0.82. The `numpy.nanstd()`

function ignores the NaN values when computing the standard deviation.

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

The `numpy.nanstd()`

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

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

parameter to specify the axis along which to compute the standard deviation.

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 `nanstd()`

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

# std dev of array print(np.nanstd(ar))

Output:

1.632993161855452

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

Use the `numpy.nanstd()`

function with `axis=1`

to get the standard deviation for each row in the array.

# std dev of each row in array print(np.nanstd(ar, axis=1))

Output:

[1. 0.]

We get the standard deviation of each row in the above 2-D array. The standard deviation of values in the first row is 1 and in the second row is 0.

Use the `numpy.nanstd()`

function with `axis=0`

to get the standard deviation of each column in the array.

# std dev of each column in array print(np.nanstd(ar, axis=0))

Output:

[0. 0. 0.]

We get the standard deviation of each column in the above 2-D array. In this example, each column has one NaN value and one non-NaN value (thus we get 0 as the standard deviation as there’s only one unique value in the column).

## Summary

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

- Using the
`numpy.std()`

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

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

function, you can specify the axis along which you want to compute the standard deviation with the`numpy.nanstd()`

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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