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The Python statistics.pstdev() function calculates the standard deviation for the entire population. A large standard deviation means that the data is widely spread out, while a small standard deviation means that the data is close to the mean. This function measures how spread out the numbers are.

A measure of mean data is dispersed in relation to the standard deviation. Unlike the variance, the standard deviation contains the same units as the data. The square root of the sample variance is the standard deviation.

Syntax

Following is the basic syntax for the statistics.pstdev() function.

statistics.pstdev(data, xbar)

Parameters

Here, the data values can be used as any sequence, list or x-bar in statistics, which is a symbol for the sample mean.

Return Value

This function returns the float value, i.e., representing the population standard deviation of the given data.

Example 1

In the below example, we are calculating the standard deviation of an entire population using the statistics.pstdev() function.

import statistics
x = statistics.pstdev([2, 4, 6, 8, 10])
print(x)

Output

The result is produced as follows −

2.8284271247461903

Example 2

Now, we are calculating the float values using the statistics.pstdev() function.

import statistics
x = statistics.pstdev([0.3, 5.5, 7.6, 0.34, 2.3])
print(x)

Output

We will get the output as shown below −

2.899975172307515

Example 3

Here, we are calculating the standard deviation using the statistics.pstdev() function.

import statistics
x = statistics.pstdev([1, 5, 6.10, 400, -0.4, -34])
print(x)

Output

When we execute the above code, we will get the following output −

151.3512223274064

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