You will often need to compute the standard deviation of a DataFrame column.

Furthermore, in statistics the standard deviation is referred as sigma.

An quite famous analysis is to approximate the range of a value given its two sigma value.

Why ?

Well as you can see 95% of your data will be located between our mu - 2 sigma and mu + 2 sigma.

So it is most probable that our variable value will end up in this range.

Let's have a look at real world example data.

Reading an example dataframe

import pandas as pd

# We read a sample dataset from the web.
df = pd.read_csv('https://raw.githubusercontent.com/mwaskom/seaborn-data/master/iris.csv')

Here we have a example dataset that is about iris flowers.

If we look at the sepal_length of versicolor irises and compute the mean and the std.

This is how we compute the standard deviation using the DataFrame.std() method.

Computing the standard deviation

sigma = df[df["species"] == "versicolor"]["sepal_length"].std()

Computing the two sigma range

mu = df[df["species"] == "versicolor"]["sepal_length"].mean()

upper_bound = mu + 2 * sigma
lower_bound = mu - 2 * sigma

print(f"The sepal length of a versicolor does have a high probability of ending up from {lower_bound} to {upper_bound}")

Here you are, you now know how to compute the two sigma range and will be able to perform statistical tests about it.