How to calculate standard deviation in Pandas

Here’s how to calculate standard deviation in Pandas.

How to calculate standard deviation in Pandas

To calculate a standard deviation in Pandas just use a std method which Pandas is offering to you. The std method calculates the standard deviation of the values in the column, and returns a single value.

import pandas as pd

my_df = pd.DataFrame({"my_column1": [9, 2, 3, 5],
                     "my_column2": [3, 7, 6, 4],
                      "my_column3": [4, 8, 8, 8]})

print(f'The standard deviation of columns:\n{my_df.std()}')

The standard deviation of columns:
my_column1    3.095696
my_column2    1.825742
my_column3    2.000000
dtype: float64

By default, the std method calculates the standard deviation using the sample standard deviation, which divides the sum of squared deviations by the number of values minus 1. If you want to calculate the population standard deviation, you can set the ddof parameter to 0:

standard_deviation = my_df['column_name'].std(ddof=0)

You can also calculate the standard deviation using the describe method of the Pandas dataframe:

statistics = my_df['column_name'].describe()
standard_deviation = statistics['std']

The describe method calculates various summary statistics for the values in the column, including the mean, standard deviation, minimum, maximum, and quartiles. By accessing the ‘std’ key in the returned statistics, you can get the standard deviation.

For more details see the documentation of std function.