How to calculate standard error in Pandas

Here’s how to calculate standard error in Pandas.

How to calculate standard error in Pandas

To calculate a standard error in Pandas just use a sem method which Pandas is offering to you.

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 error of columns:\n{my_df.sem()}')

The standard error of columns:
my_column1    1.547848
my_column2    0.912871
my_column3    1.000000
dtype: float64

In Pandas, the sem method calculates the standard error of the mean for each column of a DataFrame or for each element in a Series. The method uses the formula mentioned above and returns the standard error of the mean for each column or element.

For more details see the documentation of sem function.