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How to calculate standard error in Pandas

Here’s how to calculate standard error in Pandas.
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.

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