Python | Grouping data in Python: Techniques for grouping data by multiple columns

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Grouping by multiple variables

To group by multiple variables in Python using pandas, you can pass a list of column names to the groupby() method.

Assuming you have a pandas DataFrame with columns that you want to group by and summarize, you can use the following syntax:

import pandas as pd

# create a pandas DataFrame

df = pd.DataFrame({'Group1': ['A', 'B', 'C', 'A', 'B', 'C'],

'Group2': ['X', 'X', 'Y', 'Y', 'Z', 'Z'],

'Value': [1, 2, 3, 4, 5, 6]})

# group by the 'Group1' and 'Group2' columns and calculate the mean of the 'Value' column

grouped = df.groupby(['Group1', 'Group2']).mean()

print(grouped)

This will group the DataFrame by the 'Group1' and 'Group2' columns and calculate the mean of the 'Value' column for each group. The resulting output will be:

Value

Group1 Group2

A Y 4.0

X 1.0

B X 2.0

Z 5.0

C Y 3.0

Z 6.0

You can also use other aggregation functions like sum(), count(), min(), max(), etc. to summarize your data by group. Additionally, you can pass a dictionary of column names and aggregation functions to the agg() method to calculate multiple summaries for each group.