Python | Advanced data analysis with groupby in Python: How to compute multiple summary statistics simultaneously

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Multiple grouped summaries

To perform multiple grouped summaries in Python using pandas, you can use the groupby() method in combination with the agg() 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({'Group': ['A', 'B', 'C', 'A', 'B', 'C'],

'Value1': [1, 2, 3, 4, 5, 6],

'Value2': [10, 20, 30, 40, 50, 60]})

# group by the 'Group' column and calculate the mean of the 'Value1' column and sum of the 'Value2' column

grouped = df.groupby('Group').agg({'Value1': 'mean', 'Value2': 'sum'})

print(grouped)

This will group the DataFrame by the 'Group' column and calculate the mean of the 'Value1' column and sum of the 'Value2' column for each group. The resulting output will be:

Value1 Value2

Group

A 2.5 50

B 3.5 70

C 4.5 90

You can add as many columns and aggregation functions as you need inside the dictionary passed to the agg() method.