Get Statistics For Each Group (such As Count, Mean, Etc) Using Pandas GroupBy?

You can use the groupby method in pandas to group your data by a certain column or a combination of columns, and then use various aggregation functions to get statistics for each group.

Here is an example of how you can use groupby to get count, mean, and other statistics for each group:

import pandas as pd

# Create a sample DataFrame
data = {
    'Group': ['A', 'A', 'A', 'B', 'B', 'C', 'C'],
    'Value': [10, 20, 30, 40, 50, 60, 70]
}
df = pd.DataFrame(data)

# Group by 'Group' column and calculate statistics
grouped = df.groupby('Group').agg(
    Count=('Value', 'count'),
    Mean=('Value', 'mean'),
    Min=('Value', 'min'),
    Max=('Value', 'max')
)

print(grouped)

Output:

       Count  Mean  Min  Max
Group                       
A          3  20.0   10   30
B          2  45.0   40   50
C          2  65.0   60   70

In this example, we group the DataFrame df by the ‘Group’ column. Then we use the agg method to apply aggregation functions to the ‘Value’ column for each group. We specify the desired statistics as tuples of the form (stat_name, column) where stat_name is the name of the statistic and column is the column on which the statistic should be computed.

You can add more statistics to the agg method based on your specific requirements.

About the Author Rex

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