Python:Pandas .agg()

The .agg() method in Pandas is used with groupby() to apply one or more aggregation functions (like sum, mean, count, etc.) to grouped data. It allows flexible and powerful summarization of DataFrame data after grouping.

Syntax

DataFrameGroupBy.agg(arg, *args, **kwargs)

Parameters:

• arg: Function, string, list, or dictionary specifying the aggregation(s) to apply. Examples: 'sum', 'mean', ['min', 'max'], or {'col1': 'sum', 'col2': 'mean'}
• *args: Additional positional arguments passed to the function.
• **kwargs: Additional keyword arguments passed to the function.

Return Value:

Returns a DataFrame or Series with the aggregated results for each group.

Example

In this example, the mean Salary, maximum Salary, and total Bonus are calculated for each Department using groupby().agg():

import pandas as pd

# Sample DataFrame
data = {
    'Department': ['Sales', 'Sales', 'HR', 'HR', 'IT', 'IT'],
    'Salary': [50000, 60000, 40000, 45000, 70000, 75000],
    'Bonus': [5000, 6000, 2000, 2500, 8000, 8500]
}

df = pd.DataFrame(data)

# Group by Department and calculate multiple aggregations
result = df.groupby('Department').agg({
    'Salary': ['mean', 'max'],
    'Bonus': 'sum'
})

print(result)

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This example produces the following output:

           Salary         Bonus
             mean    max    sum
Department
HR          42500  45000   4500
IT          72500  75000  16500
Sales       55000  60000  11000

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Codebyte Example

In this example, the average Points and total Assists are computed for each Team using groupby().agg():

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