Aggregating data in Pandas: Multiple functions example

Last update on December 21 2024 07:43:26 (UTC/GMT +8 hours)

Aggregate data in a DataFrame by multiple functions.

Sample Solution:

Python Code:

import pandas as pd

# Create a sample DataFrame
data = {'Department': ['HR', 'IT', 'Finance', 'IT', 'HR', 'Finance'],
        'Salary': [50000, 60000, 45000, 70000, 55000, 60000],
        'Experience': [2, 5, 1, 7, 3, 4]}

df = pd.DataFrame(data)

# Group by 'Department' and aggregate data with multiple functions
aggregated_df = df.groupby('Department').agg({
    'Salary': ['mean', 'sum'],
    'Experience': 'max'
}).reset_index()

# Display the aggregated DataFrame
print(aggregated_df)

Output:

  Department   Salary         Experience
                 mean     sum        max
0    Finance  52500.0  105000          4
1         HR  52500.0  105000          3
2         IT  65000.0  130000          7

Explanation:

Here's a breakdown of the above code:

We create a sample DataFrame (df) with columns 'Department', 'Salary', and 'Experience'.
The df.groupby('Department') groups the DataFrame by the 'Department' column.
The agg() function is used to apply multiple aggregation functions to different columns. We calculate the mean and sum of 'Salary' and the maximum value of 'Experience'.
The result is stored in the "aggregated_df" DataFrame, and "reset_index()" is used to make the 'Department' column a regular column instead of an index.
The aggregated DataFrame is then printed.

Flowchart:

Python Code Editor:

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