pandas groupby apply multiple aggregation functions on dataframe columns

example.py

import pandas as pd
import numpy as np

# Create a sample DataFrame
data = {'category': ['A', 'A', 'B', 'B', 'A', 'B'],
        'value': [10, 20, 15, 25, 30, 35]}
df = pd.DataFrame(data)

# Group by 'category' and apply multiple aggregation functions
agg_functions = {
    'value': [
        ('_mean', 'mean'),
        ('_std', 'std'),
        ('_min', 'min'),
        ('_max', 'max'),
        ('_5th_percentile', lambda x: np.percentile(x, 5)),
        ('_95th_percentile', lambda x: np.percentile(x, 95))
    ]
}

result_df = df.groupby('category').agg(agg_functions)
result_df.columns = [f'{column[0]}{column[1]}' for column in result_df.columns]

result_df = result_df.reset_index()

result_df