GroupBy with Multiple Aggregations

Often you need multiple statistics per group. Use agg() to apply several functions at once.

df.groupby('category')['price'].agg(['mean', 'min', 'max', 'count'])

This gives mean, minimum, maximum, and count for each category.

Different aggregations per column:

df.groupby('store').agg({
    'sales': 'sum',
    'customers': 'mean',
    'rating': ['min', 'max']
})

Named aggregations for clean column names:

df.groupby('region').agg(
    total_sales=('sales', 'sum'),
    avg_order=('order_value', 'mean')
)

For comprehensive GroupBy techniques, see my Pandas course.