Multiple Named Aggregations with groupby().agg() in Pandas

Question

Given an `orders_df` (department, order_id, customer_id, revenue), write a query to compute: total revenue (`total_rev`), average revenue (`avg_rev`), total orders (`order_count`), and unique customers (`unique_buyers`) per department.

Accepted Answer

Optimal Solution:
import pandas as pd

summary_df = df.groupby('department', as_index=False).agg(
    total_rev=('revenue', 'sum'),
    avg_rev=('revenue', 'mean'),
    order_count=('order_id', 'count'),
    unique_buyers=('customer_id', 'nunique')
)

Technical Explanation:
Named aggregation syntax `new_col_name=('source_col', 'agg_func')` creates flat, descriptive column headers directly without ugly multi-level tuples.

department	order_id	customer_id	revenue
IT	101	1	5000
IT	102	2	7000
Sales	103	1	3000

department	total_rev	avg_rev	order_count	unique_buyers
IT	12000	6000	2	2
Sales	3000	3000	1	1