Pandas GroupBy Aggregation on Multiple DataFrames

Problem: GroupBy Aggregation Across Three DataFrames (Pandas) You are given three pandas.DataFrames (df1, df2, df3) containing Uber Eats order/item related data. You need to join/merge them into an analysis-ready table and use groupby plus aggregation functions to compute metrics. Requirements Merge the three DataFrames using appropriate keys (state which keys you used). Compute the average sales for each category. Return a DataFrame/table with: columns: category, avg_sales sorted by avg_sales in descending order. Notes If an order can have multiple item rows, clarify the definition of “sales” you are using (item-level vs order-level). Only the core analysis logic is required. Scale assumptions Up to 10^6 rows per DataFrame. Consider time/space efficiency. Example (one possible schema) df1(order_id, item_id, category, item_sales) df2(order_id, user_id, is_completed) df3(item_id, merchant_id) Compute the mean of item_sales per category. Example Input df1: [(1,'a','alcohol',10),(1,'b','food',20),(2,'c','alcohol',30)] df2: [(1,101,1),(2,102,0)] df3: [('a',900),('b',901),('c',902)] Output category avg_sales food 20.0 alcohol 10.0 # if filtering to completed orders only (order_id=1)