sales +----------+-------------+------------+---------+---------+----------------+ | order_id | customer_id | order_date | country | revenue | product_category| +----------+-------------+------------+---------+---------+----------------+ | 1001 | 501 | 2023-08-01 | US | 250.00 | Accessories | | 1002 | 502 | 2023-08-02 | UK | 180.00 | Apparel | | 1003 | 503 | 2023-08-02 | DE | 310.00 | Electronics | | 1004 | 501 | 2023-08-03 | US | 95.00 | Accessories | | 1005 | 504 | 2023-08-04 | CA | 420.00 | Electronics | +----------+-------------+------------+---------+---------+----------------+ ##### Scenario You receive a raw ecommerce sales table exported from multiple regional warehouses and must create a clean dataset for analysts. ##### Question Use Pandas to retrieve all European orders placed in the last 30 days and compute their total revenue. 2. Identify and remove duplicated orders, then explain how you would impute any missing revenue values. 3. Produce summary statistics and two visual insights that highlight sales trends by product_category. ##### Hints Pandas filtering, groupby-agg, isnull, fillna, duplicated, matplotlib/seaborn.