transactions id | user_id | amount | timestamp | category 1 | 1001 | 19.99 | 2023-01-01 09:00:00 | grocery 2 | 1001 | 5.50 | 2023-01-02 11:12:30 | coffee 3 | 1002 | 45.00 | 2023-01-03 14:45:10 | electronics 4 | 1003 | 12.00 | 2023-01-01 08:30:25 | transport ##### Scenario You have a daily transactions dataset that must be cleaned and summarized for a finance dashboard. ##### Question Using pandas, apply a lambda function to create a new column flagging high-value purchases (amount > $ 40). Build a dictionary that maps the raw text in the category column to standardized names (e.g., 'grocery', 'food & bev', 'coffee' -> 'Food'). Loop through this dictionary to transform the DataFrame. Aggregate total spend and number of transactions per user_id and return a tidy DataFrame. ##### Hints Show vectorized pandas code; avoid row-by-row loops except for the dictionary mapping step.