Problem: Transform and Aggregate Messy Event Data

This take-home project requires you to process user and event data using the pandas library, specifically leveraging vectorized operations without explicit loops. The objective is to clean, combine, and aggregate this data to produce country and plan-level metrics for a specific date: 2025-08-31.

Two initial DataFrames are provided:

• users: Contains user_id, signup_date, plan, monthly_fee, and country.
• events_august: Contains user_id, event_time, event_type, and amount.

A third DataFrame, events_august_extra, has the same fields as events_august but with a shuffled column order.

Tasks:

1. Data Type Conversion and Cleaning:
   • Convert users.signup_date and all event_time columns to pandas datetime objects.
   • For monthly_fee and amount columns, strip any currency symbols and whitespace, then coerce them to float type. Any conversion errors should result in NaN values.
2. Event Data Consolidation:
   • Align the columns of events_august and events_august_extra.
   • Concatenate these two DataFrames into a single events_all DataFrame.
3. Data Integration:
   • Join the users DataFrame with the events_all DataFrame using user_id as the key.
4. Metric Computation:
   • For the date 2025-08-31 only, compute the following metrics, grouped by country and plan:
     • active_users: The count of distinct users who had at least one 'login' event.
     • purchasers: The count of distinct users who had at least one 'purchase' event.
     • purchases_count: The total number of 'purchase' events.
     • revenue: The sum of amount for all 'purchase' events.
5. Output Format:
   • Return a tidy DataFrame with the columns [date, country, plan, active_users, purchasers, purchases_count, revenue].
   • The resulting DataFrame should be sorted first by country in ascending order, then by plan in ascending order.
   • The solution must explicitly state any assumptions made regarding missing amount values and timezone handling.