##### Question You are given a transaction-level table and must compute a weekly time series of how many customers have reached a year-to-date spending threshold. The problem can be solved in **SQL** or **Python (pandas)**. ## Table **transactions** - `date` (DATE) — transaction date (assume UTC, calendar dates) - `transaction_id` (STRING/INT) — primary key - `customer_id` (STRING/INT) - `dollars` (NUMERIC) — non-negative spend amount in USD ## Definitions - **Week**: a calendar week starting on Monday. Report each week by its `week_start_date` (the Monday of the week). You may pick a different, consistent week boundary if you state it. - **Year-to-date (YTD) spend as of a week**: for a given `customer_id` in calendar year `Y`, the sum of `dollars` from **Jan 1 of year Y** up to and including the **last day of that week**. ## Task For each week present in the data, compute the **number of distinct customers** whose **YTD spend as of that week** is **at least $1000**. 1. **Core metric.** For every week, count the distinct customers whose cumulative spend from the start of that calendar year through the end of the week is ≥ $1000. 2. **Multi-year handling.** If the data spans multiple calendar years, compute the metric **independently per year** — YTD resets to $0 on Jan 1 of each year. Include the `year` in the output. 3. **Ordering.** Order the results by `year`, then `week_start_date`. ## Output Return a table with: - `year` (INT) - `week_start_date` (DATE) - `num_customers_ge_1000_ytd` (INT) (If you assume a single year, the original minimal output of `week_start` and `num_customers_ge_1000_ytd` is also acceptable — the `year` column is what generalizes it to multiple years.)