You receive a CSV of ad viewing logs where the date column repeats only on the first row of each block and subsequent rows use a single dash '-' to indicate 'same as above'. Columns: date (YYYY-MM-DD or '-'), time_slot (e.g., '08:00-09:00'), ad_id, watch_seconds (INT). Tiny excerpt: +------------+-----------+-------+---------------+ | date | time_slot | ad_id | watch_seconds | +------------+-----------+-------+---------------+ | 2008-01-01 | 08:00-09:00 | A1 | 120 | | - | 08:00-09:00 | A2 | 90 | | - | 09:00-10:00 | A1 | 150 | | 2008-01-08 | 08:00-09:00 | A1 | 80 | +------------+-----------+-------+---------------+ Task A (Excel): 1) Provide the exact Excel formula to forward-fill the date in cell A2 and down the column, replacing '-' with the most recent actual date above, without using VBA or Power Query. 2) Then, using only worksheet functions, compute the time_slot with the largest total watch_seconds for the week 2008-01-01 to 2008-01-07 inclusive. Give the precise formulas (e.g., SUMIFS-based) and explain how you ensure the date filter is correct and dash rows are included after forward-fill. 3) Provide a pivot-table approach that yields the same answer and state the two field placements and the exact date filter you would set. Task B (pandas alternative): Write concise pandas code (no more than 5 lines) to: read the CSV; forward-fill the date column; filter to 2008-01-01 through 2008-01-07; group by time_slot; compute total watch_seconds; and return the single time_slot with the maximum total. Be explicit about handling '-' values.