Same-Day Ticket Resolution Rate
Problem
You are given a DataFrame `tickets` loaded from `tickets.csv` with the columns `ticket_id` (the key), `user_id`, `opened_on` and `resolved_on`. Each row is one support ticket.
A ticket is **same-day** if it was resolved on the day it was opened (`resolved_on` equals `opened_on`). Return the percentage of same-day tickets across all tickets, rounded to 2 decimal places, in a single column named `same_day_pct`.
Example `tickets`:
```text
ticket_id user_id opened_on resolved_on
1 10 2024-01-05 2024-01-05
2 11 2024-01-06 2024-01-08
3 12 2024-01-07 2024-01-07
```
Expected result is `66.67`: two of the three tickets were resolved on the same day, so 2 / 3 = 66.67%.
Input data
Example rows — the live problem includes the full dataset.
| ticket_id | user_id | opened_on | resolved_on |
|---|---|---|---|
| 1 | 10 | 2024-01-05 | 2024-01-05 |
| 2 | 11 | 2024-01-06 | 2024-01-08 |
| 3 | 12 | 2024-01-07 | 2024-01-07 |
Expected output
Your answer should return 1 row with the columns same_day_pct.
Starter code (Pandas (Python))
import pandas as pd
def same_day_rate(tickets) -> pd.DataFrame:
# Your code here
return ticketsSolve this Pandas question free
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Solution & explanation
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