Next-Day Return Rate at a Climbing Gym

Problem

A climbing gym logs every member visit.

DataFrame: `gym_visit` (from `gym_visit.csv`)

| Column | Type |
|-------------|--------|
| climber_id | int |
| wall_id | int |
| visit_day | object |
| routes_done | int |

Each row records that a climber visited the gym on a given day, used some wall, and completed some number of routes (possibly 0). `(climber_id, visit_day)` is unique.

Report the **share** of climbers who came back on the day immediately after their very first visit: among all distinct climbers, how many had a visit on the calendar day right after their earliest visit day, divided by the total number of distinct climbers. Round to **2 decimal places** and label the column `return_rate`.

**Example**

| climber_id | wall_id | visit_day | routes_done |
|------------|---------|------------|-------------|
| 7 | 2 | 2021-04-01 | 4 |
| 7 | 2 | 2021-04-02 | 6 |
| 8 | 3 | 2022-09-10 | 1 |
| 9 | 1 | 2021-04-02 | 0 |
| 9 | 5 | 2023-01-15 | 3 |

Only climber 7 returned on the day after their first visit (Apr 1 -> Apr 2), so the answer is 1 / 3 = 0.33.

Input data

Example rows — the live problem includes the full dataset.

Expected output

Your answer should return 1 row with the columns return_rate.

Starter code (Pandas (Python))

import pandas as pd

def next_day_return(gym_visit: pd.DataFrame) -> pd.DataFrame:
    # Your code here
    return gym_visit

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