You are given a pandas DataFrame `df` with the following columns: - `unit_id` (string): entity identifier (e.g., user, city, driver) - `group` (string): either `'treatment'` or `'control'` - `period` (string): either `'pre'` or `'post'` - `y` (string): outcome stored as a string (should be numeric), with **exactly one missing value** (NaN) Tasks: 1. Convert `y` from string to integer (assume all non-missing values are valid integer strings, e.g. `'12'`). 2. Impute the missing value in `y` using the **simple (unconditional) average** of the non-missing `y` values. 3. After steps (1)–(2), compute the **difference-in-differences (DiD)** estimate of the treatment effect on `y`: \[ \text{DiD} = (\overline{y}_{\text{treat, post}} - \overline{y}_{\text{treat, pre}}) - (\overline{y}_{\text{ctrl, post}} - \overline{y}_{\text{ctrl, pre}}) \] Return the scalar DiD estimate (and optionally the intermediate group-period means used).