Player First Login Device

Easy

You are analyzing telemetry from an online gaming platform. The dataset activity records daily login activity with the following columns: player_id : unique player identifier device_id : identifier of the device used on that login day event_date : login date in YYYY-MM-DD format games_played : number of games played that day Each player can appear across multiple dates, but each (player_id, event_date) pair is unique. Your task is to return the device used on the first login date for every player. Return exactly these columns: player_id device_id Result ordering is not important unless your local checker requires deterministic ordering. This problem should be solvable in both: SQL (SQLite 3.27.2) Pandas

Example 1

Input

Activity:
| player_id | device_id | event_date  | games_played |
|-----------|-----------|-------------|--------------|
| 1         | 2         | 2016-03-01  | 5            |
| 1         | 2         | 2016-05-02  | 6            |
| 2         | 3         | 2017-06-25  | 1            |
| 3         | 1         | 2016-03-02  | 0            |
| 3         | 4         | 2018-07-03  | 5            |

Output

[
  {"player_id":1,"device_id":2},
  {"player_id":2,"device_id":3},
  {"player_id":3,"device_id":1}
]

Explanation

For each player, keep the row with the earliest event_date and return that row's device_id.

Example 2

Input

Activity:
| player_id | device_id | event_date  | games_played |
|-----------|-----------|-------------|--------------|
| 10        | 42        | 2022-09-05  | 0            |
| 10        | 7         | 2023-01-10  | 8            |
| 11        | 99        | 2021-12-31  | 3            |

Output

[
  {"player_id":10,"device_id":42},
  {"player_id":11,"device_id":99}
]

Explanation

Player 10 has multiple logins, but only the earliest date (2022-09-05) matters.

Example 3

Input

Activity:
| player_id | device_id | event_date  | games_played |
|-----------|-----------|-------------|--------------|
| 4         | 12        | 2020-02-01  | 2            |
| 4         | 3         | 2021-01-01  | 9            |
| 5         | 4         | 2019-11-10  | 1            |
| 5         | 8         | 2020-01-09  | 4            |

Output

[
  {"player_id":4,"device_id":12},
  {"player_id":5,"device_id":4}
]

Explanation

Do not choose the smallest device_id. Choose the device from the earliest event_date per player.

Constraints

1 <= number of rows in activity <= 50,000
1 <= player_id <= 2,147,483,647
1 <= device_id <= 2,147,483,647
event_date is a valid date string in YYYY-MM-DD format
0 <= games_played <= 10,000
(player_id, event_date) is unique
For SQL submissions, use SQLite 3.27.2 semantics
For Pandas submissions, return a DataFrame with columns: player_id, device_id

Activity: | player_id | device_id | event_date | games_played | |-----------|-----------|-------------|--------------| | 1 | 2 | 2016-03-01 | 5 | | 1 | 2 | 2016-05-02 | 6 | | 2 | 3 | 2017-06-25 | 1 | | 3 | 1 | 2016-03-02 | 0 | | 3 | 4 | 2018-07-03 | 5 |

Activity: | player_id | device_id | event_date | games_played | |-----------|-----------|-------------|--------------| | 10 | 42 | 2022-09-05 | 0 | | 10 | 7 | 2023-01-10 | 8 | | 11 | 99 | 2021-12-31 | 3 |

Activity: | player_id | device_id | event_date | games_played | |-----------|-----------|-------------|--------------| | 4 | 12 | 2020-02-01 | 2 | | 4 | 3 | 2021-01-01 | 9 | | 5 | 4 | 2019-11-10 | 1 | | 5 | 8 | 2020-01-09 | 4 |