Player First Login Snapshot

Easy

You are given a gameplay activity log table named Activity. Each row represents one player's session activity on a calendar date: player_id: unique player identifier device_id: device used for that session event_date: session date (YYYY-MM-DD) games_played: number of games played that day (can be zero) Your task is to produce a compact onboarding snapshot: for every player, return the earliest date on which they appeared in Activity. Output columns must be exactly: player_id first_login Where first_login is the minimum event_date for that player. Result row order is not important. Supported submission formats: SQL (SQLite 3.27.2) Pandas (Python)

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         | 4         | 2017-06-25  | 1            |
| 3         | 3         | 2016-03-02  | 0            |
| 3         | 7         | 2018-07-03  | 5            |

Output

[
  {"player_id": 1, "first_login": "2016-03-01"},
  {"player_id": 2, "first_login": "2017-06-25"},
  {"player_id": 3, "first_login": "2016-03-02"}
]

Explanation

Take the minimum event_date inside each player_id group.

Example 2

Input

Activity:
| player_id | device_id | event_date  | games_played |
|-----------|-----------|-------------|--------------|
| 10        | 9         | 2020-01-10  | 0            |
| 10        | 3         | 2020-01-10  | 4            |
| 10        | 8         | 2020-03-11  | 2            |

Output

[
  {"player_id": 10, "first_login": "2020-01-10"}
]

Explanation

Same-day records are allowed across devices; the earliest date remains 2020-01-10.

Example 3

Input

Activity:
| player_id | device_id | event_date  | games_played |
|-----------|-----------|-------------|--------------|
| 7         | 2         | 2021-12-31  | 3            |
| 8         | 2         | 2022-01-01  | 1            |
| 8         | 5         | 2021-12-15  | 2            |
| 9         | 8         | 2024-02-29  | 6            |

Output

[
  {"player_id": 7, "first_login": "2021-12-31"},
  {"player_id": 8, "first_login": "2021-12-15"},
  {"player_id": 9, "first_login": "2024-02-29"}
]

Explanation

Rows can be unsorted. Grouping plus minimum date still gives each player's first activity day.

Constraints

1 <= number of rows in Activity <= 100,000
1 <= player_id <= 10^9
1 <= device_id <= 10^9
0 <= games_played <= 10^6
event_date is a valid calendar date in YYYY-MM-DD format
(player_id, event_date) combinations are unique
Use SQLite 3.27.2 semantics for SQL submissions
For Pandas, return a DataFrame with columns: player_id, first_login

Activity: | player_id | device_id | event_date | games_played | |-----------|-----------|-------------|--------------| | 1 | 2 | 2016-03-01 | 5 | | 1 | 2 | 2016-05-02 | 6 | | 2 | 4 | 2017-06-25 | 1 | | 3 | 3 | 2016-03-02 | 0 | | 3 | 7 | 2018-07-03 | 5 |

Activity: | player_id | device_id | event_date | games_played | |-----------|-----------|-------------|--------------| | 10 | 9 | 2020-01-10 | 0 | | 10 | 3 | 2020-01-10 | 4 | | 10 | 8 | 2020-03-11 | 2 |

Activity: | player_id | device_id | event_date | games_played | |-----------|-----------|-------------|--------------| | 7 | 2 | 2021-12-31 | 3 | | 8 | 2 | 2022-01-01 | 1 | | 8 | 5 | 2021-12-15 | 2 | | 9 | 8 | 2024-02-29 | 6 |