Leetcode #1097: Game Play Analysis V
In this guide, we solve Leetcode #1097 Game Play Analysis V in Python and focus on the core idea that makes the solution efficient.
You will see the intuition, the step-by-step method, and a clean Python implementation you can use in interviews.
Problem Statement
Table: Activity +--------------+---------+ | Column Name | Type | +--------------+---------+ | player_id | int | | device_id | int | | event_date | date | | games_played | int | +--------------+---------+ (player_id, event_date) is the primary key (combination of columns with unique values) of this table. This table shows the activity of players of some games.
Quick Facts
- Difficulty: Hard
- Premium: Yes
- Tags: Database
Intuition
The task is relational in nature, which maps cleanly to DataFrame operations in Python.
By treating tables as DataFrames, joins and group-bys become concise and readable.
Approach
Load the inputs as DataFrames and apply the appropriate merge, filter, or group-by.
Select or rename the columns to match the required output.
Steps:
- Load inputs as DataFrames.
- Apply merge/groupby/filter operations.
- Select the output columns.
Example
+--------------+---------+
| Column Name | Type |
+--------------+---------+
| player_id | int |
| device_id | int |
| event_date | date |
| games_played | int |
+--------------+---------+
(player_id, event_date) is the primary key (combination of columns with unique values) of this table.
This table shows the activity of players of some games.
Each row is a record of a player who logged in and played a number of games (possibly 0) before logging out on someday using some device.
Python Solution
import duckdb
import pandas as pd
def solution(activity: pd.DataFrame) -> pd.DataFrame:
con = duckdb.connect()
con.register("Activity", activity)
return con.execute("""WITH
T AS (
SELECT
player_id,
event_date,
MIN(event_date) OVER (PARTITION BY player_id) AS install_dt
FROM Activity
)
SELECT
install_dt,
COUNT(DISTINCT player_id) AS installs,
ROUND(
SUM(DATEDIFF(event_date, install_dt) = 1) / COUNT(DISTINCT player_id),
2
) AS day1_retention
FROM T
GROUP BY 1;""").df()
Complexity
The time complexity is O(n log n) (typical). The space complexity is O(n).
Edge Cases and Pitfalls
Watch for boundary values, empty inputs, and duplicate values where applicable. If the problem involves ordering or constraints, confirm the invariant is preserved at every step.
Summary
This Python solution focuses on the essential structure of the problem and keeps the implementation interview-friendly while meeting the constraints.