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Game Play Analysis I

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.
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.

 

Write a solution to find the first login date for each player.

Return the result table in any order.

The result format is in the following example.

 

Example 1:

Input: 
Activity table:
+-----------+-----------+------------+--------------+
| 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 | first_login |
+-----------+-------------+
| 1         | 2016-03-01  |
| 2         | 2017-06-25  |
| 3         | 2016-03-02  |
+-----------+-------------+

Solution Explanation for LeetCode 511: Game Play Analysis I

This problem requires finding the first login date for each player in the Activity table. The solution leverages the power of SQL's aggregate functions and grouping capabilities.

Approach

The core idea is to group the data by player_id and then find the minimum event_date within each group. This minimum date represents the first login date for that player.

SQL Solution (MySQL)

SELECT player_id, MIN(event_date) AS first_login
FROM Activity
GROUP BY player_id;
  • SELECT player_id, MIN(event_date) AS first_login: This selects the player_id and the minimum event_date for each group. The MIN(event_date) function finds the earliest date, and AS first_login assigns it an alias for clarity.
  • FROM Activity: This specifies the table to query.
  • GROUP BY player_id: This groups the rows based on player_id, ensuring that MIN() is applied separately to each player's login records.

Python Solution (using Pandas)

import pandas as pd
 
def game_analysis(activity: pd.DataFrame) -> pd.DataFrame:
    return (
        activity.groupby("player_id")
        .agg(first_login=("event_date", "min"))
        .reset_index()
    )

This Python solution utilizes the Pandas library, a powerful tool for data manipulation and analysis.

  • activity.groupby("player_id"): This groups the DataFrame activity by the player_id column, creating groups for each unique player.
  • .agg(first_login=("event_date", "min")): This performs an aggregation on each group. The agg() function applies the min() function to the event_date column within each group. The result is a new DataFrame with player_id and the minimum event_date (renamed as first_login).
  • .reset_index(): This converts the grouped result back into a regular DataFrame, resetting the index.

Time Complexity Analysis

Both the SQL and Pandas solutions have a time complexity of O(N log N) in the worst case, where N is the number of rows in the Activity table. This is because the GROUP BY operation (in SQL) and the groupby() operation (in Pandas) typically involve sorting or hashing the data, which has a logarithmic time complexity. However, optimized database systems and Pandas implementations may achieve better performance in practice. The exact complexity also depends on the specific database engine or Pandas version used and the size of the input data.

Space Complexity Analysis

The space complexity is O(M) where M is the number of unique players. This is because the result only needs to store the first login date for each unique player. In the worst case where all players are unique, M could approach N. The space complexity is dominated by the size of the output table/DataFrame.