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‎LeetCode SQL 50 Solution/550. Game Play Analysis IV/550. Game Play Analysis IV.py

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import pandas as pd
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def game_play_analysis(activity: pd.DataFrame) -> pd.DataFrame:
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# Get first login date for each player
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first_login = activity.groupby("player_id")["event_date"].min().reset_index()
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first_login.columns = ["player_id", "first_login"]
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# Merge first login date with original table
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merged = activity.merge(first_login, on="player_id")
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# Filter players who logged in the next day
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next_day_logins = merged[
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(merged["event_date"] - merged["first_login"]).dt.days == 1
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]["player_id"].nunique()
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# Total unique players
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total_players = activity["player_id"].nunique()
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# Calculate fraction
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fraction = round(next_day_logins / total_players, 2)
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return pd.DataFrame({"fraction": [fraction]})

‎LeetCode SQL 50 Solution/550. Game Play Analysis IV/550. Game Play Analysis IV.sql

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+--------------+---------+
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| player_id | int |
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| device_id | int |
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| event_date | date |
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-- | event_date | date |
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| games_played | int |
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+--------------+---------+
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(player_id, event_date) is the primary key (combination of columns with unique values) of this table.

‎LeetCode SQL 50 Solution/550. Game Play Analysis IV/readme.md

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# **550. Game Play Analysis IV**
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## **Problem Statement**
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You are given a table named `Activity`, which logs the gaming activity of players.
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### **Activity Table**
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```rb
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+--------------+---------+
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| Column Name | Type |
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+--------------+---------+
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| player_id | int |
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| device_id | int |
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| event_date | date |
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| games_played | int |
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+--------------+---------+
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```
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- **(player_id, event_date)** is the **primary key**.
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- Each row contains:
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- `player_id`: The ID of the player.
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- `event_date`: The date when the player logged in.
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- `games_played`: The number of games played before logging out.
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### **Task:**
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Find the **fraction** of players who logged in **again** the day after their **first login date**, rounded to **2 decimal places**.
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---
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## **Example 1:**
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### **Input:**
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#### **Activity Table**
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```rb
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+-----------+-----------+------------+--------------+
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| player_id | device_id | event_date | games_played |
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+-----------+-----------+------------+--------------+
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| 1 | 2 | 2016-03-01 | 5 |
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| 1 | 2 | 2016-03-02 | 6 |
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| 2 | 3 | 2017-06-25 | 1 |
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| 3 | 1 | 2016-03-02 | 0 |
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| 3 | 4 | 2018-07-03 | 5 |
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+-----------+-----------+------------+--------------+
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```
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### **Output:**
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```rb
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+-----------+
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| fraction |
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+-----------+
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| 0.33 |
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+-----------+
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```
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### **Explanation:**
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- `player_id = 1`: First login on **2016-03-01**, logs in again on **2016-03-02** ✅
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- `player_id = 2`: First login on **2017-06-25**, no next-day login ❌
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- `player_id = 3`: First login on **2016-03-02**, no next-day login ❌
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Total players = **3**, Players who logged in the next day = **1** → **1 / 3 = 0.33** ✅
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---
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## **Solution Approaches**
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### **SQL Solution (Using `JOIN` & `DATEDIFF`)**
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```sql
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SELECT
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ROUND((
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SELECT COUNT(DISTINCT a.player_id)
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FROM Activity a
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INNER JOIN (
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SELECT player_id, MIN(event_date) AS first_login
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FROM Activity
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GROUP BY player_id
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) b
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ON a.player_id = b.player_id
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AND DATEDIFF(a.event_date, b.first_login) = 1
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) /
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(SELECT COUNT(DISTINCT player_id) FROM Activity), 2) AS fraction;
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```
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**Explanation:**
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1. **Find First Login Date per Player**
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- `MIN(event_date) AS first_login`
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- **Grouped by** `player_id`
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2. **Find Players Who Logged in on the Next Day**
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- **Join** the table with itself.
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- Use `DATEDIFF(a.event_date, b.first_login) = 1` to check next-day logins.
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- Count unique `player_id`s.
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3. **Calculate Fraction**
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- Divide by total distinct `player_id`s.
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- Round to **2 decimal places**.
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---
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### **Alternative SQL Solution (Using `EXISTS`)**
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```sql
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SELECT ROUND(
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(SELECT COUNT(DISTINCT player_id)
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FROM Activity a
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WHERE EXISTS (
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SELECT 1 FROM Activity b
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WHERE a.player_id = b.player_id
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AND DATEDIFF(b.event_date, a.event_date) = 1
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)) /
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(SELECT COUNT(DISTINCT player_id) FROM Activity), 2) AS fraction;
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```
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**Explanation:**
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- Checks if a player has **ANY** login exactly **one day after**.
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- Uses `EXISTS` to optimize performance.
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---
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### **Pandas Solution**
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```python
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import pandas as pd
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def game_play_analysis(activity: pd.DataFrame) -> pd.DataFrame:
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# Get first login date for each player
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first_login = activity.groupby("player_id")["event_date"].min().reset_index()
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first_login.columns = ["player_id", "first_login"]
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# Merge first login date with original table
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merged = activity.merge(first_login, on="player_id")
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# Filter players who logged in the next day
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next_day_logins = merged[
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(merged["event_date"] - merged["first_login"]).dt.days == 1
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]["player_id"].nunique()
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# Total unique players
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total_players = activity["player_id"].nunique()
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# Calculate fraction
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fraction = round(next_day_logins / total_players, 2)
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return pd.DataFrame({"fraction": [fraction]})
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```
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**Explanation:**
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1. **Find First Login Date**
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- Group by `player_id`, get `min(event_date)`.
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2. **Merge with Original Table**
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- Check if `event_date - first_login = 1 day`.
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3. **Count Unique Players**
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- Divide by total unique `player_id`s.
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---
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## **File Structure**
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```
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📂 LeetCode550
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│── 📜 problem_statement.md
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│── 📜 sql_solution.sql
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│── 📜 sql_exists_solution.sql
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│── 📜 pandas_solution.py
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│── 📜 README.md
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```
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- `problem_statement.md` → Contains the problem description.
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- `sql_solution.sql` → SQL solution using **JOIN & DATEDIFF**.
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- `sql_exists_solution.sql` → SQL solution using **EXISTS**.
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- `pandas_solution.py` → Pandas solution.
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- `README.md` → Overview of problem and solutions.
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---
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## **Useful Links**
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- [LeetCode Problem 550](https://leetcode.com/problems/game-play-analysis-iv/)
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- [SQL `DATEDIFF()`](https://www.w3schools.com/sql/func_mysql_datediff.asp)
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- [Pandas `.groupby()`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.groupby.html)

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