Branch 1

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

@@ -0,0 +1,22 @@
+import pandas as pd
+
+def game_play_analysis(activity: pd.DataFrame) -> pd.DataFrame:
+    # Get first login date for each player
+    first_login = activity.groupby("player_id")["event_date"].min().reset_index()
+    first_login.columns = ["player_id", "first_login"]
+
+    # Merge first login date with original table
+    merged = activity.merge(first_login, on="player_id")
+
+    # Filter players who logged in the next day
+    next_day_logins = merged[
+        (merged["event_date"] - merged["first_login"]).dt.days == 1
+    ]["player_id"].nunique()
+
+    # Total unique players
+    total_players = activity["player_id"].nunique()
+
+    # Calculate fraction
+    fraction = round(next_day_logins / total_players, 2)
+
+    return pd.DataFrame({"fraction": [fraction]})

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

@@ -7,7 +7,7 @@ Table: Activity
 +--------------+---------+
 | player_id    | int     |
 | device_id    | int     |
-| event_date   | date    |
+-- | event_date   | date    |
 | games_played | int     |
 +--------------+---------+
 (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**
+
+## **Problem Statement**
+You are given a table named `Activity`, which logs the gaming activity of players.
+
+### **Activity Table**
+```rb
++--------------+---------+
+| Column Name  | Type    |
++--------------+---------+
+| player_id    | int     |
+| device_id    | int     |
+| event_date   | date    |
+| games_played | int     |
++--------------+---------+
+```
+- **(player_id, event_date)** is the **primary key**.
+- Each row contains:
+  - `player_id`: The ID of the player.
+  - `event_date`: The date when the player logged in.
+  - `games_played`: The number of games played before logging out.
+
+### **Task:**
+Find the **fraction** of players who logged in **again** the day after their **first login date**, rounded to **2 decimal places**.
+
+---
+
+## **Example 1:**
+### **Input:**
+#### **Activity Table**
+```rb
++-----------+-----------+------------+--------------+
+| player_id | device_id | event_date | games_played |
++-----------+-----------+------------+--------------+
+| 1         | 2         | 2016-03-01 | 5            |
+| 1         | 2         | 2016-03-02 | 6            |
+| 2         | 3         | 2017-06-25 | 1            |
+| 3         | 1         | 2016-03-02 | 0            |
+| 3         | 4         | 2018-07-03 | 5            |
++-----------+-----------+------------+--------------+
+```
+### **Output:**
+```rb
++-----------+
+| fraction  |
++-----------+
+| 0.33      |
++-----------+
+```
+### **Explanation:**
+- `player_id = 1`: First login on **2016-03-01**, logs in again on **2016-03-02** ✅
+- `player_id = 2`: First login on **2017-06-25**, no next-day login ❌
+- `player_id = 3`: First login on **2016-03-02**, no next-day login ❌
+
+Total players = **3**, Players who logged in the next day = **1** → **1 / 3 = 0.33** ✅
+
+---
+
+## **Solution Approaches**
+
+### **SQL Solution (Using `JOIN` & `DATEDIFF`)**
+```sql
+SELECT 
+    ROUND((
+        SELECT COUNT(DISTINCT a.player_id)
+        FROM Activity a
+        INNER JOIN (
+            SELECT player_id, MIN(event_date) AS first_login
+            FROM Activity
+            GROUP BY player_id
+        ) b
+        ON a.player_id = b.player_id
+        AND DATEDIFF(a.event_date, b.first_login) = 1
+    ) / 
+    (SELECT COUNT(DISTINCT player_id) FROM Activity), 2) AS fraction;
+```
+**Explanation:**
+1. **Find First Login Date per Player**
+   - `MIN(event_date) AS first_login`
+   - **Grouped by** `player_id`
+2. **Find Players Who Logged in on the Next Day**
+   - **Join** the table with itself.
+   - Use `DATEDIFF(a.event_date, b.first_login) = 1` to check next-day logins.
+   - Count unique `player_id`s.
+3. **Calculate Fraction**
+   - Divide by total distinct `player_id`s.
+   - Round to **2 decimal places**.
+
+---
+
+### **Alternative SQL Solution (Using `EXISTS`)**
+```sql
+SELECT ROUND(
+    (SELECT COUNT(DISTINCT player_id) 
+     FROM Activity a
+     WHERE EXISTS (
+         SELECT 1 FROM Activity b
+         WHERE a.player_id = b.player_id 
+         AND DATEDIFF(b.event_date, a.event_date) = 1
+     )) / 
+    (SELECT COUNT(DISTINCT player_id) FROM Activity), 2) AS fraction;
+```
+**Explanation:**
+- Checks if a player has **ANY** login exactly **one day after**.
+- Uses `EXISTS` to optimize performance.
+
+---
+
+### **Pandas Solution**
+```python
+import pandas as pd
+
+def game_play_analysis(activity: pd.DataFrame) -> pd.DataFrame:
+    # Get first login date for each player
+    first_login = activity.groupby("player_id")["event_date"].min().reset_index()
+    first_login.columns = ["player_id", "first_login"]
+
+    # Merge first login date with original table
+    merged = activity.merge(first_login, on="player_id")
+
+    # Filter players who logged in the next day
+    next_day_logins = merged[
+        (merged["event_date"] - merged["first_login"]).dt.days == 1
+    ]["player_id"].nunique()
+
+    # Total unique players
+    total_players = activity["player_id"].nunique()
+
+    # Calculate fraction
+    fraction = round(next_day_logins / total_players, 2)
+
+    return pd.DataFrame({"fraction": [fraction]})
+```
+**Explanation:**
+1. **Find First Login Date**
+   - Group by `player_id`, get `min(event_date)`.
+2. **Merge with Original Table**
+   - Check if `event_date - first_login = 1 day`.
+3. **Count Unique Players**
+   - Divide by total unique `player_id`s.
+
+---
+
+## **File Structure**
+```
+📂 LeetCode550
+│── 📜 problem_statement.md
+│── 📜 sql_solution.sql
+│── 📜 sql_exists_solution.sql
+│── 📜 pandas_solution.py
+│── 📜 README.md
+```
+- `problem_statement.md` → Contains the problem description.
+- `sql_solution.sql` → SQL solution using **JOIN & DATEDIFF**.
+- `sql_exists_solution.sql` → SQL solution using **EXISTS**.
+- `pandas_solution.py` → Pandas solution.
+- `README.md` → Overview of problem and solutions.
+
+---
+
+## **Useful Links**
+- [LeetCode Problem 550](https://leetcode.com/problems/game-play-analysis-iv/)
+- [SQL `DATEDIFF()`](https://www.w3schools.com/sql/func_mysql_datediff.asp)
+- [Pandas `.groupby()`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.groupby.html)