|
| 1 | +# 180. Consecutive Numbers |
| 2 | + |
| 3 | +## Problem Statement |
| 4 | +You are given a table `Logs` with the following structure: |
| 5 | + |
| 6 | +``` |
| 7 | ++-------------+---------+ |
| 8 | +| Column Name | Type | |
| 9 | ++-------------+---------+ |
| 10 | +| id | int | |
| 11 | +| num | varchar | |
| 12 | ++-------------+---------+ |
| 13 | +``` |
| 14 | +- `id` is the primary key and auto-increments starting from 1. |
| 15 | +- Find all numbers that appear **at least three times consecutively**. |
| 16 | +- Return the result table in **any order**. |
| 17 | + |
| 18 | +## Example 1: |
| 19 | + |
| 20 | +**Input:** |
| 21 | + |
| 22 | +``` |
| 23 | +Logs table: |
| 24 | ++----+-----+ |
| 25 | +| id | num | |
| 26 | ++----+-----+ |
| 27 | +| 1 | 1 | |
| 28 | +| 2 | 1 | |
| 29 | +| 3 | 1 | |
| 30 | +| 4 | 2 | |
| 31 | +| 5 | 1 | |
| 32 | +| 6 | 2 | |
| 33 | +| 7 | 2 | |
| 34 | ++----+-----+ |
| 35 | +``` |
| 36 | + |
| 37 | +**Output:** |
| 38 | + |
| 39 | +``` |
| 40 | ++-----------------+ |
| 41 | +| ConsecutiveNums | |
| 42 | ++-----------------+ |
| 43 | +| 1 | |
| 44 | ++-----------------+ |
| 45 | +``` |
| 46 | + |
| 47 | +--- |
| 48 | + |
| 49 | +## Solution Approaches |
| 50 | + |
| 51 | +### **SQL Solution (Using Self Join)** |
| 52 | +```sql |
| 53 | +SELECT DISTINCT l1.num AS ConsecutiveNums |
| 54 | +FROM Logs l1 |
| 55 | +JOIN Logs l2 ON l1.id = l2.id - 1 AND l1.num = l2.num |
| 56 | +JOIN Logs l3 ON l1.id = l3.id - 2 AND l1.num = l3.num; |
| 57 | +``` |
| 58 | + |
| 59 | +### **SQL Solution (Using Window Functions)** |
| 60 | +```sql |
| 61 | +SELECT DISTINCT num AS ConsecutiveNums |
| 62 | +FROM ( |
| 63 | + SELECT num, LAG(num,1) OVER (ORDER BY id) AS prev1, |
| 64 | + LAG(num,2) OVER (ORDER BY id) AS prev2 |
| 65 | + FROM Logs |
| 66 | +) temp |
| 67 | +WHERE num = prev1 AND num = prev2; |
| 68 | +``` |
| 69 | + |
| 70 | +### **Pandas Solution** |
| 71 | +```python |
| 72 | +import pandas as pd |
| 73 | + |
| 74 | +def consecutive_numbers(logs: pd.DataFrame) -> pd.DataFrame: |
| 75 | + logs['prev1'] = logs['num'].shift(1) |
| 76 | + logs['prev2'] = logs['num'].shift(2) |
| 77 | + |
| 78 | + result = logs[(logs['num'] == logs['prev1']) & (logs['num'] == logs['prev2'])] |
| 79 | + return pd.DataFrame({'ConsecutiveNums': result['num'].unique()}) |
| 80 | +``` |
| 81 | + |
| 82 | +--- |
| 83 | + |
| 84 | + |
| 85 | +## File Structure |
| 86 | +``` |
| 87 | +📂 Problem Name |
| 88 | + ├── 📄 README.md # Problem statement, approach, solution |
| 89 | + ├── 📄 sql_solution.sql # SQL Solution |
| 90 | + ├── 📄 pandas_solution.py # Pandas Solution |
| 91 | + └── 📄 example_input_output.txt # Sample input & expected output |
| 92 | +``` |
| 93 | + |
| 94 | +## Useful Links |
| 95 | +- [LeetCode Problem](https://leetcode.com/problems/consecutive-numbers/) 🚀 |
| 96 | +- [SQL `JOIN` Explained](https://www.w3schools.com/sql/sql_join.asp) |
| 97 | +- [MySQL `LAG()` Window Function](https://dev.mysql.com/doc/refman/8.0/en/window-function-descriptions.html) |
| 98 | + |
| 99 | +--- |
| 100 | + |
| 101 | +Feel free to contribute with optimized solutions! 💡 |
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