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dsa-solutions/lc-solutions/0600-0699/601-Human-Traffic-of-stadium.md

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+---
+id: human-traffic-of-stadium
+title: Human-Traffic-Of-Stadium
+sidebar_label: Human Traffic Of Stadium
+tags:
+  - Sql
+  - Database
+  - Pandas
+description: "This question solve important question of sql which gives us knowledge about writing of sql command."
+---
+
+# Non-negative Integers without Consecutive Ones
+
+## 1. Problem Description
+```
++---------------+---------+
+| Column Name   | Type    |
++---------------+---------+
+| id            | int     |
+| visit_date    | date    |
+| people        | int     |
++---------------+---------+
+visit_date is the column with unique values for this table.
+Each row of this table contains the visit date and visit id to the stadium with the number of people during the visit.
+As the id increases, the date increases as well.
+```
+Write a solution to display the records with three or more rows with consecutive `id`'s, and the number of people is greater than or equal to 100 for each.
+
+Return the result table ordered by `visit_date` in ascending order.
+
+The result format is in the following example.
+## 2. Examples
+
+### Example 1:
+**Input:**
+```
+Stadium table:
++------+------------+-----------+
+| id   | visit_date | people    |
++------+------------+-----------+
+| 1    | 2017-01-01 | 10        |
+| 2    | 2017-01-02 | 109       |
+| 3    | 2017-01-03 | 150       |
+| 4    | 2017-01-04 | 99        |
+| 5    | 2017-01-05 | 145       |
+| 6    | 2017-01-06 | 1455      |
+| 7    | 2017-01-07 | 199       |
+| 8    | 2017-01-09 | 188       |
++------+------------+-----------+ 
+```
+**Output:** 
+```
++------+------------+-----------+
+| id   | visit_date | people    |
++------+------------+-----------+
+| 5    | 2017-01-05 | 145       |
+| 6    | 2017-01-06 | 1455      |
+| 7    | 2017-01-07 | 199       |
+| 8    | 2017-01-09 | 188       |
++------+------------+-----------+
+```
+**Explanation:**  
+The four rows with ids 5, 6, 7, and 8 have consecutive ids and each of them has >= 100 people attended. Note that row 8 was included even though the visit_date was not the next day after row 7.
+The rows with ids 2 and 3 are not included because we need at least three consecutive ids.
+
+### Idea
+I've seen pretty many solutions using join of three tables or creating temporary tables with `n^3 `rows. With my `5-years`' working experience on data analysis, I can guarantee you this method will cause you "out of spool space" issue when you deal with a large table in big data field.
+
+I recommend you to learn and master window functions like `lead`, `lag` and use them as often as you can in your codes. These functions are very fast, and whenever you find yourself creating duplicate temp tables, you should ask yourself: can I solve this with window functions.
+
+
+## 5. Implementation (Code for 4 Languages)
+
+<Tabs>
+  <TabItem value="Pandas" label="Pandas" default>
+  ```Pandas
+ import pandas as pd
+
+def human_traffic(stadium: pd.DataFrame) -> pd.DataFrame:
+    stadium = stadium[stadium.people >= 100].sort_values(by='id')
+    third = (stadium.id.diff() == 1)\
+          & (stadium.id.diff().shift(1) == 1)
+    return stadium[third | third.shift(-1) | third.shift(-2)]
+
+
+  ```
+  </TabItem>
+
+  <TabItem value="SQL" label="SQL">
+  ```SQL
+   SELECT ID
+    , visit_date
+    , people
+FROM (
+    SELECT ID
+        , visit_date
+        , people
+        , LEAD(people, 1) OVER (ORDER BY id) nxt
+        , LEAD(people, 2) OVER (ORDER BY id) nxt2
+        , LAG(people, 1) OVER (ORDER BY id) pre
+        , LAG(people, 2) OVER (ORDER BY id) pre2
+    FROM Stadium
+) cte 
+WHERE (cte.people >= 100 AND cte.nxt >= 100 AND cte.nxt2 >= 100) 
+    OR (cte.people >= 100 AND cte.nxt >= 100 AND cte.pre >= 100)  
+    OR (cte.people >= 100 AND cte.pre >= 100 AND cte.pre2 >= 100) 
+  ```
+  </TabItem>
+
+</Tabs>
+
+### Complexity Analysis
+**Time Complexity:** $O(nlogn)$
+
+
+**Space Complexity:** $O(n)$
+
+## 10. References
+
+- [LeetCode - Human-Traffic-Of-Stadium](https://leetcode.com/problems/human-traffic-of-stadium/solutions/911779/mysql-use-window-function-for-big-data/)
+
+