Leetcode #601: Human Traffic of Stadium
In this guide, we solve Leetcode #601 Human Traffic of Stadium in Python and focus on the core idea that makes the solution efficient.
You will see the intuition, the step-by-step method, and a clean Python implementation you can use in interviews.
Problem Statement
Table: Stadium +---------------+---------+ | 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.
Quick Facts
- Difficulty: Hard
- Premium: No
- Tags: Database
Intuition
The task is relational in nature, which maps cleanly to DataFrame operations in Python.
By treating tables as DataFrames, joins and group-bys become concise and readable.
Approach
Load the inputs as DataFrames and apply the appropriate merge, filter, or group-by.
Select or rename the columns to match the required output.
Steps:
- Load inputs as DataFrames.
- Apply merge/groupby/filter operations.
- Select the output columns.
Example
+---------------+---------+
| 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.
Python Solution
import pandas as pd
def human_traffic(stadium: pd.DataFrame) -> pd.DataFrame:
df = stadium[stadium['people'] >= 100].sort_values('id')
df['grp'] = (df['id'].diff() != 1).cumsum()
sizes = df.groupby('grp')['id'].transform('count')
res = df[sizes >= 3][['id', 'visit_date', 'people']]
return res.sort_values('id')
Complexity
The time complexity is O(n log n) (typical). The space complexity is O(n).
Edge Cases and Pitfalls
Watch for boundary values, empty inputs, and duplicate values where applicable. If the problem involves ordering or constraints, confirm the invariant is preserved at every step.
Summary
This Python solution focuses on the essential structure of the problem and keeps the implementation interview-friendly while meeting the constraints.