Leetcode #2494: Merge Overlapping Events in the Same Hall
In this guide, we solve Leetcode #2494 Merge Overlapping Events in the Same Hall 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: HallEvents +-------------+------+ | Column Name | Type | +-------------+------+ | hall_id | int | | start_day | date | | end_day | date | +-------------+------+ This table may contain duplicates rows. Each row of this table indicates the start day and end day of an event and the hall in which the event is held.
Quick Facts
- Difficulty: Hard
- Premium: Yes
- 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 |
+-------------+------+
| hall_id | int |
| start_day | date |
| end_day | date |
+-------------+------+
This table may contain duplicates rows.
Each row of this table indicates the start day and end day of an event and the hall in which the event is held.
Python Solution
import duckdb
import pandas as pd
def solution(hall_events: pd.DataFrame) -> pd.DataFrame:
con = duckdb.connect()
con.register("HallEvents", hall_events)
return con.execute("""WITH
S AS (
SELECT
hall_id,
start_day,
end_day,
MAX(end_day) OVER (
PARTITION BY hall_id
ORDER BY start_day
) AS cur_max_end_day
FROM HallEvents
),
T AS (
SELECT
*,
IF(
start_day <= LAG(cur_max_end_day) OVER (
PARTITION BY hall_id
ORDER BY start_day
),
0,
1
) AS start
FROM S
),
P AS (
SELECT
*,
SUM(start) OVER (
PARTITION BY hall_id
ORDER BY start_day
) AS gid
FROM T
)
SELECT hall_id, MIN(start_day) AS start_day, MAX(end_day) AS end_day
FROM P
GROUP BY hall_id, gid;""").df()
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.