Leetcode #1132: Reported Posts II
In this guide, we solve Leetcode #1132 Reported Posts II 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: Actions +---------------+---------+ | Column Name | Type | +---------------+---------+ | user_id | int | | post_id | int | | action_date | date | | action | enum | | extra | varchar | +---------------+---------+ This table may have duplicate rows. The action column is an ENUM (category) type of ('view', 'like', 'reaction', 'comment', 'report', 'share').
Quick Facts
- Difficulty: Medium
- 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 |
+---------------+---------+
| user_id | int |
| post_id | int |
| action_date | date |
| action | enum |
| extra | varchar |
+---------------+---------+
This table may have duplicate rows.
The action column is an ENUM (category) type of ('view', 'like', 'reaction', 'comment', 'report', 'share').
The extra column has optional information about the action, such as a reason for the report or a type of reaction.
Python Solution
import duckdb
import pandas as pd
def solution(actions: pd.DataFrame, removals: pd.DataFrame) -> pd.DataFrame:
con = duckdb.connect()
con.register("Actions", actions)
con.register("Removals", removals)
return con.execute("""WITH
T AS (
SELECT
COUNT(DISTINCT t2.post_id) / COUNT(DISTINCT t1.post_id) * 100 AS percent
FROM
Actions AS t1
LEFT JOIN Removals AS t2 ON t1.post_id = t2.post_id
WHERE extra = 'spam'
GROUP BY action_date
)
SELECT ROUND(AVG(percent), 2) AS average_daily_percent
FROM T;""").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.