Leetcode #578: Get Highest Answer Rate Question
In this guide, we solve Leetcode #578 Get Highest Answer Rate Question 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: SurveyLog +-------------+------+ | Column Name | Type | +-------------+------+ | id | int | | action | ENUM | | question_id | int | | answer_id | int | | q_num | int | | timestamp | int | +-------------+------+ This table may contain duplicate rows. action is an ENUM (category) of the type: "show", "answer", or "skip".
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 |
+-------------+------+
| id | int |
| action | ENUM |
| question_id | int |
| answer_id | int |
| q_num | int |
| timestamp | int |
+-------------+------+
This table may contain duplicate rows.
action is an ENUM (category) of the type: "show", "answer", or "skip".
Each row of this table indicates the user with ID = id has taken an action with the question question_id at time timestamp.
If the action taken by the user is "answer", answer_id will contain the id of that answer, otherwise, it will be null.
q_num is the numeral order of the question in the current session.
Python Solution
import pandas as pd
def highest_answer_rate_question(survey_log: pd.DataFrame) -> pd.DataFrame:
counts = survey_log.groupby(['question_id', 'action']).size().unstack(fill_value=0)
show = counts.get('show', pd.Series(dtype=int))
answer = counts.get('answer', pd.Series(dtype=int))
rate = answer / show.replace(0, pd.NA)
rate = rate.fillna(0)
max_rate = rate.max()
best_q = rate[rate == max_rate].index.min()
return pd.DataFrame({'survey_log': [int(best_q)]})
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.