Survey Answer Rate Leader

MEDIUM20 pts

You are analyzing interaction telemetry from an online questionnaire platform.

The SurveyLog table records every user action taken on a question:

id: user identifier
action: one of show, answer, skip
question_id: question identifier
answer_id: selected answer identifier (present only when action = answer)
q_num: question position in the session
timestamp: event time represented as an integer

The table may include duplicate rows.

Define answer_rate for each question as: answer_rate = number of answer actions / number of show actions

Your task is to report the question with the highest answer_rate. If multiple questions share the same highest rate, return the smallest question_id.

For robustness in production-style logs, treat answer_rate as 0.0 when a question has zero show actions.

Return exactly one column named survey_log.

This problem should be solvable in both:

SQL (SQLite 3.27.2)
Pandas

Example

Input

SurveyLog:
| id | action | question_id | answer_id | q_num | timestamp |
|----|--------|-------------|-----------|-------|-----------|
| 5  | show   | 285         | null      | 1     | 123       |
| 5  | answer | 285         | 124124    | 1     | 124       |
| 5  | show   | 369         | null      | 2     | 125       |
| 5  | skip   | 369         | null      | 2     | 126       |

Output

[{"survey_log":285}]

Explanation

Question 285 has rate 1/1 = 1.0 and question 369 has rate 0/1 = 0.0, so 285 is selected.

Example

Input

SurveyLog:
| id | action | question_id | answer_id | q_num | timestamp |
|----|--------|-------------|-----------|-------|-----------|
| 1  | show   | 55          | null      | 1     | 11        |
| 1  | answer | 55          | 700       | 1     | 12        |
| 2  | show   | 55          | null      | 2     | 20        |
| 3  | show   | 101         | null      | 1     | 30        |
| 3  | answer | 101         | 901       | 1     | 31        |
| 4  | show   | 101         | null      | 2     | 40        |

Output

[{"survey_log":55}]

Explanation

Both questions have answer rate 1/2 = 0.5. Tie-break requires the smaller question_id, so 55 is returned.

Example

Input

SurveyLog:
| id | action | question_id | answer_id | q_num | timestamp |
|----|--------|-------------|-----------|-------|-----------|
| 8  | answer | 400         | 77        | 1     | 100       |
| 8  | skip   | 400         | null      | 1     | 101       |
| 9  | show   | 320         | null      | 2     | 102       |
| 9  | skip   | 320         | null      | 2     | 103       |
| 9  | show   | 250         | null      | 3     | 104       |
| 9  | answer | 250         | 88        | 3     | 105       |

Output

[{"survey_log":250}]

Explanation

Question 400 has zero show actions, so its rate is treated as 0.0. Question 250 has rate 1/1 = 1.0 and wins.

Accepted0/0·0% Acceptance

Constraints

1 <= number of rows in SurveyLog <= 200,000
action is one of: show, answer, skip
1 <= id <= 1,000,000,000
1 <= question_id <= 1,000,000,000
answer_id is null unless action = answer
q_num and timestamp are integers
Duplicate rows may exist
When show_count = 0, define answer_rate as 0.0
Use SQLite 3.27.2 semantics for SQL submissions
For Pandas submissions, return a DataFrame with exactly one column named survey_log

Code

Visualizer

Solutions

14px

Test Cases3

Results

Submissions

survey_log =

[{"id":5,"action":"show","question_id":285,"answer_id":null,"q_num":1,"timestamp":123},{"id":5,"action":"answer","question_id":285,"answer_id":124124,"q_num":1,"timestamp":124},{"id":5,"action":"show","question_id":369,"answer_id":null,"q_num":2,"timestamp":125},{"id":5,"action":"skip","question_id":369,"answer_id":null,"q_num":2,"timestamp":126}]

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