리트코드 : 1934. Confirmation Rate

리트코드 : 1934. Confirmation Rate

• [link]
  

---

문제

Table: Signups

+----------------+----------+
| Column Name    | Type     |
+----------------+----------+
| user_id        | int      |
| time_stamp     | datetime |
+----------------+----------+
user_id is the column of unique values for this table.
Each row contains information about the signup time for the user with ID user_id.


Table: Confirmations

+----------------+----------+
| Column Name    | Type     |
+----------------+----------+
| user_id        | int      |
| time_stamp     | datetime |
| action         | ENUM     |
+----------------+----------+
(user_id, time_stamp) is the primary key (combination of columns with unique values) for this table.
user_id is a foreign key (reference column) to the Signups table.
action is an ENUM (category) of the type ('confirmed', 'timeout')
Each row of this table indicates that the user with ID user_id requested a confirmation message at time_stamp and that confirmation message was either confirmed ('confirmed') or expired without confirming ('timeout').


The confirmation rate of a user is the number of 'confirmed' messages divided by the total number of requested confirmation messages. The confirmation rate of a user that did not request any confirmation messages is 0. Round the confirmation rate to two decimal places.

Write a solution to find the confirmation rate of each user.

Return the result table in any order.

The result format is in the following example.



Example 1:

Input: 
Signups table:
+---------+---------------------+
| user_id | time_stamp          |
+---------+---------------------+
| 3       | 2020-03-21 10:16:13 |
| 7       | 2020-01-04 13:57:59 |
| 2       | 2020-07-29 23:09:44 |
| 6       | 2020-12-09 10:39:37 |
+---------+---------------------+
Confirmations table:
+---------+---------------------+-----------+
| user_id | time_stamp          | action    |
+---------+---------------------+-----------+
| 3       | 2021-01-06 03:30:46 | timeout   |
| 3       | 2021-07-14 14:00:00 | timeout   |
| 7       | 2021-06-12 11:57:29 | confirmed |
| 7       | 2021-06-13 12:58:28 | confirmed |
| 7       | 2021-06-14 13:59:27 | confirmed |
| 2       | 2021-01-22 00:00:00 | confirmed |
| 2       | 2021-02-28 23:59:59 | timeout   |
+---------+---------------------+-----------+
Output: 
+---------+-------------------+
| user_id | confirmation_rate |
+---------+-------------------+
| 6       | 0.00              |
| 3       | 0.00              |
| 7       | 1.00              |
| 2       | 0.50              |
+---------+-------------------+
Explanation: 
User 6 did not request any confirmation messages. The confirmation rate is 0.
User 3 made 2 requests and both timed out. The confirmation rate is 0.
User 7 made 3 requests and all were confirmed. The confirmation rate is 1.
User 2 made 2 requests where one was confirmed and the other timed out. The confirmation rate is 1 / 2 = 0.5.

• 컨펌 비율 찾기

문제 풀이

MySQL

WITH TEMP AS (
    SELECT USER_ID, ROUND(COUNT(IF(ACTION='CONFIRMED',1,NULL))/COUNT(*),2) AS rate
    FROM CONFIRMATIONS
    GROUP BY USER_ID
)

SELECT S.user_id, COALESCE(T.rate,0) AS confirmation_rate
FROM SIGNUPS AS S
LEFT JOIN TEMP AS T ON T.user_id = S.user_id

• 설명

Pandas

import pandas as pd

def confirmation_rate(signups: pd.DataFrame, confirmations: pd.DataFrame) -> pd.DataFrame:

    joined = pd.merge(signups, confirmations, how='left', left_on='user_id', right_on='user_id')

    grouped = joined.groupby('user_id').agg(
        confirmation_rate=('action', lambda x: round(sum(x=='confirmed')/len(x),2))
    ).reset_index()
    return grouped

• groupby에서 lambda x의 인수는 group DataFrame으로 들어오니까, 여기서 연산해주면 된다.
• apply에서 lambda x의 인수는 row DataFrame으로 들어오니까 차이를 인지하고 lambda 작성.

코멘트

• .