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Table: Person
+-------------+---------+ | Column Name | Type | +-------------+---------+ | id | int | | email | varchar | +-------------+---------+ id is the primary key (column with unique values) for this table. Each row of this table contains an email. The emails will not contain uppercase letters.
Write a solution to delete all duplicate emails, keeping only one unique email with the smallest id
.
For SQL users, please note that you are supposed to write a DELETE
statement and not a SELECT
one.
For Pandas users, please note that you are supposed to modify Person
in place.
After running your script, the answer shown is the Person
table. The driver will first compile and run your piece of code and then show the Person
table. The final order of the Person
table does not matter.
The result format is in the following example.
Example 1:
Input: Person table: +----+------------------+ | id | email | +----+------------------+ | 1 | john@example.com | | 2 | bob@example.com | | 3 | john@example.com | +----+------------------+ Output: +----+------------------+ | id | email | +----+------------------+ | 1 | john@example.com | | 2 | bob@example.com | +----+------------------+ Explanation: john@example.com is repeated two times. We keep the row with the smallest Id = 1.
import pandas as pd
# Modify Person in place
def delete_duplicate_emails(person: pd.DataFrame) -> None:
# Sort the rows based on id (Ascending order)
person.sort_values(by="id", ascending=True, inplace=True)
# Drop the duplicates based on email.
person.drop_duplicates(subset="email", keep="first", inplace=True)
# Write your MySQL query statement below
DELETE FROM Person
WHERE id NOT IN (SELECT MIN(id) FROM (SELECT * FROM Person) AS p GROUP BY email);
# Write your MySQL query statement below
DELETE FROM Person
WHERE
id NOT IN (
SELECT id
FROM
(
SELECT
id,
ROW_NUMBER() OVER (
PARTITION BY email
ORDER BY id
) AS rk
FROM Person
) AS p
WHERE rk = 1
);
DELETE p2
FROM
person AS p1
JOIN person AS p2 ON p1.email = p2.email
WHERE
p1.id < p2.id;