2882. 删去重复的行

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题目

DataFrame customers

+-------------+--------+
| Column Name | Type   |
+-------------+--------+
| customer_id | int    |
| name        | object |
| email       | object |
+-------------+--------+

There are some duplicate rows in the DataFrame based on the email column.

Write a solution to remove these duplicate rows and keep only the first occurrence.

The result format is in the following example.

Example 1:

Input:

+-------------+---------+---------------------+
| customer_id | name    | email               |
+-------------+---------+---------------------+
| 1           | Ella    | emily@example.com   |
| 2           | David   | michael@example.com |
| 3           | Zachary | sarah@example.com   |
| 4           | Alice   | john@example.com    |
| 5           | Finn    | john@example.com    |
| 6           | Violet  | alice@example.com   |
+-------------+---------+---------------------+

Output:

+-------------+---------+---------------------+
| customer_id | name    | email               |
+-------------+---------+---------------------+
| 1           | Ella    | emily@example.com   |
| 2           | David   | michael@example.com |
| 3           | Zachary | sarah@example.com   |
| 4           | Alice   | john@example.com    |
| 6           | Violet  | alice@example.com   |
+-------------+---------+---------------------+

Explanation:

Alic (customer_id = 4) and Finn (customer_id = 5) both use john@example.com, so only the first occurrence of this email is retained.

题目大意

DataFrame customers

+-------------+--------+
| Column Name | Type   |
+-------------+--------+
| customer_id | int    |
| name        | object |
| email       | object |
+-------------+--------+

在 DataFrame 中基于 email 列存在一些重复行。

编写一个解决方案，删除这些重复行，仅保留第一次出现的行。

返回结果格式如下例所示。

示例 1:

输入：

+-------------+---------+---------------------+
| customer_id | name    | email               |
+-------------+---------+---------------------+
| 1           | Ella    | emily@example.com   |
| 2           | David   | michael@example.com |
| 3           | Zachary | sarah@example.com   |
| 4           | Alice   | john@example.com    |
| 5           | Finn    | john@example.com    |
| 6           | Violet  | alice@example.com   |
+-------------+---------+---------------------+

输出：

+-------------+---------+---------------------+
| customer_id | name    | email               |
+-------------+---------+---------------------+
| 1           | Ella    | emily@example.com   |
| 2           | David   | michael@example.com |
| 3           | Zachary | sarah@example.com   |
| 4           | Alice   | john@example.com    |
| 6           | Violet  | alice@example.com   |
+-------------+---------+---------------------+

解释：

Alice (customer_id = 4) 和 Finn (customer_id = 5) 都使用 john@example.com，因此只保留该邮箱地址的第一次出现。

解题思路

1. 去重操作：

   • Pandas 提供了 drop_duplicates 方法，支持根据指定列去重。
   • 调用 customers.drop_duplicates(subset='email')，表示以 email 列为基准，自动检测并去掉 email 列中的重复记录，保留每组重复值的第一条记录。

2. 返回新 DataFrame：

   • drop_duplicates 不修改原 DataFrame，而是返回去重后的新 DataFrame。
   • 如果希望对原 DataFrame 就地修改，需显式传递 inplace=True 参数。

复杂度分析

• 时间复杂度：O(n)，其中 n 是行数，去重操作需要对 email 列进行遍历和哈希检查。
• 空间复杂度：O(n)，去重操作需要一个临时哈希。

代码

import pandas as pd

def dropDuplicateEmails(customers: pd.DataFrame) -> pd.DataFrame:
  return customers.drop_duplicates('email')