9/25 PANDAS (合併列表 )

Table: Person

+-------------+---------+
| Column Name | Type    |
+-------------+---------+
| personId    | int     |
| lastName    | varchar |
| firstName   | varchar |
+-------------+---------+
personId is the primary key (column with unique values) for this table.
This table contains information about the ID of some persons and their first and last names.

 

Table: Address

+-------------+---------+
| Column Name | Type    |
+-------------+---------+
| addressId   | int     |
| personId    | int     |
| city        | varchar |
| state       | varchar |
+-------------+---------+
addressId is the primary key (column with unique values) for this table.
Each row of this table contains information about the city and state of one person with ID = PersonId.

 

Write a solution to report the first name, last name, city, and state of each person in the Person table. If the address of a personId is not present in the Address table, report null instead.

Return the result table in any order.

The result format is in the following example.

 

Example 1:

Input: 
Person table:
+----------+----------+-----------+
| personId | lastName | firstName |
+----------+----------+-----------+
| 1        | Wang     | Allen     |
| 2        | Alice    | Bob       |
+----------+----------+-----------+
Address table:
+-----------+----------+---------------+------------+
| addressId | personId | city          | state      |
+-----------+----------+---------------+------------+
| 1         | 2        | New York City | New York   |
| 2         | 3        | Leetcode      | California |
+-----------+----------+---------------+------------+
Output: 
| firstName | lastName | city          | state    |
+-----------+----------+---------------+----------+
| Allen     | Wang     | Null          | Null     |
| Bob       | Alice    | New York City | New York |
+-----------+----------+---------------+----------+
Explanation: 
There is no address in the address table for the personId = 1 so we return null in their city and state. 

addressId = 1 contains information about the address of personId = 2. 

import pandas as pd

def combine_two_tables(person: pd.DataFrame, address: pd.DataFrame) -> pd.DataFrame:

    res=person.merge(address,how='left',on='personId')

    print(res)

    return res[['firstName','lastName','city','state']]