Leecode pandas groupby keep cols 1070. Product Sales Analysis III

Leecode pandas
1070. Product Sales Analysis III
Table: Sales

±------------±------+
| Column Name | Type |
±------------±------+
| sale_id | int |
| product_id | int |
| year | int |
| quantity | int |
| price | int |
±------------±------+
(sale_id, year) is the primary key (combination of columns with unique values) of this table.
product_id is a foreign key (reference column) to Product table.
Each row of this table shows a sale on the product product_id in a certain year.
Note that the price is per unit.

Table: Product

±-------------±--------+
| Column Name | Type |
±-------------±--------+
| product_id | int |
| product_name | varchar |
±-------------±--------+
product_id is the primary key (column with unique values) of this table.
Each row of this table indicates the product name of each product.

Write a solution to select the product id, year, quantity, and price for the first year of every product sold.

Return the resulting table in any order.

The result format is in the following example.

Example 1:

Input

Sales =

sale_id	product_id	year	quantity	price
1	100	2008	10	5000
2	100	2009	12	5000
7	200	2011	15	9000

Product =

product_id	product_name
100	Nokia
200	Apple
300	Samsung

Output

product_id	first_year	quantity	price
100	2008	10	5000
200	2011	15	9000

My solution

import pandas as pd

def sales_analysis(sales: pd.DataFrame, product: pd.DataFrame) -> pd.DataFrame:
    min_yr = sales.groupby('product_id')['year'].agg(min).reset_index()
    joint = min_yr.merge(sales, on=['product_id', 'year'], how='left')
    selected = joint[['product_id', 'year', 'quantity', 'price']].rename(columns={'year': 'first_year'})

    return selected

df after groupby:

product_id	year
100	2008
200	2011