Welcome to Subscribe On Youtube
2890. Reshape Data Melt
Description
DataFrame report
+-------------+--------+
| Column Name | Type |
+-------------+--------+
| product | object |
| quarter_1 | int |
| quarter_2 | int |
| quarter_3 | int |
| quarter_4 | int |
+-------------+--------+
Write a solution to reshape the data so that each row represents sales data for a product in a specific quarter.
The result format is in the following example.
Example 1:
Input: +-------------+-----------+-----------+-----------+-----------+ | product | quarter_1 | quarter_2 | quarter_3 | quarter_4 | +-------------+-----------+-----------+-----------+-----------+ | Umbrella | 417 | 224 | 379 | 611 | | SleepingBag | 800 | 936 | 93 | 875 | +-------------+-----------+-----------+-----------+-----------+ Output: +-------------+-----------+-------+ | product | quarter | sales | +-------------+-----------+-------+ | Umbrella | quarter_1 | 417 | | SleepingBag | quarter_1 | 800 | | Umbrella | quarter_2 | 224 | | SleepingBag | quarter_2 | 936 | | Umbrella | quarter_3 | 379 | | SleepingBag | quarter_3 | 93 | | Umbrella | quarter_4 | 611 | | SleepingBag | quarter_4 | 875 | +-------------+-----------+-------+ Explanation: The DataFrame is reshaped from wide to long format. Each row represents the sales of a product in a quarter.
Solutions
-
import pandas as pd def meltTable(report: pd.DataFrame) -> pd.DataFrame: return pd.melt(report, id_vars=['product'], var_name='quarter', value_name='sales')