Reshape Data: Melt

Problem Description

Given a DataFrame report containing sales data for various products across four quarters, write a solution to reshape the data so that each row represents sales data for a product in a specific quarter.

Key Insights

• The input DataFrame is in a wide format, where sales figures for each quarter are in separate columns.
• The output should be in a long format, where each product has multiple rows corresponding to each quarter.
• We need to transform the quarter columns into a single column while retaining the product names and their respective sales.

Space and Time Complexity

Time Complexity: O(n) - where n is the number of products, as we are iterating through each product and each quarter. Space Complexity: O(n) - for storing the reshaped data in the new format.

Solution

To solve this problem, we will use a data transformation technique to convert the DataFrame from a wide format to a long format. Specifically, we will:

1. Identify the columns that need to be reshaped (the quarter columns).
2. Use a method to "melt" the DataFrame, which will consolidate the quarter columns into a single column while keeping the product names intact.
3. The resulting DataFrame will consist of three columns: product, quarter, and sales.

Code Solutions