Reshape Data- Melt

Problem

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.

Examples

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.

## Solution

### Method 1 - Pandas Melt

#### Intuition
We want to convert the DataFrame from wide to long format, so each row is a product-quarter-sales triple. This is a classic use case for pandas `melt`.

#### Approach
Use `pd.melt` to unpivot the DataFrame, renaming columns as needed.

#### Code

{{< code_tabs >}}
##### Python (pandas)
```python
def melt_report(report: pd.DataFrame) -> pd.DataFrame:
    result = report.melt(id_vars=['product'], var_name='quarter', value_name='sales')
    return result

Complexity

• ⏰ Time complexity: O(n) — where n is the number of cells in the DataFrame.
• 🧺 Space complexity: O(n) — for the output DataFrame.