Reshape Data: Melt
<pre>
DataFrame <code>report</code>
+-------------+--------+
| Column Name | Type   |
+-------------+--------+
| product     | object |
| quarter_1   | int    |
| quarter_2   | int    |
| quarter_3   | int    |
| quarter_4   | int    |
+-------------+--------+
</pre>

<p>Write a solution to <strong>reshape</strong> the data so that each row represents sales data for a product in a specific quarter.</p>

<p>The result format is in the following example.</p>

<p>&nbsp;</p>
<p><strong class="example">Example 1:</strong></p>

<pre>
<strong>Input:
</strong>+-------------+-----------+-----------+-----------+-----------+
| product     | quarter_1 | quarter_2 | quarter_3 | quarter_4 |
+-------------+-----------+-----------+-----------+-----------+
| Umbrella    | 417       | 224       | 379       | 611       |
| SleepingBag | 800       | 936       | 93        | 875       |
+-------------+-----------+-----------+-----------+-----------+
<strong>Output:</strong>
+-------------+-----------+-------+
| 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   |
+-------------+-----------+-------+
<strong>Explanation:</strong>
The DataFrame is reshaped from wide to long format. Each row represents the sales of a product in a quarter.
</pre>

[]
[]
[{"value": "pythondata", "text": "Pandas", "defaultCode": "import pandas as pd\n\ndef meltTable(report: pd.DataFrame) -> pd.DataFrame:\n    "}]
{
  "pythondata": [
    "report = pd.DataFrame([], columns=['product', 'quarter_1', 'quarter_2', 'quarter_3', 'quarter_4']).astype({'product':'object', 'quarter_1':'Int64', 'quarter_2':'Int64', 'quarter_3':'Int64', 'quarter_4':'Int64'})"
  ],
  "database": true,
  "name": "meltTable",
  "languages": [
    "pythondata"
  ]
}
{"pythondata": ["Pandas", "<p>Python 3.10 with Pandas 2.2.2 and NumPy 1.26.4</p>"]}
[]