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            "title": "Reshape Data: Melt",
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            "content": "<pre>\nDataFrame <code>report</code>\n+-------------+--------+\n| Column Name | Type   |\n+-------------+--------+\n| product     | object |\n| quarter_1   | int    |\n| quarter_2   | int    |\n| quarter_3   | int    |\n| quarter_4   | int    |\n+-------------+--------+\n</pre>\n\n<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>\n\n<p>The result format is in the following example.</p>\n\n<p>&nbsp;</p>\n<p><strong class=\"example\">Example 1:</strong></p>\n\n<pre>\n<strong>Input:\n</strong>+-------------+-----------+-----------+-----------+-----------+\n| product     | quarter_1 | quarter_2 | quarter_3 | quarter_4 |\n+-------------+-----------+-----------+-----------+-----------+\n| Umbrella    | 417       | 224       | 379       | 611       |\n| SleepingBag | 800       | 936       | 93        | 875       |\n+-------------+-----------+-----------+-----------+-----------+\n<strong>Output:</strong>\n+-------------+-----------+-------+\n| product     | quarter   | sales |\n+-------------+-----------+-------+\n| Umbrella    | quarter_1 | 417   |\n| SleepingBag | quarter_1 | 800   |\n| Umbrella    | quarter_2 | 224   |\n| SleepingBag | quarter_2 | 936   |\n| Umbrella    | quarter_3 | 379   |\n| SleepingBag | quarter_3 | 93    |\n| Umbrella    | quarter_4 | 611   |\n| SleepingBag | quarter_4 | 875   |\n+-------------+-----------+-------+\n<strong>Explanation:</strong>\nThe DataFrame is reshaped from wide to long format. Each row represents the sales of a product in a quarter.\n</pre>\n",
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                    "code": "import pandas as pd\n\ndef meltTable(report: pd.DataFrame) -> pd.DataFrame:\n    ",
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            "sampleTestCase": "{\"headers\":{\"report\":[\"product\",\"quarter_1\",\"quarter_2\",\"quarter_3\",\"quarter_4\"]},\"rows\":{\"report\":[[\"Umbrella\",417,224,379,611],[\"SleepingBag\",800,936,93,875]]}}",
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