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            "questionId": "3073",
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            "title": "Reshape Data: Melt",
            "titleSlug": "reshape-data-melt",
            "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",
            "translatedTitle": "重塑数据：融合",
            "translatedContent": "<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>编写一个解决方案，将数据 <strong>重塑</strong> 成每一行表示特定季度产品销售数据的形式。</p>\n\n<p>结果格式如下例所示：</p>\n\n<p>&nbsp;</p>\n\n<p><strong class=\"example\">示例 1：</strong></p>\n\n<pre>\n<strong>输入：\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>输出：</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>解释：</strong>\nDataFrame 已从宽格式重塑为长格式。每一行表示一个季度内产品的销售情况。\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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