leetcode-problemset/leetcode-cn/originData/fill-missing-data.json

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        "question": {
            "questionId": "3070",
            "questionFrontendId": "2887",
            "categoryTitle": "pandas",
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            "title": "Fill Missing Data",
            "titleSlug": "fill-missing-data",
            "content": "<pre>\nDataFrame <code>products</code>\n+-------------+--------+\n| Column Name | Type   |\n+-------------+--------+\n| name        | object |\n| quantity    | int    |\n| price       | int    |\n+-------------+--------+\n</pre>\n\n<p>Write a solution to fill in the missing value as <code><strong>0</strong></code> in the <code>quantity</code> column.</p>\n\n<p>The result format is in the following example.</p>\n\n<p>&nbsp;</p>\n<pre>\n<strong class=\"example\">Example 1:</strong>\n<strong>Input:</strong>+-----------------+----------+-------+\n| name            | quantity | price |\n+-----------------+----------+-------+\n| Wristwatch      | None     | 135   |\n| WirelessEarbuds | None     | 821   |\n| GolfClubs       | 779      | 9319  |\n| Printer         | 849      | 3051  |\n+-----------------+----------+-------+\n<strong>Output:\n</strong>+-----------------+----------+-------+\n| name            | quantity | price |\n+-----------------+----------+-------+\n| Wristwatch      | 0        | 135   |\n| WirelessEarbuds | 0        | 821   |\n| GolfClubs       | 779      | 9319  |\n| Printer         | 849      | 3051  |\n+-----------------+----------+-------+\n<strong>Explanation:</strong> \nThe quantity for Wristwatch and WirelessEarbuds are filled by 0.</pre>\n",
            "translatedTitle": "填充缺失值",
            "translatedContent": "<pre>\nDataFrame <code>products</code>\n+-------------+--------+\n| Column Name | Type   |\n+-------------+--------+\n| name        | object |\n| quantity    | int    |\n| price       | int    |\n+-------------+--------+\n</pre>\n\n<p>编写一个解决方案，在 <code>quantity</code> 列中将缺失的值填充为&nbsp;<code><strong>0</strong></code>。</p>\n\n<p>返回结果如下示例所示。</p>\n\n<p>&nbsp;</p>\n<strong class=\"example\">示例 1：</strong>\n\n<pre>\n<strong>输入：\n</strong>+-----------------+----------+-------+\n| name            | quantity | price |\n+-----------------+----------+-------+\n| Wristwatch      | 32       | 135   |\n| WirelessEarbuds | None     | 821   |\n| GolfClubs       | None     | 9319  |\n| Printer         | 849      | 3051  |\n+-----------------+----------+-------+\n<strong>输出：\n</strong>+-----------------+----------+-------+\n| name            | quantity | price |\n+-----------------+----------+-------+\n| Wristwatch      | 32       | 135   |\n| WirelessEarbuds | 0        | 821   |\n| GolfClubs       | 0        | 9319  |\n| Printer         | 849      | 3051  |\n+-----------------+----------+-------+\n<b>解释：</b>\nToaster 和 Headphones 的数量被填充为 0。</pre>\n",
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                    "lang": "Pandas",
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                    "code": "import pandas as pd\n\ndef fillMissingValues(products: pd.DataFrame) -> pd.DataFrame:\n    ",
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            "hints": [
                "Consider using a build-in function in pandas library to fill the missing values of specified columns."
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            "sampleTestCase": "{\"headers\":{\"products\":[\"name\",\"quantity\",\"price\"]},\"rows\":{\"products\":[[\"Wristwatch\",null,135],[\"WirelessEarbuds\",null,821],[\"GolfClubs\",779,9319],[\"Printer\",849,3051]]}}",
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