{
    "data": {
        "question": {
            "questionId": "3070",
            "questionFrontendId": "2887",
            "categoryTitle": "pandas",
            "boundTopicId": 2467496,
            "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",
            "isPaidOnly": false,
            "difficulty": "Easy",
            "likes": 0,
            "dislikes": 0,
            "isLiked": null,
            "similarQuestions": "[]",
            "contributors": [],
            "langToValidPlayground": "{\"cpp\": false, \"java\": false, \"python\": false, \"python3\": false, \"mysql\": false, \"mssql\": false, \"oraclesql\": false, \"c\": false, \"csharp\": false, \"javascript\": false, \"typescript\": false, \"bash\": false, \"php\": false, \"swift\": false, \"kotlin\": false, \"dart\": false, \"golang\": false, \"ruby\": false, \"scala\": false, \"html\": false, \"pythonml\": false, \"rust\": false, \"racket\": false, \"erlang\": false, \"elixir\": false, \"pythondata\": false, \"react\": false, \"vanillajs\": false, \"postgresql\": false}",
            "topicTags": [],
            "companyTagStats": null,
            "codeSnippets": [
                {
                    "lang": "Pandas",
                    "langSlug": "pythondata",
                    "code": "import pandas as pd\n\ndef fillMissingValues(products: pd.DataFrame) -> pd.DataFrame:\n    ",
                    "__typename": "CodeSnippetNode"
                }
            ],
            "stats": "{\"totalAccepted\": \"1.3K\", \"totalSubmission\": \"1.8K\", \"totalAcceptedRaw\": 1276, \"totalSubmissionRaw\": 1821, \"acRate\": \"70.1%\"}",
            "hints": [
                "Consider using a build-in function in pandas library to fill the missing values of specified columns."
            ],
            "solution": null,
            "status": null,
            "sampleTestCase": "{\"headers\":{\"products\":[\"name\",\"quantity\",\"price\"]},\"rows\":{\"products\":[[\"Wristwatch\",null,135],[\"WirelessEarbuds\",null,821],[\"GolfClubs\",779,9319],[\"Printer\",849,3051]]}}",
            "metaData": "{\n  \"pythondata\": [\n    \"products = pd.DataFrame([], columns=['name', 'quantity', 'price']).astype({'name':'object', 'quantity':'Int64', 'price':'Int64'})\"\n  ],\n  \"database\": true,\n  \"name\": \"fillMissingValues\",\n  \"languages\": [\n    \"pythondata\"\n  ]\n}",
            "judgerAvailable": true,
            "judgeType": "large",
            "mysqlSchemas": [],
            "enableRunCode": true,
            "envInfo": "{\"pythondata\":[\"Pandas\",\"<p>Python 3.10 with Pandas 2.0.2 and NumPy 1.25.0<\\/p>\"]}",
            "book": null,
            "isSubscribed": false,
            "isDailyQuestion": false,
            "dailyRecordStatus": null,
            "editorType": "CKEDITOR",
            "ugcQuestionId": null,
            "style": "LEETCODE",
            "exampleTestcases": "{\"headers\":{\"products\":[\"name\",\"quantity\",\"price\"]},\"rows\":{\"products\":[[\"Wristwatch\",null,135],[\"WirelessEarbuds\",null,821],[\"GolfClubs\",779,9319],[\"Printer\",849,3051]]}}",
            "__typename": "QuestionNode"
        }
    }
}