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{
"data": {
"question": {
"questionId": "3070",
"questionFrontendId": "2887",
"boundTopicId": null,
"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 | 32 | 135 |\n| WirelessEarbuds | None | 821 |\n| GolfClubs | None | 9319 |\n| Printer | 849 | 3051 |\n+-----------------+----------+-------+\n<strong>Output:\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<strong>Explanation:</strong> \nThe quantity for Toaster and Headphones are filled by 0.</pre>\n",
"translatedTitle": null,
"translatedContent": null,
"isPaidOnly": false,
"difficulty": "Easy",
"likes": 8,
"dislikes": 1,
"isLiked": null,
"similarQuestions": "[]",
"exampleTestcases": "{\"headers\":{\"products\":[\"name\",\"quantity\",\"price\"]},\"rows\":{\"products\":[[\"Wristwatch\",32,135],[\"WirelessEarbuds\",null,821],[\"GolfClubs\",null,9319],[\"Printer\",849,3051]]}}",
"categoryTitle": "Algorithms",
"contributors": [],
"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\": \"283\", \"totalSubmission\": \"297\", \"totalAcceptedRaw\": 283, \"totalSubmissionRaw\": 297, \"acRate\": \"95.3%\"}",
"hints": [
"Consider using a build-in function in pandas library to fill the missing values of specified columns."
],
"solution": {
"id": "2112",
"canSeeDetail": true,
"paidOnly": false,
"hasVideoSolution": false,
"paidOnlyVideo": true,
"__typename": "ArticleNode"
},
"status": null,
"sampleTestCase": "{\"headers\":{\"products\":[\"name\",\"quantity\",\"price\"]},\"rows\":{\"products\":[[\"Wristwatch\",32,135],[\"WirelessEarbuds\",null,821],[\"GolfClubs\",null,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,
"enableTestMode": false,
"enableDebugger": true,
"envInfo": "{\"pythondata\": [\"Pandas\", \"<p>Python 3.10 with Pandas 2.0.2 and NumPy 1.25.0</p>\"]}",
"libraryUrl": null,
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"challengeQuestion": null,
"__typename": "QuestionNode"
}
}
}