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59 lines
4.1 KiB
JSON
59 lines
4.1 KiB
JSON
{
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"question": {
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"questionId": "3070",
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"questionFrontendId": "2887",
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"boundTopicId": null,
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"title": "Fill Missing Data",
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"titleSlug": "fill-missing-data",
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"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> </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",
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"translatedTitle": null,
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"difficulty": "Easy",
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"likes": 26,
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"similarQuestions": "[]",
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"exampleTestcases": "{\"headers\":{\"products\":[\"name\",\"quantity\",\"price\"]},\"rows\":{\"products\":[[\"Wristwatch\",null,135],[\"WirelessEarbuds\",null,821],[\"GolfClubs\",779,9319],[\"Printer\",849,3051]]}}",
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"categoryTitle": "pandas",
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"contributors": [],
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"codeSnippets": [
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{
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"lang": "Pandas",
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"langSlug": "pythondata",
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"code": "import pandas as pd\n\ndef fillMissingValues(products: pd.DataFrame) -> pd.DataFrame:\n ",
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}
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"hints": [
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"Consider using a build-in function in pandas library to fill the missing values of specified columns."
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],
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"solution": {
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"id": "2112",
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},
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"status": null,
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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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"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}",
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"envInfo": "{\"pythondata\": [\"Pandas\", \"<p>Python 3.10 with Pandas 2.0.2 and NumPy 1.25.0</p>\"]}",
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