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59 lines
4.8 KiB
JSON
59 lines
4.8 KiB
JSON
{
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"data": {
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"question": {
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"questionId": "3072",
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"questionFrontendId": "2889",
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"boundTopicId": null,
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"title": "Reshape Data: Pivot",
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"titleSlug": "reshape-data-pivot",
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"content": "<pre>\nDataFrame <code>weather</code>\n+-------------+--------+\n| Column Name | Type |\n+-------------+--------+\n| city | object |\n| month | object |\n| temperature | int |\n+-------------+--------+\n</pre>\n\n<p>Write a solution to <strong>pivot</strong> the data so that each row represents temperatures for a specific month, and each city is a separate 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+--------------+----------+-------------+\n| city | month | temperature |\n+--------------+----------+-------------+\n| Jacksonville | January | 13 |\n| Jacksonville | February | 23 |\n| Jacksonville | March | 38 |\n| Jacksonville | April | 5 |\n| Jacksonville | May | 34 |\n| ElPaso | January | 20 |\n| ElPaso | February | 6 |\n| ElPaso | March | 26 |\n| ElPaso | April | 2 |\n| ElPaso | May | 43 |\n+--------------+----------+-------------+\n<strong>Output:</strong><code>\n+----------+--------+--------------+\n| month | ElPaso | Jacksonville |\n+----------+--------+--------------+\n| April | 2 | 5 |\n| February | 6 | 23 |\n| January | 20 | 13 |\n| March | 26 | 38 |\n| May | 43 | 34 |\n+----------+--------+--------------+</code>\n<strong>Explanation:\n</strong>The table is pivoted, each column represents a city, and each row represents a specific month.</pre>\n",
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"translatedTitle": null,
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"isPaidOnly": false,
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"difficulty": "Easy",
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"likes": 33,
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"similarQuestions": "[]",
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"exampleTestcases": "{\"headers\":{\"weather\":[\"city\",\"month\",\"temperature\"]},\"rows\":{\"weather\":[[\"Jacksonville\",\"January\",13],[\"Jacksonville\",\"February\",23],[\"Jacksonville\",\"March\",38],[\"Jacksonville\",\"April\",5],[\"Jacksonville\",\"May\",34],[\"ElPaso\",\"January\",20],[\"ElPaso\",\"February\",6],[\"ElPaso\",\"March\",26],[\"ElPaso\",\"April\",2],[\"ElPaso\",\"May\",43]]}}",
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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 pivotTable(weather: pd.DataFrame) -> pd.DataFrame:\n ",
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"__typename": "CodeSnippetNode"
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}
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],
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"stats": "{\"totalAccepted\": \"7.6K\", \"totalSubmission\": \"9.6K\", \"totalAcceptedRaw\": 7569, \"totalSubmissionRaw\": 9619, \"acRate\": \"78.7%\"}",
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"hints": [
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"Consider using a built-in function in pandas library to transform the data"
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],
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"solution": {
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"id": "2106",
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"status": null,
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"sampleTestCase": "{\"headers\":{\"weather\":[\"city\",\"month\",\"temperature\"]},\"rows\":{\"weather\":[[\"Jacksonville\",\"January\",13],[\"Jacksonville\",\"February\",23],[\"Jacksonville\",\"March\",38],[\"Jacksonville\",\"April\",5],[\"Jacksonville\",\"May\",34],[\"ElPaso\",\"January\",20],[\"ElPaso\",\"February\",6],[\"ElPaso\",\"March\",26],[\"ElPaso\",\"April\",2],[\"ElPaso\",\"May\",43]]}}",
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"metaData": "{\n \"pythondata\": [\n \"weather = pd.DataFrame([], columns=['city', 'month', 'temperature']).astype({'city':'object', 'month':'object', 'temperature':'Int64'})\"\n ],\n \"database\": true,\n \"name\": \"pivot_table\",\n \"languages\": [\n \"pythondata\"\n ],\n \"manual\": true\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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