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55 lines
6.8 KiB
JSON
55 lines
6.8 KiB
JSON
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"questionId": "3072",
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"questionFrontendId": "2889",
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"categoryTitle": "pandas",
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"boundTopicId": 2467493,
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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": "数据重塑：透视",
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"translatedContent": "<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>编写一个解决方案，以便将数据 <strong>旋转</strong>，使得每一行代表特定月份的温度，而每个城市都是一个单独的列。</p>\n\n<p>输出结果格式如下示例所示。</p>\n\n<p> </p>\n<b>示例 1:</b>\n\n<pre>\n<b>输入：</b>\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<code><b>输出：</b>\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>解释：\n</strong>表格被旋转，每一列代表一个城市，每一行代表特定的月份。</pre>\n",
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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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"Consider using a built-in function in pandas library to transform the data"
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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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"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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