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leetcode/originData/reshape-data-melt.json

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+{
+    "data": {
+        "question": {
+            "questionId": "3073",
+            "questionFrontendId": "2890",
+            "boundTopicId": null,
+            "title": "Reshape Data: Melt",
+            "titleSlug": "reshape-data-melt",
+            "content": "<pre>\nDataFrame <code>report</code>\n+-------------+--------+\n| Column Name | Type   |\n+-------------+--------+\n| product     | object |\n| quarter_1   | int    |\n| quarter_2   | int    |\n| quarter_3   | int    |\n| quarter_4   | int    |\n+-------------+--------+\n</pre>\n\n<p>Write a solution to <strong>reshape</strong> the data so that each row represents sales data for a product in a specific quarter.</p>\n\n<p>The result format is in the following example.</p>\n\n<p>&nbsp;</p>\n<p><strong class=\"example\">Example 1:</strong></p>\n\n<pre>\n<strong>Input:\n</strong>+-------------+-----------+-----------+-----------+-----------+\n| product     | quarter_1 | quarter_2 | quarter_3 | quarter_4 |\n+-------------+-----------+-----------+-----------+-----------+\n| Umbrella    | 417       | 224       | 379       | 611       |\n| SleepingBag | 800       | 936       | 93        | 875       |\n+-------------+-----------+-----------+-----------+-----------+\n<strong>Output:</strong>\n+-------------+-----------+-------+\n| product     | quarter   | sales |\n+-------------+-----------+-------+\n| Umbrella    | quarter_1 | 417   |\n| SleepingBag | quarter_1 | 800   |\n| Umbrella    | quarter_2 | 224   |\n| SleepingBag | quarter_2 | 936   |\n| Umbrella    | quarter_3 | 379   |\n| SleepingBag | quarter_3 | 93    |\n| Umbrella    | quarter_4 | 611   |\n| SleepingBag | quarter_4 | 875   |\n+-------------+-----------+-------+\n<strong>Explanation:</strong>\nThe DataFrame is reshaped from wide to long format. Each row represents the sales of a product in a quarter.\n</pre>\n",
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+            "translatedContent": null,
+            "isPaidOnly": false,
+            "difficulty": "Easy",
+            "likes": 9,
+            "dislikes": 1,
+            "isLiked": null,
+            "similarQuestions": "[]",
+            "exampleTestcases": "{\"headers\":{\"report\":[\"product\",\"quarter_1\",\"quarter_2\",\"quarter_3\",\"quarter_4\"]},\"rows\":{\"report\":[[\"Umbrella\",417,224,379,611],[\"SleepingBag\",800,936,93,875]]}}",
+            "categoryTitle": "Algorithms",
+            "contributors": [],
+            "topicTags": [],
+            "companyTagStats": null,
+            "codeSnippets": [
+                {
+                    "lang": "Pandas",
+                    "langSlug": "pythondata",
+                    "code": "import pandas as pd\n\ndef meltTable(report: pd.DataFrame) -> pd.DataFrame:\n    ",
+                    "__typename": "CodeSnippetNode"
+                }
+            ],
+            "stats": "{\"totalAccepted\": \"165\", \"totalSubmission\": \"187\", \"totalAcceptedRaw\": 165, \"totalSubmissionRaw\": 187, \"acRate\": \"88.2%\"}",
+            "hints": [
+                "Consider using a built-in function in pandas library to transform the data"
+            ],
+            "solution": {
+                "id": "2105",
+                "canSeeDetail": true,
+                "paidOnly": false,
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+                "__typename": "ArticleNode"
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+            "status": null,
+            "sampleTestCase": "{\"headers\":{\"report\":[\"product\",\"quarter_1\",\"quarter_2\",\"quarter_3\",\"quarter_4\"]},\"rows\":{\"report\":[[\"Umbrella\",417,224,379,611],[\"SleepingBag\",800,936,93,875]]}}",
+            "metaData": "{\n  \"pythondata\": [\n    \"report = pd.DataFrame([], columns=['product', 'quarter_1', 'quarter_2', 'quarter_3', 'quarter_4']).astype({'product':'object', 'quarter_1':'Int64', 'quarter_2':'Int64', 'quarter_3':'Int64', 'quarter_4':'Int64'})\"\n  ],\n  \"database\": true,\n  \"name\": \"meltTable\",\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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+}