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leetcode-cn/originData/get-the-size-of-a-dataframe.json

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+{
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+        "question": {
+            "questionId": "3076",
+            "questionFrontendId": "100017",
+            "categoryTitle": "Algorithms",
+            "boundTopicId": 2467483,
+            "title": "Get the Size of a DataFrame",
+            "titleSlug": "get-the-size-of-a-dataframe",
+            "content": "<pre>\nDataFrame <code>players:</code>\n+-------------+--------+\n| Column Name | Type   |\n+-------------+--------+\n| player_id   | int    |\n| name        | object |\n| age         | int    |\n| position    | object |\n| ...         | ...    |\n+-------------+--------+\n</pre>\n\n<p>Write a solution to calculate and display the <strong>number of rows and columns</strong> of <code>players</code>.</p>\n\n<p>Return the result as an array:</p>\n\n<p><code>[number of rows, number of columns]</code></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| player_id | name     | age | position    | team               |\n+-----------+----------+-----+-------------+--------------------+\n| 846       | Mason    | 21  | Forward     | RealMadrid         |\n| 749       | Riley    | 30  | Winger      | Barcelona          |\n| 155       | Bob      | 28  | Striker     | ManchesterUnited   |\n| 583       | Isabella | 32  | Goalkeeper  | Liverpool          |\n| 388       | Zachary  | 24  | Midfielder  | BayernMunich       |\n| 883       | Ava      | 23  | Defender    | Chelsea            |\n| 355       | Violet   | 18  | Striker     | Juventus           |\n| 247       | Thomas   | 27  | Striker     | ParisSaint-Germain |\n| 761       | Jack     | 33  | Midfielder  | ManchesterCity     |\n| 642       | Charlie  | 36  | Center-back | Arsenal            |\n+-----------+----------+-----+-------------+--------------------+<strong>\nOutput:\n</strong>[10, 5]\n<strong>Explanation:</strong>\nThis DataFrame contains 10 rows and 5 columns.\n</pre>\n",
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+            "translatedContent": null,
+            "isPaidOnly": false,
+            "difficulty": "Easy",
+            "likes": 0,
+            "dislikes": 0,
+            "isLiked": null,
+            "similarQuestions": "[]",
+            "contributors": [],
+            "langToValidPlayground": "{\"cpp\": false, \"java\": false, \"python\": false, \"python3\": false, \"mysql\": false, \"mssql\": false, \"oraclesql\": false, \"c\": false, \"csharp\": false, \"javascript\": false, \"typescript\": false, \"bash\": false, \"php\": false, \"swift\": false, \"kotlin\": false, \"dart\": false, \"golang\": false, \"ruby\": false, \"scala\": false, \"html\": false, \"pythonml\": false, \"rust\": false, \"racket\": false, \"erlang\": false, \"elixir\": false, \"pythondata\": false, \"react\": false, \"vanillajs\": false, \"postgresql\": false}",
+            "topicTags": [],
+            "companyTagStats": null,
+            "codeSnippets": [
+                {
+                    "lang": "Pandas",
+                    "langSlug": "pythondata",
+                    "code": "import pandas as pd\n\ndef getDataframeSize(players: pd.DataFrame) -> List[int]:\n    ",
+                    "__typename": "CodeSnippetNode"
+                }
+            ],
+            "stats": "{\"totalAccepted\": \"18\", \"totalSubmission\": \"18\", \"totalAcceptedRaw\": 18, \"totalSubmissionRaw\": 18, \"acRate\": \"100.0%\"}",
+            "hints": [
+                "Consider using a built-in function in pandas library to get the size of a DataFrame."
+            ],
+            "solution": null,
+            "status": null,
+            "sampleTestCase": "{\"headers\":{\"players\":[\"player_id\",\"name\",\"age\",\"position\",\"team\"]},\"rows\":{\"players\":[[846,\"Mason\",21,\"Forward\",\"RealMadrid\"],[749,\"Riley\",30,\"Winger\",\"Barcelona\"],[155,\"Bob\",28,\"Striker\",\"ManchesterUnited\"],[583,\"Isabella\",32,\"Goalkeeper\",\"Liverpool\"],[388,\"Zachary\",24,\"Midfielder\",\"BayernMunich\"],[883,\"Ava\",23,\"Defender\",\"Chelsea\"],[355,\"Violet\",18,\"Striker\",\"Juventus\"],[247,\"Thomas\",27,\"Striker\",\"ParisSaint-Germain\"],[761,\"Jack\",33,\"Midfielder\",\"ManchesterCity\"],[642,\"Charlie\",36,\"Center-back\",\"Arsenal\"]]}}",
+            "metaData": "{\n  \"pythondata\": [\n    \"players = pd.DataFrame([], columns=['player_id', 'name', 'age', 'position', 'team']).astype({'player_id':'Int64', 'name':'object', 'age':'Int64', 'position':'object', 'team':'object'})\"\n  ],\n  \"database\": true,\n  \"name\": \"get_size\",\n  \"languages\": [\n    \"pythondata\"\n  ],\n  \"manual\": true\n}",
+            "judgerAvailable": true,
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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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+            "isDailyQuestion": false,
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+            "ugcQuestionId": null,
+            "style": "LEETCODE",
+            "exampleTestcases": "{\"headers\":{\"players\":[\"player_id\",\"name\",\"age\",\"position\",\"team\"]},\"rows\":{\"players\":[[846,\"Mason\",21,\"Forward\",\"RealMadrid\"],[749,\"Riley\",30,\"Winger\",\"Barcelona\"],[155,\"Bob\",28,\"Striker\",\"ManchesterUnited\"],[583,\"Isabella\",32,\"Goalkeeper\",\"Liverpool\"],[388,\"Zachary\",24,\"Midfielder\",\"BayernMunich\"],[883,\"Ava\",23,\"Defender\",\"Chelsea\"],[355,\"Violet\",18,\"Striker\",\"Juventus\"],[247,\"Thomas\",27,\"Striker\",\"ParisSaint-Germain\"],[761,\"Jack\",33,\"Midfielder\",\"ManchesterCity\"],[642,\"Charlie\",36,\"Center-back\",\"Arsenal\"]]}}",
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+        }
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+}