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

{
    "data": {
        "question": {
            "questionId": "3076",
            "questionFrontendId": "2878",
            "boundTopicId": null,
            "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",
            "translatedTitle": null,
            "translatedContent": null,
            "isPaidOnly": false,
            "difficulty": "Easy",
            "likes": 42,
            "dislikes": 6,
            "isLiked": null,
            "similarQuestions": "[]",
            "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\"]]}}",
            "categoryTitle": "pandas",
            "contributors": [],
            "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\": \"15.3K\", \"totalSubmission\": \"18.3K\", \"totalAcceptedRaw\": 15311, \"totalSubmissionRaw\": 18324, \"acRate\": \"83.6%\"}",
            "hints": [
                "Consider using a built-in function in pandas library to get the size of a DataFrame."
            ],
            "solution": {
                "id": "2109",
                "canSeeDetail": true,
                "paidOnly": false,
                "hasVideoSolution": false,
                "paidOnlyVideo": true,
                "__typename": "ArticleNode"
            },
            "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,
            "judgeType": "large",
            "mysqlSchemas": [],
            "enableRunCode": true,
            "enableTestMode": false,
            "enableDebugger": false,
            "envInfo": "{\"pythondata\": [\"Pandas\", \"<p>Python 3.10 with Pandas 2.0.2 and NumPy 1.25.0</p>\"]}",
            "libraryUrl": null,
            "adminUrl": null,
            "challengeQuestion": null,
            "__typename": "QuestionNode"
        }
    }
}