{ "data": { "question": { "questionId": "3064", "questionFrontendId": "2888", "boundTopicId": null, "title": "Reshape Data: Concatenate", "titleSlug": "reshape-data-concatenate", "content": "
\nDataFrame\n\ndf1
\n+-------------+--------+\n| Column Name | Type |\n+-------------+--------+\n| student_id | int |\n| name | object |\n| age | int |\n+-------------+--------+\n\nDataFramedf2
\n+-------------+--------+\n| Column Name | Type |\n+-------------+--------+\n| student_id | int |\n| name | object |\n| age | int |\n+-------------+--------+\n\n
Write a solution to concatenate these two DataFrames vertically into one DataFrame.
\n\nThe result format is in the following example.
\n\n\n
Example 1:
\n\n\nInput:\ndf1\n+------------+---------+-----+\n| student_id | name | age |\n+------------+---------+-----+\n| 1 | Mason | 8 |\n| 2 | Ava | 6 |\n| 3 | Taylor | 15 |\n| 4 | Georgia | 17 |\n+------------+---------+-----+\ndf2\n+------------+------+-----+\n| student_id | name | age |\n+------------+------+-----+\n| 5 | Leo | 7 |\n| 6 | Alex | 7 |\n+------------+------+-----+\nOutput:\n+------------+---------+-----+\n| student_id | name | age |\n+------------+---------+-----+\n| 1 | Mason | 8 |\n| 2 | Ava | 6 |\n| 3 | Taylor | 15 |\n| 4 | Georgia | 17 |\n| 5 | Leo | 7 |\n| 6 | Alex | 7 |\n+------------+---------+-----+\nExplanation:\nThe two DataFramess are stacked vertically, and their rows are combined.\n", "translatedTitle": null, "translatedContent": null, "isPaidOnly": false, "difficulty": "Easy", "likes": 26, "dislikes": 4, "isLiked": null, "similarQuestions": "[]", "exampleTestcases": "{\"headers\":{\"df1\":[\"student_id\",\"name\",\"age\"],\"df2\":[\"student_id\",\"name\",\"age\"]},\"rows\":{\"df1\":[[1,\"Mason\",8],[2,\"Ava\",6],[3,\"Taylor\",15],[4,\"Georgia\",17]],\"df2\":[[5,\"Leo\",7],[6,\"Alex\",7]]}}", "categoryTitle": "pandas", "contributors": [], "topicTags": [], "companyTagStats": null, "codeSnippets": [ { "lang": "Pandas", "langSlug": "pythondata", "code": "import pandas as pd\n\ndef concatenateTables(df1: pd.DataFrame, df2: pd.DataFrame) -> pd.DataFrame:\n ", "__typename": "CodeSnippetNode" } ], "stats": "{\"totalAccepted\": \"8.9K\", \"totalSubmission\": \"10.1K\", \"totalAcceptedRaw\": 8901, \"totalSubmissionRaw\": 10061, \"acRate\": \"88.5%\"}", "hints": [ "Consider using a built-in function in pandas library with the appropriate axis argument." ], "solution": { "id": "2102", "canSeeDetail": true, "paidOnly": false, "hasVideoSolution": false, "paidOnlyVideo": true, "__typename": "ArticleNode" }, "status": null, "sampleTestCase": "{\"headers\":{\"df1\":[\"student_id\",\"name\",\"age\"],\"df2\":[\"student_id\",\"name\",\"age\"]},\"rows\":{\"df1\":[[1,\"Mason\",8],[2,\"Ava\",6],[3,\"Taylor\",15],[4,\"Georgia\",17]],\"df2\":[[5,\"Leo\",7],[6,\"Alex\",7]]}}", "metaData": "{\n \"pythondata\": [\n \"df1 = pd.DataFrame([], columns=['student_id', 'name', 'age']).astype({'student_id':'Int64', 'name':'object', 'age':'Int64'})\",\n \"df2 = pd.DataFrame([], columns=['student_id', 'name', 'age']).astype({'student_id':'Int64', 'name':'object', 'age':'Int64'})\"\n ],\n \"database\": true,\n \"name\": \"concatenateTables\",\n \"languages\": [\n \"pythondata\"\n ]\n}", "judgerAvailable": true, "judgeType": "large", "mysqlSchemas": [], "enableRunCode": true, "enableTestMode": false, "enableDebugger": false, "envInfo": "{\"pythondata\": [\"Pandas\", \"
Python 3.10 with Pandas 2.0.2 and NumPy 1.25.0
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