mirror of
https://gitee.com/coder-xiaomo/leetcode-problemset
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76 lines
6.8 KiB
JSON
76 lines
6.8 KiB
JSON
{
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"data": {
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"question": {
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"questionId": "3589",
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"questionFrontendId": "3278",
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"categoryTitle": "Database",
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"boundTopicId": 2900887,
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"title": "Find Candidates for Data Scientist Position II",
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"titleSlug": "find-candidates-for-data-scientist-position-ii",
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"content": null,
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"translatedTitle": "寻找数据科学家职位的候选人 II",
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"translatedContent": null,
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"isPaidOnly": true,
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"difficulty": "Medium",
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"likes": 1,
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"topicTags": [
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{
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"name": "Database",
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"slug": "database",
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"translatedName": "数据库",
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"__typename": "TopicTagNode"
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}
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],
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"companyTagStats": null,
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"codeSnippets": null,
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"stats": "{\"totalAccepted\": \"206\", \"totalSubmission\": \"424\", \"totalAcceptedRaw\": 206, \"totalSubmissionRaw\": 424, \"acRate\": \"48.6%\"}",
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"hints": [],
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"solution": null,
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"status": null,
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"sampleTestCase": "{\"headers\":{\"Candidates\":[\"candidate_id\",\"skill\",\"proficiency\"],\"Projects\":[\"project_id\",\"skill\",\"importance\"]},\"rows\":{\"Candidates\":[[101,\"Python\",5],[101,\"Tableau\",3],[101,\"PostgreSQL\",4],[101,\"TensorFlow\",2],[102,\"Python\",4],[102,\"Tableau\",5],[102,\"PostgreSQL\",4],[102,\"R\",4],[103,\"Python\",3],[103,\"Tableau\",5],[103,\"PostgreSQL\",5],[103,\"Spark\",4]],\"Projects\":[[501,\"Python\",4],[501,\"Tableau\",3],[501,\"PostgreSQL\",5],[502,\"Python\",3],[502,\"Tableau\",4],[502,\"R\",2]]}}",
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"metaData": "{\"mysql\":[\"Create table if not exists Candidates(candidate_id int, skill varchar(50), proficiency int)\",\"Create table if not exists Projects( project_id int, skill varchar(50), importance int)\"],\"mssql\":[\"Create table Candidates(candidate_id int, skill varchar(50), proficiency int)\",\"Create table Projects( project_id int, skill varchar(50), importance int)\"],\"oraclesql\":[\"Create table Candidates(candidate_id number, skill varchar2(50), proficiency number)\",\"Create table Projects(project_id number, skill varchar2(50), importance number)\"],\"database\":true,\"name\":\"find_best_candidates\",\"postgresql\":[\"CREATE TABLE IF NOT EXISTS Candidates (\\n candidate_id INT,\\n skill VARCHAR(50),\\n proficiency INT\\n);\\n\",\"CREATE TABLE IF NOT EXISTS Projects (\\n project_id INT,\\n skill VARCHAR(50),\\n importance INT\\n);\\n\"],\"pythondata\":[\"Candidates = pd.DataFrame({\\n 'candidate_id': pd.Series(dtype='int'),\\n 'skill': pd.Series(dtype='str'),\\n 'proficiency': pd.Series(dtype='int')\\n})\",\"Projects = pd.DataFrame({\\n 'project_id': pd.Series(dtype='int'),\\n 'skill': pd.Series(dtype='str'),\\n 'importance': pd.Series(dtype='int')\\n})\"],\"database_schema\":{\"Candidates\":{\"candidate_id\":\"INT\",\"skill\":\"VARCHAR(50)\",\"proficiency\":\"INT\"},\"Projects\":{\"project_id\":\"INT\",\"skill\":\"VARCHAR(50)\",\"importance\":\"INT\"}}}",
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"judgerAvailable": true,
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"judgeType": "large",
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"mysqlSchemas": [
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"Create table if not exists Candidates(candidate_id int, skill varchar(50), proficiency int)",
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"Create table if not exists Projects( project_id int, skill varchar(50), importance int)",
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"Truncate table Candidates",
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"insert into Candidates (candidate_id, skill, proficiency) values ('101', 'Python', '5')",
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"insert into Candidates (candidate_id, skill, proficiency) values ('101', 'Tableau', '3')",
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"insert into Candidates (candidate_id, skill, proficiency) values ('101', 'PostgreSQL', '4')",
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"insert into Candidates (candidate_id, skill, proficiency) values ('101', 'TensorFlow', '2')",
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"insert into Candidates (candidate_id, skill, proficiency) values ('102', 'Python', '4')",
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"insert into Candidates (candidate_id, skill, proficiency) values ('102', 'Tableau', '5')",
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"insert into Candidates (candidate_id, skill, proficiency) values ('102', 'PostgreSQL', '4')",
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"insert into Candidates (candidate_id, skill, proficiency) values ('102', 'R', '4')",
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"insert into Candidates (candidate_id, skill, proficiency) values ('103', 'Python', '3')",
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"insert into Candidates (candidate_id, skill, proficiency) values ('103', 'Tableau', '5')",
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"insert into Candidates (candidate_id, skill, proficiency) values ('103', 'PostgreSQL', '5')",
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"insert into Candidates (candidate_id, skill, proficiency) values ('103', 'Spark', '4')",
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"Truncate table Projects",
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"insert into Projects (project_id, skill, importance) values ('501', 'Python', '4')",
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"insert into Projects (project_id, skill, importance) values ('501', 'Tableau', '3')",
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"insert into Projects (project_id, skill, importance) values ('501', 'PostgreSQL', '5')",
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"insert into Projects (project_id, skill, importance) values ('502', 'Python', '3')",
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"insert into Projects (project_id, skill, importance) values ('502', 'Tableau', '4')",
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"insert into Projects (project_id, skill, importance) values ('502', 'R', '2')"
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],
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"enableRunCode": true,
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"envInfo": "{\"mysql\":[\"MySQL\",\"<p>\\u7248\\u672c\\uff1a<code>MySQL 8.0<\\/code><\\/p>\"],\"mssql\":[\"MS SQL Server\",\"<p>mssql server 2019.<\\/p>\"],\"oraclesql\":[\"Oracle\",\"<p>Oracle Sql 11.2.<\\/p>\"],\"pythondata\":[\"Pandas\",\"<p>Python 3.10 with Pandas 2.2.2 and NumPy 1.26.4<\\/p>\"],\"postgresql\":[\"PostgreSQL\",\"<p>PostgreSQL 16<\\/p>\"]}",
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"book": null,
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"isDailyQuestion": false,
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"editorType": "CKEDITOR",
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"ugcQuestionId": null,
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"style": "LEETCODE",
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"exampleTestcases": "{\"headers\":{\"Candidates\":[\"candidate_id\",\"skill\",\"proficiency\"],\"Projects\":[\"project_id\",\"skill\",\"importance\"]},\"rows\":{\"Candidates\":[[101,\"Python\",5],[101,\"Tableau\",3],[101,\"PostgreSQL\",4],[101,\"TensorFlow\",2],[102,\"Python\",4],[102,\"Tableau\",5],[102,\"PostgreSQL\",4],[102,\"R\",4],[103,\"Python\",3],[103,\"Tableau\",5],[103,\"PostgreSQL\",5],[103,\"Spark\",4]],\"Projects\":[[501,\"Python\",4],[501,\"Tableau\",3],[501,\"PostgreSQL\",5],[502,\"Python\",3],[502,\"Tableau\",4],[502,\"R\",2]]}}",
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"__typename": "QuestionNode"
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}
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}
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} |