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Diff for: ‎README.md

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# 力扣题库（完整版）
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> 最后更新日期： **2024.07.16**
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> 最后更新日期： **2024.08.06**
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>
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> 使用脚本前请务必仔细完整阅读本 `README.md` 文件
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Diff for: ‎leetcode-cn/origin-data.json

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Diff for: ‎leetcode-cn/originData/[no content]alt-and-tab-simulation.json

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{
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"data": {
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"question": {
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"questionId": "3538",
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"questionFrontendId": "3237",
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"categoryTitle": "Algorithms",
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"boundTopicId": 2865158,
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"title": "Alt and Tab Simulation",
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"titleSlug": "alt-and-tab-simulation",
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"content": null,
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"translatedTitle": "Alt 和 Tab模拟",
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"translatedContent": null,
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"isPaidOnly": true,
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"difficulty": "Medium",
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"likes": 0,
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"isLiked": null,
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"similarQuestions": "[]",
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"contributors": [],
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"topicTags": [
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{
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"name": "Array",
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"slug": "array",
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"translatedName": "数组",
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"__typename": "TopicTagNode"
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},
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{
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"name": "Hash Table",
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"slug": "hash-table",
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"translatedName": "哈希表",
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"__typename": "TopicTagNode"
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},
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{
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"name": "Simulation",
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"slug": "simulation",
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"translatedName": "模拟",
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],
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"companyTagStats": null,
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"stats": "{\"totalAccepted\": \"51\", \"totalSubmission\": \"56\", \"totalAcceptedRaw\": 51, \"totalSubmissionRaw\": 56, \"acRate\": \"91.1%\"}",
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"hints": [
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"Let’s answer the queries offline.",
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"Start from the <b>last</b> query until you reach the first query.",
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"Each time, append the value of that query to a <code>ans</code> array if it wasn’t already appended.",
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"Then start from the <b>beginning</b> of <code>windows</code> and append to <code>ans</code> array if it wasn’t already appended."
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],
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"solution": null,
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"status": null,
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"sampleTestCase": "[1,2,3]\n[3,3,2]",
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"metaData": "{\n \"name\": \"simulationResult\",\n \"params\": [\n {\n \"name\": \"windows\",\n \"type\": \"integer[]\"\n },\n {\n \"type\": \"integer[]\",\n \"name\": \"queries\"\n }\n ],\n \"return\": {\n \"type\": \"integer[]\"\n }\n}",
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"judgerAvailable": true,
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Diff for: ‎leetcode-cn/originData/[no content]ceo-subordinate-hierarchy.json

+64
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1+
{
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"data": {
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"question": {
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"questionId": "3549",
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"questionFrontendId": "3236",
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"categoryTitle": "Database",
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"title": "CEO Subordinate Hierarchy",
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"titleSlug": "ceo-subordinate-hierarchy",
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"content": null,
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"hints": [],
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"sampleTestCase": "{\"headers\":{\"Employees\":[\"employee_id\",\"employee_name\",\"manager_id\",\"salary\"]},\"rows\":{\"Employees\":[[1,\"Alice\",null,150000],[2,\"Bob\",1,120000],[3,\"Charlie\",1,110000],[4,\"David\",2,105000],[5,\"Eve\",2,100000],[6,\"Frank\",3,95000],[7,\"Grace\",3,98000],[8,\"Helen\",5,90000]]}}",
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"metaData": "{\"mysql\":[\"Create table if not exists employees(employee_id int, employee_name varchar(100), manager_id int, salary int)\"],\"mssql\":[\"Create table employees(employee_id int, employee_name varchar(100), manager_id int, salary int)\"],\"oraclesql\":[\"Create table employees(employee_id NUMBER, employee_name varchar2(100), manager_id NUMBER, salary NUMBER)\"],\"database\":true,\"name\":\"find_subordinates\",\"manual\":false,\"postgresql\":[\"CREATE TABLE IF NOT EXISTS employees (\\n employee_id INT,\\n employee_name VARCHAR(100),\\n manager_id INT,\\n salary INT\\n);\\n\"],\"pythondata\":[\"Employees = pd.DataFrame(columns=['employee_id', 'employee_name', 'manager_id', 'salary']).astype({\\n 'employee_id': pd.Int64Dtype(),\\n 'employee_name': 'str',\\n 'manager_id': pd.Int64Dtype(),\\n 'salary': pd.Int64Dtype()\\n})\"],\"database_schema\":{\"employees\":{\"employee_id\":\"INT\",\"employee_name\":\"VARCHAR(100)\",\"manager_id\":\"INT\",\"salary\":\"INT\"}}}",
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"judgerAvailable": true,
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"judgeType": "large",
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"mysqlSchemas": [
40+
"Create table if not exists employees(employee_id int, employee_name varchar(100), manager_id int, salary int)",
41+
"Truncate table Employees",
42+
"insert into Employees (employee_id, employee_name, manager_id, salary) values ('1', 'Alice', 'None', '150000')",
43+
"insert into Employees (employee_id, employee_name, manager_id, salary) values ('2', 'Bob', '1', '120000')",
44+
"insert into Employees (employee_id, employee_name, manager_id, salary) values ('3', 'Charlie', '1', '110000')",
45+
"insert into Employees (employee_id, employee_name, manager_id, salary) values ('4', 'David', '2', '105000')",
46+
"insert into Employees (employee_id, employee_name, manager_id, salary) values ('5', 'Eve', '2', '100000')",
47+
"insert into Employees (employee_id, employee_name, manager_id, salary) values ('6', 'Frank', '3', '95000')",
48+
"insert into Employees (employee_id, employee_name, manager_id, salary) values ('7', 'Grace', '3', '98000')",
49+
"insert into Employees (employee_id, employee_name, manager_id, salary) values ('8', 'Helen', '5', '90000')"
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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 2.0.2 and NumPy 1.25.0<\\/p>\"],\"postgresql\":[\"PostgreSQL\",\"<p>PostgreSQL 16<\\/p>\"]}",
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"book": null,
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"isSubscribed": false,
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"dailyRecordStatus": null,
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"editorType": "CKEDITOR",
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"ugcQuestionId": null,
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"style": "LEETCODE",
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"exampleTestcases": "{\"headers\":{\"Employees\":[\"employee_id\",\"employee_name\",\"manager_id\",\"salary\"]},\"rows\":{\"Employees\":[[1,\"Alice\",null,150000],[2,\"Bob\",1,120000],[3,\"Charlie\",1,110000],[4,\"David\",2,105000],[5,\"Eve\",2,100000],[6,\"Frank\",3,95000],[7,\"Grace\",3,98000],[8,\"Helen\",5,90000]]}}",
61+
"__typename": "QuestionNode"
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}
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}
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}

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