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https://gitee.com/coder-xiaomo/leetcode-problemset
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96 lines
7.4 KiB
JSON
96 lines
7.4 KiB
JSON
{
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"question": {
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"questionId": "620",
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"questionFrontendId": "620",
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"boundTopicId": null,
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"title": "Not Boring Movies",
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"titleSlug": "not-boring-movies",
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"content": "<p>Table: <code>Cinema</code></p>\n\n<pre>\n+----------------+----------+\n| Column Name | Type |\n+----------------+----------+\n| id | int |\n| movie | varchar |\n| description | varchar |\n| rating | float |\n+----------------+----------+\nid is the primary key (column with unique values) for this table.\nEach row contains information about the name of a movie, its genre, and its rating.\nrating is a 2 decimal places float in the range [0, 10]\n</pre>\n\n<p> </p>\n\n<p>Write a solution to report the movies with an odd-numbered ID and a description that is not <code>"boring"</code>.</p>\n\n<p>Return the result table ordered by <code>rating</code> <strong>in descending order</strong>.</p>\n\n<p>The result format is in the following example.</p>\n\n<p> </p>\n<p><strong class=\"example\">Example 1:</strong></p>\n\n<pre>\n<strong>Input:</strong> \nCinema table:\n+----+------------+-------------+--------+\n| id | movie | description | rating |\n+----+------------+-------------+--------+\n| 1 | War | great 3D | 8.9 |\n| 2 | Science | fiction | 8.5 |\n| 3 | irish | boring | 6.2 |\n| 4 | Ice song | Fantacy | 8.6 |\n| 5 | House card | Interesting | 9.1 |\n+----+------------+-------------+--------+\n<strong>Output:</strong> \n+----+------------+-------------+--------+\n| id | movie | description | rating |\n+----+------------+-------------+--------+\n| 5 | House card | Interesting | 9.1 |\n| 1 | War | great 3D | 8.9 |\n+----+------------+-------------+--------+\n<strong>Explanation:</strong> \nWe have three movies with odd-numbered IDs: 1, 3, and 5. The movie with ID = 3 is boring so we do not include it in the answer.\n</pre>\n",
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"translatedContent": null,
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"isPaidOnly": false,
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"difficulty": "Easy",
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"likes": 983,
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"similarQuestions": "[]",
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"exampleTestcases": "{\"headers\":{\"cinema\":[\"id\", \"movie\", \"description\", \"rating\"]},\"rows\":{\"cinema\":[[1, \"War\", \"great 3D\", 8.9], [2, \"Science\", \"fiction\", 8.5], [3, \"irish\", \"boring\", 6.2], [4, \"Ice song\", \"Fantacy\", 8.6], [5, \"House card\", \"Interesting\", 9.1]]}}",
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"categoryTitle": "Database",
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"contributors": [],
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{
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"name": "Database",
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"codeSnippets": [
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{
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"lang": "MySQL",
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"langSlug": "mysql",
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"code": "# Write your MySQL query statement below\n",
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"__typename": "CodeSnippetNode"
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},
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{
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"lang": "MS SQL Server",
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"langSlug": "mssql",
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"code": "/* Write your T-SQL query statement below */\n",
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"__typename": "CodeSnippetNode"
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},
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{
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"lang": "Oracle",
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"langSlug": "oraclesql",
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"code": "/* Write your PL/SQL query statement below */\n",
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"__typename": "CodeSnippetNode"
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},
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{
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"lang": "Pandas",
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"langSlug": "pythondata",
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"code": "import pandas as pd\n\ndef not_boring_movies(cinema: pd.DataFrame) -> pd.DataFrame:\n ",
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"__typename": "CodeSnippetNode"
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},
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{
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"lang": "PostgreSQL",
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"langSlug": "postgresql",
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"code": "-- Write your PostgreSQL query statement below\n",
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"__typename": "CodeSnippetNode"
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}
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],
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"stats": "{\"totalAccepted\": \"344.5K\", \"totalSubmission\": \"473.2K\", \"totalAcceptedRaw\": 344525, \"totalSubmissionRaw\": 473164, \"acRate\": \"72.8%\"}",
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"hints": [],
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"solution": {
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},
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"status": null,
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"sampleTestCase": "{\"headers\":{\"cinema\":[\"id\", \"movie\", \"description\", \"rating\"]},\"rows\":{\"cinema\":[[1, \"War\", \"great 3D\", 8.9], [2, \"Science\", \"fiction\", 8.5], [3, \"irish\", \"boring\", 6.2], [4, \"Ice song\", \"Fantacy\", 8.6], [5, \"House card\", \"Interesting\", 9.1]]}}",
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"metaData": "{\"mysql\": [\"Create table If Not Exists cinema (id int, movie varchar(255), description varchar(255), rating float(2, 1))\"], \"mssql\": [\"Create table cinema (id int, movie varchar(255), description varchar(255), rating decimal(2, 1))\"], \"oraclesql\": [\"Create table cinema (id int, movie varchar(255), description varchar(255), rating number(2, 1))\"], \"database\": true, \"name\": \"not_boring_movies\", \"pythondata\": [\"cinema = pd.DataFrame([], columns=['id', 'movie', 'description', 'rating']).astype({'id':'Int64', 'movie':'object', 'description':'object', 'rating':'Float64'})\"], \"postgresql\": [\"Create table If Not Exists cinema (id int, movie varchar(255), description varchar(255), rating NUMERIC(3, 1))\"], \"manual\": false, \"database_schema\": {\"cinema\": {\"id\": \"INT\", \"movie\": \"VARCHAR(255)\", \"description\": \"VARCHAR(255)\", \"rating\": \"FLOAT(2, 1)\"}}}",
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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 cinema (id int, movie varchar(255), description varchar(255), rating float(2, 1))",
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"Truncate table cinema",
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"insert into cinema (id, movie, description, rating) values ('1', 'War', 'great 3D', '8.9')",
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"insert into cinema (id, movie, description, rating) values ('2', 'Science', 'fiction', '8.5')",
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"insert into cinema (id, movie, description, rating) values ('3', 'irish', 'boring', '6.2')",
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"insert into cinema (id, movie, description, rating) values ('4', 'Ice song', 'Fantacy', '8.6')",
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"insert into cinema (id, movie, description, rating) values ('5', 'House card', 'Interesting', '9.1')"
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],
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"enableRunCode": true,
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"enableTestMode": false,
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"envInfo": "{\"mysql\": [\"MySQL\", \"<p><code>MySQL 8.0</code>.</p>\"], \"mssql\": [\"MS SQL Server\", \"<p><code>mssql server 2019</code>.</p>\"], \"oraclesql\": [\"Oracle\", \"<p><code>Oracle Sql 11.2</code>.</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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}
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