mirror of
https://gitee.com/coder-xiaomo/leetcode-problemset
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104 lines
12 KiB
JSON
104 lines
12 KiB
JSON
{
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"data": {
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"question": {
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"questionId": "1480",
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"questionFrontendId": "1341",
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"boundTopicId": null,
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"title": "Movie Rating",
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"titleSlug": "movie-rating",
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"content": "<p>Table: <code>Movies</code></p>\n\n<pre>\n+---------------+---------+\n| Column Name | Type |\n+---------------+---------+\n| movie_id | int |\n| title | varchar |\n+---------------+---------+\nmovie_id is the primary key (column with unique values) for this table.\ntitle is the name of the movie.\n</pre>\n\n<p> </p>\n\n<p>Table: <code>Users</code></p>\n\n<pre>\n+---------------+---------+\n| Column Name | Type |\n+---------------+---------+\n| user_id | int |\n| name | varchar |\n+---------------+---------+\nuser_id is the primary key (column with unique values) for this table.\n</pre>\n\n<p> </p>\n\n<p>Table: <code>MovieRating</code></p>\n\n<pre>\n+---------------+---------+\n| Column Name | Type |\n+---------------+---------+\n| movie_id | int |\n| user_id | int |\n| rating | int |\n| created_at | date |\n+---------------+---------+\n(movie_id, user_id) is the primary key (column with unique values) for this table.\nThis table contains the rating of a movie by a user in their review.\ncreated_at is the user's review date. \n</pre>\n\n<p> </p>\n\n<p>Write a solution to:</p>\n\n<ul>\n\t<li>Find the name of the user who has rated the greatest number of movies. In case of a tie, return the lexicographically smaller user name.</li>\n\t<li>Find the movie name with the <strong>highest average</strong> rating in <code>February 2020</code>. In case of a tie, return the lexicographically smaller movie name.</li>\n</ul>\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> \nMovies table:\n+-------------+--------------+\n| movie_id | title |\n+-------------+--------------+\n| 1 | Avengers |\n| 2 | Frozen 2 |\n| 3 | Joker |\n+-------------+--------------+\nUsers table:\n+-------------+--------------+\n| user_id | name |\n+-------------+--------------+\n| 1 | Daniel |\n| 2 | Monica |\n| 3 | Maria |\n| 4 | James |\n+-------------+--------------+\nMovieRating table:\n+-------------+--------------+--------------+-------------+\n| movie_id | user_id | rating | created_at |\n+-------------+--------------+--------------+-------------+\n| 1 | 1 | 3 | 2020-01-12 |\n| 1 | 2 | 4 | 2020-02-11 |\n| 1 | 3 | 2 | 2020-02-12 |\n| 1 | 4 | 1 | 2020-01-01 |\n| 2 | 1 | 5 | 2020-02-17 | \n| 2 | 2 | 2 | 2020-02-01 | \n| 2 | 3 | 2 | 2020-03-01 |\n| 3 | 1 | 3 | 2020-02-22 | \n| 3 | 2 | 4 | 2020-02-25 | \n+-------------+--------------+--------------+-------------+\n<strong>Output:</strong> \n+--------------+\n| results |\n+--------------+\n| Daniel |\n| Frozen 2 |\n+--------------+\n<strong>Explanation:</strong> \nDaniel and Monica have rated 3 movies ("Avengers", "Frozen 2" and "Joker") but Daniel is smaller lexicographically.\nFrozen 2 and Joker have a rating average of 3.5 in February but Frozen 2 is smaller lexicographically.\n</pre>\n",
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"translatedTitle": null,
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"isPaidOnly": false,
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"difficulty": "Medium",
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"likes": 418,
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"dislikes": 148,
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"isLiked": null,
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"similarQuestions": "[]",
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"exampleTestcases": "{\"headers\": {\"Movies\": [\"movie_id\", \"title\"], \"Users\": [\"user_id\", \"name\"], \"MovieRating\": [\"movie_id\", \"user_id\", \"rating\", \"created_at\"]}, \"rows\": {\"Movies\": [[1, \"Avengers\"], [2, \"Frozen 2\"], [3, \"Joker\"]], \"Users\": [[1, \"Daniel\"], [2, \"Monica\"], [3, \"Maria\"], [4, \"James\"]], \"MovieRating\": [[1, 1, 3, \"2020-01-12\"], [1, 2, 4, \"2020-02-11\"], [1, 3, 2, \"2020-02-12\"], [1, 4, 1, \"2020-01-01\"], [2, 1, 5, \"2020-02-17\"], [2, 2, 2, \"2020-02-01\"], [2, 3, 2, \"2020-03-01\"], [3, 1, 3, \"2020-02-22\"], [3, 2, 4, \"2020-02-25\"]]}}",
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"categoryTitle": "Database",
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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 movie_rating(movies: pd.DataFrame, users: pd.DataFrame, movie_rating: 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\": \"56.5K\", \"totalSubmission\": \"139.1K\", \"totalAcceptedRaw\": 56523, \"totalSubmissionRaw\": 139116, \"acRate\": \"40.6%\"}",
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"hints": [],
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"solution": null,
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"status": null,
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"sampleTestCase": "{\"headers\": {\"Movies\": [\"movie_id\", \"title\"], \"Users\": [\"user_id\", \"name\"], \"MovieRating\": [\"movie_id\", \"user_id\", \"rating\", \"created_at\"]}, \"rows\": {\"Movies\": [[1, \"Avengers\"], [2, \"Frozen 2\"], [3, \"Joker\"]], \"Users\": [[1, \"Daniel\"], [2, \"Monica\"], [3, \"Maria\"], [4, \"James\"]], \"MovieRating\": [[1, 1, 3, \"2020-01-12\"], [1, 2, 4, \"2020-02-11\"], [1, 3, 2, \"2020-02-12\"], [1, 4, 1, \"2020-01-01\"], [2, 1, 5, \"2020-02-17\"], [2, 2, 2, \"2020-02-01\"], [2, 3, 2, \"2020-03-01\"], [3, 1, 3, \"2020-02-22\"], [3, 2, 4, \"2020-02-25\"]]}}",
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"metaData": "{\"mysql\": [\"Create table If Not Exists Movies (movie_id int, title varchar(30))\", \"Create table If Not Exists Users (user_id int, name varchar(30))\", \"Create table If Not Exists MovieRating (movie_id int, user_id int, rating int, created_at date)\"], \"mssql\": [\"Create table Movies (movie_id int, title varchar(30))\", \"Create table Users (user_id int, name varchar(30))\", \"Create table MovieRating (movie_id int, user_id int, rating int, created_at date)\"], \"oraclesql\": [\"Create table Movies (movie_id int, title varchar(30))\", \"Create table Users (user_id int, name varchar(30))\", \"Create table MovieRating (movie_id int, user_id int, rating int, created_at date)\", \"ALTER SESSION SET nls_date_format='YYYY-MM-DD'\"], \"database\": true, \"name\": \"movie_rating\", \"pythondata\": [\"Movies = pd.DataFrame([], columns=['movie_id', 'title']).astype({'movie_id':'Int64', 'title':'object'})\", \"Users = pd.DataFrame([], columns=['user_id', 'name']).astype({'user_id':'Int64', 'name':'object'})\", \"MovieRating = pd.DataFrame([], columns=['movie_id', 'user_id', 'rating', 'created_at']).astype({'movie_id':'Int64', 'user_id':'Int64', 'rating':'Int64', 'created_at':'datetime64[ns]'})\"], \"postgresql\": [\"\\nCreate table If Not Exists Movies (movie_id int, title varchar(30))\", \"Create table If Not Exists Users (user_id int, name varchar(30))\", \"Create table If Not Exists MovieRating (movie_id int, user_id int, rating int, created_at date)\"], \"database_schema\": {\"Movies\": {\"movie_id\": \"INT\", \"title\": \"VARCHAR(30)\"}, \"Users\": {\"user_id\": \"INT\", \"name\": \"VARCHAR(30)\"}, \"MovieRating\": {\"movie_id\": \"INT\", \"user_id\": \"INT\", \"rating\": \"INT\", \"created_at\": \"DATE\"}}}",
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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 Movies (movie_id int, title varchar(30))",
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"Create table If Not Exists Users (user_id int, name varchar(30))",
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"Create table If Not Exists MovieRating (movie_id int, user_id int, rating int, created_at date)",
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"Truncate table Movies",
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"insert into Movies (movie_id, title) values ('1', 'Avengers')",
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"insert into Movies (movie_id, title) values ('2', 'Frozen 2')",
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"insert into Movies (movie_id, title) values ('3', 'Joker')",
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"Truncate table Users",
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"insert into Users (user_id, name) values ('1', 'Daniel')",
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"insert into Users (user_id, name) values ('2', 'Monica')",
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"insert into Users (user_id, name) values ('3', 'Maria')",
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"insert into Users (user_id, name) values ('4', 'James')",
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"Truncate table MovieRating",
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"insert into MovieRating (movie_id, user_id, rating, created_at) values ('1', '1', '3', '2020-01-12')",
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"insert into MovieRating (movie_id, user_id, rating, created_at) values ('1', '2', '4', '2020-02-11')",
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"insert into MovieRating (movie_id, user_id, rating, created_at) values ('1', '3', '2', '2020-02-12')",
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"insert into MovieRating (movie_id, user_id, rating, created_at) values ('1', '4', '1', '2020-01-01')",
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"insert into MovieRating (movie_id, user_id, rating, created_at) values ('2', '1', '5', '2020-02-17')",
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"insert into MovieRating (movie_id, user_id, rating, created_at) values ('2', '2', '2', '2020-02-01')",
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"insert into MovieRating (movie_id, user_id, rating, created_at) values ('2', '3', '2', '2020-03-01')",
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"insert into MovieRating (movie_id, user_id, rating, created_at) values ('3', '1', '3', '2020-02-22')",
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"insert into MovieRating (movie_id, user_id, rating, created_at) values ('3', '2', '4', '2020-02-25')"
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],
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"enableRunCode": true,
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"enableTestMode": false,
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"enableDebugger": 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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