leetcode-problemset/leetcode/originData/movie-rating.json

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        "question": {
            "questionId": "1480",
            "questionFrontendId": "1341",
            "boundTopicId": null,
            "title": "Movie Rating",
            "titleSlug": "movie-rating",
            "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>&nbsp;</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>&nbsp;</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)&nbsp;for this table.\nThis table contains the rating of a movie by a user in their review.\ncreated_at is the user&#39;s review date. \n</pre>\n\n<p>&nbsp;</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&nbsp;result format is in the following example.</p>\n\n<p>&nbsp;</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 (&quot;Avengers&quot;, &quot;Frozen 2&quot; and &quot;Joker&quot;) 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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            "isPaidOnly": false,
            "difficulty": "Medium",
            "likes": 418,
            "dislikes": 148,
            "isLiked": null,
            "similarQuestions": "[]",
            "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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                    "lang": "MySQL",
                    "langSlug": "mysql",
                    "code": "# Write your MySQL query statement below\n",
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                    "lang": "MS SQL Server",
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                {
                    "lang": "Oracle",
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                    "code": "/* Write your PL/SQL query statement below */\n",
                    "__typename": "CodeSnippetNode"
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                {
                    "lang": "Pandas",
                    "langSlug": "pythondata",
                    "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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                },
                {
                    "lang": "PostgreSQL",
                    "langSlug": "postgresql",
                    "code": "-- Write your PostgreSQL query statement below\n",
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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\"]]}}",
            "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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                "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)",
                "Truncate table Movies",
                "insert into Movies (movie_id, title) values ('1', 'Avengers')",
                "insert into Movies (movie_id, title) values ('2', 'Frozen 2')",
                "insert into Movies (movie_id, title) values ('3', 'Joker')",
                "Truncate table Users",
                "insert into Users (user_id, name) values ('1', 'Daniel')",
                "insert into Users (user_id, name) values ('2', 'Monica')",
                "insert into Users (user_id, name) values ('3', 'Maria')",
                "insert into Users (user_id, name) values ('4', 'James')",
                "Truncate table MovieRating",
                "insert into MovieRating (movie_id, user_id, rating, created_at) values ('1', '1', '3', '2020-01-12')",
                "insert into MovieRating (movie_id, user_id, rating, created_at) values ('1', '2', '4', '2020-02-11')",
                "insert into MovieRating (movie_id, user_id, rating, created_at) values ('1', '3', '2', '2020-02-12')",
                "insert into MovieRating (movie_id, user_id, rating, created_at) values ('1', '4', '1', '2020-01-01')",
                "insert into MovieRating (movie_id, user_id, rating, created_at) values ('2', '1', '5', '2020-02-17')",
                "insert into MovieRating (movie_id, user_id, rating, created_at) values ('2', '2', '2', '2020-02-01')",
                "insert into MovieRating (movie_id, user_id, rating, created_at) values ('2', '3', '2', '2020-03-01')",
                "insert into MovieRating (movie_id, user_id, rating, created_at) values ('3', '1', '3', '2020-02-22')",
                "insert into MovieRating (movie_id, user_id, rating, created_at) values ('3', '2', '4', '2020-02-25')"
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