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https://gitee.com/coder-xiaomo/leetcode-problemset
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107 lines
19 KiB
JSON
107 lines
19 KiB
JSON
{
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"data": {
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"question": {
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"questionId": "3996",
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"questionFrontendId": "3642",
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"categoryTitle": "Database",
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"boundTopicId": 3745957,
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"title": "Find Books with Polarized Opinions",
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"titleSlug": "find-books-with-polarized-opinions",
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"content": "<p>Table: <code>books</code></p>\n\n<pre>\n+-------------+---------+\n| Column Name | Type |\n+-------------+---------+\n| book_id | int |\n| title | varchar |\n| author | varchar |\n| genre | varchar |\n| pages | int |\n+-------------+---------+\nbook_id is the unique ID for this table.\nEach row contains information about a book including its genre and page count.\n</pre>\n\n<p>Table: <code>reading_sessions</code></p>\n\n<pre>\n+----------------+---------+\n| Column Name | Type |\n+----------------+---------+\n| session_id | int |\n| book_id | int |\n| reader_name | varchar |\n| pages_read | int |\n| session_rating | int |\n+----------------+---------+\nsession_id is the unique ID for this table.\nEach row represents a reading session where someone read a portion of a book. session_rating is on a scale of 1-5.\n</pre>\n\n<p>Write a solution to find books that have <strong>polarized opinions</strong> - books that receive both very high ratings and very low ratings from different readers.</p>\n\n<ul>\n\t<li>A book has polarized opinions if it has <code>at least one rating ≥ 4</code> and <code>at least one rating ≤ 2</code></li>\n\t<li>Only consider books that have <strong>at least </strong><code>5</code><strong> reading sessions</strong></li>\n\t<li>Calculate the <strong>rating spread</strong> as (<code>highest_rating - lowest_rating</code>)</li>\n\t<li>Calculate the <strong>polarization score</strong> as the number of extreme ratings (<code>ratings ≤ 2 or ≥ 4</code>) divided by total sessions</li>\n\t<li><strong>Only include</strong> books where <code>polarization score ≥ 0.6</code> (at least <code>60%</code> extreme ratings)</li>\n</ul>\n\n<p>Return <em>the result table ordered by polarization score in <strong>descending</strong> order, then by title in <strong>descending</strong> order</em>.</p>\n\n<p>The result format is in the following example.</p>\n\n<p> </p>\n<p><strong class=\"example\">Example:</strong></p>\n\n<div class=\"example-block\">\n<p><strong>Input:</strong></p>\n\n<p>books table:</p>\n\n<pre class=\"example-io\">\n+---------+------------------------+---------------+----------+-------+\n| book_id | title | author | genre | pages |\n+---------+------------------------+---------------+----------+-------+\n| 1 | The Great Gatsby | F. Scott | Fiction | 180 |\n| 2 | To Kill a Mockingbird | Harper Lee | Fiction | 281 |\n| 3 | 1984 | George Orwell | Dystopian| 328 |\n| 4 | Pride and Prejudice | Jane Austen | Romance | 432 |\n| 5 | The Catcher in the Rye | J.D. Salinger | Fiction | 277 |\n+---------+------------------------+---------------+----------+-------+\n</pre>\n\n<p>reading_sessions table:</p>\n\n<pre class=\"example-io\">\n+------------+---------+-------------+------------+----------------+\n| session_id | book_id | reader_name | pages_read | session_rating |\n+------------+---------+-------------+------------+----------------+\n| 1 | 1 | Alice | 50 | 5 |\n| 2 | 1 | Bob | 60 | 1 |\n| 3 | 1 | Carol | 40 | 4 |\n| 4 | 1 | David | 30 | 2 |\n| 5 | 1 | Emma | 45 | 5 |\n| 6 | 2 | Frank | 80 | 4 |\n| 7 | 2 | Grace | 70 | 4 |\n| 8 | 2 | Henry | 90 | 5 |\n| 9 | 2 | Ivy | 60 | 4 |\n| 10 | 2 | Jack | 75 | 4 |\n| 11 | 3 | Kate | 100 | 2 |\n| 12 | 3 | Liam | 120 | 1 |\n| 13 | 3 | Mia | 80 | 2 |\n| 14 | 3 | Noah | 90 | 1 |\n| 15 | 3 | Olivia | 110 | 4 |\n| 16 | 3 | Paul | 95 | 5 |\n| 17 | 4 | Quinn | 150 | 3 |\n| 18 | 4 | Ruby | 140 | 3 |\n| 19 | 5 | Sam | 80 | 1 |\n| 20 | 5 | Tara | 70 | 2 |\n+------------+---------+-------------+------------+----------------+\n</pre>\n\n<p><strong>Output:</strong></p>\n\n<pre class=\"example-io\">\n+---------+------------------+---------------+-----------+-------+---------------+--------------------+\n| book_id | title | author | genre | pages | rating_spread | polarization_score |\n+---------+------------------+---------------+-----------+-------+---------------+--------------------+\n| 1 | The Great Gatsby | F. Scott | Fiction | 180 | 4 | 1.00 |\n| 3 | 1984 | George Orwell | Dystopian | 328 | 4 | 1.00 |\n+---------+------------------+---------------+-----------+-------+---------------+--------------------+\n</pre>\n\n<p><strong>Explanation:</strong></p>\n\n<ul>\n\t<li><strong>The Great Gatsby (book_id = 1):</strong>\n\n\t<ul>\n\t\t<li>Has 5 reading sessions (meets minimum requirement)</li>\n\t\t<li>Ratings: 5, 1, 4, 2, 5</li>\n\t\t<li>Has ratings ≥ 4: 5, 4, 5 (3 sessions)</li>\n\t\t<li>Has ratings ≤ 2: 1, 2 (2 sessions)</li>\n\t\t<li>Rating spread: 5 - 1 = 4</li>\n\t\t<li>Extreme ratings (≤2 or ≥4): All 5 sessions (5, 1, 4, 2, 5)</li>\n\t\t<li>Polarization score: 5/5 = 1.00 (≥ 0.6, qualifies)</li>\n\t</ul>\n\t</li>\n\t<li><strong>1984 (book_id = 3):</strong>\n\t<ul>\n\t\t<li>Has 6 reading sessions (meets minimum requirement)</li>\n\t\t<li>Ratings: 2, 1, 2, 1, 4, 5</li>\n\t\t<li>Has ratings ≥ 4: 4, 5 (2 sessions)</li>\n\t\t<li>Has ratings ≤ 2: 2, 1, 2, 1 (4 sessions)</li>\n\t\t<li>Rating spread: 5 - 1 = 4</li>\n\t\t<li>Extreme ratings (≤2 or ≥4): All 6 sessions (2, 1, 2, 1, 4, 5)</li>\n\t\t<li>Polarization score: 6/6 = 1.00 (≥ 0.6, qualifies)</li>\n\t</ul>\n\t</li>\n\t<li><strong>Books not included:</strong>\n\t<ul>\n\t\t<li>To Kill a Mockingbird (book_id = 2): All ratings are 4-5, no low ratings (≤2)</li>\n\t\t<li>Pride and Prejudice (book_id = 4): Only 2 sessions (< 5 minimum)</li>\n\t\t<li>The Catcher in the Rye (book_id = 5): Only 2 sessions (< 5 minimum)</li>\n\t</ul>\n\t</li>\n</ul>\n\n<p>The result table is ordered by polarization score in descending order, then by book title in descending order.</p>\n</div>\n",
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"difficulty": "Easy",
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"likes": 0,
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"similarQuestions": "[]",
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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",
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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 */",
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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 */",
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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 find_polarized_books(books: pd.DataFrame, reading_sessions: 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",
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"__typename": "CodeSnippetNode"
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}
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],
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"stats": "{\"totalAccepted\": \"73\", \"totalSubmission\": \"115\", \"totalAcceptedRaw\": 73, \"totalSubmissionRaw\": 115, \"acRate\": \"63.5%\"}",
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"hints": [],
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"solution": null,
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"status": null,
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"sampleTestCase": "{\"headers\":{\"books\":[\"book_id\",\"title\",\"author\",\"genre\",\"pages\"],\"reading_sessions\":[\"session_id\",\"book_id\",\"reader_name\",\"pages_read\",\"session_rating\"]},\"rows\":{\"books\":[[1,\"The Great Gatsby\",\"F. Scott\",\"Fiction\",180],[2,\"To Kill a Mockingbird\",\"Harper Lee\",\"Fiction\",281],[3,\"1984\",\"George Orwell\",\"Dystopian\",328],[4,\"Pride and Prejudice\",\"Jane Austen\",\"Romance\",432],[5,\"The Catcher in the Rye\",\"J.D. Salinger\",\"Fiction\",277]],\"reading_sessions\":[[1,1,\"Alice\",50,5],[2,1,\"Bob\",60,1],[3,1,\"Carol\",40,4],[4,1,\"David\",30,2],[5,1,\"Emma\",45,5],[6,2,\"Frank\",80,4],[7,2,\"Grace\",70,4],[8,2,\"Henry\",90,5],[9,2,\"Ivy\",60,4],[10,2,\"Jack\",75,4],[11,3,\"Kate\",100,2],[12,3,\"Liam\",120,1],[13,3,\"Mia\",80,2],[14,3,\"Noah\",90,1],[15,3,\"Olivia\",110,4],[16,3,\"Paul\",95,5],[17,4,\"Quinn\",150,3],[18,4,\"Ruby\",140,3],[19,5,\"Sam\",80,1],[20,5,\"Tara\",70,2]]}}",
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"metaData": "{\"mysql\":[\"CREATE TABLE books (\\n book_id INT,\\n title VARCHAR(255),\\n author VARCHAR(255),\\n genre VARCHAR(100),\\n pages INT\\n)\",\"CREATE TABLE reading_sessions (\\n session_id INT,\\n book_id INT,\\n reader_name VARCHAR(255),\\n pages_read INT,\\n session_rating INT\\n)\"],\"mssql\":[\"CREATE TABLE books (\\n book_id INT,\\n title VARCHAR(255),\\n author VARCHAR(255),\\n genre VARCHAR(100),\\n pages INT\\n)\",\"CREATE TABLE reading_sessions (\\n session_id INT,\\n book_id INT,\\n reader_name VARCHAR(255),\\n pages_read INT,\\n session_rating INT\\n)\"],\"oraclesql\":[\"CREATE TABLE books (\\n book_id NUMBER,\\n title VARCHAR2(255),\\n author VARCHAR2(255),\\n genre VARCHAR2(100),\\n pages NUMBER\\n)\",\"CREATE TABLE reading_sessions (\\n session_id NUMBER,\\n book_id NUMBER,\\n reader_name VARCHAR2(255),\\n pages_read NUMBER,\\n session_rating NUMBER\\n)\"],\"database\":true,\"name\":\"find_polarized_books\",\"postgresql\":[\"CREATE TABLE books (\\n book_id SERIAL PRIMARY KEY,\\n title VARCHAR(255),\\n author VARCHAR(255),\\n genre VARCHAR(100),\\n pages INTEGER\\n);\\n\",\"CREATE TABLE reading_sessions (\\n session_id SERIAL PRIMARY KEY,\\n book_id INTEGER,\\n reader_name VARCHAR(255),\\n pages_read INTEGER,\\n session_rating INTEGER\\n);\\n\"],\"pythondata\":[\"books = pd.DataFrame({\\n \\\"book_id\\\": pd.Series(dtype=\\\"int64\\\"), # SERIAL -> int64\\n \\\"title\\\": pd.Series(dtype=\\\"string\\\"), # VARCHAR -> string dtype\\n \\\"author\\\": pd.Series(dtype=\\\"string\\\"), # VARCHAR -> string dtype\\n \\\"genre\\\": pd.Series(dtype=\\\"string\\\"), # VARCHAR -> string dtype\\n \\\"pages\\\": pd.Series(dtype=\\\"int64\\\") # INTEGER -> int64\\n})\",\"reading_sessions = pd.DataFrame({\\n \\\"session_id\\\": pd.Series(dtype=\\\"int64\\\"), # NUMBER -> int64\\n \\\"book_id\\\": pd.Series(dtype=\\\"int64\\\"), # NUMBER -> int64\\n \\\"reader_name\\\": pd.Series(dtype=\\\"string\\\"), # VARCHAR2 -> string dtype\\n \\\"pages_read\\\": pd.Series(dtype=\\\"int64\\\"), # NUMBER -> int64\\n \\\"session_rating\\\": pd.Series(dtype=\\\"int64\\\") # NUMBER -> int64\\n})\"],\"database_schema\":{\"books\":{\"book_id\":\"INT\",\"title\":\"VARCHAR(255)\",\"author\":\"VARCHAR(255)\",\"genre\":\"VARCHAR(100)\",\"pages\":\"INT\"},\"reading_sessions\":{\"session_id\":\"INT\",\"book_id\":\"INT\",\"reader_name\":\"VARCHAR(255)\",\"pages_read\":\"INT\",\"session_rating\":\"INT\"}}}",
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"judgerAvailable": true,
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"judgeType": "large",
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"mysqlSchemas": [
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"CREATE TABLE books (\n book_id INT,\n title VARCHAR(255),\n author VARCHAR(255),\n genre VARCHAR(100),\n pages INT\n)",
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"CREATE TABLE reading_sessions (\n session_id INT,\n book_id INT,\n reader_name VARCHAR(255),\n pages_read INT,\n session_rating INT\n)",
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"Truncate table books",
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"insert into books (book_id, title, author, genre, pages) values ('1', 'The Great Gatsby', 'F. Scott', 'Fiction', '180')",
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"insert into books (book_id, title, author, genre, pages) values ('2', 'To Kill a Mockingbird', 'Harper Lee', 'Fiction', '281')",
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"insert into books (book_id, title, author, genre, pages) values ('3', '1984', 'George Orwell', 'Dystopian', '328')",
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"insert into books (book_id, title, author, genre, pages) values ('4', 'Pride and Prejudice', 'Jane Austen', 'Romance', '432')",
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"insert into books (book_id, title, author, genre, pages) values ('5', 'The Catcher in the Rye', 'J.D. Salinger', 'Fiction', '277')",
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"Truncate table reading_sessions",
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"insert into reading_sessions (session_id, book_id, reader_name, pages_read, session_rating) values ('1', '1', 'Alice', '50', '5')",
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"insert into reading_sessions (session_id, book_id, reader_name, pages_read, session_rating) values ('2', '1', 'Bob', '60', '1')",
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"insert into reading_sessions (session_id, book_id, reader_name, pages_read, session_rating) values ('3', '1', 'Carol', '40', '4')",
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"insert into reading_sessions (session_id, book_id, reader_name, pages_read, session_rating) values ('4', '1', 'David', '30', '2')",
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"insert into reading_sessions (session_id, book_id, reader_name, pages_read, session_rating) values ('5', '1', 'Emma', '45', '5')",
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"insert into reading_sessions (session_id, book_id, reader_name, pages_read, session_rating) values ('6', '2', 'Frank', '80', '4')",
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"insert into reading_sessions (session_id, book_id, reader_name, pages_read, session_rating) values ('7', '2', 'Grace', '70', '4')",
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"insert into reading_sessions (session_id, book_id, reader_name, pages_read, session_rating) values ('8', '2', 'Henry', '90', '5')",
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"insert into reading_sessions (session_id, book_id, reader_name, pages_read, session_rating) values ('9', '2', 'Ivy', '60', '4')",
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"insert into reading_sessions (session_id, book_id, reader_name, pages_read, session_rating) values ('10', '2', 'Jack', '75', '4')",
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"insert into reading_sessions (session_id, book_id, reader_name, pages_read, session_rating) values ('11', '3', 'Kate', '100', '2')",
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"insert into reading_sessions (session_id, book_id, reader_name, pages_read, session_rating) values ('12', '3', 'Liam', '120', '1')",
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"insert into reading_sessions (session_id, book_id, reader_name, pages_read, session_rating) values ('13', '3', 'Mia', '80', '2')",
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"insert into reading_sessions (session_id, book_id, reader_name, pages_read, session_rating) values ('14', '3', 'Noah', '90', '1')",
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"insert into reading_sessions (session_id, book_id, reader_name, pages_read, session_rating) values ('15', '3', 'Olivia', '110', '4')",
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"insert into reading_sessions (session_id, book_id, reader_name, pages_read, session_rating) values ('16', '3', 'Paul', '95', '5')",
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"insert into reading_sessions (session_id, book_id, reader_name, pages_read, session_rating) values ('17', '4', 'Quinn', '150', '3')",
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"insert into reading_sessions (session_id, book_id, reader_name, pages_read, session_rating) values ('18', '4', 'Ruby', '140', '3')",
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"insert into reading_sessions (session_id, book_id, reader_name, pages_read, session_rating) values ('19', '5', 'Sam', '80', '1')",
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"insert into reading_sessions (session_id, book_id, reader_name, pages_read, session_rating) values ('20', '5', 'Tara', '70', '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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"style": "LEETCODE",
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"exampleTestcases": "{\"headers\":{\"books\":[\"book_id\",\"title\",\"author\",\"genre\",\"pages\"],\"reading_sessions\":[\"session_id\",\"book_id\",\"reader_name\",\"pages_read\",\"session_rating\"]},\"rows\":{\"books\":[[1,\"The Great Gatsby\",\"F. Scott\",\"Fiction\",180],[2,\"To Kill a Mockingbird\",\"Harper Lee\",\"Fiction\",281],[3,\"1984\",\"George Orwell\",\"Dystopian\",328],[4,\"Pride and Prejudice\",\"Jane Austen\",\"Romance\",432],[5,\"The Catcher in the Rye\",\"J.D. Salinger\",\"Fiction\",277]],\"reading_sessions\":[[1,1,\"Alice\",50,5],[2,1,\"Bob\",60,1],[3,1,\"Carol\",40,4],[4,1,\"David\",30,2],[5,1,\"Emma\",45,5],[6,2,\"Frank\",80,4],[7,2,\"Grace\",70,4],[8,2,\"Henry\",90,5],[9,2,\"Ivy\",60,4],[10,2,\"Jack\",75,4],[11,3,\"Kate\",100,2],[12,3,\"Liam\",120,1],[13,3,\"Mia\",80,2],[14,3,\"Noah\",90,1],[15,3,\"Olivia\",110,4],[16,3,\"Paul\",95,5],[17,4,\"Quinn\",150,3],[18,4,\"Ruby\",140,3],[19,5,\"Sam\",80,1],[20,5,\"Tara\",70,2]]}}",
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