178. Rank Scores – Leetcode Solutions

Description

Table: Scores

+-------------+---------+
| Column Name | Type    |
+-------------+---------+
| id          | int     |
| score       | decimal |
+-------------+---------+
id is the primary key (column with unique values) for this table.
Each row of this table contains the score of a game. Score is a floating point value with two decimal places.

Write a solution to find the rank of the scores. The ranking should be calculated according to the following rules:

• The scores should be ranked from the highest to the lowest.
• If there is a tie between two scores, both should have the same ranking.
• After a tie, the next ranking number should be the next consecutive integer value. In other words, there should be no holes between ranks.

Return the result table ordered by score in descending order.

The result format is in the following example.

Examples:

Example 1:

Input: 
Scores table:
+----+-------+
| id | score |
+----+-------+
| 1  | 3.50  |
| 2  | 3.65  |
| 3  | 4.00  |
| 4  | 3.85  |
| 5  | 4.00  |
| 6  | 3.65  |
+----+-------+
Output: 
+-------+------+
| score | rank |
+-------+------+
| 4.00  | 1    |
| 4.00  | 1    |
| 3.85  | 2    |
| 3.65  | 3    |
| 3.65  | 3    |
| 3.50  | 4    |
+-------+------+

Solution in Pandas

Step-by-step solution with detailed comments:

1. Import necessary libraries: We’ll need Pandas for handling the data.
2. Sort the DataFrame: Sort the scores in descending order.
3. Calculate the ranks: Use the rank method with the appropriate parameters to handle ties and ensure consecutive ranking.
4. Create the result DataFrame: Select and rename the necessary columns to match the desired output format.

import pandas as pd

def order_scores(scores: pd.DataFrame) -> pd.DataFrame:
    # Step 1: Sort the scores in descending order
    scores_sorted = scores.sort_values(by='score', ascending=False)
    
    # Step 2: Calculate ranks
    # method='dense' gives consecutive rank numbers without gaps
    scores_sorted['rank'] = scores_sorted['score'].rank(method='dense', ascending=False).astype(int)
    
    # Step 3: Select and rename the necessary columns
    result = scores_sorted[['score', 'rank']]
    
    return result

Solution in MySQL

-- Solution to rank the scores
SELECT
    score,
    DENSE_RANK() OVER (ORDER BY score DESC) AS `rank`
FROM
    Scores
ORDER BY
    score DESC;

Solution in PostgreSQL

SELECT
    score,
    DENSE_RANK() OVER (ORDER BY score DESC) AS rank
FROM
    Scores
ORDER BY
    score DESC;