Class Score Spread Analyzer

Medium

An academic analytics team wants a fast way to measure score dispersion in a class snapshot. You are given one table: Scores columns: student_id: unique student identifier student_name: student display name assignment1: score in assignment 1 assignment2: score in assignment 2 assignment3: score in assignment 3 Task: Compute each student's total score: total_score = assignment1 + assignment2 + assignment3 Find the largest and smallest total scores in the class. Return their difference: difference_in_score = max(total_score) - min(total_score) Output requirements: Return exactly one column named: difference_in_score Return one row. Supported environments: SQL (SQLite 3.27.2) Pandas (Python)

Example 1

Input

Scores:
| student_id | student_name | assignment1 | assignment2 | assignment3 |
|------------|--------------|-------------|-------------|-------------|
| 309        | Olivia       | 88          | 47          | 87          |
| 321        | Ethan        | 98          | 95          | 37          |
| 338        | Ava          | 100         | 64          | 43          |
| 423        | Mason        | 60          | 44          | 47          |
| 896        | Nora         | 32          | 37          | 50          |
| 235        | Lucas        | 31          | 53          | 69          |

Output

[
  {"difference_in_score": 111}
]

Explanation

The highest total is 230 and the lowest total is 119, so the spread is 111.

Example 2

Input

Scores:
| student_id | student_name | assignment1 | assignment2 | assignment3 |
|------------|--------------|-------------|-------------|-------------|
| 10         | Isha         | 80          | 70          | 60          |
| 11         | Omar         | 90          | 60          | 60          |
| 12         | Lina         | 75          | 75          | 60          |
| 13         | Arjun        | 40          | 50          | 60          |

Output

[
  {"difference_in_score": 60}
]

Explanation

Totals are 210, 210, 210, and 150. Spread is 210 - 150 = 60.

Example 3

Input

Scores:
| student_id | student_name | assignment1 | assignment2 | assignment3 |
|------------|--------------|-------------|-------------|-------------|
| 901        | Zoya         | 95          | 88          | 91          |

Output

[
  {"difference_in_score": 0}
]

Explanation

With only one student, highest and lowest totals are identical.

Constraints

1 <= number of rows in Scores <= 50,000
Scores.student_id is unique
0 <= assignment1, assignment2, assignment3 <= 100
1 <= length of student_name <= 100
All score values are integers
SQL submissions are evaluated on SQLite 3.27.2
Pandas submissions must return a DataFrame with one column: difference_in_score

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Class Score Spread Analyzer

Medium

An academic analytics team wants a fast way to measure score dispersion in a class snapshot. You are given one table: Scores columns: student_id: unique student identifier student_name: student display name assignment1: score in assignment 1 assignment2: score in assignment 2 assignment3: score in assignment 3 Task: Compute each student's total score: total_score = assignment1 + assignment2 + assignment3 Find the largest and smallest total scores in the class. Return their difference: difference_in_score = max(total_score) - min(total_score) Output requirements: Return exactly one column named: difference_in_score Return one row. Supported environments: SQL (SQLite 3.27.2) Pandas (Python)

Example 1

Input

Scores:
| student_id | student_name | assignment1 | assignment2 | assignment3 |
|------------|--------------|-------------|-------------|-------------|
| 309        | Olivia       | 88          | 47          | 87          |
| 321        | Ethan        | 98          | 95          | 37          |
| 338        | Ava          | 100         | 64          | 43          |
| 423        | Mason        | 60          | 44          | 47          |
| 896        | Nora         | 32          | 37          | 50          |
| 235        | Lucas        | 31          | 53          | 69          |

Output

[
  {"difference_in_score": 111}
]

Explanation

The highest total is 230 and the lowest total is 119, so the spread is 111.

Example 2

Input

Scores:
| student_id | student_name | assignment1 | assignment2 | assignment3 |
|------------|--------------|-------------|-------------|-------------|
| 10         | Isha         | 80          | 70          | 60          |
| 11         | Omar         | 90          | 60          | 60          |
| 12         | Lina         | 75          | 75          | 60          |
| 13         | Arjun        | 40          | 50          | 60          |

Output

[
  {"difference_in_score": 60}
]

Explanation

Totals are 210, 210, 210, and 150. Spread is 210 - 150 = 60.

Example 3

Input

Scores:
| student_id | student_name | assignment1 | assignment2 | assignment3 |
|------------|--------------|-------------|-------------|-------------|
| 901        | Zoya         | 95          | 88          | 91          |

Output

[
  {"difference_in_score": 0}
]

Explanation

With only one student, highest and lowest totals are identical.

Constraints

1 <= number of rows in Scores <= 50,000
Scores.student_id is unique
0 <= assignment1, assignment2, assignment3 <= 100
1 <= length of student_name <= 100
All score values are integers
SQL submissions are evaluated on SQLite 3.27.2
Pandas submissions must return a DataFrame with one column: difference_in_score