00:00:00

Description

Editorial

Engineering vs Marketing Peak Salary Gap

EASY10 pts

A compensation analytics team compares top-end pay across key departments.

Table: Salaries

emp_name: employee name
department: business function label
salary: salary amount

Data guarantees:

(emp_name, department) is unique.
There is at least one row for department = Engineering.
There is at least one row for department = Marketing.

Task: Compute one metric called salary_difference:

highest_engineering_salary = maximum salary among Engineering rows
highest_marketing_salary = maximum salary among Marketing rows
salary_difference = absolute(highest_marketing_salary - highest_engineering_salary)

Return exactly one row with one column:

salary_difference

Supported submission environments:

SQL (SQLite 3.27.2)
Pandas (Python)

Example

Input

Salaries:
| emp_name | department  | salary |
|----------|-------------|--------|
| Kathy    | Engineering | 50000  |
| Roy      | Marketing   | 30000  |
| Charles  | Engineering | 45000  |
| Jack     | Engineering | 85000  |
| Benjamin | Marketing   | 34000  |
| Anthony  | Marketing   | 42000  |
| Edward   | Engineering | 102000 |
| Terry    | Engineering | 44000  |
| Evelyn   | Marketing   | 53000  |
| Arthur   | Engineering | 32000  |

Output

[
  {"salary_difference":49000}
]

Explanation

Top Engineering salary is 102000 and top Marketing salary is 53000, so the absolute difference is 49000.

Example

Input

Salaries:
| emp_name | department  | salary |
|----------|-------------|--------|
| Ana      | Engineering | 78000  |
| Ben      | Engineering | 91000  |
| Cara     | Marketing   | 125000 |
| Drew     | Marketing   | 103000 |
| Eli      | Sales       | 220000 |

Output

[
  {"salary_difference":34000}
]

Explanation

Only Engineering and Marketing are compared. Sales does not affect the metric.

Example

Input

Salaries:
| emp_name | department  | salary |
|----------|-------------|--------|
| P1       | Engineering | 60000  |
| P2       | Marketing   | 60000  |
| P3       | Engineering | 51000  |
| P4       | Marketing   | 58000  |

Output

[
  {"salary_difference":0}
]

Explanation

Both departments share the same highest salary (60000), so the absolute difference is 0.

Accepted0/0·0% Acceptance

Constraints

2 <= number of rows in Salaries <= 1200
(emp_name, department) is unique
1 <= salary <= 1000000
department is a non-empty string
emp_name is a non-empty string
At least one row exists with department = Engineering
At least one row exists with department = Marketing
Return exactly one row and one column: salary_difference
For SQL submissions, use SQLite 3.27.2 semantics
For Pandas submissions, return a DataFrame with exactly one column named salary_difference

Code

Visualizer

Solutions

14px

Test Cases3

Results

Submissions

Salaries =

[{"emp_name":"Kathy","department":"Engineering","salary":50000},{"emp_name":"Roy","department":"Marketing","salary":30000},{"emp_name":"Charles","department":"Engineering","salary":45000},{"emp_name":"Jack","department":"Engineering","salary":85000},{"emp_name":"Benjamin","department":"Marketing","salary":34000},{"emp_name":"Anthony","department":"Marketing","salary":42000},{"emp_name":"Edward","department":"Engineering","salary":102000},{"emp_name":"Terry","department":"Engineering","salary":44000},{"emp_name":"Evelyn","department":"Marketing","salary":53000},{"emp_name":"Arthur","department":"Engineering","salary":32000}]

Loading problem...

101

00:00:00

Description

Editorial

Engineering vs Marketing Peak Salary Gap

EASY10 pts

A compensation analytics team compares top-end pay across key departments.

Table: Salaries

emp_name: employee name
department: business function label
salary: salary amount

Data guarantees:

(emp_name, department) is unique.
There is at least one row for department = Engineering.
There is at least one row for department = Marketing.

Task: Compute one metric called salary_difference:

highest_engineering_salary = maximum salary among Engineering rows
highest_marketing_salary = maximum salary among Marketing rows
salary_difference = absolute(highest_marketing_salary - highest_engineering_salary)

Return exactly one row with one column:

salary_difference

Supported submission environments:

SQL (SQLite 3.27.2)
Pandas (Python)

Example

Input

Salaries:
| emp_name | department  | salary |
|----------|-------------|--------|
| Kathy    | Engineering | 50000  |
| Roy      | Marketing   | 30000  |
| Charles  | Engineering | 45000  |
| Jack     | Engineering | 85000  |
| Benjamin | Marketing   | 34000  |
| Anthony  | Marketing   | 42000  |
| Edward   | Engineering | 102000 |
| Terry    | Engineering | 44000  |
| Evelyn   | Marketing   | 53000  |
| Arthur   | Engineering | 32000  |

Output

[
  {"salary_difference":49000}
]

Explanation

Top Engineering salary is 102000 and top Marketing salary is 53000, so the absolute difference is 49000.

Example

Input

Salaries:
| emp_name | department  | salary |
|----------|-------------|--------|
| Ana      | Engineering | 78000  |
| Ben      | Engineering | 91000  |
| Cara     | Marketing   | 125000 |
| Drew     | Marketing   | 103000 |
| Eli      | Sales       | 220000 |

Output

[
  {"salary_difference":34000}
]

Explanation

Only Engineering and Marketing are compared. Sales does not affect the metric.

Example

Input

Salaries:
| emp_name | department  | salary |
|----------|-------------|--------|
| P1       | Engineering | 60000  |
| P2       | Marketing   | 60000  |
| P3       | Engineering | 51000  |
| P4       | Marketing   | 58000  |

Output

[
  {"salary_difference":0}
]

Explanation

Both departments share the same highest salary (60000), so the absolute difference is 0.

Accepted0/0·0% Acceptance

Constraints

2 <= number of rows in Salaries <= 1200
(emp_name, department) is unique
1 <= salary <= 1000000
department is a non-empty string
emp_name is a non-empty string
At least one row exists with department = Engineering
At least one row exists with department = Marketing
Return exactly one row and one column: salary_difference
For SQL submissions, use SQLite 3.27.2 semantics
For Pandas submissions, return a DataFrame with exactly one column named salary_difference

Code

Visualizer

Solutions

14px

Test Cases3

Results

Submissions

Salaries =

Engineering vs Marketing Peak Salary Gap

Hints

Engineering vs Marketing Peak Salary Gap

Hints