Payroll Gender Code Toggle Report

Easy

A payroll data pipeline stores employee compensation records in a Salary table: id: unique employee identifier name: employee name sex: one-character gender marker ('m' or 'f') salary: current salary amount A downstream integration expects the opposite marker convention, so every row must be transformed as follows:'m' becomes 'f''f' becomes 'm' Return a transformed result table with exactly these columns: id name sex salary Additional requirements: Keep id, name, and salary unchanged. Sort the output by id in ascending order. Supported submissions: SQL (SQLite 3.27.2) Pandas (Python)

Example 1

Input

Salary:
| id | name | sex | salary |
|----|------|-----|--------|
| 1  | A    | m   | 2500   |
| 2  | B    | f   | 1500   |
| 3  | C    | m   | 5500   |
| 4  | D    | f   | 500    |

Output

[
  {"id":1,"name":"A","sex":"f","salary":2500},
  {"id":2,"name":"B","sex":"m","salary":1500},
  {"id":3,"name":"C","sex":"f","salary":5500},
  {"id":4,"name":"D","sex":"m","salary":500}
]

Explanation

Each row is transformed independently by flipping sex code m<->f while preserving all other columns.

Example 2

Input

Salary:
| id | name  | sex | salary |
|----|-------|-----|--------|
| 11 | Nia   | f   | 8000   |
| 7  | Omar  | m   | 7200   |
| 15 | Leah  | f   | 8100   |
| 9  | Jonah | m   | 7100   |

Output

[
  {"id":7,"name":"Omar","sex":"f","salary":7200},
  {"id":9,"name":"Jonah","sex":"f","salary":7100},
  {"id":11,"name":"Nia","sex":"m","salary":8000},
  {"id":15,"name":"Leah","sex":"m","salary":8100}
]

Explanation

Input order can be arbitrary. Output must still be sorted by id.

Example 3

Input

Salary:
| id | name | sex | salary |
|----|------|-----|--------|
| 100 | Kai | m   | 9000   |
| 101 | Kai | m   | 9000   |
| 102 | Kai | f   | 9000   |

Output

[
  {"id":100,"name":"Kai","sex":"f","salary":9000},
  {"id":101,"name":"Kai","sex":"f","salary":9000},
  {"id":102,"name":"Kai","sex":"m","salary":9000}
]

Explanation

Rows are not grouped or deduplicated. Every record is toggled independently, even with repeated names and salaries.

Constraints

1 <= number of rows in Salary <= 10,000
1 <= id <= 1,000,000,000
id is unique
1 <= length of name <= 100
sex is either 'm' or 'f'
0 <= salary <= 1,000,000,000
Use SQLite 3.27.2 semantics for SQL submissions
For Pandas submissions, return columns: id, name, sex, salary

[ {"id":7,"name":"Omar","sex":"f","salary":7200}, {"id":9,"name":"Jonah","sex":"f","salary":7100}, {"id":11,"name":"Nia","sex":"m","salary":8000}, {"id":15,"name":"Leah","sex":"m","salary":8100} ]