Day6. String Methods - Calculate Special Bonus

Problem

Table: Employees

Column Name	Type
employee_id	int
name	varchar
salary	int

employee_id is the primary key (column with unique values) for this table.
Each row of this table indicates the employee ID, employee name, and salary.

Write a solution to calculate the bonus of each employee. The bonus of an employee is 100% of their salary if the ID of the employee is an odd number and the employee's name does not start with the character 'M'. The bonus of an employee is 0 otherwise.

Return the result table ordered by employee_id.

The result format is in the following example.

Example 1:

Input:
Employees table:

employee_id	name	salary
2	Meir	3000
3	Michael	3800
7	Addilyn	7400
8	Juan	6100
9	Kannon	7700

Output:

employee_id	bonus
2	0
3	0
7	7400
8	0
9	7700

Explanation:
The employees with IDs 2 and 8 get 0 bonus because they have an even employee_id.
The employee with ID 3 gets 0 bonus because their name starts with 'M'.
The rest of the employees get a 100% bonus.

Solution

# my solution
import pandas as pd

def calculate_special_bonus(employees: pd.DataFrame) -> pd.DataFrame:
   employees['salary'][(employees['employee_id'] % 2 == 0) | (employees['name'].str[0]=='M')] = 0
   employees = employees.rename(columns={'salary': 'bonus'})
   employees = employees.sort_values(by=['employee_id'])
   return employees[['employee_id', 'bonus']]

# check result
data = [[2, 'Meir', 3000], [3, 'Michael', 3800], [7, 'Addilyn', 7400], [8, 'Juan', 6100], [9, 'Kannon', 7700]]
Employees = pd.DataFrame(data, columns=['employee_id', 'name', 'salary']).astype({'employee_id':'int64', 'name':'object', 'salary':'int64'})

calculate_special_bonus(Employees)

Learn new method

When I checked popular solution, It was a little different from my solution. Today I learn about sort_values method through Pandas API reference.[1] Because I had never used sort_values method, before I solved this problem.

pandas.DataFrame.sort_values

DataFrame.sort_values(by, *, axis=0, ascending=True, inplace=False, kind='quicksort', na_position='last', ignore_index=False, key=None) sort by the values along either axis. To use this method, You have to set parameter by as str or list of str to sort by certain axis. Let's look into some example.

import pandas as pd
import numpy as np

df = pd.DataFrame({
    'col1': ['A', 'A', 'B', np.nan, 'D', 'C'],
    'col2': [2, 1, 9, 8, 7, 4],
    'col3': [0, 1, 9, 4, 2, 3],
    'col4': ['a', 'B', 'c', 'D', 'e', 'F']
})
df

df.sort_values(by=['col1'])

df.sort_values(by='col1', ascending=False, na_position='first')

If you check above result, You could know that parameter by need to sort by certain axis. In addition, If you use the other parameters, You could set sort option such as sort inverse, etc...

df.sort_values(by=['col1', 'col2'])

Also, You could sort by many axis.

reference

Problem
leetcode - 30 Days of Pandas / Calculate Special Bonus [0]

Pandas API reference
pandas.DataFrame.sort_values — pandas 2.0.3 documentation [1]