Skip to content

You just got hired as the first and only data practitioner at a small business experiencing exponential growth. The company needs more structured processes, guidelines, and standards. Your first mission is to structure the human resources data. The data is currently scattered across teams and files and comes in various formats: Excel files, CSVs, JSON files...

You'll work with the following data in the datasets folder:

  • Office addresses are currently saved in office_addresses.csv. If the value for office is NaN, then the employee is remote.
  • Employee addresses are saved on the first tab of employee_information.xlsx.
  • Employee emergency contacts are saved on the second tab of employee_information.xlsx; this tab is called emergency_contacts. However, this sheet was edited at some point, and the headers were removed! The HR manager let you know that they should be: employee_id, last_name, first_name, emergency_contact, emergency_contact_number, and relationship.
  • Employee roles, teams, and salaries have been exported from the company's human resources management system into a JSON file titled employee_roles.json. Here are the first few lines of that file:
{"A2R5H9":
  {
    "title": "CEO",
    "monthly_salary": "$4500",
    "team": "Leadership"
  },
 ...
}

Imports

import pandas as pd

Reading Data

Office Addresses

off_add = pd.read_csv('datasets/office_addresses.csv')
print(off_add.head())         # Cheking data
print(list(off_add.columns))

Employees Information

1: Addresses

addresses_cols = ['employee_id', 'employee_country', 'employee_city',
                  'employee_street', 'employee_street_number']

emp_inf_add = pd.read_excel('datasets/employee_information.xlsx',
                            sheet_name = 0, 
                            usecols    = addresses_cols)
print(emp_inf_add.head())     # Cheking data
print(list(emp_inf_add.columns))

2: Emergency Contacts

emergency_contacts_header = ['employee_id', 'last_name', 'first_name',
                             'emergency_contact', 'emergency_contact_number',
                             'relationship']

emp_inf_eme_con = pd.read_excel('datasets/employee_information.xlsx',
                                sheet_name = 1, 
                                header     = None,
                                names      = emergency_contacts_header)
print(emp_inf_eme_con.head())     # Cheking data
print(list(emp_inf_eme_con.columns))

Employess Roles

emp_rol = pd.read_json('datasets/employee_roles.json',
                       orient = 'index')
print(emp_rol.head())         # Cheking data
print(list(emp_rol.columns))

Merging Data

Addresses and Office

employees = emp_inf_add.merge(off_add, 
                                how = 'left',
                                left_on  = 'employee_country',
                                right_on = 'office_country')
print(employees.head())
print(list(employees.columns))

Employees and Roles

employees = employees.merge(emp_rol,
                            left_on     = 'employee_id',
                            right_index = True)
print(employees.head())
print(list(employees.columns))
‌
‌
‌