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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"
  },
 ...
}

import pandas as pd

#load office address
office_address = pd.read_csv('datasets/office_addresses.csv')

#cols from address
addresses_cols = ["employee_id", "employee_country", "employee_city", "employee_street", "employee_street_number"]

#load employee address
emp_address = pd.read_excel('datasets/employee_information.xlsx', usecols= addresses_cols)

#cols for contacts
contact_cols = ['employee_id', 'last_name', 'first_name', 'emergency_contact', 'emergency_contact_number', 'relationship']

#load employee contacts
emp_em_cont = pd.read_excel('datasets/employee_information.xlsx', sheet_name= "emergency_contacts", header = None, names = contact_cols)

#load employee roles
emp_roles = pd.read_json('datasets/employee_roles.json', orient= 'index')


#merge emp_address with office_address
employees = emp_address.merge(office_address, left_on ='employee_country', right_on="office_country", how = 'left')

#merge employees with emp_roles
employees = employees.merge(emp_roles, left_on='employee_id', right_on = emp_roles.index)

#merge employees with emp_em_count
employees= employees.merge(emp_em_cont, on = 'employee_id')


#fill null values in office columns
for col in ['office', 'office_country', 'office_city', 'office_street', 'office_street_number']:
    employees[col].fillna('Remote', inplace = True)
    
    
#final columns
final_cols = ['employee_id', 'first_name', 'last_name', 'employee_country', 'employee_city', 'employee_street', 'employee_street_number', 'emergency_contact', 'emergency_contact_number', 'relationship', 'monthly_salary', 'team', 'title', 'office', 'office_country', 'office_city', 'office_street', 'office_street_number']

#final dataframe
employees_final = employees[final_cols]

#set index
employees_final.set_index('employee_id', inplace = True)

employees_final