Project: Consolidating Employee Data

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
pd.set_option('display.max_columns', None)
# Start coding here... 
# Reading in office addresses
office_addresses = pd.read_csv("datasets/office_addresses.csv")
print(office_addresses)

# Employee address columns to keep:
address_cols = ["employee_id", 
                "employee_country",
                "employee_city",
                "employee_street",
                "employee_street_number"]
# Reading in employee addresses
employee_addresses = pd.read_excel("datasets/employee_information.xlsx", usecols=address_cols)
print(employee_addresses)

# Initialize headers for emergency contacts
contacts_headers = ["employee_id", 
                    "last_name", 
                    "first_name", 
                    "emergency_contact", 
                    "emergency_contact_number", 
                    "relationship"]
# Emergency contacts
emergency_contacts = pd.read_excel("datasets/employee_information.xlsx",
                                   sheet_name="emergency_contacts",
                                   header=None,names=contacts_headers)
print(emergency_contacts)

# Employee roles
employee_roles = pd.read_json("datasets/employee_roles.json").transpose()
print(employee_roles)

# Merging DataFrames
# Merging office and employee addresses
employees = pd.merge(employee_addresses, office_addresses, how="left", left_on="employee_country", right_on="office_country")
print(employees)

# Merging employees with roles
employees = employees.merge(employee_roles, left_on="employee_id", right_on=employee_roles.index)
print(employees)

# Merging employees with emergency contacts
employees = employees.merge(emergency_contacts, on="employee_id")
print(employees)

# Cleaning and Reformatting
# Changing NaN to Remote
for col in employees.columns:
    if col.startswith("office"):
        employees[col].fillna("Remote", inplace=True)
print(employees)


# Setting custom column order, excluding index
custom_column_order = [
    "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 employees_final DataFrame
employees_final = employees[custom_column_order]
employees_final.set_index("employee_id", inplace=True)
print(employees_final)