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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
    import pandas as pd
    
    # Read in office_addresses.csv
    offices = pd.read_csv("datasets/office_addresses.csv")
    
    # Declare a list of columns to keep from addresses
    addresses_cols = ["employee_id", "employee_country", "employee_city", "employee_street", "employee_street_number"]
    
    # Read in employee_information.xlsx
    addresses = pd.read_excel("datasets/employee_information.xlsx",
                             usecols=addresses_cols)
    
    # Declare a list of new column names
    emergency_contacts_header = ["employee_id", "last_name", "first_name",
                                 "emergency_contact", "emergency_contact_number", "relationship"]
    
    # Read in employee_information.xlsx
    emergency_contacts = pd.read_excel("datasets/employee_information.xlsx", 
                                       sheet_name="emergency_contacts", 
                                       header=None,
                                       names=emergency_contacts_header)
    
    # Read in employee_roles.json
    roles = pd.read_json("datasets/employee_roles.json", orient="index")
    
    # Merge addresses with offices
    employees = addresses.merge(offices, left_on="employee_country", right_on="office_country", how="left")
    
    # Merge employees with roles
    employees = employees.merge(roles, left_on="employee_id", right_on=roles.index)
    
    # Merge employees with emergency_contacts
    employees = employees.merge(emergency_contacts, 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)
        
    # Create final columns
    final_columns = ["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"]
    
    # Subset for the required columns
    employees_final = employees[final_columns]
    
    # Set employee_id as the index
    employees_final.set_index("employee_id", inplace=True)