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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
import numpy as np
# Start coding here... 
## read all files
office_addresses=pd.read_csv('datasets/office_addresses.csv')
xls= pd.ExcelFile('datasets/employee_information.xlsx')

# Print sheet names
print(xls.sheet_names)
employee_addresses=xls.parse(0)
emergency_contacts=xls.parse(1,header=None, names=['employee_id', 'last_name', 'first_name', 'emergency_contact', 'emergency_contact_number', 'relationship'])
# read the json file 
employee_roles=pd.read_json('datasets/employee_roles.json', orient='index')
employee_roles.index.name= 'employee_id'

# merging all files into employees_final
df=employee_addresses.merge(office_addresses, left_on='employee_country', right_on='office_country', how='left')
df=df.merge(emergency_contacts, on='employee_id', how='left' )
df=df.merge(employee_roles, on='employee_id', how='left' )


#### Change any NaN values in column names starting with office to the word "Remote"

df["office"].fillna("Remote", inplace = True)
df["office_country"].fillna("Remote", inplace = True)
df["office_city"].fillna("Remote", inplace = True)  
df["office_street"].fillna("Remote", inplace = True)
df["office_street_number"].fillna("Remote", inplace = True)

### drop 2 dupplicated columns 'employee_last_name', 'employee_first_name'
df.drop(columns=['employee_last_name', 'employee_first_name'])
df1=df[['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']]
## set employee_id is the index
employees_final=df1.set_index('employee_id')
employees_final