Building an Automated Leave Management System with Python

Managing employee leave manually is inefficient and error-prone. By automating leave requests, approvals, and tracking with Python, MongoDB, and Pandas, businesses can improve efficiency and reduce administrative workload.

Lillqvist Strat specializes in automation that streamlines HR processes, saving time and minimizing errors.

---

1. Leave Management Basics

An automated leave management system should:

• Store leave records in MongoDB.
• Automate leave request approvals using Python logic.
• Analyze leave trends using Pandas.
• Send notifications for leave requests and approvals.

---

2. MongoDB Leave Records

We use MongoDB to store leave requests, track employee leave balances, and ensure data integrity.

Setting Up the Database

from pymongo import MongoClient

client = MongoClient("mongodb://localhost:27017/")
db = client["leave_management"]
leave_collection = db["leave_requests"]

Defining the Leave Request Schema

leave_request = {
    "employee_id": "EMP123",
    "name": "John Doe",
    "leave_type": "Annual",
    "start_date": "2025-03-01",
    "end_date": "2025-03-05",
    "status": "Pending",
    "reason": "Vacation"
}

leave_collection.insert_one(leave_request)

---

3. Python Approval Logic

Leave requests should be approved or rejected based on leave balance, team availability, and company policies.

Automated Leave Approval Process

def approve_leave(employee_id):
    leave_request = leave_collection.find_one({"employee_id": employee_id, "status": "Pending"})

    if leave_request:
        leave_collection.update_one(
            {"employee_id": employee_id, "status": "Pending"},
            {"$set": {"status": "Approved"}}
        )
        print(f"✅ Leave approved for {leave_request['name']}.")
    else:
        print("No pending leave request found.")

approve_leave("EMP123")

Rejecting Leave Requests

def reject_leave(employee_id, reason):
    leave_request = leave_collection.find_one({"employee_id": employee_id, "status": "Pending"})

    if leave_request:
        leave_collection.update_one(
            {"employee_id": employee_id, "status": "Pending"},
            {"$set": {"status": "Rejected", "rejection_reason": reason}}
        )
        print(f"❌ Leave request rejected for {leave_request['name']}. Reason: {reason}")
    else:
        print("No pending leave request found.")

reject_leave("EMP123", "High workload during requested period")

---

4. Pandas Leave Trends Analysis

To analyze leave patterns and optimize workforce planning, we use Pandas.

import pandas as pd

# Load leave data into Pandas
leave_data = list(leave_collection.find({}, {"_id": 0}))
df = pd.DataFrame(leave_data)

# Analyze leave types
leave_summary = df.groupby("leave_type")["employee_id"].count()
print(leave_summary)

# Export leave records to Excel
df.to_excel("leave_report.xlsx", index=False)

---

5. Automated Notifications

Employees and managers should receive notifications for leave approvals and rejections.

Sending Email Notifications

import smtplib
from email.mime.text import MIMEText

def send_notification(email, subject, message):
    sender_email = "your_company@example.com"
    password = "yourpassword"

    msg = MIMEText(message)
    msg["Subject"] = subject
    msg["From"] = sender_email
    msg["To"] = email

    with smtplib.SMTP("smtp.example.com", 587) as server:
        server.starttls()
        server.login(sender_email, password)
        server.sendmail(sender_email, email, msg.as_string())

# Example: Notify employee of approved leave
send_notification("employee@example.com", "Leave Request Approved", "Your leave request has been approved.")

---

Conclusion

Automating leave management with Python, MongoDB, and Pandas eliminates manual errors, streamlines approvals, and provides data-driven insights into employee leave trends.

Lillqvist Strat helps businesses implement automated HR solutions that save time and improve efficiency. Let’s automate your leave management today!