Automation has become an essential part of my daily workflow as a data professional. What started as a simple script to generate weekly reports has evolved into a comprehensive automation system that saves me hours every week.
The Problem
Every Monday morning, I used to spend 2-3 hours manually collecting data from various sources, cleaning it, and generating reports for different stakeholders. This repetitive task was not only time-consuming but also prone to human error.
The Solution
I developed a Python automation pipeline using pandas, requests, and scheduled cron jobs. The system automatically:
- Fetches data from multiple APIs and databases
- Cleans and processes the data using pandas
- Generates standardized reports in multiple formats
- Sends email notifications with attached reports
Implementation Details
The core of the automation relies on pandas for data manipulation and the schedule library for timing. Here's a simplified version of the main script:
import pandas as pd
import schedule
import time
from datetime import datetime
def generate_weekly_report():
# Fetch data from various sources
data = fetch_data_sources()
# Process and clean data
cleaned_data = process_data(data)
# Generate report
report = create_report(cleaned_data)
# Send via email
send_report(report)
# Schedule the job for every Monday at 8 AM
schedule.every().monday.at("08:00").do(generate_weekly_report)
while True:
schedule.run_pending()
time.sleep(60)Results
The automation system has been running reliably for 6 months, saving approximately 12 hours per month. More importantly, it has eliminated human errors and ensured consistent report delivery.
Key Takeaways
Start small with simple automation tasks and gradually build complexity. Python's ecosystem provides excellent tools for automation, and the investment in setting up these systems pays off quickly in time savings and reliability.