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How I Automated My Entire Workflow with Python + 5 Handy Libraries 🚀

If you’re still doing repetitive work manually… stop. Let Python handle it like I did.

Pythonworld

— 3 min read
How I Automated My Entire Workflow with Python + 5 Handy Libraries 🚀
Photo by AFINIS Group ® - AFINIS GASKET® Production on Unsplash

I was wasting hours on boring tasks — until I turned Python into my personal assistant.

How I Automated My Entire Workflow with Python + 5 Handy Libraries 🚀

Let’s be honest — manual tasks are the enemy of productivity. Whether it’s renaming hundreds of files, scraping data, or cleaning up spreadsheets, I used to spend hours doing things that felt repetitive and mind-numbing.

Then Python entered the chat.

Over the past year, I’ve automated almost every part of my daily workflow using Python. From pulling reports and sending emails to organizing files and tracking my time — it’s all on autopilot now.

In this post, I’ll walk you through exactly how I did it, and introduce you to 5 handy Python libraries that made it all possible.

The Philosophy: Automate Boring Stuff, Focus on Impact

Before diving into the tools, here’s my guiding principle:

If you do the same task more than twice — automate it.

With this mindset, I started identifying friction points in my day — things like:

  • Logging work hours manually
  • Moving files into organized folders
  • Scraping data for reports
  • Updating Google Sheets
  • Sending weekly summary emails

I turned each into a Python-powered system. Here’s how.

1. schedule — For Running Things Like Clockwork

At the heart of my automation is the schedule library — a super-lightweight tool for running Python jobs at specific intervals.

import schedule 
import time 
 
def send_daily_summary(): 
    print("Sending email...") 
 
schedule.every().day.at("18:00").do(send_daily_summary) 
 
while True: 
    schedule.run_pending() 
    time.sleep(60)

I use it to:

  • Run scripts every morning and evening
  • Send daily reminders
  • Kick off cleanup tasks at night
It’s simpler than cron, and lives right inside your Python script.

2. smtplib + email — Sending Emails from Python

Imagine you create a daily report and have to email it to your team every day.

I automated this entire flow using smtplib + the built-in email library:

import smtplib 
from email.message import EmailMessage 
 
msg = EmailMessage() 
msg['Subject'] = 'Daily Report' 
msg['From'] = 'me@example.com' 
msg['To'] = 'team@example.com' 
msg.set_content("Please find attached the report.") 
 
with open("report.pdf", "rb") as f: 
    msg.add_attachment(f.read(), maintype='application', subtype='pdf', filename='report.pdf') 
 
with smtplib.SMTP_SSL('smtp.gmail.com', 465) as smtp: 
    smtp.login("me@example.com", "app_password") 
    smtp.send_message(msg)
Boom 💥 — automated emails, zero manual effort.

3. pandas — The Swiss Army Knife for Data Tasks

If you’re not using pandas yet, you’re leaving time on the table.

I use it to:

  • Read and clean CSV/Excel reports
  • Merge data from multiple sources
  • Generate summaries and pivot tables
  • Output clean final reports
import pandas as pd 
 
df = pd.read_csv("sales.csv") 
summary = df.groupby("Region")["Revenue"].sum() 
summary.to_excel("summary.xlsx")
Pair this with email automation, and you’ve got a powerful reporting pipeline.

4. watchdog — Automating File Monitoring

Need to take action when a new file is added to a folder?

Enter watchdog — a brilliant library for monitoring file system changes.

Example: When I download a bank statement, Python immediately:

  • Renames it
  • Moves it to the correct folder
  • Updates my expense tracker
from watchdog.observers import Observer 
from watchdog.events import FileSystemEventHandler 
import shutil, time, os 
 
class FileHandler(FileSystemEventHandler): 
    def on_created(self, event): 
        if event.src_path.endswith(".pdf"): 
            shutil.move(event.src_path, "/Users/me/Documents/Statements") 
 
observer = Observer() 
observer.schedule(FileHandler(), path="/Users/me/Downloads", recursive=False) 
observer.start() 
 
try: 
    while True: 
        time.sleep(1) 
except KeyboardInterrupt: 
    observer.stop() 
observer.join()
Set it once. Forget it forever.

5. gspread — Google Sheets Automation

I often track things like content calendars, time logs, and metrics in Google Sheets.

With gspread, I update them directly from Python — no need to open a browser.

import gspread 
from oauth2client.service_account import ServiceAccountCredentials 
 
scope = ["https://spreadsheets.google.com/feeds", "https://www.googleapis.com/auth/drive"] 
creds = ServiceAccountCredentials.from_json_keyfile_name("creds.json", scope) 
client = gspread.authorize(creds) 
 
sheet = client.open("My Automation Tracker").sheet1 
sheet.append_row(["2025-07-01", "Blog post published", "âś…"])

Perfect for:

  • Adding rows from a script
  • Syncing data across systems
  • Keeping real-time dashboards up to date

Bonus: Putting It All Together

Here’s a real example of a daily workflow I automated:

  1. At 9 AM, schedule kicks off my reporting script
  2. The script uses pandas to process CSVs
  3. Generates a report and saves it
  4. Sends it via smtplib to my team
  5. Updates Google Sheet with task status using gspread

All this happens while I sip my morning coffee.

Final Thoughts

Learning Python isn’t just for building apps — it’s a superpower for automation.

If you’re tired of doing the same task every day, it’s probably time to let Python take over. Start with small wins — automate a recurring file cleanup or report generation. Over time, you’ll free up hours every week.

Trust me, once you automate your workflow, there’s no going back.

Have a favorite Python library for automation? Drop it in the comments — I’d love to discover more gems!

If this post helped you, don’t forget to clap 👏 and share with fellow devs looking to escape the grind.

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