5 Python Automation Projects You Can Build This Weekend

As a Python developer, the excitement of creating project solutions for real-world problems is familiar. However, brainstorming the perfect project idea can often lead to a creative block.

If you’re eager to learn and want to kickstart your automation skills this weekend, here are 5 Python automation projects, each increasing in difficulty. These projects will not only enhance your Python abilities but also result in something practical by the end of the weekend.

Remember, the guiding principle for these projects is simple: focus on solving a problem rather than just using a tool. This approach will lead to more meaningful learning and useful outcomes.

1 Email Auto Cleaner (Beginner)

Is your inbox cluttered with unread newsletters, promotional emails, and spam? Automate the cleanup process with Python!

This simple email cleaner will filter out promotional emails from your Gmail account.

What You’ll Need:

import re
import imaplib
import email
from email.header import decode_header

# Connect to the Gmail server
mail = imaplib.IMAP4_SSL("imap.gmail.com")
mail.login("your_email@gmail.com", "your_password")
mail.select("inbox")

# Search for all emails
status, messages = mail.search(None, "ALL")
email_ids = messages[0].split()

# Loop through all emails and filter out unwanted ones
for email_id in email_ids:
    status, msg_data = mail.fetch(email_id, "(RFC822)")
    for response_part in msg_data:
        if isinstance(response_part, tuple):
            msg = email.message_from_bytes(response_part[1])
            subject, encoding = decode_header(msg["Subject"])[0]

            # Check if the subject contains a promotional keyword
            if re.search(r"(sale|promotion|offer|newsletter)", subject, re.IGNORECASE):
                mail.store(email_id, '+FLAGS', '\\Deleted')  # Mark as deleted
                print(f"Deleted: {subject}")

# Expunge deleted messages
mail.expunge()
mail.logout()

This program scans your inbox for keywords in email subject lines, such as “sale” or “promotion,” and automatically deletes those emails. You can enhance this project by adding functionality to auto-unsubscribe from these mailing lists!

2 Social Media Post Scheduler (Beginner)

For content creators, marketers, or anyone working online, social media is vital. However, manually scheduling posts can be time-consuming. This automation project allows you to schedule posts on Twitter at specific times.

What You’ll Need:

import tweepy
import schedule
import time

# Twitter API authentication
auth = tweepy.OAuth1UserHandler(
    consumer_key="your_consumer_key",
    consumer_secret="your_consumer_secret",
    access_token="your_access_token",
    access_token_secret="your_access_token_secret"
)
api = tweepy.API(auth)

# Function to post a tweet
def post_tweet():
    tweet = "Automated tweet using Python! #PythonRocks"
    api.update_status(tweet)
    print("Tweet posted!")

# Schedule the tweet to be posted at 9 AM every day
schedule.every().day.at("09:00").do(post_tweet)

# Keep the script running
while True:
    schedule.run_pending()
    time.sleep(60)

This simple scheduler will automatically post a tweet every day at 9:00 AM. Want to schedule multiple posts? Just modify the scheduling logic.

Quick Pause

If you’re eager to learn Python through hands-on, project-based examples, PYTHON CRASH COURSE by Eric Matthes is a bestselling programming book with over 1,500,000 copies sold!

3 Automatic Data Scraper (Intermediate)

Need to gather web data? Whether it’s price data from an e-commerce site or stock market information, Python makes it easy to scrape and automate data extraction.

This project implements a scraper that extracts product names and prices from an e-commerce website.

What You’ll Need:

import requests
from bs4 import BeautifulSoup

# URL of the website to scrape
url = "https://example.com/products"

# Send a GET request to the website
response = requests.get(url)
soup = BeautifulSoup(response.text, "html.parser")

# Find product names and prices
products = soup.find_all("div", class_="product")

for product in products:
    name = product.find("h2").text
    price = product.find("span", class_="price").text
    print(f"Product: {name}, Price: {price}")

This script fetches product names and prices from the website, allowing you to automate data gathering for price tracking or market analysis.

4 File Organizer (Intermediate)

If you have many projects, your file system can quickly become chaotic. This automation project organizes files in a directory based on their type (PDF, images, documents, etc.) and moves them into appropriate folders.

What You’ll Need:

import os
import shutil

# Path to the directory you want to organize
source_dir = "your_directory_path"

# Define file type categories
categories = {
    "Images": [".jpg", ".png", ".gif"],
    "PDFs": [".pdf"],
    "Documents": [".docx", ".txt"],
    "Spreadsheets": [".xls", ".csv"]
}

# Loop through all files in the source directory
for filename in os.listdir(source_dir):
    file_path = os.path.join(source_dir, filename)

    if os.path.isfile(file_path):
        file_extension = os.path.splitext(filename)[1].lower()

        # Check the file extension and move to the appropriate folder
        for category, extensions in categories.items():
            if file_extension in extensions:
                category_path = os.path.join(source_dir, category)
                if not os.path.exists(category_path):
                    os.makedirs(category_path)
                shutil.move(file_path, os.path.join(category_path, filename))
                print(f"Moved {filename} to {category}")
                break

This script sorts your files into folders based on type, helping maintain a clean workspace.

5 Personal Movie Recommendation System (Advanced)

If you’re interested in machine learning, this project uses past movie ratings to recommend movies you might enjoy. It employs collaborative filtering to suggest movies based on user preferences.

What You’ll Need:

import pandas as pd
from sklearn.metrics.pairwise import cosine_similarity
import numpy as np

# Example user-movie rating data (load your own dataset)
data = {
    'user': ['Alice', 'Bob', 'Charlie', 'David'],
    'Movie A': [5, 4, 1, 0],
    'Movie B': [3, 5, 2, 0],
    'Movie C': [4, 3, 5, 0],
    'Movie D': [0, 1, 3, 5]
}
df = pd.DataFrame(data)

# Compute the cosine similarity between users
user_ratings = df.drop('user', axis=1).values
cosine_sim = cosine_similarity(user_ratings)

# Function to recommend movies based on similarity
def recommend_movies(user_id):
    similar_users = list(enumerate(cosine_sim[user_id]))
    similar_users = sorted(similar_users, key=lambda x: x[1], reverse=True)

    recommended_movies = []
    for i, _ in similar_users[1:]:
        recommended_movies.append(df.columns[i + 1])

    return recommended_movies

# Example: Recommend movies for user 0 (Alice)
recommended = recommend_movies(0)
print(f"Recommended movies for Alice: {recommended}")

This system calculates similarities between users based on their movie ratings and recommends movies accordingly. If you’ve used Netflix or Spotify, you’ve interacted with a similar recommendation engine.

Final Thoughts

All these projects enhance your skills in various ways. Choose one that excites you, making learning feel less like a chore and more like progress.

When you’re motivated, learning doesn’t feel like work — it feels like progress.

Abdur Rahman - Python in Plain English