Automating Excel Reports with Python and OpenPyXL

import requests
from bs4 import BeautifulSoup
import pandas as pd

def scrape_website(url):
    response = requests.get(url)
    if response.status_code == 200:
        soup = BeautifulSoup(response.text, 'html.parser')
        data = []
        for item in soup.select('.some-class'):
            data.append(item.text.strip())
        df = pd.DataFrame(data, columns=['Extracted Data'])
        df.to_csv('output.csv', index=False)
        print("Data saved to output.csv")
    else:
        print("Failed to retrieve data")

scrape_website('https://example.com')

This simple script extracts specific content from a webpage and saves it to a CSV file. With a few tweaks, it could be customized for different websites and data types.

In this tutorial, we’ll create a facial recognition-based attendance system using Python. This project combines computer vision, machine learning, and database management to automate attendance tracking for workplaces, schools, or events. By the end, you’ll have a working application that can detect faces, recognize individuals, and log their attendance into a database.

Traditional attendance systems like manual sign-ins or RFID cards are time-consuming and prone to errors. Facial recognition offers a fast, contactless, and reliable solution.

import schedule
import time

# Schedule the price checker to run every hour
schedule.every(1).hour.do(check_price)

while True:
    schedule.run_pending()
    time.sleep(1)

Run the script, and it will automatically check the product price every hour and send email alerts if the price drops below your threshold.