Python Automation Development Tutorials, Guides & Insights

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.

Overview

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.

import requests
from bs4 import BeautifulSoup

# Target URL of the product
URL = "https://www.example.com/product-page"

# Headers to mimic a real browser
HEADERS = {
    "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36"
}

def get_product_price():
    response = requests.get(URL, headers=HEADERS)
    soup = BeautifulSoup(response.content, "html.parser")

    # Extract product name and price using CSS selectors
    product_name = soup.find("h1", {"class": "product-title"}).text.strip()
    price = soup.find("span", {"class": "price"}).text.strip()

    return product_name, float(price.replace("$", ""))

product, price = get_product_price()
print(f"{product}: ${price}")

Allow users to specify a desired price threshold and compare it with the current price.

This will output the rows of data as tuples. The first row will contain the headers.

Let’s calculate the total sales for each product and add it as a new column: