Python Tutorial 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.

nltk هي مكتبة قوية لمعالجة اللغة الطبيعية. أولاً، سنقوم بتنزيل الموارد اللازمة:

import nltk

# تنزيل الموارد الأساسية
nltk.download('punkt')
nltk.download('wordnet')

def read_large_file(file_path):
    with open(file_path, "r") as file:
        for line in file:
            yield line.strip()

# Process a large file
for line in read_large_file("large_file.txt"):
    print(line)

Leverage aiohttp for non-blocking web scraping:

Pydantic models can easily be converted to and from JSON:

# Serialize to JSON
user_json = user.json()
print(user_json)

# Deserialize from JSON
user_data = '{"id": 1, "name": "John Doe", "age": 25}'
user = User.parse_raw(user_data)
print(user)

myenv\Scripts\activate

To deactivate the virtual environment, simply run: