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.

import nltk

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

لنبدأ بإنشاء مجموعة بيانات بسيطة تتضمن الأسئلة الشائعة والردود:

def generate_numbers(start, end):
    for i in range(start, end):
        yield i

def filter_even(numbers):
    for num in numbers:
        if num % 2 == 0:
            yield num

def square(numbers):
    for num in numbers:
        yield num ** 2

# Chaining
numbers = generate_numbers(1, 10)
even_numbers = filter_even(numbers)
squared_numbers = square(even_numbers)

print(list(squared_numbers))  # Output: [4, 16, 36, 64]

The yield from statement allows a generator to delegate part of its operations to another generator.

from pydantic import BaseSettings

class Settings(BaseSettings):
    app_name: str = "My App"
    admin_email: str

    class Config:
        env_prefix = "MYAPP_"

settings = Settings()
print(settings.app_name)
print(settings.admin_email)

• Use Pydantic models for API request and response validation.
• Leverage type coercion to simplify data handling.
• Use nested models for complex data structures.
• Manage application settings and configurations with Pydantic's BaseSettings.
• Take advantage of constrained types for stricter validation rules.

A virtual environment is an isolated environment that allows you to run and manage Python projects with their own dependencies. This isolation prevents conflicts between packages and versions, making it easier to manage multiple projects on the same machine.

Virtual environments help maintain a clean and organized development environment by: