.btn {
  padding: 12px 24px;
  border: none;
  border-radius: 8px;
  background: linear-gradient(45deg, #6c63ff, #4cc9f0);
  color: white;
  font-weight: bold;
  cursor: pointer;
  transition: transform 0.3s, box-shadow 0.3s;
}

.btn:hover {
  transform: translateY(-2px);
  box-shadow: 0 5px 15px rgba(108, 99, 255, 0.4);
}

import React, { useState, useEffect } from 'react';

function App() {
  const [data, setData] = useState([]);
  
  useEffect(() => {
    fetch('/api/data')
      .then(response => response.json())
      .then(data => setData(data));
  }, []);
  
  return (
    

      
مرحباً بالعالم!
      

        {data.map(item => (
          
{item.name}
        ))}
      
    
  );
}

export default App;

-- استعلام لاستخراج البيانات مع JOIN وتحسين الأداء
SELECT 
    u.name, 
    u.email, 
    COUNT(o.id) as order_count,
    SUM(o.total) as total_spent
FROM users u
INNER JOIN orders o ON u.id = o.user_id
WHERE u.created_at > '2023-01-01'
GROUP BY u.id
HAVING COUNT(o.id) > 5
ORDER BY total_spent DESC
LIMIT 10;

import pandas as pd
import numpy as np

class DataProcessor:
    def __init__(self, data):
        self.data = data
        
    def clean_data(self):
        """تنظيف البيانات من القيم الفارغة"""
        self.data = self.data.dropna()
        return self
        
    def normalize(self, columns):
        """تطبيع البيانات في أعمدة محددة"""
        for col in columns:
            self.data[col] = (self.data[col] - self.data[col].mean()) / self.data[col].std()
        return self
        
    def get_processed_data(self):
        """إرجاع البيانات المعالجة"""
        return self.data

# مثال للاستخدام
processor = DataProcessor(df)
processed_data = processor.clean_data().normalize(['age', 'income']).get_processed_data()