🚀 Python ⭐ Advanced

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Python

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.py

💻 Source Code

hello.py

#!/usr/bin/env python3
"""
Python Advanced Sample - Demonstrating Most Powerful Features
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Features demonstrated:
- Decorators & Function Wrapping
- Generators & Iterators
- Context Managers
- Async/Await & Coroutines
- Type Hints & Annotations
- Dataclasses
- List/Dict/Set Comprehensions
- Magic Methods
"""

from typing import Generator, Optional, List, Dict, Any
from dataclasses import dataclass, field
from contextlib import contextmanager
from functools import wraps
import asyncio
from datetime import datetime

# Decorator with arguments
def timing_decorator(label: str = "Function"):
    """Decorator to measure execution time"""
    def decorator(func):
        @wraps(func)
        def wrapper(*args, **kwargs):
            start = datetime.now()
            result = func(*args, **kwargs)
            elapsed = (datetime.now() - start).total_seconds()
            print(f"{label} '{func.__name__}' took {elapsed:.4f}s")
            return result
        return wrapper
    return decorator

# Context Manager
@contextmanager
def resource_manager(name: str):
    """Context manager for resource handling"""
    print(f"Acquiring resource: {name}")
    try:
        yield name
    finally:
        print(f"Releasing resource: {name}")

# Dataclass with advanced features
@dataclass
class Language:
    """Modern Python dataclass"""
    name: str
    paradigm: str
    typing: str
    year: int
    features: List[str] = field(default_factory=list)
    
    def __post_init__(self):
        self.name = self.name.upper()
    
    def add_feature(self, feature: str) -> None:
        self.features.append(feature)
    
    @property
    def is_modern(self) -> bool:
        return self.year >= 2010

# Generator function
def fibonacci(n: int) -> Generator[int, None, None]:
    """Generate Fibonacci sequence using generators"""
    a, b = 0, 1
    for _ in range(n):
        yield a
        a, b = b, a + b

# Async function demonstrating coroutines
async def fetch_data(url: str, delay: float = 0.1) -> Dict[str, Any]:
    """Simulate async data fetching"""
    await asyncio.sleep(delay)
    return {
        "url": url,
        "status": "success",
        "data": f"Data from {url}",
        "timestamp": datetime.now().isoformat()
    }

async def async_main() -> None:
    """Demonstrate concurrent async operations"""
    urls = ["api.example.com/1", "api.example.com/2", "api.example.com/3"]
    
    # Concurrent async execution
    tasks = [fetch_data(url) for url in urls]
    results = await asyncio.gather(*tasks)
    
    print("\n=== Async Results ===")
    for result in results:
        print(f"✓ {result['url']}: {result['status']}")

# Class with magic methods
class SmartDict(dict):
    """Enhanced dictionary with magic methods"""
    
    def __missing__(self, key):
        return f"Key '{key}' not found"
    
    def __add__(self, other):
        result = SmartDict(self)
        result.update(other)
        return result

@timing_decorator(label="Main Program")
def main():
    """Main function demonstrating Python's most powerful features"""
    
    print("🐍 PYTHON ADVANCED FEATURES SHOWCASE\n")
    
    # 1. List Comprehensions with conditions
    print("=== List Comprehensions ===")
    squares = [x**2 for x in range(10) if x % 2 == 0]
    print(f"Even squares: {squares}")
    
    # 2. Dict Comprehensions
    word_lengths = {word: len(word) for word in ["Python", "Java", "Rust"]}
    print(f"Word lengths: {word_lengths}")
    
    # 3. Set Comprehensions
    unique_squares = {x**2 for x in [1, 2, 2, 3, 3, 4]}
    print(f"Unique squares: {unique_squares}")
    
    # 4. Generators
    print("\n=== Generators ===")
    fib_sequence = list(fibonacci(10))
    print(f"Fibonacci(10): {fib_sequence}")
    
    # 5. Generator expression (memory efficient)
    sum_of_squares = sum(x**2 for x in range(1000000))
    print(f"Sum of squares (1M): {sum_of_squares:,}")
    
    # 6. Context Manager
    print("\n=== Context Manager ===")
    with resource_manager("Database Connection"):
        print("Using resource...")
    
    # 7. Dataclasses
    print("\n=== Dataclasses ===")
    python = Language("python", "Multi-paradigm", "Dynamic", 1991)
    python.add_feature("Decorators")
    python.add_feature("Generators")
    python.add_feature("Async/Await")
    print(f"Language: {python.name}, Modern: {python.is_modern}")
    print(f"Features: {', '.join(python.features)}")
    
    # 8. Magic Methods
    print("\n=== Magic Methods (SmartDict) ===")
    dict1 = SmartDict({"a": 1, "b": 2})
    dict2 = SmartDict({"c": 3, "d": 4})
    combined = dict1 + dict2
    print(f"Combined dict: {combined}")
    print(f"Missing key: {combined['missing']}")
    
    # 9. Unpacking and destructuring
    print("\n=== Unpacking ===")
    first, *middle, last = [1, 2, 3, 4, 5, 6]
    print(f"First: {first}, Middle: {middle}, Last: {last}")
    
    # 10. Walrus operator (Python 3.8+)
    print("\n=== Walrus Operator ===")
    if (count := len(fib_sequence)) > 5:
        print(f"Fibonacci sequence has {count} elements")
    
    # 11. Match statement (Python 3.10+)
    print("\n=== Pattern Matching ===")
    status_code = 200
    match status_code:
        case 200:
            print("✓ Success")
        case 404:
            print("✗ Not Found")
        case _:
            print("? Other status")
    
    # 12. Run async operations
    print("\n=== Async/Await ===")
    asyncio.run(async_main())
    
    print("\n✅ All advanced Python features demonstrated!")

if __name__ == "__main__":
    main()

▶️ Example Output

This is what the above Python code produces when executed:

Console Output

🐍 PYTHON ADVANCED FEATURES SHOWCASE

=== List Comprehensions ===
Even squares: [0, 4, 16, 36, 64]
Word lengths: {'Python': 6, 'Java': 4, 'Rust': 4}
Unique squares: {16, 1, 4, 9}

=== Generators ===
Fibonacci(10): [0, 1, 1, 2, 3, 5, 8, 13, 21, 34]
Sum of squares (1M): 333,332,833,333,500,000

=== Context Manager ===
Acquiring resource: Database Connection
Using resource...
Releasing resource: Database Connection

=== Dataclasses ===
Language: PYTHON, Modern: False
Features: Decorators, Generators, Async/Await

=== Magic Methods (SmartDict) ===
Combined dict: {'a': 1, 'b': 2, 'c': 3, 'd': 4}
Missing key: Key 'missing' not found

=== Unpacking ===
First: 1, Middle: [2, 3, 4, 5], Last: 6

=== Walrus Operator ===
Fibonacci sequence has 10 elements

=== Pattern Matching ===
✓ Success

=== Async/Await ===

=== Async Results ===
✓ api.example.com/1: success
✓ api.example.com/2: success
✓ api.example.com/3: success

✅ All advanced Python features demonstrated!
Main Program 'main' took 0.1784s

💡 Interactive Demo: The code above shows Python's advanced features including decorators, generators, async/await, dataclasses, and more. Each section demonstrates a specific feature with its output.

📈 2026 Trends & Industry Role

Python's evolving role and trajectory in the 2026 technology landscape

🤖 AI & Machine Learning Dominance

Python remains the de facto language for AI/ML and data science in 2026, anchored by a constantly evolving ecosystem of libraries — TensorFlow, PyTorch, Pandas, scikit-learn, and Hugging Face. Ongoing performance improvements continue to consolidate Python's lead in developer productivity for model training, data pipelines, and inference workflows.

⚡ High-Performance Extensions via Rust & Zig

A growing trend in 2026 is integrating Python with memory-safe, high-performance languages like Rust and Zig for performance-critical extensions. Tools like PyO3 (Rust bindings) and Zig's C ABI compatibility let developers write hot code paths at C-like speeds without leaving the Python ecosystem for the majority of the codebase.

🚀 Ecosystem Performance Improvements

Python's runtime is actively improving: CPython's free-threaded mode (PEP 703), the Faster CPython project, and JIT compilation advances in Python 3.13+ are closing the performance gap. These improvements, combined with async I/O and native parallelism, keep Python competitive for high-throughput services.

🔑 Key Takeaway: Python is enhanced by its vast ecosystem and ongoing performance improvements, consolidating its lead in AI/ML and developer productivity. Its seamless integration with Rust and Zig extensions ensures C-like performance is always within reach. → View Full 2026 Language Trends

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📊 Language Information

💻 Source Code

▶️ Example Output

📈 2026 Trends & Industry Role

🤖 AI & Machine Learning Dominance

⚡ High-Performance Extensions via Rust & Zig

🚀 Ecosystem Performance Improvements

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