# Python Version Adoption (2026)
┌─────────────────────────────────────────┐
│ Python 3.12+  → 85% (Stable)           │
│ Python 3.13   → 10% (New Features)     │
│ Python 3.14+  → 5%  (Experimental)     │
└─────────────────────────────────────────┘

# Data processing pipeline
if (data := fetch_data()) and (clean := clean_data(data)):
    result = process(clean)
    
# While loop optimization
while (chunk := file.read(8192)):
    process_chunk(chunk)

# List comprehension with condition
results = [y for x in data if (y := transform(x)) is not None]

from functools import lru_cache
from typing import TypeVar
import hashlib
import pickle

T = TypeVar('T')

class SmartCache:
    """Intelligent caching with memory awareness"""
    
    def __init__(self, maxsize: int = 128):
        self._cache = {}
        self.maxsize = maxsize
        
    def __call__(self, func):
        async def wrapper(*args, **kwargs):
            # Create cache key with hash
            key = self._create_key(func, args, kwargs)
            
            if key in self._cache:
                return self._cache[key]
            
            result = await func(*args, **kwargs)
            
            # Manage cache size
            if len(self._cache) >= self.maxsize:
                self._cache.pop(next(iter(self._cache)))
            
            self._cache[key] = result
            return result
        return wrapper
    
    def _create_key(self, func, args, kwargs):
        """Create unique hash key"""
        data = pickle.dumps((func.__name__, args, sorted(kwargs.items())))
        return hashlib.sha256(data).hexdigest()

import asyncio
from asyncio import TaskGroup, timeout
import httpx

class AsyncProcessor:
    """Modern async patterns for 2026"""
    
    async def process_batch(self, items: list):
        """Using TaskGroups (Python 3.11+)"""
        results = []
        async with TaskGroup() as tg:
            for item in items:
                task = tg.create_task(self.process_item(item))
                results.append(task)
        return [r.result() for r in results]
    
    async def with_timeout(self):
        """Timeout handling in 2026"""
        try:
            async with timeout(5.0):
                return await self.fetch_data()
        except TimeoutError:
            return {"error": "Timeout"}
    
    async def parallel_requests(self, urls: list):
        """Parallel HTTP requests"""
        async with httpx.AsyncClient() as client:
            tasks = [client.get(url) for url in urls]
            responses = await asyncio.gather(*tasks, return_exceptions=True)
            return [r for r in responses if not isinstance(r, Exception)]

from typing import TypeVar, Generic, Protocol, Self
from typing import reveal_type  # Debugging tool
from dataclasses import dataclass
from enum import StrEnum

T = TypeVar('T')
K = TypeVar('K')
V = TypeVar('V')

# Protocol for structural typing
class Renderable(Protocol):
    def render(self) -> str: ...

@dataclass
class Response(Generic[T]):
    """Generic response with 2026 type hints"""
    data: T
    status: int
    metadata: dict[str, str] | None = None
    
    @classmethod
    def create(cls, data: T) -> Self:
        return cls(data=data, status=200)
    
    def map(self, func: Callable[[T], K]) -> Response[K]:
        return Response(func(self.data), self.status)

from contextlib import contextmanager, asynccontextmanager
from typing import AsyncIterator

class DatabaseConnection:
    """Modern context manager pattern"""
    
    def __init__(self, dsn: str):
        self.dsn = dsn
        self._connection = None
    
    async def __aenter__(self) -> Self:
        self._connection = await self._connect()
        return self
    
    async def __aexit__(self, *exc_info) -> None:
        await self._connection.close()
    
    @asynccontextmanager
    async def transaction(self) -> AsyncIterator[None]:
        """Nested context manager"""
        try:
            await self.begin()
            yield
            await self.commit()
        except Exception:
            await self.rollback()
            raise

from typing import Literal
from pydantic import BaseModel
import instructor  # For structured LLM outputs

class Sentiment(BaseModel):
    """Structured output from LLM"""
    sentiment: Literal["positive", "negative", "neutral"]
    confidence: float
    explanation: str

class AIService:
    def __init__(self):
        # Using instructor for structured outputs
        self.client = instructor.from_openai(OpenAI())
    
    async def analyze_sentiment(self, text: str) -> Sentiment:
        """Get structured sentiment analysis"""
        return await self.client.chat.completions.create(
            model="gpt-4",
            response_model=Sentiment,
            messages=[
                {"role": "system", "content": "Analyze sentiment"},
                {"role": "user", "content": text}
            ]
        )

@app.websocket("/ws/{client_id}")
async def websocket_endpoint(websocket: WebSocket, client_id: str):
    await websocket.accept()
    try:
        while True:
            data = await websocket.receive_text()
            # Process data
            await websocket.send_text(f"Echo: {data}")
    except Exception:
        await websocket.close()

@app.get("/cached-data")
async def get_data(
    redis: Annotated[Redis, Depends(get_redis)],
    ttl: int = 3600
):
    """Cached endpoint with Redis"""
    cache_key = "data_key"
    
    if cached := await redis.get(cache_key):
        return {"cached": True, "data": cached}
    
    data = await expensive_operation()
    await redis.setex(cache_key, ttl, data)
    return {"cached": False, "data": data}