Python Mastery Final

Comprehensive review, advanced patterns, and best practices.

Overview

Final comprehensive review.

Advanced Patterns Summary

# 1. Metaclasses
class RegistryMeta(type):
    _registry = {}
    
    def __new__(cls, name, bases, attrs):
        new_class = super().__new__(cls, name, bases, attrs)
        if name != 'Base':
            cls._registry[name] = new_class
        return new_class

# 2. Descriptors
class Validated:
    def __set_name__(self, owner, name):
        self.name = name
    
    def __get__(self, obj, objtype=None):
        if obj is None:
            return self
        return getattr(obj, f'_{self.name}', None)
    
    def __set__(self, obj, value):
        setattr(obj, f'_{self.name}', value)

# 3. Generators
def pipeline(*functions):
    def generator(data):
        for func in functions:
            data = func(data)
        return data
    return generator

# 4. Context Managers
from contextlib import contextmanager

@contextmanager
def managed_resource():
    resource = acquire()
    try:
        yield resource
    finally:
        release(resource)

# 5. Decorators
def memoize(func):
    cache = {}
    @wraps(func)
    def wrapper(*args):
        if args not in cache:
            cache[args] = func(*args)
        return cache[args]
    return wrapper

Best Practices

# 1. Use type hints
def greet(name: str) -> str:
    return f"Hello, {name}!"

# 2. Use dataclasses for data containers
from dataclasses import dataclass

@dataclass
class Point:
    x: float
    y: float

# 3. Use pathlib for paths
from pathlib import Path

path = Path("data") / "file.txt"

# 4. Use context managers for resources
with open("file.txt") as f:
    data = f.read()

# 5. Use generators for large datasets
def large_dataset():
    for i in range(1000000):
        yield i

Practice

Build a complete application using all patterns.