Yaron

Python Class Decorators

Python class decorators extend the functionality of classes in a manner similar to how function decorators extend functions. They allow you to modify or enhance the behavior of classes without directly altering their source code. Class decorators are particularly useful for adding features like validation, caching, or logging to classes in a flexible and reusable way.

The Python wraps Decorator

One common challenge with decorators is that they can unintentionally strip away important metadata from the functions they decorate. Fortunately, there is a solution to this problem in the form of the Python wraps decorator, which is available in the functools module. You can use wraps in any decorator you create to preserve the original metadata.

Python Decorators With Arguments

Python decorators are functions that allow you to enhance the functionality of other functions without modifying their original code directly. Decorators can accept arguments themselves, which can make them even more versatile. These arguments can modify the behavior of the decorator or the decorated function in various ways.

Python Decorators

Python decorators allow you to add functionality to existing functions without modifying their original code directly. They are especially useful for adding common functionality such as logging, authentication, performance monitoring, and caching to specific functions you implement. To crate a decorator, you don't need to use new Python features beyond that of functions, however Python does have specialized @ syntax that can simplify the process.

Python Generator Expressions

Python generator expressions offer a succinct and efficient way of creating simple generators. Generator expression syntax is similar to that of list comprehensions. And much like those, generator expressions allow you to generate sequences by applying expressions to items in an iterable and potentially filtering them based on specified conditions.

Python Generators

Python generators allow you to easily create efficient sequences of values using iterators. They can be implemented with special functions called generator functions, which use the yield keyword to yield values one at a time, rather than returning them all at once. Generators can also be created with generator expressions, which are similar to list comprehensions but produce values lazily as well.

Python Closures

Python closures are functions bundled up with variables they access in outer, non-local scopes of enclosing functions. Closures are essential when inner functions need access to variables from their enclosing scope because without them, these variables would be lost once the enclosing function completes execution.

The Python nonlocal Keyword

The Python nonlocal keyword is used to access and modify variables from an outer (but not global) scope in nested functions (i.e. non-local variables). It tells Python that a variable assigned in a nested function should refer to a variable in the nearest enclosing scope that is not global. Otherwise, Python will create a new variable in the local scope of the nested function.

The Python global Keyword

The global keyword in Python is used to access and modify global variables from within a function's local scope. It tells Python that a variable assigned in a function should refer to a global variable. Otherwise, Python will create a local variable of the same name.

Understanding Python Scopes

A scope is the extent of visibility and accessibility of named entities such as variables, functions, and classes, within a program. It determines where in your code you can access a particular name or identifier. There are three types of Python scopes: build-in, global, and local.