python

Asynchronous HTTP Requests in Python with aiohttp and asyncio

Asynchronous HTTP Requests in Python with aiohttp and asyncio
Asynchronous code has increasingly become a mainstay of Python development. With asyncio becoming part of the standard library and many third party packages providing features compatible with it, this paradigm is not going away anytime soon. Let's walk through how to use the aiohttp library to take advantage of this for making asynchronous HTTP requests, which is one of the most common use cases for non-blocking code. What is non-blocking code?

Python Logging Config via Dictionary and Config File

Python Logging Config via Dictionary and Config File
Logging is one of the most important aspects of software development. The developers log events like successful completion, warnings, errors, fatal errors, etc. These logs can help developers in various ways like find out the root cause in case of when some failure happens in the future, look for slow parts of the code for optimization, etc. Python provides a module named logging which has a vast API that can be used to log events in Python.

Underscores in Python

Underscores in Python
Single and double underscores have a meaning in Python variable and method names. Some of that meaning is merely by convention and intended as a hint to the programmer — and some of it is enforced by the Python interpreter. If you're wondering "What's the meaning of single and double underscores in Python variable and method names?" I'll do my best to get you the answer here. In this article I’ll discuss the following five underscore patterns and naming conventions and how they affect the behavior of your Python programs:

How Validate Object Attributes in Python

How Validate Object Attributes in Python
Generally speaking, type checking and value checking are handled by Python in a flexible and implicit way. Python has introduced typing module since Python3 which provides runtime support for type hints. But for value checking, there is no unified way to validate values due to its many possibilities. One of the scenarios where we need value checking is when we initialize a class instance. We want to ensure valid input attributes in the first stage, for example, an email address should have the correct format xxx@xxxxx.

Python Decorators Guide

Python Decorators Guide
The Power of Python Decorators At their core, Python’s decorators allow you to extend and modify the behavior of a callable (functions, methods, and classes) without permanently modifying the callable itself. Any sufficiently generic functionality you can tack on to an existing class or function’s behavior makes a great use case for decoration. This includes the following: logging enforcing access control and authentication instrumentation and timing functions rate-limiting caching and more Sure, decorators are relatively complicated to wrap your head around for the first time, but they’re a highly useful feature that you’ll often encounter in third-party frameworks and the Python standard library.

Fabulous Python Decorators

Fabulous Python Decorators
@cache @functools.cache(user_function) Simple lightweight unbounded function cache. Sometimes called "memoize" . Returns the same as lru_cache(maxsize=None), creating a thin wrapper around a dictionary lookup for the function arguments. Because it never needs to evict (remove) old values, this is smaller and faster than lru_cache() with a size limit. example: from functools import cache @cache def factorial(n): return n * factorial(n-1) if n else 1 >>> factorial(10) # no previously cached result, makes 11 recursive calls 3628800 >>> factorial.
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