Python

FastAPI framework, high performance, easy to learn, fast to code, ready for production

In this tutorial, you'll explore Python's __pycache__ folder. You'll learn about when and why the interpreter creates these folders, and you'll customize their default behavior. Finally, you'll take a look under the hood of the cached .pyc files.

Python's logging module isn't the only way to create logs. There are several third-party packages you can use, too. One of the most popular is Loguru. Loguru intends to remove all the boilerplate you get with the Python logging API. You will find that Loguru greatly simplifies creating logs in Python.

Python logging made (stupidly) simple.

Symbolica is a blazing fast computer algebra system.

This tutorial dives into Python sequences, which is one of the main categories of data types. You'll learn about the properties that make an object a sequence and how to create user-defined sequences.

In this video, Ben Finkel covers how to create an asynchronous API call in Python. A lot of internet resources have detailed articles that discuss all the in...

Learn how to speed up slow Python code with concurrent programming and the cutting-edge asyncio library./bbr/br/ Python is flexible, versatile, and easy to learn. It can also be very slow compared to lower-level languages. Python Concurrency with asyncio/i teaches you how to boost Python's performance by applying a variety of concurrency techniques. You'll learn how the complex-but-powerful asyncio library can achieve concurrency with just a single thread and use asyncio's APIs to run multiple web requests and database queries simultaneously. The book covers using asyncio with the entire Python concurrency landscape, including multiprocessing and multithreading.

05bit/peewee-async, peewee-async Asynchronous interface for peewee ORM powered by asyncio. Important notes Since version 0.6.0a only peewee 3.5+ is supported If you still

In this video, I will show you how to take a slow running script with many API calls and convert it to an async version that will run much faster. I use AIOH...

Threading in Python is simple. It allows you to manage concurrent threads doing work at the same time. The library is called "threading", you create "Thread" objects, and they run target functions for you. You can start potentially hundreds of threads that will operate in parallel, and work through tasks faster.

Easily learn how to download multiple images in Python using synchronous and asynchronous code.

We can run multiple concurrent asyncio event loops by starting and running each new event loop in a separate thread. Each thread can host and manage one event loop. This means we can start one thread per event loop we require, allowing a program to potentially scale from thousands to millions of coroutines. In this […]

Develop Python-based apps with confidence by mastering the tools needed to ship well-tested, high-quality code.

This tutorial looks at how to develop and test an asynchronous API with FastAPI, Postgres, pytest, and Docker using Test-driven Development (TDD).

We can define coroutine methods on custom Python objects. This allows methods on custom Python objects to use async/await syntax, such as awaiting other coroutines and tasks and allows the custom coroutine methods themselves to be awaited within our asyncio programs. In this tutorial, you will discover how to define object methods as coroutines. Let’s […]

Today I learned how to use named tuples to improve readability and flexibility of test parametrisations in pytest.

This tutorial explores lazy evaluation in Python and looks at the advantages and disadvantages of using lazy and eager evaluation methods. By the end of this tutorial, you'll clearly understand which approach is best for you, depending on your needs.

py2wasm converts your Python programs to WebAssembly, running them at 3x faster speeds

Introduction In this article, we will discuss the importance of code linting and how to...

I use f-strings every day. The irony is I also every day end up searching the Web to find the correct format to use. Until one day I thought a

Discover the power of Pydantic, Python's most popular data parsing, validation, and serialization library. In this hands-on tutorial, you'll learn how to make your code more robust, trustworthy, and easier to debug with Pydantic.

No one cares if you can reverse a linked list — they want a one-click way to use your software on their machine. Docker makes that…

In this tutorial, you'll learn how to store and retrieve data using Python, SQLite, and SQLAlchemy as well as with flat files. Using SQLite with Python brings with it the additional benefit of accessing data with SQL. By adding SQLAlchemy, you can work with data in terms of objects and methods.

Loading complete JSON files into Python can use too much memory, leading to slowness or crashes. The solution: process JSON data one chunk at a time.