Asynchronous programming, a feature of modern languages, allows programs to juggle multiple operations without waiting or getting hung up on any one of them. In Python 3.13 and higher, this powerful tool can be leveraged to efficiently handle tasks like network or file I/O, where most of the program's time is spent waiting for tasks to finish. By mastering asynchronous programming, developers can significantly increase productivity and streamline tasks.

In traditional programming, a web scraping application that opens 100 network connections would typically open one connection, wait for the results, then open the next and wait for the results, and so on. However, with asynchronous programming, all 100 connections can be opened at once, and the program can switch between active connections as they return results. This approach eliminates time wasted waiting for any one thing to finish, allowing for more efficient use of resources.

Asynchronous programming is particularly useful when dealing with tasks that involve long wait times, I/O operations, or multiple I/O operations happening simultaneously. Examples of tasks that work well with async include web scraping, network services, and programs that coordinate results from multiple sources that take a long time to return values.

In Python, asynchronous programming is achieved using the `async` and `await` keywords. The `async` keyword is used to define asynchronous functions, also known as coroutines, which can use the `await` keyword to wait for results from other coroutines without blocking. Coroutines can only be called from other async functions, and the `asyncio` library is used to bridge async and the rest of Python.

The `asyncio` library provides several key functions, including `asyncio.run()` to launch an async function from non-asynchronous code, and `asyncio.gather()` to run multiple async functions and wait for all of their results. By using `asyncio`, developers can efficiently run multiple tasks concurrently and gather their results in a single batch.

In addition to `asyncio`, event loops and task objects are essential components of Python async apps. Event loops manage the execution of coroutines, and task objects provide a way to control the behavior of coroutines from outside the event loop. By using event loops and task objects, developers can gain more control over the behavior of their async applications.

So, why use async instead of threads or multiprocessing? Async is about concurrency, whereas threads and multiprocessing are about parallelism. Async provides several key advantages, including being more lightweight, easier to reason about, and more readily cancellable than threads. Additionally, async can be used in conjunction with multiprocessing to delegate CPU-intensive jobs to a process pool without blocking the central process.

With the introduction of Python 3.13, multithreading has also undergone significant changes, allowing Python threads to run with full concurrency. However, even with these changes, async tasks will still scale better than threads at the high end.

For developers looking to get started with asynchronous programming in Python, the best approach is to build simple async apps and explore the growing number of async-powered libraries and middleware. The official documentation for `asyncio` and the `aio-libs` repository are valuable resources for learning more about asynchronous programming in Python.