How to Use Asyncio for Asynchronous Event-Driven Programming in Python

1. Introduction

In this tutorial, you will learn how to use the asyncio module in Python to create and handle asynchronous events. Asynchronous events are those that occur independently of the main program flow, and can be triggered by user input, network activity, timers, or other external factors. Asynchronous programming is a way of writing code that can handle multiple concurrent events without blocking or waiting for them to finish.

Asyncio is a built-in module in Python that provides a framework for writing asynchronous code. It allows you to define coroutines, which are special functions that can pause and resume their execution at certain points. Coroutines can be scheduled and managed by an event loop, which is a central controller that runs and coordinates the coroutines. Asyncio also provides futures and tasks, which are objects that represent the results or outcomes of coroutines.

By using asyncio, you can write code that can perform multiple tasks concurrently, such as fetching data from multiple sources, processing user input, or running background jobs. This can improve the performance and responsiveness of your applications, as well as make your code more readable and maintainable.

To follow this tutorial, you will need a basic understanding of Python and its syntax. You will also need Python 3.7 or higher, as some features of asyncio are only available in newer versions. You can check your Python version by running the following command in your terminal:

python --version

If you are ready, let’s get started!

2. What is Asynchronous Programming?

Asynchronous programming is a way of writing code that can handle multiple concurrent events without blocking or waiting for them to finish. This means that your code can perform other tasks while waiting for an event to occur, such as a network request, a user input, or a timer. This can improve the performance and responsiveness of your applications, as well as make your code more readable and maintainable.

But what exactly are events, and how can you handle them asynchronously? To answer these questions, let’s first understand the difference between synchronous and asynchronous programming.

2.1. Synchronous vs Asynchronous

Synchronous programming is the traditional way of writing code, where each line of code is executed one after another, and the program waits for each operation to finish before moving on to the next one. For example, if you have a function that makes a network request, the program will wait until the request is completed and the response is received before executing the next line of code. This is also called blocking, because the program is blocked from doing anything else until the current operation is done.

Asynchronous programming, on the other hand, is a way of writing code that can handle multiple concurrent events without blocking or waiting for them to finish. For example, if you have a function that makes a network request, the program will not wait for the request to complete, but instead will continue executing the next line of code. The network request will be handled by a separate process or thread, and the program will be notified when the response is ready. This is also called non-blocking, because the program can do other things while the current operation is in progress.

To illustrate the difference between synchronous and asynchronous programming, let’s look at a simple example. Suppose you want to write a program that fetches data from three different websites and prints the results. Here is how you would do it synchronously in Python:

import requests

# Define a function that fetches data from a given URL
def fetch_data(url):
    response = requests.get(url) # Make a network request and wait for the response
    return response.text # Return the response text

# Define the URLs to fetch data from
urls = ["https://example.com", "https://example.org", "https://example.net"]

# Loop through the URLs and fetch data synchronously
for url in urls:
    data = fetch_data(url) # Call the function and wait for the result
    print(data) # Print the result

This code will work, but it will be slow and inefficient, because it will fetch data from one URL at a time, and wait for each request to finish before moving on to the next one. If each request takes 1 second to complete, the whole program will take at least 3 seconds to run.

Now, let’s see how you would do it asynchronously in Python using asyncio:

import asyncio
import aiohttp

# Define a coroutine that fetches data from a given URL
async def fetch_data(url):
    async with aiohttp.ClientSession() as session: # Create an asynchronous session
        async with session.get(url) as response: # Make an asynchronous request and wait for the response
            return await response.text() # Return the response text

# Define the URLs to fetch data from
urls = ["https://example.com", "https://example.org", "https://example.net"]

# Define a coroutine that loops through the URLs and fetches data asynchronously
async def main():
    tasks = [] # Create an empty list of tasks
    for url in urls:
        task = asyncio.create_task(fetch_data(url)) # Create an asynchronous task for each URL
        tasks.append(task) # Add the task to the list of tasks
    results = await asyncio.gather(*tasks) # Wait for all the tasks to finish and gather the results
    for result in results:
        print(result) # Print the result

# Run the main coroutine
asyncio.run(main())

This code will also work, but it will be much faster and more efficient, because it will fetch data from all the URLs concurrently, and not wait for each request to finish before moving on to the next one. If each request takes 1 second to complete, the whole program will take only 1 second to run.

As you can see, asynchronous programming can make your code more performant and responsive, especially when dealing with I/O-bound tasks, such as network requests, file operations, or user input. However, asynchronous programming also comes with its own challenges and complexities, such as managing concurrency, handling errors, and writing readable and maintainable code. In the next section, we will explore some of the benefits and challenges of asynchronous programming in more detail.

2.2. Benefits and Challenges of Asynchronous Programming

Asynchronous programming has many benefits, such as:

• Performance: Asynchronous code can run faster and more efficiently than synchronous code, as it can handle multiple concurrent events without blocking or waiting for them to finish. This can improve the throughput and scalability of your applications, as well as reduce the resource consumption and latency.
• Responsiveness: Asynchronous code can make your applications more responsive and user-friendly, as they can handle user input, network activity, or other external events without freezing or slowing down the main program flow. This can enhance the user experience and satisfaction, as well as prevent potential errors or crashes.
• Readability: Asynchronous code can make your code more readable and maintainable, as it can avoid nested callbacks, complex loops, or conditional statements that can make synchronous code hard to follow and debug. Asyncio also provides a clear and consistent syntax for writing asynchronous code, using keywords such as async and await.

However, asynchronous programming also has some challenges, such as:

• Concurrency: Asynchronous code can introduce concurrency issues, such as race conditions, deadlocks, or data corruption, when multiple tasks access or modify the same data or resources. You need to be careful and use proper synchronization mechanisms, such as locks, semaphores, or queues, to ensure the correctness and safety of your code.
• Errors: Asynchronous code can make error handling more difficult, as errors can occur at any point in the execution of a task, and can propagate or affect other tasks. You need to use proper exception handling techniques, such as try, except, or finally, to catch and handle errors gracefully and prevent them from crashing your application.
• Debugging: Asynchronous code can make debugging more challenging, as the order and timing of the execution of tasks can be unpredictable and hard to trace. You need to use proper debugging tools, such as logging, breakpoints, or stack traces, to monitor and inspect the state and behavior of your code.

As you can see, asynchronous programming can offer many advantages, but also requires some extra care and attention. In the next section, we will explore how asyncio works in Python, and how it can help you write asynchronous code more easily and effectively.

3. How Asyncio Works in Python

Asyncio is a built-in module in Python that provides a framework for writing asynchronous code. It allows you to define coroutines, which are special functions that can pause and resume their execution at certain points. Coroutines can be scheduled and managed by an event loop, which is a central controller that runs and coordinates the coroutines. Asyncio also provides futures and tasks, which are objects that represent the results or outcomes of coroutines.

In this section, we will explain how asyncio works in Python, and how it can help you write asynchronous code more easily and effectively. We will cover the following topics:

• What is the event loop and how does it run and manage coroutines?
• What are coroutines and how do you define and schedule them?
• What are futures and tasks and how do they represent the results or outcomes of coroutines?
• What are the keywords async and await and how do they enable you to write asynchronous code?

By the end of this section, you will have a solid understanding of the core concepts and components of asyncio, and how they work together to create and handle asynchronous events in Python.

3.1. The Event Loop

The event loop is the core component of asyncio, and it is responsible for running and managing coroutines. The event loop is a single-threaded loop that can execute multiple coroutines concurrently, by switching between them at appropriate points. The event loop also handles asynchronous events, such as network requests, timers, signals, or user input, and dispatches them to the corresponding coroutines.

The event loop has two main functions: running and scheduling coroutines.

Running coroutines means executing them until they are completed or paused. The event loop runs one coroutine at a time, and switches to another one when the current one is blocked or waiting for an event. The event loop uses a queue to store the coroutines that are ready to run, and pops them from the queue in a first-in, first-out (FIFO) order. The event loop also uses a selector to monitor the events that are pending or ready, and notifies the coroutines that are waiting for them.

Scheduling coroutines means adding them to the queue of the event loop, so that they can be run later. The event loop provides several methods to schedule coroutines, such as create_task, ensure_future, gather, or run_until_complete. These methods create tasks, which are subclasses of futures, and represent the execution or outcome of coroutines. We will discuss futures and tasks in more detail in the next section.

To use the event loop, you need to import the asyncio module and use the asyncio.run function. This function creates a new event loop, runs a given coroutine until it is completed, and then closes the loop. For example, the following code creates and runs a simple coroutine that prints “Hello, world!” after one second:

import asyncio

# Define a coroutine that prints "Hello, world!" after one second
async def hello():
    await asyncio.sleep(1) # Pause the coroutine for one second
    print("Hello, world!") # Print the message

# Run the coroutine using the asyncio.run function
asyncio.run(hello())

This code will output:

Hello, world!

In the next section, we will explain what coroutines are and how to define and schedule them using the event loop.

3.2. Coroutines

Coroutines are the main building blocks of asyncio, and they are special functions that can pause and resume their execution at certain points. Coroutines can be used to create and handle asynchronous events, such as network requests, timers, or user input, without blocking or waiting for them to finish. Coroutines can also communicate with each other, by sending and receiving data, or by waiting for other coroutines to complete.

To define a coroutine, you need to use the async keyword before the def statement. This tells Python that the function is a coroutine, and that it can use the await keyword inside it. For example, the following code defines a simple coroutine that prints “Hello, world!” after one second:

import asyncio

# Define a coroutine that prints "Hello, world!" after one second
async def hello():
    await asyncio.sleep(1) # Pause the coroutine for one second
    print("Hello, world!") # Print the message

The await keyword is used to pause the execution of the coroutine until the awaited object is completed or ready. The awaited object can be another coroutine, a future, a task, or any other object that supports the __await__ method. In this case, the awaited object is asyncio.sleep(1), which is a coroutine that returns after one second. When the coroutine is paused, the event loop can run other coroutines or handle other events, until the awaited object is ready.

To schedule a coroutine, you need to use one of the methods provided by the event loop, such as create_task, ensure_future, gather, or run_until_complete. These methods create a task, which is a subclass of a future, and represent the execution or outcome of a coroutine. We will discuss futures and tasks in more detail in the next section. For example, the following code schedules the hello coroutine using the asyncio.run function, which creates and runs a new event loop:

import asyncio

# Define a coroutine that prints "Hello, world!" after one second
async def hello():
    await asyncio.sleep(1) # Pause the coroutine for one second
    print("Hello, world!") # Print the message

# Run the coroutine using the asyncio.run function
asyncio.run(hello())

This code will output:

Hello, world!

In the next section, we will explain what futures and tasks are and how they represent the results or outcomes of coroutines.

3.3. Futures and Tasks

Futures and tasks are objects that represent the results or outcomes of coroutines. They are subclasses of the asyncio.Future class, and they implement the __await__ method, which means they can be awaited by other coroutines. Futures and tasks are also awaitable objects, which means they can be used with the await keyword.

Futures and tasks have some common attributes and methods, such as:

• result(): Returns the result of the coroutine, or raises an exception if the coroutine failed.
• exception(): Returns the exception raised by the coroutine, or None if the coroutine succeeded.
• done(): Returns True if the coroutine is done, either by returning a result or raising an exception.
• cancelled(): Returns True if the coroutine was cancelled.
• cancel(): Cancels the coroutine, if it is not done yet.
• add_done_callback(): Adds a callback function that will be called when the coroutine is done.

The main difference between futures and tasks is that futures are low-level objects that are created and managed by the event loop, while tasks are high-level objects that are created and managed by the programmer. Tasks are also subclasses of futures, which means they inherit all the attributes and methods of futures, and add some extra features, such as:

• get_coro(): Returns the coroutine object that the task is executing.
• get_name(): Returns the name of the task, which can be useful for debugging.
• set_name(): Sets the name of the task, which can be useful for debugging.
• current_task(): Returns the current task that is being executed by the event loop.
• all_tasks(): Returns a set of all the tasks that are currently scheduled by the event loop.

To create a future, you need to use the loop.create_future() method, where loop is the event loop object. This method returns a new future object that is not associated with any coroutine. You can then set the result or the exception of the future using the set_result() or set_exception() methods. For example, the following code creates a future and sets its result to “Hello, world!”:

import asyncio

# Create a new event loop
loop = asyncio.new_event_loop()

# Create a new future
future = loop.create_future()

# Set the result of the future
future.set_result("Hello, world!")

# Print the result of the future
print(future.result())

This code will output:

Hello, world!

To create a task, you need to use one of the methods provided by the event loop, such as create_task(), ensure_future(), gather(), or run_until_complete(). These methods take a coroutine object as an argument, and return a new task object that is associated with the coroutine. The task will then run the coroutine and store its result or exception. For example, the following code creates a task from the hello() coroutine that we defined earlier, and runs it using the asyncio.run() function:

import asyncio

# Define a coroutine that prints "Hello, world!" after one second
async def hello():
    await asyncio.sleep(1) # Pause the coroutine for one second
    print("Hello, world!") # Print the message

# Create and run a task from the coroutine using the asyncio.run function
asyncio.run(asyncio.create_task(hello()))

This code will output:

Hello, world!

In the next section, we will explain what the keywords async and await are and how they enable you to write asynchronous code.

3.4. Awaiting and Yielding

The keywords async and await are essential for writing asynchronous code in Python. They enable you to define and use coroutines, which are special functions that can pause and resume their execution at certain points. They also enable you to communicate with the event loop, which is the central controller that runs and manages coroutines.

The async keyword is used to declare a function as a coroutine. This means that the function can use the await keyword inside it, and that it can be scheduled and run by the event loop. For example, the following code defines a coroutine that prints “Hello, world!” after one second:

import asyncio

# Define a coroutine that prints "Hello, world!" after one second
async def hello():
    await asyncio.sleep(1) # Pause the coroutine for one second
    print("Hello, world!") # Print the message

The await keyword is used to pause the execution of the coroutine until the awaited object is completed or ready. The awaited object can be another coroutine, a future, a task, or any other object that supports the __await__ method. When the coroutine is paused, the event loop can run other coroutines or handle other events, until the awaited object is ready. For example, the following code defines a coroutine that fetches data from a given URL:

import asyncio
import aiohttp

# Define a coroutine that fetches data from a given URL
async def fetch_data(url):
    async with aiohttp.ClientSession() as session: # Create an asynchronous session
        async with session.get(url) as response: # Make an asynchronous request and wait for the response
            return await response.text() # Return the response text

The await keyword can also be used to yield control to the event loop, without waiting for any specific object. This can be useful to allow other coroutines to run, or to avoid blocking the event loop for too long. For example, the following code defines a coroutine that prints numbers from 1 to 10, with a one-second delay between each number:

import asyncio

# Define a coroutine that prints numbers from 1 to 10, with a one-second delay between each number
async def count():
    for i in range(1, 11):
        print(i) # Print the number
        await asyncio.sleep(1) # Pause the coroutine for one second
        await asyncio.sleep(0) # Yield control to the event loop

In this section, we have learned how to use the async and await keywords to write asynchronous code in Python. We have also learned how to communicate with the event loop, which is the core component of asyncio. In the next section, we will learn how to use asyncio for asynchronous event-driven programming, and how to create and run an event loop, define and schedule coroutines, manage and cancel tasks, and handle errors and exceptions.

4. How to Use Asyncio for Asynchronous Event-Driven Programming

In this section, we will learn how to use asyncio for asynchronous event-driven programming, which is a programming paradigm that involves reacting to events that occur independently of the main program flow. Events can be triggered by user input, network activity, timers, or other external factors. Asynchronous event-driven programming can improve the performance and responsiveness of your applications, as well as make your code more readable and maintainable.

To use asyncio for asynchronous event-driven programming, you need to follow four main steps:

1. Create and run an event loop, which is the core component of asyncio that runs and manages coroutines.
2. Define and schedule coroutines, which are special functions that can pause and resume their execution at certain points.
3. Manage and cancel tasks, which are objects that represent the execution or outcome of coroutines.
4. Handle errors and exceptions, which are inevitable in any programming project.

In the following subsections, we will explain each of these steps in more detail, and provide some examples and tips on how to use them effectively.

4.1. Creating and Running an Event Loop

The event loop is the core component of asyncio that runs and manages coroutines. It is responsible for executing the coroutines, handling the events, and coordinating the concurrent tasks. The event loop also provides some methods and functions to create and schedule coroutines, manage and cancel tasks, handle errors and exceptions, and control the execution flow.

To use the event loop, you need to create and run it. There are different ways to do this, depending on your use case and preference. Here are some of the most common ways:

• asyncio.run(coro): This is the simplest and recommended way to run a coroutine using asyncio. It creates a new event loop, runs the given coroutine until it is complete, and then closes the loop. It also handles any errors and exceptions that may occur. This function is only available in Python 3.7 or higher. For example, the following code runs the hello() coroutine using the asyncio.run() function:

import asyncio

# Define a coroutine that prints "Hello, world!" after one second
async def hello():
    await asyncio.sleep(1) # Pause the coroutine for one second
    print("Hello, world!") # Print the message

# Run the coroutine using the asyncio.run function
asyncio.run(hello())

• loop = asyncio.new_event_loop() and loop.run_until_complete(coro): This is another way to run a coroutine using asyncio. It creates a new event loop, runs the given coroutine until it is complete, and then returns the result or raises an exception. It does not close the loop, so you can reuse it for other coroutines. You can also use the loop object to access other methods and functions of the event loop. For example, the following code runs the hello() coroutine using the run_until_complete() method:

import asyncio

# Define a coroutine that prints "Hello, world!" after one second
async def hello():
    await asyncio.sleep(1) # Pause the coroutine for one second
    print("Hello, world!") # Print the message

# Create a new event loop
loop = asyncio.new_event_loop()

# Run the coroutine using the run_until_complete method
loop.run_until_complete(hello())

# Close the loop
loop.close()

• asyncio.get_event_loop() and loop.run_forever(): This is another way to run a coroutine using asyncio. It gets the current event loop, or creates a new one if none exists, and runs it indefinitely. You can use the loop object to schedule and manage coroutines, tasks, and other events. You can also use the loop.stop() method to stop the loop. For example, the following code runs the count() coroutine using the run_forever() method:

import asyncio

# Define a coroutine that prints numbers from 1 to 10, with a one-second delay between each number
async def count():
    for i in range(1, 11):
        print(i) # Print the number
        await asyncio.sleep(1) # Pause the coroutine for one second
        await asyncio.sleep(0) # Yield control to the event loop

# Get the current event loop, or create a new one if none exists
loop = asyncio.get_event_loop()

# Schedule the coroutine using the create_task method
task = loop.create_task(count())

# Run the loop indefinitely
loop.run_forever()

# Stop the loop after 10 seconds
loop.call_later(10, loop.stop)

In this subsection, we have learned how to create and run an event loop, which is the core component of asyncio that runs and manages coroutines. In the next subsection, we will learn how to define and schedule coroutines, which are special functions that can pause and resume their execution at certain points.

4.2. Defining and Scheduling Coroutines

Coroutines are special functions that can pause and resume their execution at certain points. They are the basic building blocks of asynchronous programming in Python, and they allow you to write code that can handle multiple concurrent events without blocking or waiting for them to finish. Coroutines are also awaitable objects, which means they can be used with the await keyword.

To define a coroutine, you need to use the async keyword before the function definition. This tells Python that the function is a coroutine, and that it can use the await keyword inside it. For example, the following code defines a coroutine that prints “Hello, world!” after one second:

import asyncio

# Define a coroutine that prints "Hello, world!" after one second
async def hello():
    await asyncio.sleep(1) # Pause the coroutine for one second
    print("Hello, world!") # Print the message

To schedule a coroutine, you need to use one of the methods or functions provided by the event loop, such as create_task(), ensure_future(), gather(), or run_until_complete(). These methods or functions take a coroutine object as an argument, and return a task object that is associated with the coroutine. A task is a subclass of a future, which is an object that represents the result or outcome of a coroutine. Tasks and futures are also awaitable objects, which means they can be used with the await keyword.

For example, the following code schedules the hello() coroutine using the create_task() method, and then awaits the task using the await keyword:

import asyncio

# Define a coroutine that prints "Hello, world!" after one second
async def hello():
    await asyncio.sleep(1) # Pause the coroutine for one second
    print("Hello, world!") # Print the message

# Define a main coroutine that schedules and awaits the hello coroutine
async def main():
    task = asyncio.create_task(hello()) # Create a task from the coroutine
    await task # Wait for the task to finish

# Run the main coroutine using the asyncio.run function
asyncio.run(main())

This code will output:

Hello, world!

In this subsection, we have learned how to define and schedule coroutines, which are special functions that can pause and resume their execution at certain points. In the next subsection, we will learn how to manage and cancel tasks, which are objects that represent the execution or outcome of coroutines.

4.3. Managing and Cancelling Tasks

Tasks are objects that represent the execution or outcome of coroutines. They are subclasses of futures, which are objects that represent the result or state of an asynchronous operation. Tasks and futures are also awaitable objects, which means they can be used with the await keyword.

To create a task from a coroutine, you can use one of the methods or functions provided by the event loop, such as create_task(), ensure_future(), or gather(). These methods or functions take a coroutine object as an argument, and return a task object that is associated with the coroutine. For example, the following code creates a task from the hello() coroutine using the create_task() method:

import asyncio

# Define a coroutine that prints "Hello, world!" after one second
async def hello():
    await asyncio.sleep(1) # Pause the coroutine for one second
    print("Hello, world!") # Print the message

# Define a main coroutine that creates and awaits the hello task
async def main():
    task = asyncio.create_task(hello()) # Create a task from the coroutine
    await task # Wait for the task to finish

# Run the main coroutine using the asyncio.run function
asyncio.run(main())

To manage a task, you can use some of the methods and attributes of the task object, such as done(), result(), exception(), add_done_callback(), or get_name(). These methods and attributes allow you to check the status, result, exception, callback, or name of the task. For example, the following code prints the name and result of the hello() task after it is done:

import asyncio

# Define a coroutine that prints "Hello, world!" after one second
async def hello():
    await asyncio.sleep(1) # Pause the coroutine for one second
    print("Hello, world!") # Print the message
    return "Hello, world!" # Return the message

# Define a main coroutine that creates and awaits the hello task
async def main():
    task = asyncio.create_task(hello()) # Create a task from the coroutine
    task.set_name("hello_task") # Set the name of the task
    await task # Wait for the task to finish
    print(task.get_name()) # Print the name of the task
    print(task.result()) # Print the result of the task

# Run the main coroutine using the asyncio.run function
asyncio.run(main())

To cancel a task, you can use the cancel() method of the task object, or the asyncio.CancelledError exception. These methods and exceptions allow you to stop the execution of the task before it is complete, and raise an exception in the coroutine. You can also use the cancelled() method of the task object to check if the task was cancelled. For example, the following code cancels the count() task after 5 seconds, and handles the exception in the coroutine:

import asyncio

# Define a coroutine that prints numbers from 1 to 10, with a one-second delay between each number
async def count():
    try:
        for i in range(1, 11):
            print(i) # Print the number
            await asyncio.sleep(1) # Pause the coroutine for one second
            await asyncio.sleep(0) # Yield control to the event loop
    except asyncio.CancelledError: # Handle the cancellation exception
        print("The count task was cancelled")

# Define a main coroutine that creates and cancels the count task
async def main():
    task = asyncio.create_task(count()) # Create a task from the coroutine
    await asyncio.sleep(5) # Wait for 5 seconds
    task.cancel() # Cancel the task
    await task # Wait for the task to finish

# Run the main coroutine using the asyncio.run function
asyncio.run(main())

In this subsection, we have learned how to manage and cancel tasks, which are objects that represent the execution or outcome of coroutines. In the next subsection, we will learn how to handle errors and exceptions, which are inevitable in any programming project.

4.4. Handling Errors and Exceptions

Errors and exceptions are inevitable in any programming project, and asynchronous programming is no exception. Errors and exceptions can occur in your code, in the event loop, or in the external events that you are handling. You need to be able to handle them properly, otherwise your program may crash, behave unexpectedly, or produce incorrect results.

To handle errors and exceptions in asyncio, you can use the standard Python mechanisms, such as try, except, finally, and raise. You can also use some of the methods and attributes of the task and future objects, such as result(), exception(), set_exception(), and add_done_callback(). These methods and attributes allow you to get, set, or handle the exception of a task or a future.

For example, the following code defines a coroutine that raises a ValueError exception, and then handles it using the try and except blocks:

import asyncio

# Define a coroutine that raises a ValueError exception
async def error():
    raise ValueError("Something went wrong")

# Define a main coroutine that handles the exception
async def main():
    try:
        await error() # Await the error coroutine
    except ValueError as e: # Handle the ValueError exception
        print(e) # Print the exception message

# Run the main coroutine using the asyncio.run function
asyncio.run(main())

This code will output:

Something went wrong

Alternatively, the following code defines a coroutine that raises a ValueError exception, and then handles it using the exception() method of the task object:

import asyncio

# Define a coroutine that raises a ValueError exception
async def error():
    raise ValueError("Something went wrong")

# Define a main coroutine that handles the exception
async def main():
    task = asyncio.create_task(error()) # Create a task from the coroutine
    await task # Wait for the task to finish
    e = task.exception() # Get the exception of the task
    if e: # If there is an exception
        print(e) # Print the exception message

# Run the main coroutine using the asyncio.run function
asyncio.run(main())

This code will also output:

Something went wrong

In this subsection, we have learned how to handle errors and exceptions in asyncio, using the standard Python mechanisms and the methods and attributes of the task and future objects. In the next section, we will conclude this tutorial and summarize the main points that we have covered.

5. Conclusion

In this tutorial, you have learned how to use asyncio for asynchronous event-driven programming in Python. You have learned the concepts and benefits of asynchronous programming, and how to use coroutines, futures, tasks, and the event loop to handle multiple concurrent events without blocking or waiting for them to finish. You have also learned how to manage and cancel tasks, and how to handle errors and exceptions in asyncio.

Asyncio is a powerful and versatile module that can help you write performant and responsive applications, especially when dealing with I/O-bound tasks, such as network requests, file operations, or user input. Asyncio can also make your code more readable and maintainable, by allowing you to write code that follows the natural flow of the events, rather than using callbacks or threads.

However, asyncio also has its own challenges and complexities, such as managing concurrency, handling errors, and writing readable and maintainable code. You need to be familiar with the syntax and semantics of asyncio, and follow some best practices and guidelines to avoid common pitfalls and errors. You also need to be aware of the limitations and compatibility issues of asyncio, and how to use it with other modules and libraries.

Asyncio is a constantly evolving and improving module, and new features and enhancements are being added to it regularly. You can find more information and documentation about asyncio on the official Python website: https://docs.python.org/3/library/asyncio.html. You can also find many examples and tutorials on how to use asyncio for various purposes and projects on the internet.

We hope you have enjoyed this tutorial and learned something useful from it. Thank you for reading and happy coding!