Python Async Io Tutorial – Complete Guide

Welcome to this intriguing journey of exploring Python async IO! Whether you are a beginner or an experienced coder, this tutorial will help you understand this complex yet indispensable concept in Python programming. Our goal is to make this journey engaging and valuable, and by the end of this text, you’ll have a robust understanding of async IO and its practical applications.

What is Python async IO?

Async IO, standing for asynchronous input/output, is a concurrent programming design within Python. It’s a module that has functions for working with asynchronous IO, using coroutines and streams. It lets your programs handle more tasks at once, boosting overall performance.

Python’s async IO is primarily utilized to write single-threaded concurrent code using coroutines and multiplexing IO access over sockets and other resources. In simpler terms, it’s heavily used for writing code that handles high IO tasks like web scraping, reading and writing to databases, or managing websockets.

Learning async IO opens up a plethora of opportunities for implementing efficient and high-performance applications. It’s a powerful tool for any Python programmer aiming to enhance their code’s performance while dealing with high IO tasks. This understanding is especially vital for managing high traffic websites or data-intensive applications.

So why wait? Let’s dive into the depths of asynchronous IO in Python!

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Basic Syntax and Understanding Async/Await

Python uses two essential keywords, ‘async’ and ‘await’, to establish the functionality of async IO. We’ll start by emphasizing these to set a strong foundation.


The ‘async’ keyword in Python denotes a function or IO operation as asynchronous, meaning it’ll return a coroutine object which can then be scheduled with an ‘event loop.

async def function_name():  
  print('Hello from the async function')


The ‘await’ keyword, on the other hand, is used to wait for a coroutine to complete. It can only be used inside an ‘async def’ function.

async def function_name():  
  await asyncio.sleep(1)  

Both snippets won’t show the expected output if run in a typical Python environment. That’s because these are coroutines that need to be scheduled to run in an event loop, which brings us to the next part of our tutorial.

Understanding Event Loop

The event loop is the fundamental part of asynchronous IO in Python. It’s a loop that can register, execute and cancel calls. It also supports the execution of coroutines directly.

Creating an Event Loop

In order to use async IO in python, an event loop must be created to manage and handle all asynchronous tasks. Here’s how you can do it:

import asyncio  

loop = asyncio.get_event_loop()

Scheduling a Coroutine

Scheduling a coroutine in meant for the event loop can be done by the following function:

coroutine = function_name()  

Running Multiple Coroutines

You can also run multiple coroutines concurrently using ‘asyncio.gather()’:

async def f1():
async def f2():

loop = asyncio.get_event_loop()
loop.run_until_complete(asyncio.gather(f1(), f2()))  

This tutorial was meant to be an easy-to-follow guide on Python async IO. Moving forward, you can now experiment with different async functions and take your python application performance to the next level!

Handling IO Operations

Handling IO operations in Python using async IO can drastically boost the overall performance of your applications. To demonstrate this, let’s look at a basic example of reading a file using traditional IO and then do the same using async IO.

Traditional IO

The traditional method of reading a file in Python goes something like this:

def read_file():
  with open('file.txt', 'r') as f:


Async IO

A similar operation using async IO:

import asyncio

async def read_file():
  with open('file.txt', 'r') as f:

loop = asyncio.get_event_loop()

In async IO, the read operation will not block the entire program and will only take up as much time as required to complete the operation, making the overall code more efficient.

Working with Requests

Another important use-case of async IO is handling network requests. Let’s look at how we can make two requests asynchronously:

Traditional Requests

Firstly, the traditional way of making requests:

import requests

def fetch_data(url):
  response = requests.get(url)


Async Requests

And now the async way of making the same requests:

import aiohttp
import asyncio

async def fetch_data(session, url):
  async with session.get(url) as response:
    return await response.text()

async def main():
  async with aiohttp.ClientSession() as session:
    data1 = await fetch_data(session, '')
    data2 = await fetch_data(session, '')

loop = asyncio.get_event_loop()

In the async version, both requests are executed concurrently, reducing the overall wait time and significantly improving the performance of your program when dealing with IO operations.

Working with Databases

Lastly, let’s look at how we can interact with a database asynchronously:

import asyncio
import aiomysql

loop = asyncio.get_event_loop()

async def get_name(id):
  conn = await aiomysql.connect(host='localhost', user='root', password='password', db='mydb', loop=loop)
  cur = await conn.cursor()
  await cur.execute('SELECT name FROM employees WHERE id=%s', (id,))
  print(await r.fetchone())


Again, this code snippet becomes significantly more efficient when you have to handle tons of requests or operations concurrently. So, with a good understanding of async IO, you’re now ready to make your Python programs a lot more efficient!

Where To Go Next?

Now that you have acquired the basics of Python async IO, it’s important to keep the momentum going! With Python being highly in demand—particularly in data science—expanding your Python skillset can open doors to endless opportunities. That’s where we would recommend taking the next step with our Python Mini-Degree.

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The courses are suitable for everyone—whether you are a beginner with no prior coding experience or an experienced programmer looking to acquire a new skill. The flexibility we offer allows you to progress at your own pace and access the materials anytime, anywhere, and on any device.

If you’re seeking to explore the expanse of Python further, do check out our extensive collection of Python courses. Designed to suit all levels of expertise, these courses promise to empower your coding knowledge beyond comprehension. You can access our Python courses here.


Once you have conquered the intricacies of Python async IO, you are on the right path to becoming a proficient concurrent programmer. As you venture into this territory, you’ll cultivate a pragmatic approach to enhancing your programs’ performance and throughput. So, don’t let this momentum fade away. Continually seek to grasp and master complex programming intricacies, and you’ll soon find yourself on par with the best in the industry.

At Zenva, we seek to accompany you on your learning journey. So why not propel your coding abilities even further with our Python Mini-Degree? Remember, every line of code you write brings you one step closer to your ultimate coding goals. So, keep at it and happy coding!

Did you come across any errors in this tutorial? Please let us know by completing this form and we’ll look into it!

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