How to use asyncio in Python

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by dot icon Updated: Sep 26, 2023

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Asyncio is flexible and can handle many tasks, from basic I/O to web scraping. The ability to do different tasks simultaneously makes it good for waiting for outside resources. I/O or networks could relate to these tasks.  

Features of Asyncio:  

  • Concurrency  
  • Non-Blocking I/O  
  • Coroutines  
  • Event Loop  
  • Cancellable Tasks  
  • Timeouts and Delays  
  • Networking  
  • Interoperability  
  • Extensibility  
  • Ecosystem  
  • Parallelism  

Types of Tasks Used in Asyncio:  

I/O-Bound Operations:  

  • File I/O: Reading from or writing to files without blocking the program's execution.  
  • Database Access: Database Access is performing database queries or updates asynchronously. This is useful for applications that rely on databases.  
  • Socket Communication: Socket communication involves managing network sockets. Managing network clients and handling multiple chat server connections is important.  

Network Operations:  

  • HTTP Requests: Making HTTP requests to web APIs or websites asynchronously. Libraries such as AIOHTTP use asyncio and are good for web scraping and web services.  
  • DNS Lookups: DNS Lookups are important for efficient network communication. They resolve domain names to IP addresses.  
  • WebSocket Communication: You can build real-time applications using WebSocket communication. Examples include chat applications and online gaming.  

 Web Scraping:  

  • Crawling Websites: To crawl websites, you fetch web pages and extract data from many sites at once.  
  • Rate Limiting: Scrape slower to prevent server overload or being blocked. If needed, try again.  


To sum up, asyncio is a useful tool for developers. One of its advantages is that it can handle multiple tasks simultaneously. It also allows code to run without blocking other processes. This means it can respond quickly to requests. Additionally, it supports various programming languages. It makes asynchronous programming easier to understand and use for many applications. This improves software performance and user experience. If you build web apps network services, or handle I/O tasks, asyncio can help you.  

Here is the example of how to use asyncio in Python.

Fig: Preview of the output that you will get on running this code from your IDE.


In this solution we are using asyncio library of Python.


Follow the steps carefully to get the output easily.

  1. Download and Install the PyCharm Community Edition on your computer.
  2. Open the terminal and install the required libraries with the following commands.
  3. Install asyncio - pip install asyncio.
  4. Create a new Python file on your IDE.
  5. Copy the snippet using the 'copy' button and paste it into your Python file.
  6. Run the current file to generate the output.

I hope you found this useful.

I found this code snippet by searching for 'Using asyncio in Python 'in Kandi. You can try any such use case!

Environment Tested

I tested this solution in the following versions. Be mindful of changes when working with other versions.

  1. PyCharm Community Edition 2022.3.1
  2. The solution is created in Python 3.11.1 Version
  3.  asyncio 

Using this solution, we can be able to use asyncio in Python with simple steps. This process also facilities an easy way to use, hassle-free method to create a hands-on working version of code which would help us to use asyncio in Python.

Dependent Library

pytest-asyncioby pytest-dev

Python doticonstar image 1153 doticonVersion:v0.21.0doticon
License: Permissive (Apache-2.0)

Asyncio support for pytest


            pytest-asyncioby pytest-dev

            Python doticon star image 1153 doticonVersion:v0.21.0doticon License: Permissive (Apache-2.0)

            Asyncio support for pytest

                      You can search for any dependent library on kandi like 'pytest-asynico'.


                      1. What is the concurrency landscape of Python, and why is asyncio important?   

                      Python can handle multiple tasks simultaneously, offering various options for concurrency. Each option has its strengths and use cases. Asyncio, a part of Python, is crucial. It offers a specific approach to concurrency for certain tasks.  


                      2. How does the asyncio module work for a Working Python Developer?   

                      Python developers need to understand the asyncio module and use it effectively. This is especially true when working with I/O-bound and asynchronous tasks.  

                      1. Import asyncio: Add the asyncio module to your Python script or application.  
                      2. Define Coroutines: To work with asyncio, you'll write asynchronous code using coroutines. Coroutines are functions marked with async def and defined using the async keyword.  
                      3. Event Loop: The main part of asyncio is the event loop. It's a scheduler that handles events and doesn't block. You create an event loop using asyncio.get_event_loop() or asyncio.new_event_loop().  
                      4.  Running Multiple Coroutines Concurrently: Asyncio allows you to run multiple Coroutines concurrently.  
                      5. Handling Exceptions: You have two options to handle exceptions in your coroutines. You can use try-except blocks or asyncio's asyncio.ensure_future(). This helps you handle exceptions gracefully.  
                      6. Timeouts and Delays: To stop running coroutines, you can use .cancel() on the asyncio.Task. Cancellation helps manage resources and stop unnecessary tasks.  
                      7. Timeouts and Delays: You can use Asyncio's built-in features to set these in your code. This is helpful when setting time limits or scheduling tasks at certain times.  
                      8. Testing: To effectively test asyncio code, use frameworks like pytest and libraries.  
                      9. Profiling and Optimization: Use Python's profiling tools or third-party profilers to speed up. Optimize your code based on the profiling results.  


                      3. What features in the Python standard library make asynchronous programming easier?   

                      The Python standard library has features and modules that make asynchronous programming easier. This is especially helpful when working with asyncio.  

                      1. asyncio Module: The asyncio module is a big part of the standard library. In Python, it lays the groundwork for asynchronous programming. This includes the event loop, coroutines, and tools to manage.  
                      2. async and await Keywords: Python introduced the words "async" and "await". It shows that functions and code blocks can happen at different times. Writing and reading code like this makes asynchronous code appear as synchronous code.  
                      3. asyncio.Queue: The asyncio.Queue class is a safe queue that lets you pass data between coroutines. It is asynchronous. It's a valuable tool for coordinating and communicating between asynchronous tasks.  


                      4. What makes Asynchronous Python more efficient than traditional programming techniques?   

                      Python's asyncio can make certain tasks more efficient than traditional programming methods.  

                      • Non-Blocking I/O  
                      • Concurrency without Threads  
                      • Scalability  
                      • Responsiveness  
                      • Reduced Context Switching  
                      • Simplified Code  
                      • Resource Efficiency  
                      • Predictable Performance  


                      5. How does Asynchronous IO help develop faster and more effective code?   

                      Asynchronous I/O (Input/Output) can make code faster and more effective.  

                      • Improved Responsiveness  
                      • Reduced Idle Time  
                      • Concurrency  
                      • Scalability  
                      • Simplified Code  
                      • Resource Efficiency  
                      • Predictable Performance  
                      • Reduced Overhead 


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