Python Concurrency Learning Paths

Python concurrency is a big topic and can be challenging for beginners.

This page provides you with step-by-step learning paths that you can use to get started and get good at Python concurrency.

You might want to bookmark this page and come back to it again and again.

New to Concurrency in Python

The first step is to get familiar with the basics of concurrency in Python.

• Step 1: Get familiar with the Python concurrency landscape
  • Why Learn Python Concurrency
  • Concurrent Programming in Python
  • Asynchronous Programming in Python
• Step 2: Get familiar with the Python concurrency APIs that are available
  • How to Choose a Python Concurrency API
• Step 3: Get familiar with the API docs, books, and glossary of terms.
  • Python Concurrency API Documentation
  • Python Concurrency Books
  • Python Concurrency Glossary of Terms

You can then dive into one of the specific concurrency APIs below.

Threading Learning Path

Python provides thread-based concurrency in the “threading” module.

This section provides a learning path that you can use to get started and get effective with the threading module, super fast.

• Step 1: Discover what a thread is in Python.
  • What is a Thread in Python
  • Thread Life-Cycle in Python
• Step 2: Discover how to run code in a new thread.
  • How to Run a Function in a New Thread in Python
  • How to Extend the Thread Class in Python
• Step 3: Discover how to get the most out of threads.
  • Threading in Python: The Complete Guide
  • Python Threading Jump-Start (my book)

Next, you can browse all tutorials or dive into a topic below to learn more.

Multiprocessing Learning Path

Python provides process-based concurrency in the “multiprocessing” module.

This section provides a learning path that you can use to get started and get effective with the multiprocessing module, super fast.

• Step 1: Discover what a process is in Python.
  • Process Life-Cycle in Python
  • Thread vs Process in Python
• Step 2: Discover how to run code in a new child process.
  • Run a Function in a Child Process
  • How to Extend the Process Class in Python
• Step 3: Discover how to get the most out of processes.
  • Multiprocessing in Python: The Complete Guide
  • Python Multiprocessing Jump-Start (my book)

Next, you can browse all tutorials or dive into a topic below to learn more.

Asyncio Learning Path

Python provides coroutine-based concurrency in the “asyncio” module.

This section provides a learning path that you can use to get started and get effective with the asyncio module, super fast.

• Step 1: Discover asyncio programs in Python.
  • Asyncio Hello World Tutorial in Python
  • How to Run an Asyncio Program in Python
• Step 2: Discover how to create and run tasks in asyncio.
  • What is a Coroutine
  • What is an Asyncio Task
  • How to Create Asyncio Tasks in Python
• Step 3: Discover how to get the most out of asyncio.
  • Asyncio in Python: The Complete Guide
  • All Books on Asyncio
  • Python Asyncio Jump-Start (my beginner book)
  • Python Asyncio Mastery (my intermediate book)

Next, you can browse all tutorials or dive into a topic below to learn more.

ThreadPoolExecutor Learning Path

Python provides thread pool-based concurrency with the “ThreadPoolExecutor” class.

This section provides a learning path that you can use to get started and get effective with the ThreadPoolExecutor class, super fast.

• Step 1: Discover the ThreadPoolExecutor class in Python.
  • What Is the ThreadPoolExecutor
  • ThreadPoolExecutor in Python: Quick-Start Guide
• Step 2: Discover how to run code in the ThreadPoolExecutor.
  • How to Use as_completed() with the ThreadPoolExecutor
  • How to Use map() with the ThreadPoolExecutor
  • How to Use submit() with the ThreadPoolExecutor
  • What are Futures in the ThreadPoolExecutor
• Step 3: Discover how to get the most out of ThreadPoolExecutor.
  • ThreadPoolExecutor in Python: The Complete Guide
  • Python ThreadPoolExecutor Jump-Start (my book)

Next, you can browse all tutorials or dive into a topic below to learn more.

ProcessPoolExecutor Learning Path

Python provides process pool-based concurrency with the “ProcessPoolExecutor” class.

This section provides a learning path that you can use to get started and get effective with the ProcessPoolExecutor class, super fast.

• Step 1: Discover the ProcessPoolExecutor class in Python.
  • What is the ProcessPoolExecutor
  • ProcessPoolExecutor Quick-Start Guide
• Step 2: Discover how to run code in the ProcessPoolExecutor.
  • How to Use map() With the ProcessPoolExecutor in Python
  • How to use as_completed() With the ProcessPoolExecutor in Python
  • What Are Future Objects in the ProcessPoolExecutor
• Step 3: Discover how to get the most out of ProcessPoolExecutor.
  • ProcessPoolExecutor in Python: The Complete Guide
  • Python ProcessPoolExecutor Jump-Start (my book)

Next, you can browse all tutorials or dive into a topic below to learn more.

ThreadPool Learning Path

Python provides thread pool-based concurrency with the “multiprocessing.pool.ThreadPool” class.

This section provides a learning path that you can use to get started and get effective with the ThreadPool class, super fast.

• Step 1: Discover the ThreadPool class in Python.
  • ThreadPool Class in Python
  • ThreadPool Example
• Step 2: Discover how to run code in the ThreadPool.
  • ThreadPool apply() vs map() vs imap() vs starmap()
• Step 3: Discover how to get the most out of ThreadPool.
  • ThreadPool in Python: The Complete Guide
  • Python ThreadPool Jump-Start (my book)

Next, you can browse all tutorials or dive into a topic below to learn more.

Multiprocessing Pool Learning Path

Python provides process pool-based concurrency with the “multiprocessing.Pool” class.

This section provides a learning path that you can use to get started and get effective with the multiprocessing.Pool class, super fast.

• Step 1: Discover the multiprocessing.Pool class in Python.
  • Multiprocessing Pool Class in Python
  • Multiprocessing Pool Example
• Step 2: Discover how to run code in the multiprocessing.Pool.
  • Pool apply() vs map() vs imap() vs starmap()
• Step 3: Discover how to get the most out of multiprocessing.Pool.
  • Multiprocessing Pool in Python: The Complete Guide
  • Python Multiprocessing Pool Jump-Start (my book)

Next, you can browse all tutorials or dive into a topic below to learn more.

File I/O Concurrency Learning Path

Working with files can be slow.

This section provides a learning path that you can use to get started and get effective at using Python concurrency to speed up file I/O operations, super fast.

• Step 1: Review Python concurrency techniques.
  • Python Concurrency: A Whirlwind Tour
• Step 2: Review Python file I/O operations.
  • Python File IO: A Whirlwind Tour
  • AIOFiles for AsyncIO in Python
• Step 3: Discover how to perform file I/O concurrently.
  • Faster File I/O With Concurrency
  • Concurrent File I/O 7-Day Course (or the email course)
  • Concurrent File I/O in Python (my book!)

Next, you can browse all tutorials or dive into a topic below to learn more.

NumPy Parallelism Learning Path

NumPy is a third-party library for working with arrays of numbers efficiently.

It is widely used as the basis in other libraries such as SciPy and Pandas and is central to Python’s use in deep learning and machine learning.

This section provides a learning path that you can use to get started and get effective with parallelism in NumPy, super fast.

• Step 1: Discover why NumPy Concurrency is important.
  • Why Numpy Parallelism is Important
• Step 2: Discover the Cost of a naive approach to NumPy parallelism
  • Performance Cost of Naive Parallelism in NumPy
  • NumPy vs the Global Interpreter Lock (GIL)
• Step 3: Discover how Get The Most Out Of NumPy
  • Concurrent NumPy 7-Day Course (or the email course)
  • Concurrent NumPy in Python (my book!)

Next, you can browse all tutorials or dive into a topic below to learn more.

Benchmarking Learning Path

Python code can be slow.

We must benchmark our code in order to know how slow it is and whether the changes we have made make it faster or not.

Benchmarking is the way to faster Python code and this learning path will show you how to benchmark your code.

• Step 1: Discover the importance of benchmarking.
  • Importance of Python Execution Time Performance Benchmarking
• Step 2: Discover how to benchmark code in Python.
  • How to benchmark a Python Statement
  • How to benchmark a Python Function
  • How to Benchmark a Python Program
• Step 3: Discover how to master the craft of benchmarking.
  • Python Benchmarking 7-Day Crash Course (or the email course)
  • Python Benchmarking (my book!)

Next, you can browse all tutorials or dive into a topic below to learn more.

Need More Help?

Do you need more help with Python concurrency?
Perhaps you need help with a topic not covered above?

• Contact me directly and I will do my best to help.