Python Tutorials

Python - Concurrent Execution

Concurrent Execution

Concurrent execution in Python means we can execute multiple tasks or tasks at the same time, not one after the other, which helps improve performance, especially for I/O-bound tasks There are several ways to achieve concurrency in Python:

Threading

Threading is a method of concurrent execution where multiple threads run in the same process. Higher-level threading, or the ability to conduct many tasks concurrently within a single process, is supported by threading.

Python's global interpreter lock (GIL) may be limiting for CPU-bound tasks, but threading still works well for I/O-bound tasks (like file I/O or network requests).

Example

The following example demonstrates the use of threading:

import threading
import time

def task(name, duration):
    print(f"Task {name} starting")
    time.sleep(duration)
    print(f"Task {name} completed")

# Create threads
thread1 = threading.Thread(target=task, args=("A", 2))
thread2 = threading.Thread(target=task, args=("B", 3))

# Start threads
thread1.start()
thread2.start()

# Wait for both threads to complete
thread1.join()
thread2.join()

print("Both tasks are done!")

Output

Task A starting
Task B starting
Task A completed
Task B completed
Both tasks are done!

Multiprocessing

Multiprocessing allows multiple applications to execute in parallel, effectively bypassing the GIL. This is useful for CPU-bound tasks as each program gets its own Python definition and memory location. Programmers can use several processors for concurrent execution through multiprocessing, which enhances performance for CPU-bound activities.

Example

The following example demonstrates the use of multiprocessing:
import multiprocessing
import time

def task(name, duration):
    print(f"Task {name} starting")
    time.sleep(duration)
    print(f"Task {name} completed")

if __name__ == "__main__":
    process1 = multiprocessing.Process(target=task, args=("A", 2))
    process2 = multiprocessing.Process(target=task, args=("B", 3))

    process1.start()
    process2.start()

    process1.join()
    process2.join()

    print("Both processes are done!")

Output

Task A starting
Task B starting
Task A completed
Task B completed
Both processes are done!

AsyncIO

AsyncIO is a framework for writing single-threaded concurrent code using async and await syntax. It is mainly used for I/O-bound tasks and is used when several I/O-bound tasks need to be performed simultaneously.

Example

In this example, we demonstrate the use of asyncio:
import asyncio
async def task(name, duration):
    print(f"Task {name} starting")
    await asyncio.sleep(duration)
    print(f"Task {name} completed")

async def main():
    # Create tasks
    task1 = asyncio.create_task(task("A", 2))
    task2 = asyncio.create_task(task("B", 3))
    # Wait for all tasks to complete
    await task1
    await task2
    print("Both tasks are done!")
asyncio.run(main())

Output

Task A starting
Task B starting
Task A completed
Task B completed
Both tasks are done!

Futures (concurrent.futures)

The concurrent.futures module provides a high-level interface for asynchronously executing functions using threads or processes.

Example

The following example demonstrates the use of futures (concurrent.futures):
from concurrent.futures import ThreadPoolExecutor
import time
def task(name, duration):
    print(f"Task {name} starting")
    time.sleep(duration)
    print(f"Task {name} completed")

with ThreadPoolExecutor() as executor:
    futures = [executor.submit(task, "A", 2), executor.submit(task, "B", 3)]
    for future in futures:
        future.result()
print("Both tasks are done!")

Output

Task A starting
Task B starting
Task A completed
Task B completed
Both tasks are done!

Summary:

  • Threading: It is best for I/O-bound tasks but has limitations for CPU-bound tasks due to the GIL.
  • Multiprocessing: It is best for CPU-bound tasks.
  • AsyncIO: It is efficient for I/O-bound tasks in a single-threaded event loop.
  • Futures: It is a high-level API for threads and processes.

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Python

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Concurrent Execution

Concurrent execution in Python means we can execute multiple tasks or tasks at the same time, not one after the other, which helps improve performance, especially for I/O-bound tasks There are several ways to achieve concurrency in Python:

Threading

Threading is a method of concurrent execution where multiple threads run in the same process. Higher-level threading, or the ability to conduct many tasks concurrently within a single process, is supported by threading.

Python's global interpreter lock (GIL) may be limiting for CPU-bound tasks, but threading still works well for I/O-bound tasks (like file I/O or network requests).

Example

The following example demonstrates the use of threading:

python
import threading
import time

def task(name, duration):
    print(f"Task {name} starting")
    time.sleep(duration)
    print(f"Task {name} completed")

# Create threads
thread1 = threading.Thread(target=task, args=("A", 2))
thread2 = threading.Thread(target=task, args=("B", 3))

# Start threads
thread1.start()
thread2.start()

# Wait for both threads to complete
thread1.join()
thread2.join()

print("Both tasks are done!")

Output

Task A starting
Task B starting
Task A completed
Task B completed
Both tasks are done!

Multiprocessing

Multiprocessing allows multiple applications to execute in parallel, effectively bypassing the GIL. This is useful for CPU-bound tasks as each program gets its own Python definition and memory location. Programmers can use several processors for concurrent execution through multiprocessing, which enhances performance for CPU-bound activities.

Example

The following example demonstrates the use of multiprocessing:
python
import multiprocessing
import time

def task(name, duration):
    print(f"Task {name} starting")
    time.sleep(duration)
    print(f"Task {name} completed")

if __name__ == "__main__":
    process1 = multiprocessing.Process(target=task, args=("A", 2))
    process2 = multiprocessing.Process(target=task, args=("B", 3))

    process1.start()
    process2.start()

    process1.join()
    process2.join()

    print("Both processes are done!")

Output

Task A starting
Task B starting
Task A completed
Task B completed
Both processes are done!

AsyncIO

AsyncIO is a framework for writing single-threaded concurrent code using async and await syntax. It is mainly used for I/O-bound tasks and is used when several I/O-bound tasks need to be performed simultaneously.

Example

In this example, we demonstrate the use of asyncio:
python
import asyncio
async def task(name, duration):
    print(f"Task {name} starting")
    await asyncio.sleep(duration)
    print(f"Task {name} completed")

async def main():
    # Create tasks
    task1 = asyncio.create_task(task("A", 2))
    task2 = asyncio.create_task(task("B", 3))
    # Wait for all tasks to complete
    await task1
    await task2
    print("Both tasks are done!")
asyncio.run(main())

Output

Task A starting
Task B starting
Task A completed
Task B completed
Both tasks are done!

Futures (concurrent.futures)

The concurrent.futures module provides a high-level interface for asynchronously executing functions using threads or processes.

Example

The following example demonstrates the use of futures (concurrent.futures):
from concurrent.futures import ThreadPoolExecutor
python
import time
def task(name, duration):
    print(f"Task {name} starting")
    time.sleep(duration)
    print(f"Task {name} completed")

with ThreadPoolExecutor() as executor:
    futures = [executor.submit(task, "A", 2), executor.submit(task, "B", 3)]
    for future in futures:
        future.result()
print("Both tasks are done!")

Output

Task A starting
Task B starting
Task A completed
Task B completed
Both tasks are done!

Summary:

  • Threading: It is best for I/O-bound tasks but has limitations for CPU-bound tasks due to the GIL.
  • Multiprocessing: It is best for CPU-bound tasks.
  • AsyncIO: It is efficient for I/O-bound tasks in a single-threaded event loop.
  • Futures: It is a high-level API for threads and processes.

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Frequently Asked Questions for Python

Python is commonly used for developing websites and software, task automation, data analysis, and data visualisation. Since it's relatively easy to learn, Python has been adopted by many non-programmers, such as accountants and scientists, for a variety of everyday tasks, like organising finances.


Python's syntax is a lot closer to English and so it is easier to read and write, making it the simplest type of code to learn how to write and develop with. The readability of C++ code is weak in comparison and it is known as being a language that is a lot harder to get to grips with.

Learning Curve: Python is generally considered easier to learn for beginners due to its simplicity, while Java is more complex but provides a deeper understanding of how programming works. Performance: Java has a higher performance than Python due to its static typing and optimization by the Java Virtual Machine (JVM).

Python can be considered beginner-friendly, as it is a programming language that prioritizes readability, making it easier to understand and use. Its syntax has similarities with the English language, making it easy for novice programmers to leap into the world of development.

To start coding in Python, you need to install Python and set up your development environment. You can download Python from the official website, use Anaconda Python, or start with DataLab to get started with Python in your browser.

Learning Curve: Python is generally considered easier to learn for beginners due to its simplicity, while Java is more complex but provides a deeper understanding of how programming works.

Python alone isn't going to get you a job unless you are extremely good at it. Not that you shouldn't learn it: it's a great skill to have since python can pretty much do anything and coding it is fast and easy. It's also a great first programming language according to lots of programmers.

The point is that Java is more complicated to learn than Python. It doesn't matter the order. You will have to do some things in Java that you don't in Python. The general programming skills you learn from using either language will transfer to another.


Read on for tips on how to maximize your learning. In general, it takes around two to six months to learn the fundamentals of Python. But you can learn enough to write your first short program in a matter of minutes. Developing mastery of Python's vast array of libraries can take months or years.


6 Top Tips for Learning Python

  • Choose Your Focus. Python is a versatile language with a wide range of applications, from web development and data analysis to machine learning and artificial intelligence.
  • Practice regularly.
  • Work on real projects.
  • Join a community.
  • Don't rush.
  • Keep iterating.

The following is a step-by-step guide for beginners interested in learning Python using Windows.

  • Set up your development environment.
  • Install Python.
  • Install Visual Studio Code.
  • Install Git (optional)
  • Hello World tutorial for some Python basics.
  • Hello World tutorial for using Python with VS Code.

Best YouTube Channels to Learn Python

  • Corey Schafer.
  • sentdex.
  • Real Python.
  • Clever Programmer.
  • CS Dojo (YK)
  • Programming with Mosh.
  • Tech With Tim.
  • Traversy Media.

Python can be written on any computer or device that has a Python interpreter installed, including desktop computers, servers, tablets, and even smartphones. However, a laptop or desktop computer is often the most convenient and efficient option for coding due to its larger screen, keyboard, and mouse.

Write your first Python programStart by writing a simple Python program, such as a classic "Hello, World!" script. This process will help you understand the syntax and structure of Python code.

  • Google's Python Class.
  • Microsoft's Introduction to Python Course.
  • Introduction to Python Programming by Udemy.
  • Learn Python - Full Course for Beginners by freeCodeCamp.
  • Learn Python 3 From Scratch by Educative.
  • Python for Everybody by Coursera.
  • Learn Python 2 by Codecademy.

  • Understand why you're learning Python. Firstly, it's important to figure out your motivations for wanting to learn Python.
  • Get started with the Python basics.
  • Master intermediate Python concepts.
  • Learn by doing.
  • Build a portfolio of projects.
  • Keep challenging yourself.

Top 5 Python Certifications - Best of 2024
  • PCEP (Certified Entry-level Python Programmer)
  • PCAP (Certified Associate in Python Programmer)
  • PCPP1 & PCPP2 (Certified Professional in Python Programming 1 & 2)
  • Certified Expert in Python Programming (CEPP)
  • Introduction to Programming Using Python by Microsoft.

The average salary for Python Developer is ₹5,55,000 per year in the India. The average additional cash compensation for a Python Developer is within a range from ₹3,000 - ₹1,20,000.

The Python interpreter and the extensive standard library are freely available in source or binary form for all major platforms from the Python website, https://www.python.org/, and may be freely distributed.

If you're looking for a lucrative and in-demand career path, you can't go wrong with Python. As one of the fastest-growing programming languages in the world, Python is an essential tool for businesses of all sizes and industries. Python is one of the most popular programming languages in the world today.

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