Python Multithreaded Programming

Multithreading allows your Python programs to run multiple tasks concurrently, improving performance and responsiveness. It is widely used in applications involving I/O operations, real-time processing, and user interactions—such as GUI apps and web servers. In a typical single-threaded program, the execution starts from the top and flows line by line. But with multithreading, multiple threads run simultaneously, executing different parts of the code at the same time.

What is a Thread?

A thread is the smallest unit of execution within a process. It runs independently but shares the same memory space with other threads in the same process. Threads are managed by the operating system using time-sharing, which allows efficient task switching. Python supports two types of threads:

• Kernel threads
• User-space threads

However, for application-level programming, Python primarily provides high-level support through the threading module.

Why Use Threads?

Threads help make programs faster, more efficient, and responsive. Here's why threading is useful:

• Parallel Computation: On multi-core systems, threads run in parallel, speeding up processing.
• Non-blocking I/O: Threads handle input/output operations concurrently, improving overall efficiency.
• Asynchronous Event Handling: Threads can manage simultaneous actions like mouse and keyboard input.
• Standardization: Threading is a widely accepted method across programming languages for concurrent execution.

Threading Modules in Python

Python provides two modules for thread management:

• _thread module (low-level, outdated)
• threading module (preferred, high-level)

The threading module builds on _thread and provides a more object-oriented approach. You should always use the threading module for new applications.

Benefits of Threading

• Multiple threads can access shared data space.
• Threads use less memory, making them lightweight.
• Threads allow the program to remain responsive.
• Global variables can be accessed and modified by all threads.

Each thread has:

• A starting point
• An execution phase
• An end

It also maintains an instruction pointer to track its position during execution.

Creating a New Thread in Python

To create and start a new thread, use the Thread class from the threading module.

Syntax:

threading.Thread(target=function, args=(arg1, arg2)).start()

• target: The function the thread should run
• args: A tuple of arguments passed to the function

Example:

import threading

# Define a function to run in a thread
def coder(number):
    print(f'Coder ID: {number}')

# Create and start 5 threads
threads = []
for k in range(5):
    t = threading.Thread(target=coder, args=(k,))
    threads.append(t)
    t.start()

Output:

Coder ID: 0
Coder ID: 1
Coder ID: 2
Coder ID: 3
Coder ID: 4

Each thread executes the coder() function with a unique argument, allowing concurrent execution.

Key Methods of the Thread Class

The Thread class in the threading module offers several important methods:

These methods help manage and monitor thread execution.

Conclusion

In this tutorial, you learned the fundamentals of multithreaded programming in Python. You explored what threads are and how they allow concurrent execution within a single process. You discovered why threads are useful—especially for parallel computation, non-blocking I/O, and asynchronous events. You also learned how to implement threads using Python's threading module, which provides a clean, object-oriented way to create and manage threads. You saw how to start new threads with the Thread class, pass arguments to thread functions, and use key methods like start(), run(), and is_alive(). With this understanding, you can now build Python programs that perform multiple tasks simultaneously, making them more efficient and responsive.