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Multithreading in Python 3

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Multithreading in Python 3

A thread is the smallest unit of a program or process executed independently or scheduled by the Operating System. In the computer system, an Operating System achieves multitasking by dividing the process into threads. A thread is a lightweight process that ensures the execution of the process separately on the system. In Python 3, when multiple processors are running on a program, each processor runs simultaneously to execute its tasks separately.

Multithreading in Python 3

Python Multithreading

Multithreading is a threading technique in Python programming to run multiple threads concurrently by rapidly switching between threads with a CPU help (called context switching). Besides, it allows sharing of its data space with the main threads inside a process that share information and communication with other threads easier than individual processes. Multithreading aims to perform multiple tasks simultaneously, which increases performance, speed and improves the rendering of the application.

Note: The Python Global Interpreter Lock (GIL) allows running a single thread at a time, even the machine has multiple processors.

Benefits of Multithreading in Python

Following are the benefits to create a multithreaded application in Python, as follows:

  1. It ensures effective utilization of computer system resources.
  2. Multithreaded applications are more responsive.
  3. It shares resources and its state with sub-threads (child) which makes it more economical.
  4. It makes the multiprocessor architecture more effective due to similarity.
  5. It saves time by executing multiple threads at the same time.
  6. The system does not require too much memory to store multiple threads.

When to use Multithreading in Python?

It is a very useful technique for time-saving and improving the performance of an application. Multithreading allows the programmer to divide application tasks into sub-tasks and simultaneously run them in a program. It allows threads to communicate and share resources such as files, data, and memory to the same processor. Furthermore, it increases the user’s responsiveness to continue running a program even if a part of the application is the length or blocked.

How to achieve multithreading in Python?

There are two main modules of multithreading used to handle threads in Python.

  1. The thread module
  2. The threading module

Thread modules

It is started with Python 3, designated as obsolete, and can only be accessed with _thread that supports backward compatibility.


To implement the thread module in Python, we need to import a thread module and then define a function that performs some action by setting the target with a variable.



Calculate the square root of the given number   Square is:  16   Square is:  25   Square is:  36   Square is:  49   Square is:  4   Calculate the cube of the given number   Cube is:  64   Cube is:  125   Cube is:  216   Cube is:  343   Cube is:  8   Total time taken by threads is: 3.005793809890747  

Threading Modules

The threading module is a high-level implementation of multithreading used to deploy an application in Python. To use multithreading, we need to import the threading module in Python Program.

Thread Class Methods

Methods Description
start() A start() method is used to initiate the activity of a thread. And it calls only once for each thread so that the execution of the thread can begin.
run() A run() method is used to define a thread’s activity and can be overridden by a class that extends the threads class.
join() A join() method is used to block the execution of another code until the thread terminates.

Follow the given below steps to implement the threading module in Python Multithreading:

1. Import the threading module

Create a new thread by importing the threading module, as shown.


A threading module is made up of a Thread class, which is instantiated to create a Python thread.

2. Declaration of the thread parameters: It contains the target function, argument, and kwargs as the parameter in the Thread() class.

  • Target: It defines the function name that is executed by the thread.
  • Args: It defines the arguments that are passed to the target function name.

For example:

In the above code, we invoked the print_hello() function as the target parameter. The print_hello() contains one parameter n, which passed to the args parameter.

3. Start a new thread: To start a thread in Python multithreading, call the thread class’s object. The start() method can be called once for each thread object; otherwise, it throws an exception error.


4. Join method: It is a join() method used in the thread class to halt the main thread’s execution and waits till the complete execution of the thread object. When the thread object is completed, it starts the execution of the main thread in Python.



Hello, how old are you? 20  Thank you  

When the above program is executed, the join() method halts the execution of the main thread and waits until the thread t1 is completely executed. Once the t1 is successfully executed, the main thread starts its execution.

Note: If we do not use the join() method, the interpreter can execute any print statement inside the Python program. Generally, it executes the first print statement because the interpreter executes the lines of codes from the program’s start.

5. Synchronizing Threads in Python

It is a thread synchronization mechanism that ensures no two threads can simultaneously execute a particular segment inside the program to access the shared resources. The situation may be termed as critical sections. We use a race condition to avoid the critical section condition, in which two threads do not access resources at the same time.

Let’s write a program to use the threading module in Python Multithreading.



Calculate the square root of the given number   Calculate the cube of the given number   Square is:  16   Cube is:  64   Square is:  25   Cube is:  125   Square is:  36   Cube is:  216   Square is:  49   Cube is:  343   Square is:  4   Cube is:  8   Total time taken by threads is: 1.5140972137451172   Again executing the main thread   Thread 1 and Thread 2 have finished their execution.  

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