`threading`

The Python threading module provides a higher-level interface for working with threads, allowing you to run multiple operations concurrently within the same process.

It simplifies the management of threads, making it easier to write concurrent applications in Python.

Here’s a quick example:

Python
      
        
      
    
>>> import threading

>>> def greet():
...     print("Hello, World!")
...

>>> thread = threading.Thread(target=greet)
>>> thread.start()
>>> thread.join()
Hello, World!

Key Features

Creates and manages threads for concurrent execution
Synchronizes operations across multiple threads
Provides thread-safe data structure and synchronization primitives
Supports background processing
Offers thread-local storage for per-thread data
Enables naming and identification of threads
Coordinates execution using events and condition variables
Detects and manages active threads within the process

Frequently Used Classes and Functions

Object	Type	Description
`threading.Thread`	Class	Represents a single thread of control
`threading.Lock`	Class	Implements a basic lock (mutex)
`threading.RLock`	Class	Implements a reentrant lock that can be acquired multiple times by the same thread
`threading.Event`	Class	Provides a mechanism for communicating between threads
`threading.Condition`	Class	Provides thread synchronization via a condition variable
`threading.Semaphore`	Class	Limits access to a shared resource
`threading.active_count()`	Function	Returns the number of currently active threads
`threading.current_thread()`	Function	Returns the current `Thread` object

Examples

Creating and starting a thread:

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>>> import threading

>>> def print_numbers():
...     for i in range(5):
...         print(i)
...

>>> thread = threading.Thread(target=print_numbers)
>>> thread.start()
>>> thread.join()
0
1
2
3
4

Using a lock to synchronize threads:

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>>> lock = threading.Lock()

>>> def thread_safe_increment(counter):
...     with lock:
...         counter[0] += 1
...

>>> counter = [0]
>>> threads = [
...     threading.Thread(target=thread_safe_increment, args=(counter,))
...     for _ in range(10)
... ]

>>> for thread in threads:
...     thread.start()
...

>>> for thread in threads:
...     thread.join()
...

>>> counter[0]
10

Common Use Cases

Running I/O-bound tasks concurrently
Managing access to shared resources with locks and semaphores
Coordinating task execution using events and condition variables
Performing background or periodic tasks in applications
Improving user interface responsiveness by offloading work to threads
Isolating data for each thread with thread-local storage
Executing parallel operations for faster data processing
Simulating concurrent scenarios for testing and development

Real-World Example

In the example below, you’ll demonstrate how to download multiple web pages concurrently using threads:

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>>> import threading, requests

>>> urls = [
...     "https://www.example.com",
...     "https://www.python.org",
...     "https://www.realpython.com"
... ]

>>> def fetch_url(url):
...     response = requests.get(url)
...     print(f"{url}: {response.status_code}")
...

>>> threads = [
...     threading.Thread(target=fetch_url, args=(url,))
...     for url in urls
... ]

>>> for thread in threads:
...     thread.start()
...

>>> for thread in threads:
...     thread.join()
...
https://www.example.com: 200
https://www.python.org: 200
https://www.realpython.com: 200

In this example, the threading module allows you to fetch multiple web pages concurrently. This significantly reduces the total time required compared to fetching them sequentially or synchronously.

Tutorial

An Intro to Threading in Python

In this intermediate-level tutorial, you'll learn how to use threading in your Python programs. You'll see how to create threads, how to coordinate and synchronize them, and how to handle common problems that arise in threading.

intermediate best-practices

For additional information on related topics, take a look at the following resources: