In today’s digital era, learning Python threading is an invaluable skill that can catapult your programming prowess to a whole new level. Understanding threads can open up new avenues in your coding journey, letting you optimize your applications by parallelizing tasks and improving their efficiency. But, what are Python threads, how are they used, and why should you invest your time to learn them? Let’s delve in deeper to understand these concepts.
Table of contents
What are Python Threads?
In Python, threads are used to run multiple tasks concurrently. They are a way to achieve multitasking, where different parts of a single program run parallelly, hence maximizing computational power and efficiency. A thread is the smallest unit of processing that can be performed in an OS (Operating System).
What are Python Threads Used For?
Python threads are used to handle tasks that can be divided into smaller subtasks and executed simultaneously. They allow the execution of multiple tasks in a single process space, thereby improving overall program efficiency. They are immensely useful in scenarios where there are I/O or network-bound tasks that spend a lot of time waiting for input or data.
Why Should You Learn Python Threads?
Learning Python threads can help you write more efficient, faster, and scalable programs. If you are building a heavy-duty application, understanding threads can help you optimize your code by breaking down larger tasks into smaller, parallelizable tasks. Also, having knowledge of threads can open up opportunities in the realms of game development and AI programming, where thread utilization is pivotal for performance optimization.
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Python Threading Basics
Let’s get started with the basics of Python threading. In Python, the _thread and threading modules provide tools for creating and managing threads. We’ll use the threading module, as it offers a more modern and higher-level approach to handling threads.
To use threads in your Python program, you must first import the threading module:
import threading
Creating a Thread
A thread in Python is represented by the Thread class in the threading module. To create a thread, we instantiate this class:
t = threading.Thread(target=my_function)
Here, my_function is the function that you want the thread to execute.
Starting a Thread
We can start a thread by calling its start method:
t.start()
Waiting for a Thread to Finish
We can make the main thread wait for another thread to finish by using the join method:
t.join()
This will halt the execution of the main thread until the thread t has finished executing. It is particularly important when you need to make sure a thread has completed its task before the main thread proceeds.
Example of Python Threading
Let’s create an example to see all these methods in action:
import threading
import time
def count(nums):
for num in nums:
time.sleep(1)
print(num)
# Create thread
t = threading.Thread(target=count, args=([1, 2, 3, 4, 5],))
# Start thread
t.start()
# Main thread continues to execute
for i in range(0, 3):
time.sleep(1)
print("Main Thread")
# Wait for the thread to finish
t.join()
# Continue with main thread
print("Thread has finished execution")This example illustrates threading basics in Python. In further sections, we’ll examine more complex tasks involving multiple threads.
Multiple Threads
In Python, we can create multiple threads to run tasks simultaneously. Here’s a simple example that showcases this:
import threading
import time
def count(nums, threadName):
for num in nums:
time.sleep(1)
print(threadName, num)
# Create threads
t1 = threading.Thread(target=count, args=([1, 2, 3, 4, 5],'Thread-1', ))
t2 = threading.Thread(target=count, args=([6, 7, 8, 9, 10],'Thread-2', ))
# Start threads
t1.start()
t2.start()
# Wait for both threads to finish
t1.join()
t2.join()
print("All threads have finished execution")In this example, we have two threads ‘Thread-1’ and ‘Thread-2’ executing the same function with different arguments simultaneously.
Threading with Locks
Python provides a concept called “locks” to deal with the concurrency issues. A lock allows only one thread to enter the part that’s locked, at a time. This is a way to prevent race conditions.
import threading
# A shared variable
num = 0
# A lock
num_lock = threading.Lock()
def add_one():
global num
with num_lock:
num += 1
# Create 1000 threads that each add one to num
for _ in range(1000):
t = threading.Thread(target=add_one)
t.start()
print(num)In this code, we create a global `num_lock` using the `Lock` class from the `threading` module. This lock ensures that each thread can only perform the `num += 1` operation individually without interference from other threads.
Daemon Threads
Daemon threads are background threads that do not prevent the program from exiting. Even if these threads are still executing, the program will terminate if all non-daemon threads have finished. Here’s an example to illustrate:
import threading
import time
def daemon():
while True:
time.sleep(1)
print("Daemon Thread")
def non_daemon():
print("Non-Daemon Thread")
# Create Daemon thread
d = threading.Thread(target=daemon)
d.daemon = True
# Create Non-Daemon thread
nd = threading.Thread(target=non_daemon)
# Start threads
d.start()
nd.start()In this example, the daemon thread is set using the property daemon = True. When the non-daemon thread completes, the Python program ends along with any daemon threads that are still running.
Thread Local Data
Thread Local Data is data that is unique to a thread. It allows threads to have their own copies of variables. Here’s an example to illustrate:
import threading
# Create thread local data
local_data = threading.local()
def display():
try:
print(local_data.value)
except AttributeError:
print("No value assigned")
def assign_value(value):
local_data.value = value
display()
t1 = threading.Thread(target=assign_value, args=('Thread 1',))
t2 = threading.Thread(target=assign_value, args=('Thread 2',))
t1.start()
t2.start()In this example, local_data is a threading.local() object. Each thread has a different instance of this object, so changes in one thread will not affect the others.
Where to Go Next?
Python threading is just a stepping stone on your coding journey. If you wish to delve deeper into the Python world and take your Python game up a notch, check out the Python Mini-Degree at Zenva Academy.
At Zenva, we cater to both beginners and professionals through our Python Mini-Degree program. Here you will have access to a comprehensive collection of courses covering a multitude of topics, from coding basics and algorithms to advanced topics such as game development and app creation.
Here’s what the Python Mini-Degree at Zenva can offer you:
- Learn Python, a popular language renowned for its simplicity and versatility.
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- The course content is regularly updated to keep pace with industry developments and trends.
- A self-paced learning strategy, facilitating learning at your own convenience.
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Conclusion
As we’ve demonstrated, Python threads provide a platform to create efficient and faster applications. They not only unveil new programming strategies but also enrich your Python skills that will surely grab the attention of your future employers or clients. Learning and mastering Python threads can be a game-changer on your programming career path.
The journey to becoming a Python expert may be complex, but it’s certainly worth the effort. And remember – you are not alone. At Zenva, we are committed to providing you with the skills and knowledge you need to excel. Let’s conquer the Python world together!
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