Python is an interpreter language.
It is in Python’s traits that the interpreter compiles the code to bytecode and then is executed via the Python virtual machine. This is the standard process with the reference implementation of Python, CPython. What if there were multiple threads that are to be executed by the interpreter simultaneously.
By default, Python has not made it possible by using a mechanism called Global Interpreter Lock. This Global Interpreter Lock can be identified as a mechanism like mutex that exerts constraints on the Python code limiting the number of threads in the state of execution to one. In Layman’s terms, this means only one thread can be executed at any given time in Python using the standard interpreter.
You can skip to a specific section of this Python GIL tutorial using the table of contents below:
From a thread synchronization standpoint, a critical section of a program can be identified as a space or a resource that is potentially being accessed by processes that run concurrently. The race condition phenomenon can occur when multiple threads are trying to access a shared resource which is also known as a critical section. Since it is not generally known how the scheduling algorithm has scheduled threads to be executed at a given time, the order or the priority of execution is unknown. This may result in a racing condition making multiple threads race for the same resource.
In order to prevent any unpredictable consequences of race conditions, various thread synchronization mechanisms are used to control and limit access to this critical section forcing the threads or the processes to wait until the occupied thread is complete. There are many thread synchronization mechanisms such as mutex, conditions, semaphores to be employed in such scenarios.
A mutex, also known as mutual exclusion, can be defined as a mutually exclusive flag that keeps an eye out for the critical section in a code. The threads that are attempting to access this critical section are supposed to acquire a lock on this section preventing any race conditions.
Welcome to my Blog , In this article, you are going to learn the top 10 python tips and tricks.
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Welcome to my Blog, In this article, we will learn python lambda function, Map function, and filter function.
Lambda function in python: Lambda is a one line anonymous function and lambda takes any number of arguments but can only have one expression and python lambda syntax is
Syntax: x = lambda arguments : expression
Now i will show you some python lambda function examples:
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Python is awesome, it’s one of the easiest languages with simple and intuitive syntax but wait, have you ever thought that there might ways to write your python code simpler?
In this tutorial, you’re going to learn a variety of Python tricks that you can use to write your Python code in a more readable and efficient way like a pro.
Swapping value in Python
Instead of creating a temporary variable to hold the value of the one while swapping, you can do this instead
>>> FirstName = "kalebu" >>> LastName = "Jordan" >>> FirstName, LastName = LastName, FirstName >>> print(FirstName, LastName) ('Jordan', 'kalebu')
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Today you’re going to learn how to use Python programming in a way that can ultimately save a lot of space on your drive by removing all the duplicates.
In many situations you may find yourself having duplicates files on your disk and but when it comes to tracking and checking them manually it can tedious.
Heres a solution
Instead of tracking throughout your disk to see if there is a duplicate, you can automate the process using coding, by writing a program to recursively track through the disk and remove all the found duplicates and that’s what this article is about.
But How do we do it?
If we were to read the whole file and then compare it to the rest of the files recursively through the given directory it will take a very long time, then how do we do it?
The answer is hashing, with hashing can generate a given string of letters and numbers which act as the identity of a given file and if we find any other file with the same identity we gonna delete it.
There’s a variety of hashing algorithms out there such as
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Magic Methods are the special methods which gives us the ability to access built in syntactical features such as ‘<’, ‘>’, ‘==’, ‘+’ etc…
You must have worked with such methods without knowing them to be as magic methods. Magic methods can be identified with their names which start with __ and ends with __ like init, call, str etc. These methods are also called Dunder Methods, because of their name starting and ending with Double Underscore (Dunder).
Now there are a number of such special methods, which you might have come across too, in Python. We will just be taking an example of a few of them to understand how they work and how we can use them.
class AnyClass: def __init__(): print("Init called on its own") obj = AnyClass()
The first example is _init, _and as the name suggests, it is used for initializing objects. Init method is called on its own, ie. whenever an object is created for the class, the init method is called on its own.
The output of the above code will be given below. Note how we did not call the init method and it got invoked as we created an object for class AnyClass.
Init called on its own
Let’s move to some other example, add gives us the ability to access the built in syntax feature of the character +. Let’s see how,
class AnyClass: def __init__(self, var): self.some_var = var def __add__(self, other_obj): print("Calling the add method") return self.some_var + other_obj.some_var obj1 = AnyClass(5) obj2 = AnyClass(6) obj1 + obj2
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