Python's Hidden Power: Property Decorators

When I review Python code, I often look past syntax and focus on decisions. Take this line: if user_id in users: grant_access() It works. But what matters is what users actually is. A list → Python checks items one by one A set or dict → Python jumps straight to the answer Same line of code. Very different performance. With large data, these choices decide whether a system feels instant or slow. This is the kind of detail that separates: • someone who writes Python • from someone who understands how Python behaves I recently wrote a complete breakdown of how Python searches data internally—linear search, binary search, and hash lookup—using real examples and benchmarks. It’s not about algorithms. It’s about choosing the right data structure upfront. Full breakdown 👇 https://lnkd.in/gT2uaZER #Python #SoftwareEngineering #BackendEngineering #Performance #CodeQuality

Although it may not be a big deal in other object oriented language to know the difference between Class and Object, it is a big deal in Python. This is especially evident and important when using libraries and/or frameworks that expects you to pass a callback function as one of the arguments to its own function or method. Why Python? Cause Python expects an extra "self" parameter to be passed in instance methods that other PLs don't. You'll encounter this unique behavior when you pass a class, instead of an object, along with the callable as this is a function (not a method) that expects "self", thus you get too many arguments, expected "n" arguments but got "n+1"... type of error, because of that extra argument being passed by Python. And this is why I always tell people that I mentor to not ignore or skip the basics or essentials, as this knowledge becomes critically valuable in most edge cases. #python #class #object

🧠 “Interesting Python Nugget” (Lists) Python List Trick You’ll Actually Use Did you know you can remove duplicates from a list in ONE line? nums = [1, 2, 2, 3, 4, 4] unique_nums = list(set(nums)) ✔ Simple ✔ Fast ✔ Super handy for real projects Python has tons of these tiny gems that save time and make code cleaner. 📬 We explain one Python concept every day — short, clear, and practical. Want more? Subscribe and learn Python daily ✨ link in the comments Please sign up and follow #PythonChallenge #PythonLearning #CodeChallenge #PythonDaily #PyDaily

How FINALLY keyword in Python can silently change your function’s behaviour? How does the try except flow works? 1. When Python enters a try block, it pushes a "cleanup" instruction onto the stack. 2. When you hit a return statement inside try, Python doesn't actually exit the function immediately, it just "saves" the return value. 3. If you return inside the finally block itself, the original return value is discarded. More worse - This same thing happens with exceptions too. If your try block raises a error, but your finally block has a return or a break, the error vanishes! Takeaway - 1. Never use return, break, or continue inside a finally block. It can lead to "silent failures" and unexpected bugs. 2. Finally is meant only for cleanup (closing files, releasing locks) and not logic. I’m deep-diving into Python internals. Do follow along and tell your experiences in comments. #Python #PythonInternals #SoftwareEngineering #BackendDevelopment

🔺 BIG Python Sin:  Using a mutable default argument Don't use a mutable object as a default argument. Default arguments in Python are evaluated once, at function definition time, not each time the function is called. If you have a default argument like: items=[], it will be shared across all calls, and you will get an accumulating state you didn’t ask for. 👑 The best way is to have an immutable object as a default argument. That way, each call will get a fresh list as in the example below.

Am I too late? I just discovered match-case in Python! If you have used "switch-case" in other languages, "match-case" is Python’s way of doing something similar, but with more flexibility. It helps handle multiple conditions in a clean, readable way. Where it really comes in handy: 1. Routing logic in applications (choosing actions based on user input). 2. Handling different types of messages or events. 3. Simplifying long if / elif / else chains. 4. Working with structured data like tuples, lists, or dictionaries. Honestly, it makes your code much easier to read and maintain when there are multiple possibilities to consider. If you are just finding out about it like I did, I would definitely recommend checking it out and getting familiar with how it works, you might be surprised. If you have used it before, I’d love to hear your take on it. #Python #BackendDevelopment

What Really Happens When You Pass a Variable to a Function in Python? In Python, variables don’t hold values — they hold references to objects. When you pass a variable to a function: 👉 Python passes the reference, not a copy of the object. This model is often called “pass-by-object-reference” (or call by sharing). Why this confuses people? Immutable objects (int, str, tuple): Reassignment inside a function creates a new object → original stays unchanged. Mutable objects (list, dict, set): In-place modification changes the same object → caller sees the change. So it feels like: Immutable → pass by value Mutable → pass by reference But that’s just an illusion. The real rule Python always passes a reference to an object. What you do with that reference determines the outcome. #Python #ProgrammingConcepts #PythonInternals #Mutable #Immutable #CleanCode

🐍📰 Three Ways of Storing and Accessing Lots of Images in Python In this tutorial, you'll cover three ways of storing and accessing lots of images in Python. You'll also see the performance benefits and drawbacks of each one #python

Why range(1,000,000) is cheap, but list(range(1,000,000)) is costly in Python? TL;DR: Iteration Protocol in Python needs to know only next item and not full list. The "Next Page" Rule Iteration in Python isn't about having a collection of items; it’s about knowing how to get the next item. Two special methods make this possible: 1. __iter__() → tells Python “I can be looped over” 2. __next__() → returns the next value, one at a time When there’s nothing left, StopIteration tells Python to stop the loop. Why this matters? When we use a list, we pay for all the memory upfront. When we use the Iteration Protocol, we only pay for one item at a time. This is called Lazy Evaluation. Takeaway - If the object represents a collection or a stream of data, implement __iter__ and __next__. It makes the code more memory-efficient and much more "Pythonic." I’m deep-diving into the Python protocols this week and will share my learnings. Do follow along and tell your experiences in comments. #Python #PythonInternals #SoftwareEngineering #BackendDevelopment

Tuples in Python: Immutable (cannot be changed after creation), ordered collection that allows duplicate elements. Syntax:() Two methods: Index(),count() Python 3.14.0 (tags/v3.14.0:ebf955d, Oct 7 2025, 10:15:03) [MSC v.1944 64 bit (AMD64)] on win32 Enter "help" below or click "Help" above for more information. >>> #tuples() >>> a=(4,5.7,"python",4+9j,True,False) >>> print(a) (4, 5.7, 'python', (4+9j), True, False) >>> type(a) <class 'tuple'> >>> len(a) 6 >>> a.count("python") 1 >>> a.index(True) 4 Pooja Chinthakayala Mam,Saketh Kallepu Sir,Uppugundla Sairam Sir