Python's 'Else' in For Loops: A Hidden Gem

Why .join() is faster than str1 + str2 in Python? Why string concatenation gets really slow in Python? Strings are immutable in Python. When we write str1 + str2, following steps are done - 1. Request a brand new block of memory large enough for both. 2. Copy every single character from str1 and str2 into the new block. 3. Destroy the old strings (eventually) If we do this 10,000 times in a loop, we are copying millions of characters over and over again. When we use ''.join(list_of_strings), Python is significantly smarter. It performs following 2 pass operation - 1. It iterates through the entire list once to calculate the total length of the final string. 2. it allocates one single block of memory of that exact size and copies all the strings into their respective slots at once. Instead of n reallocations and n copies, one allocation and one copy! Takeaway - -> Need to join 2 or 3 strings - a + b is fine and very readable. -> Need to join strings in a loop - Always collect them in a list first and use ''.join(my_list) at the very end. I’m deep-diving into Python internals and performance. Do follow along and tell your experiences in comments. #Python #PythonInternals #SoftwareEngineering #BackendDevelopment

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

Python Tuples: When Data Shouldn’t Change By now, I’ve learned that Python has multiple sequence types: • Strings → immutable • Lists → mutable • Tuples → immutable (and very intentional) So why do we need tuples when lists already exist? 💡 Because sometimes order = meaning. Example: A tuple like (first_name, middle_initial, last_name) Each position has a fixed purpose. Changing it would break the logic, so Python simply doesn’t allow it. That’s why tuples are perfect for: 🔹 Returning multiple values from a function 🔹 Grouping related data that must stay consistent 🔹 Unpacking values cleanly into variables Lists are flexible. Tuples are reliable. Knowing when to use each one is where real Python understanding starts #Python #LearningInPublic #DataStructures #ProgrammingBasics #DeveloperJourney

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

🧵Python Strings: Small Details, Big Power Today I learned how Python lets us access exactly what we need from a string,nothing more, nothing less. 🔹 Indexing Python starts counting from 0, not 1 First character → index 0 Last character → length - 1 (or just use -1 😉) 🔹 Negative indexes Don’t know the string length? No problem. Python lets you count from the end. 🔹 Slicing strings Want part of a string? Use slices like text[:4] or text[4:] Just like range(): start included, end excluded. 💡 Key takeaway: Indexing feels confusing at first, but practice turns it into muscle memory. Learning Python one slice at a time #Python #LearningInPublic #ProgrammingBasics #Coursera #DeveloperJourney #CodingTip

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

Python keeps rewarding curiosity. Slicing is one such elegant piece of it. So far, most slicing I’ve seen is on built-in sequences like lists, strings, or tuples. But what surprised me is that we can define our own sequences and still use slicing on them. The real trick behind slicing is the slice object, which looks like: slice(start, stop, step) When we write something like: seq[start:stop:step] Python internally does something close to: seq.__getitem__(slice(start, stop, step)) #PythonLearning #BackendEngineering #FinTechCareers

The coolest move in Python syntax: The "Moonwalk" loop. 🕺 Beginners often overcomplicate counting backwards in Python. I’ve seen clunky while loops, manually decrementing counters, or creating lists just to reverse them [::-1]. Stop working so hard. The "Pro" move is to unlock the often-ignored third parameter of the built-in range() function: the step. The syntax is: range(start, stop, step) By default, the step is positive (+1), moving you forward. But if you set the step to -1, you tell Python to slide backwards. In the visual below: Start at 10. Stop before 0 (Remember, the stop index is exclusive!). Step back by -1 each time. It’s clean, readable, and honestly, it just looks way cooler than a messy while loop. 😎 Want to learn a new Python trick every morning? We turn boring documentation into fun, bite-sized lessons. Get them delivered straight to your inbox daily. 👉 Join the PyDaily community here: https://lnkd.in/ducXvs-y #Python #CodingTips #LearnPython #SoftwareEngineering #Developer #PyDaily

🧠 Python Feature That Feels Like Mind Reading: List Comprehensions Most beginners write this 👇 squares = [] for x in range(5): squares.append(x * x) Python says… one clean line 😎 ✅ Pythonic Way squares = [x * x for x in range(5)] 🧒 Simple Explanation Imagine telling a robot 🤖: “Give me squares of numbers from 0 to 4.” Python listens once and does it instantly. 💡 Why Developers Love This ✔ Short and readable ✔ Faster to write ✔ Used everywhere in real projects ✔ Interview favorite ⚡ With Condition even_squares = [x*x for x in range(10) if x % 2 == 0] 💻 Python isn’t about writing long code. 💻 It’s about writing expressive code 🐍✨ 💻 Once you master list comprehensions, there’s no going back. #Python #PythonTips #CleanCode #LearnPython #DeveloperLife #Programming #List #ListComprehension