Python's built-in sorted() outperforms custom sorting algorithms

🧠 Python Feature That Makes Type Checking Smarter: typing.Protocol Duck typing… but official 🦆✨ 🤔 The Problem 💻 Python is dynamically typed. 💻 But sometimes you want structure without inheritance. ❌ Traditional Way class Bird: def fly(self): ... def start_flying(bird: Bird): bird.fly() This forces inheritance. ✅ Pythonic Way with Protocol from typing import Protocol class Flyable(Protocol): def fly(self) -> None: ... def start_flying(obj: Flyable): obj.fly() 💫 Now ANY object with fly() works. 💫 No inheritance required 🎯 🧒 Simple Explanation 🦆 If it quacks like a duck and walks like a duck… 🦆 it doesn’t need to be a Duck class. 🦆 That’s Protocol. 💡 Why This Is Powerful ✔ Structural typing ✔ Cleaner architecture ✔ Better static analysis ✔ Used heavily in modern Python frameworks ⚡ Real Example class Drone: def fly(self): print("Flying") start_flying(Drone()) # Works! 🐍 Python believes in behavior, not hierarchy 🐍 typing.Protocol makes duck typing formal and powerful. #Python #PythonTips #PythonTricks #AdvancedPython #CleanCode #LearnPython #Programming #DeveloperLife #DailyCoding #100DaysOfCode

Confession: I used to write terrible Python. Jupyter notebooks with cells numbered out of order. No type hints. Global variables everywhere. Functions called "process_data_v2_final_FINAL." Sound familiar? The turning point was when I had to hand off a project to another engineer. They stared at my code for two days and said, "I genuinely can't figure out what this does." I was mortified. Since then I've become almost religious about production-grade Python: type hints with mypy, Pydantic for validation, FastAPI for serving, async where it matters, proper package management with uv. The difference between a data scientist and an ML engineer isn't what models they know. It's whether another human can read, run, and maintain their code six months later. If your code only works when you run it in the exact right order in your specific notebook — that's not engineering. That's a magic trick. Write code like someone else will maintain it. Because they will. #Python #SoftwareEngineering #FastAPI #MachineLearning #CleanCode #Coding

🧠 Python Concept That Runs When Class Is Called: __call__ on Classes Objects can be callable… but classes can customize calls too 👀 🤔 The Surprise When you do: obj = MyClass() Python actually does: obj = MyClass.__call__() 🧪 Example class Logger: def __call__(self, msg): print("LOG:", msg) log = Logger() log("Hello") 🎯 Class instances become functions 🧠 But Classes Themselves Use __call__ class Meta(type): def __call__(cls, *args, **kwargs): print("Creating instance") return super().__call__(*args, **kwargs) class User(metaclass=Meta): pass u = User() ✅ Output Creating instance Metaclass intercepted construction. 🧒 Simple Explanation 🥤 Imagine a vending machine 🥤 Press button → machine runs → gives item 🥤 That press = __call__. 💡 Why This Is Powerful ✔ Factories ✔ Dependency injection ✔ Framework hooks ✔ Callable objects ✔ Advanced APIs ⚡ Real Uses 💻 PyTorch modules 💻 FastAPI dependencies 💻 Decorator classes 💻 DSL builders 🐍 In Python, calling isn’t just for functions 🐍 Classes and objects can redefine what “()” means. 🐍 __call__ is the hook behind that power. #Python #PythonTips #PythonTricks #AdvancedPython #CleanCode #LearnPython #Programming #DeveloperLife #DailyCoding #100DaysOfCode

🧠 Is Your Python Code Making the Right Decisions? In my last post, we talked about "Identifiers"—the boxes where we store data. But data sitting in a box is useless. To make your program think, calculate, and react, you need the engine room of Python: Operators. If variables are the nouns, operators are the verbs. They make things happen. Here is the 3-part toolkit you use in almost every script: 1️⃣ The Mathematicians (Arithmetic Operators) 🧮 You know the basics (+, -, *, /). But Python has two secret weapons for data handling: 🔹 Floor Division (//): Rounds the result down to the nearest whole number. (e.g., 7 // 2 is 3, not 3.5). 🔹 Modulus (%): Gives you the remainder of a division. Crucial for checking if a number is even or odd! (e.g., 10 % 3 is 1). 2️⃣ The Judges (Comparison Operators) ⚖️ These operators ask questions and only accept "True" or "False" as answers. 🔸 They are the gatekeepers for your if statements. 🔸 Watch out: = assigns a value. == compares two values. Mixing these up is a classic rookie mistake! 3️⃣ The Traffic Controllers (Logical Operators) 🚦 When one condition isn't enough, you need these to combine them. 🔹 and: Both conditions must be met to pass. 🔹 or: Only one needs to be met to pass. 🔹 not: Reverses the logic (True becomes False). ♻️ Repost if you found this breakdown helpful. ➕ Follow me to catch Part 3 of this Python Basics series! #PythonDeveloper #CodingLife #DataScience #SoftwareEngineering #LearnToCode #connections

Python doesn’t have is_sorted() — and that’s intentional. If you need to validate order without re-sorting the data, this is the most efficient pattern: def is_sorted(t): return all(x <= y for x, y in zip(t, t[1:])) Why this works: - zip(t, t[1:]) compares adjacent elements - all() short-circuits on the first violation - Time complexity: O(n) - Stops early if unsorted Most developers reach for: t == tuple(sorted(t)) That’s O(n log n) and allocates memory — even if the tuple is already sorted. When validating: - Timestamps before binary search - Sorted IDs before merge operations - Monotonic sensor readings Use pairwise comparison — not sorting. Full breakdown (with benchmarks and edge cases): https://lnkd.in/gxGiudfR #Python #SoftwareEngineering #Performance

🚀 Day-38 of #100DaysOfCode 🐍 Python Sorting Algorithm Challenge Today I implemented Selection Sort from scratch to sort a list of numbers provided by the user—without using any built-in sorting methods. 🔹 What is Selection Sort? Selection Sort repeatedly selects the smallest element from the unsorted portion of the list and places it at the correct position. 🔹 Concepts Practiced: ✔ Nested loops ✔ Minimum element selection logic ✔ Index tracking ✔ In-place swapping 🔹 Approach: Iterate through the list Find the minimum element in the remaining unsorted part Swap it with the current index Repeat until the list is fully sorted 🔹 Key Insight: Selection Sort has a time complexity of O(n²), making it useful for understanding sorting fundamentals rather than large datasets. Working through such algorithms builds strong foundational knowledge of sorting and array manipulation 💡 #Python #SelectionSort #SortingAlgorithms #CorePython #100DaysOfCode #Day38 #LearnPython #CodingPractice #PythonDeveloper

#Projects 🚀 Excited to share my latest project: Thread Shed 🧵✨ https://lnkd.in/gssBsFwy Working with complex string data in Python has always fascinated me. This project was born out of the challenge of handling messy, layered text structures and turning them into something clean, efficient, and insightful. 🔹 Why it matters: Speeds up data parsing and transformation Handles intricate string manipulations with clarity Demonstrates how concurrency can simplify real-world text-heavy workflows 👉 I’d love to hear how others approach complex string challenges in Python. Do you lean on regex, threading, or something else entirely? #Python #Threading #StringProcessing #DataEngineering #Innovation Would you like me to make this post more technical (with code snippets and performance metrics) or more story-driven (focusing on your personal journey and motivation)?

Python vs Julia - Performance Test Python script - https://lnkd.in/ejAYdMzR Julia script - https://lnkd.in/eYU34qHy I ran a small performance comparison between Python and Julia across several typical scenarios: > numeric loop > Newton’s method > Monte Carlo simulation > small matrix loop > text processing The radar chart below shows the results. What I observed Python was faster in: - Numeric loop - Small matrix loop Julia was faster in: - Newton’s method (by ~10x) - Text processing (almost 3x) - Monte Carlo (moderate advantage) So… is Julia faster than Python? Not universally. And Python is definitely not "slow by default" Performance depends on: - how the code is written - the workload type - compilation model - memory behavior - whether libraries are involved Julia shines in compute-heavy numerical routines, especially when the code structure allows the JIT compiler to optimize aggressively. Python performs extremely well when leveraging optimized internals and mature ecosystem tools. The bigger picture Julia is still much younger than Python, but: - It was designed for high-performance scientific computing - It compiles via LLVM - It removes the "two-language problem" (prototype vs production language) Its potential in scientific computing, HPC, and ML is significant. Will it replace Python? Probably not. Will it carve out a strong niche in performance-critical domains? Very likely. Curious to hear your experience - Have you used Julia in production, or do you stick with Python for performance work? #python #julia #performance #monte_carlo

🐍 Python Challenge What will be the output of the following code? funcs = [lambda x: x * i for i in range(3)] print(funcs) Options: A) 0 B) 2 C) 4 D) TypeError 🧠 Many developers expect the answer to be 2 because funcs[1] seems like it should use i = 1. But the correct answer is actually: ✅ 4 💡 Why? This happens because of a concept in Python called Late Binding. Inside the list comprehension, the lambda functions do not store the value of i at the time they are created. Instead, they reference the same variable i, whose final value after the loop finishes is: i = 2 So all functions in the list behave like this: lambda x: x * 2 When we execute: funcs it becomes: 2 * 2 = 4 🎯 Key Lesson When using lambda inside loops or list comprehensions, Python captures the variable itself, not its value at creation time. 💬 Question for Python learners: How would you fix this code so that each lambda keeps its own value of i? #Python #AI #DataAnalytics #LearningInPublic #PythonTips #30DayChallenge