Two numbers. Same math. Different answers. 🤯 Copy code Python x = 10**100 y = 10**100 + 1 print(x == y) Output: False Python handles this without blinking. No overflow. No precision loss. Now watch this: Python a = 1e16 b = a + 1 print(a == b) Output: True At this scale, adding 1 changes nothing. The value is already beyond what a float can accurately represent. Integers in Python grow as large as memory allows. Floats live within fixed precision and range boundaries. Same language. Same operator. Very different realities. Next time a number behaves “strangely”, remember — it’s not a bug, it’s a boundary. #Python #DataScience #ProgrammingInsights #NumericalComputing #TechThoughts

🚀 Using `next()` with Generators (Python) The `next()` function is used to retrieve the next value from a generator. When `next()` is called on a generator object, the generator executes until it encounters a `yield` statement. The value yielded is returned by `next()`. If the generator has no more values to yield (e.g., it has reached the end of the function), `next()` raises a `StopIteration` exception. This exception signals that the generator has been exhausted. #Python #PythonDev #DataScience #WebDev #professional #career #development

The 5 Legacy Python Habits You Need to Break Today 🚫 1️⃣ Using os.path instead of pathlib. 2️⃣ Ignoring the "Walrus Operator" (:=) in loops. 3️⃣ Writing "Naked" Python without Type Hints. 4️⃣ Avoiding threads due to old GIL fears. 5️⃣ Long if-elif chains instead of Pattern Matching. Why? Because modern Python (3.12+) is faster, cleaner, and built for the AI era. Check out my new article on the shifting Python landscape. 🔗 [https://lnkd.in/gSWqqz59] #Python313 #SoftwareEngineering #CodingHabits #BackendDevelopment #TechTrends2026

Today I Learnt: How Bubble Sort Organizes Data! I’ve been continuing my Python algorithm journey today by implementing Bubble Sort. The logic is simple but fascinating: it repeatedly compares adjacent numbers and swaps them if they are in the wrong order. This process continues until the largest numbers "bubble up" to their correct positions at the end of the list. What I practiced today: Nested Loops: Using one loop to control the passes and another to compare adjacent elements Optimization: Using len(data) - 1 - i in the inner loop to avoid checking already sorted elements. In-place Swapping: Leveraging Python’s clean syntax to swap variables without a temporary "third" container. It’s not the fastest algorithm for huge datasets, but it’s a foundational concept for understanding how sorting works under the hood! #Python #Coding #Algorithms #DataStructures #BubbleSort #WebDevelopment #ContinuousLearning

🚀 Just pushed a new machine learning implementation to GitHub! I built **Multiple Linear Regression from scratch** using **vectorized gradient descent in Python/NumPy** to compare performance with traditional loops. Vectorization makes ML code *dramatically faster* by leveraging optimized C/Fortran kernels and SIMD instructions! 🧠💡 :contentReference[oaicite:1]{index=1} 💻 Repository: https://lnkd.in/gppzrgrn 📌 Highlights: ✅ Fully vectorized linear regression training ✅ Gradient descent implemented from first principles ✅ Demonstrated performance improvement over loop‑based code ✅ Clear explanation and concepts inside README If you're learning ML fundamentals or want to see how vectorization boosts efficiency in numerical code, check it out! #MachineLearning #Python #NumPy #GradientDescent #Vectorization #DataScience #MLfromScratch #ANDREWNG

If this image made you pause for a second — good. 👀 Same code. Same value. But Python says True here and False there. This is not: ❌ a bug ❌ randomness ❌ Python being inconsistent This is Python memory management at work. Behind the scenes, Python is deciding: Should I reuse memory? Is this object safe to share? Is this value common enough to cache? Can reference counting clean this up? Or should the Garbage Collector step in? Most developers learn Python syntax. Very few learn how Python thinks about memory. That gap is where: interview traps happen `is` vs `==` confusion starts performance bugs hide I explained this clearly, step-by-step (with diagrams) in my Medium article 👇 👉 Python Memory Management Explained (Interning, GC, Reference Counting) https://lnkd.in/grVdVSns Once you read it, this image will make complete sense — and Python will stop feeling “magical”. #Python #PythonInternals #MemoryManagement #BackendDeveloper #SoftwareEngineering #LearnPython

📊 Python Pandas | Selecting Data from a Series Learning how to access data efficiently is a key step in mastering Pandas. In this example, I practiced selecting values from a Pandas Series using: 🔹 .loc[] → label-based indexing 🔹 .iloc[] → position-based indexing Understanding the difference between these two helps write clearer and more reliable data analysis code. Small steps, consistent practice, and steady progress 🚀 #Python #Pandas #DataAnalysis #DataScience #LearningPython #CodingJourney #PythonProgramming

🚀 Day 8/100 | #100DaysOfCode | Python String Power 🐍✨ Learning Python step by step, and today was all about understanding strings more deeply 🔍 Here’s what I practiced today 👇 🔹 String Indexing Accessing characters using positions — helped me understand how data is stored in strings. 🔹 swapcase() Turns lowercase into uppercase and vice versa — simple but very useful! 🔹 count() To check how many times a character or word appears in a string. 🔹 find() & index() Both help in finding positions of characters, but today I learned the key difference between them — great for debugging and validations. 🔹 split() & join() Breaking strings into words and joining them back — super important for real-world text processing. Small concepts, but they build a strong foundation for bigger programs 💯 Trying to stay consistent and improve a little every single day 🚀 Learning in public, learning with purpose 💻✨ #Python #100DaysOfCode #LearningPython #CodingJourney #DeveloperInMaking #TechSkills #Consistency #DailyLearning #FromBasicsToBuild

🚀 Python Learning – Day 17 Today I explored more NumPy concepts related to data structure and layout: Array shape Reshaping arrays Understanding axis These are important when working with real datasets. 🔹 Shape of an Array import numpy as np arr = np.array([[1, 2, 3], [4, 5, 6]]) print(arr.shape) 🔹 Reshaping an Array new_arr = arr.reshape(3, 2) print(new_arr) 🔹 Using Axis print(arr.sum(axis=0)) # column-wise print(arr.sum(axis=1)) # row-wise Understanding shape and axis helps avoid mistakes in data analysis. Moving forward with NumPy basics. 🔥 #Python #NumPy #DataAnalytics #LearningJourney #DailyLearning