Bubble Sort Algorithm Implementation in Python

Stop choosing between [] and () at random. It’s the difference between a high-performance script and a MemoryError. 🐍 In Python, List Comprehensions and Generator Expressions might look similar, but under the hood, they handle your system's RAM very differently. I’ve put together a slide deck to break down: ✅ The 'Eager' vs. 'Lazy' evaluation model. ✅ Real-world memory benchmarks. ✅ When to prioritize Speed over Memory. Swipe through to see which one you should be using for your next data pipeline! 👇 What’s your go-to? Do you default to Lists for simplicity, or Generators for efficiency? Let’s discuss in the comments! #Python #Coding #SoftwareEngineering #DataScience #Backend #Performance

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

🚀 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

Sorting is not just about order — it’s about control over data 🔧 Today’s Python 🐍 practice revolved around ordering and sorting data across multiple structures. From simple integer lists to student records and dictionaries, the focus was on understanding how sorting behaves, not just applying it. I worked with both in-place sorting and non-destructive sorting to see how data mutates (or doesn’t). This extended naturally into sorting tuples and dictionaries using custom keys, a pattern that shows up everywhere — rankings, scoreboards, reports, and backend logic. Once you understand how to sort by intent instead of default behavior, the code becomes far more expressive and reliable. Small operations like sorting often decide whether data is usable or misleading. Getting this right early pays off later. #Python #Sorting #DataHandling #ProgrammingLogic #SoftwareDevelopment

Today I had a list full of duplicate values. 🐍 Instead of writing loops to remove them, I converted the list into a set — and instantly got only unique elements. Then I used: • intersection( ) → to find common values • union( ) → to merge datasets • difference( ) → to know what’s missing • symmetric_difference( ) → to find what’s not common at all This is how I realized: logic is not about more code, it’s about the right structure. Which Python concept saved your time recently? 👇 #python #codingjourney #datacleaning #learningbydoing

Day 333: Why Lists aren't always enough (Deque) 🌀 Optimizing Queues Here is a computer science fact: Popping the first item of a standard Python list is slow ($O(n)$) because Python has to shift every other item in memory. If you are implementing a Queue, a Sliding Window algorithm, or BFS (Breadth-First Search), you need deque (Double Ended Queue). It allows you to add or remove items from both ends instantly (O(1)). from collections import deque # A list where popping from the left is fast queue = deque(["User1", "User2", "User3"]) queue.popleft() # "User1" leaves instantly queue.append("User4") # "User4" joins the back print(queue) Challenge: Try solving the "Sliding Window Maximum" problem on LeetCode using a list vs. a deque. The speed difference is massive. #Algorithms #DataStructures #Python #CodingInterviews

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

Day 4/30: Game Theory & Data Mapping in Python 🐍💧🔫 For Day 4 of my #30DaysOfPython challenge, I built a classic: Snake, Water, Gun. While it looks like a simple game, the underlying logic is a masterclass in Data Mapping and Conditional Matrices. The Analytics Breakdown: ✅ Dictionary Mapping: I used Python Dictionaries to translate user input ("s", "w", "g") into numerical values. This is exactly how we "encode" categorical data for machine learning models! ✅ Reverse Mapping: I created a second dictionary to "decode" computer results back into human-readable labels—a key part of data storytelling. ✅ Nested Logic: Managing multiple outcomes (Win, Loss, Draw) using structured if-elif-else blocks. Building this helped me understand how to handle user-generated strings and map them to backend logic seamlessly. Next stop: Day 5! Full code below! 👇 #Python #CodingChallenge #GameTheory #Logic #DataScience #LearningInPublic #Day4

𝐃𝐚𝐲 5 | 𝐍𝐮𝐦𝐏𝐲 𝐀𝐫𝐫𝐚𝐲 𝐂𝐫𝐞𝐚𝐭𝐢𝐨𝐧 & 𝐕𝐞𝐜𝐭𝐨𝐫 𝐎𝐩𝐞𝐫𝐚𝐭𝐢𝐨𝐧𝐬 📊 Not long ago, these Python libraries for analysis felt unfamiliar and almost mysterious. But by showing up daily and working through them step by step, the logic is starting to click. What once looked strange is becoming intuitive, and it’s getting more interesting each day. Today was about multi-dimensional arrays and understanding how NumPy handles operations across axes. ✔️ Created NumPy arrays from nested lists ✔️ Extracted negative values using boolean indexing ✔️ Performed element-wise multiplication ✔️ Calculated sums across different axes ✔️ Built and reshaped a 3D array with arange() ✔️ Practiced slicing specific rows ✔️ Compared values across rows using vectorized logic Day 5 done. Looking forward to day 6 🚀 𝐎𝐬𝐭𝐢𝐧𝐚𝐭𝐨 𝐑𝐢𝐠𝐨𝐫𝐞 #NumPy #Python #DataAnalytics #DataScience #LearningInPublic #50_days_of_data_analysis_with_python