A simple example of how large performance gains can come from restructuring a computation: 𝗣𝗿𝗼𝗯𝗹𝗲𝗺 Compute all pairwise squared Euclidean distances between vectors. 𝗔𝗽𝗽𝗿𝗼𝗮𝗰𝗵 𝟭: 𝗡𝗮𝗶𝘃𝗲 𝗣𝘆𝘁𝗵𝗼𝗻  • Double loop over all pairs  • Element-wise computation per pair 𝗔𝗽𝗽𝗿𝗼𝗮𝗰𝗵 𝟮: 𝗩𝗲𝗰𝘁𝗼𝗿𝗶𝘇𝗲𝗱 𝗡𝘂𝗺𝗣𝘆  • Express computation using array broadcasting  • Execute work in optimized C instead of Python loops 𝗥𝗲𝘀𝘂𝗹𝘁𝘀 (𝟱𝟬𝟬 × 𝟭𝟬 𝗺𝗮𝘁𝗿𝗶𝘅) In a recent trial, I had the following results:  • Naive Python: ~0.62 s  • Vectorized NumPy: ~0.013 s  • ~48× speedup  • 0.0 maximum error between implementations The key difference is not just implementation — it’s how the computation is expressed. By restructuring the problem into a form that operates on entire arrays at once, we:  • Eliminate Python interpreter overhead  • Enable vectorized execution  • Improve cache efficiency  • Allow low-level optimizations to take effect This pattern shows up frequently in high-performance numerical code — especially when working with large datasets or computationally intensive workloads. I’ve been exploring these kinds of optimizations recently and plan to share more examples.

A small helper tool. This is very much an early early “release” if it can even be called that, but I know that if I go off to make it an actual fortified repo, I’ll get busy and forget about it. The biggest problem it aims to tackle: processing large video datasets into a manageable, streamlined format whilst remaining and being built primarily in python. #opensource #opensauce #dohashtagsdoanythingonlinkedin #computervision

🐍 These are important data structures used to store multiple values. 📊 List • Ordered • Mutable (can change) • Allows duplicates Example: [1, 2, 3, 3] 🔒 Tuple • Ordered • Immutable (cannot change) • Allows duplicates Example: (1, 2, 3, 3) 🔁 Set • Unordered • Mutable • Does NOT allow duplicates Example: {1, 2, 3} 💡 Key Difference: List → Changeable + Ordered Tuple → Fixed + Ordered Set → Unique values only 🎯 Choosing the right data structure helps in writing efficient and clean Python code. #Python #DataScience #MachineLearning #AI #LearningInPublic #Programming

🧠 Python Concept: List Comprehension Write powerful loops in one clean line. ❌ Traditional Way squares = [] for i in range(5): squares.append(i * i) print(squares) Output [0, 1, 4, 9, 16] ✅ Pythonic Way squares = [i * i for i in range(5)] print(squares) Same result, less code. ⚡ With Condition even_squares = [i * i for i in range(10) if i % 2 == 0] print(even_squares) Output [0, 4, 16, 36, 64] 🧒 Simple Explanation Imagine telling a robot: 👉 “Give me squares of numbers from 0–4.” 👉 Instead of repeating instructions, you give one rule. 👉 That rule = list comprehension. 💡 Why This Matters ✔ Shorter code ✔ Faster execution ✔ More readable loops ✔ Very Pythonic 🐍 Python often replaces multiple lines with a single elegant expression 🐍 List comprehensions are one of the most powerful examples of that philosophy. #Python #PythonTips #PythonTricks #AdvancedPython #List #ListComprehension #Tech #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

🚀 Day 4/60 – Operators in Python (Make Your Code Powerful) Variables store data. Operators make data useful. Let’s learn the basics 👇 ➕ 1️⃣ Arithmetic Operators (Math) a = 10 b = 3 print(a + b) # 13 print(a - b) # 7 print(a * b) # 30 print(a / b) # 3.33 print(a % b) # 1 (remainder) 🔍 2️⃣ Comparison Operators (True/False) print(10 > 5) # True print(10 < 5) # False print(10 == 10) # True print(10 != 5) # True Used in decision making. 🔗 3️⃣ Logical Operators (Combine Conditions) print(True and False) # False print(True or False) # True print(not True) # False ⚡ Real Example age = 20 if age >= 18: print("You can vote") Operators help you build logic like this. ❌ Common Mistake if age = 18: # ❌ Wrong Correct: if age == 18: # ✅ Comparison 🔥 Pro Tip = → assignment == → comparison Never mix them. 🔥 Challenge for today Write a program: 👉 Take a number 👉 Check if it is even or odd Hint 👇 num % 2 Comment “DONE” when you solve it ✅ Follow Adeel Sajjad if you’re serious about learning Python in 60 days 🚀 #Python #LearnPython #PythonProgramming #Coding #Programming

🚀 A small Python problem reminded me why algorithm efficiency matters. Even with 2+ years of experience as a Data Engineer, I like revisiting core programming fundamentals. 👉 Today's problem: Find the Second Largest Number in a list. Example: [10, 20, 5, 8, 20] My first instinct was the simple approach: • Sort the list   • Pick the second element from the end But sorting gives us *O(n log n)* complexity. A better approach is solving it in *one pass (O(n))*. Idea: Track two variables while iterating: • largest • second largest Python implementation: lst = [10, 20, 5, 8, 20] largest = second = float('-inf') for num in lst:   if num > largest:     second = largest     largest = num   elif num > second and num != largest:     second = num print(second) Output: 10 💡 Takeaway 👉 Even simple problems show how important efficient algorithms are. 👉 In data engineering pipelines where we process massive datasets, single-pass logic can make a real difference. How would you solve this problem? #DataEngineering #Python #Algorithms #CodingPractice #LearningInPublic

Python in Data Science #009 I feel like I’ve lost count of how many times I saw “feature importance” in a slide deck, nodded along. Sometimes I realize it is telling a comforting story, not the true one. The model workes, but the explanation is quiet misleading. I always default to permutation importance for explanations and treat impurity-based importance as a rough heuristic. Tree models (RF/GB/XGB) often expose impurity-based importance (the built-in “gain”/“gini” style). It’s fast, but it’s biased toward continuous/high-cardinality features, and it can inflate variables that simply offer more split opportunities. Permutation importance asks a more practical question: “If I shuffle this feature, how much does my metric drop?” That trade-off matters: permutation is slower and can get messy with highly correlated features (importance gets shared or diluted), but it’s much closer to “what the model actually uses” on the data distribution you care about. Also important: compute it on a validation set, not the training set, or you’ll explain overfitting.#datascience #machinelearning #python

Machine Learning Time Series Data using matrixprofilets #machinelearning #datascience #timeseriesdata #matrixprofilets An Open Source Python Time Series Library For Motif Discovery using Matrix Profile. matrixprofile-ts is a Python 2 and 3 library for evaluating time series data using the Matrix Profile algorithms developed by the Keogh and Mueen research group at UC Riverside and the University of New Mexico. Current implementations include MASS, STMP, STAMP, STAMPI, STOMP, SCRIMP++ and FLUSS. https://lnkd.in/gg3yFZdq

🧠 Python Concept That Explains Why += Can Mutate: In-place vs New Objects (__iadd__) Why does this behave differently? 👀 a = [1, 2] b = a a += [3] print(a) # [1, 2, 3] print(b) # [1, 2, 3] But: x = (1, 2) y = x x += (3,) print(x) # (1, 2, 3) print(y) # (1, 2) Same += … different result 🤯 🤔 The Reason: __iadd__ Python tries: 1️⃣ __iadd__ (in-place add) 2️⃣ else → __add__ (new object) 🧪 Lists implement __iadd__ list.__iadd__(self, other) So list is modified in place. 🧪 Tuples don’t So Python creates a new tuple. 🧒 Simple Explanation List = clay 🧱 You reshape same clay. Tuple = brick 🧱 You must make new brick. 💡 Why This Matters ✔ Mutability understanding ✔ Side-effects bugs ✔ Performance ✔ Data structures ✔ Interview classic ⚡ Key Insight id(a) == id(a += ...) True for mutable types False for immutable types 💻 In Python, += doesn’t always mean “new value”. 🐍 Sometimes it means “modify in place” 🐍 The difference comes from __iadd__. #Python #PythonTips #PythonTricks #AdvancedPython #CleanCode #LearnPython #Programming #DeveloperLife #DailyCoding #100DaysOfCode