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

🌟 New Blog Just Published! 🌟 📌 5 Python Scripts to Automate Data Cleaning and Cut Hours 🚀 📖 Data-driven projects waste 60-80% of their timeline on cleaning alone. That means if you budget three months for a model, two of those months disappear before you ever touch an algorithm. Make sense?... 🔗 Read more: https://lnkd.in/dXZPJPBa 🚀✨ #python-data-cleani #pandas-automation #data-cleaning-scri

🐻❄Pandas Tip: Instead of looping through rows, use vectorized operations in Pandas. They are faster, cleaner, and more Pythonic.Vectorized operations mean performing calculations on entire columns (arrays) at once, instead of processing data row by row using loops. Example: Python under pandas library: df["total"] = df["price"] * df["quantity"] 🚀 This approach improves performance significantly, especially on large datasets. Why Avoid Loops in Pandas? Using loops (for, iterrows()): 😐Slow for large datasets 😐Harder to read and maintain 😐Doesn’t utilize Pandas’ full power Using vectorization: 😊Faster execution 😊Cleaner and shorter code 😊Better memory usage #Python #Pandas #DataEngineering #DataScience

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

🚀 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

𝐃𝐚𝐲 𝟒: 𝐓𝐲𝐩𝐞 𝐂𝐚𝐬𝐭𝐢𝐧𝐠 𝐢𝐧 𝐏𝐲𝐭𝐡𝐨𝐧 🐍 Type casting means converting a value from one data type to another. 𝐓𝐲𝐩𝐞𝐬 𝐨𝐟 𝐓𝐲𝐩𝐞𝐜𝐚𝐬𝐭𝐢𝐧𝐠 𝐚𝐫𝐞: 1️⃣ 𝐼𝑚𝑝𝑙𝑖𝑐𝑖𝑡 𝑐𝑜𝑛𝑣𝑒𝑟𝑠𝑖𝑜n: ✔️ Python automatically converts compatible data types. Example: 10 + 2.5 → float ❌ Strings are not converted automatically: Example: 10 + "5" → TypeError 2️⃣ 𝐸𝑥𝑝𝑙𝑖𝑐𝑖𝑡 𝑐𝑜𝑛𝑣𝑒𝑟𝑠𝑖𝑜𝑛: ✔️ Done manually using built-in functions: int(), float(), str(), list(), etc. ✍️𝐍𝐨𝐭𝐞: Understanding type casting helps prevent errors and write cleaner code. This is just the beginning. Learning step by step and sharing everything publicly. #Python #LearningInPublic #DataScience #Consistency

Diving Deeper into Python Strings! Until now, I was building strings in the old-school way: using + to concatenate and str() to convert numbers. It works… but it’s messy. Today, I explored a cleaner, more powerful approach: format(). Here’s what I learned: {} are placeholders for variables. .format() handles type conversions automatically, no more str() headaches! Named placeholders make strings readable and flexible, even if variable order changes. Format numbers, align text, and make outputs neat, perfect for prices, tables, logs, or debug messages. 💡 String formatting isn’t just cleaner syntax, it makes your code readable, maintainable, and professional. Once you get it, your logs and outputs will look polished! #Python #Coding #StringFormatting #CleanCode #LearnPython #ProgrammingTips

𝗗𝗮𝘆 𝟱/𝟭𝟬𝟬: Why your Python loops are slowing down your AI 🏎️ If you are using 𝘧𝘰𝘳 loops to process numerical data, you are likely leaving a 10x–100x speed improvement on the table. Today, I dove into NumPy, the backbone of scientific computing in Python. The secret sauce? 𝗩𝗲𝗰𝘁𝗼𝗿𝗶𝘇𝗮𝘁𝗶𝗼𝗻. Instead of processing items one by one (the slow way), NumPy uses optimized C code to perform operations on entire arrays at once. 𝗠𝘆 𝟯 𝗚𝗮𝗺𝗲-𝗖𝗵𝗮𝗻𝗴𝗶𝗻𝗴 𝗧𝗮𝗸𝗲𝗮𝘄𝗮𝘆𝘀 𝗧𝗼𝗱𝗮𝘆: 𝗩𝗲𝗰𝘁𝗼𝗿𝗶𝘇𝗲𝗱 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀: Adding two arrays of 1 million numbers takes one line: 𝗮𝗿𝗿𝟭 + 𝗮𝗿𝗿𝟮. No loops required. 𝗧𝗵𝗲 𝗣𝗼𝘄𝗲𝗿 𝗼𝗳 𝗥𝗲𝘀𝗵𝗮𝗽𝗶𝗻𝗴: I learned why a 1D array (𝟱,) is NOT the same as a 2D array (𝟭, 𝟱). Most ML libraries like Scikit-Learn will throw an error if you don't get your dimensions right! 𝗜𝗱𝗲𝗻𝘁𝗶𝘁𝘆 𝗠𝗮𝘁𝗿𝗶𝗰𝗲𝘀 & 𝗭𝗲𝗿𝗼𝘀: Functions like 𝗻𝗽.𝗲𝘆𝗲() and 𝗻𝗽.𝘇𝗲𝗿𝗼𝘀() are essential for initializing model weights before training even begins. 𝗧𝗵𝗲 𝗩𝗲𝗿𝗱𝗶𝗰𝘁: If you want to work with Big Data, stop thinking in loops and start thinking in Arrays. #100DaysOfML #Python #NumPy #DataScience #Coding #Performance #MachineLearning

🐍 Day 22 — Functions in Python Day 22 of #python365ai 🧩 Functions group reusable code. Example: def greet(name): print("Hello", name) 📌 Why this matters: Functions improve readability and reduce repetition. 📘 Practice task: Write a function that adds two numbers. #python365ai #PythonFunctions #CleanCode #LearnPython

BTS to our latest DKAW Lecture titled "Python for Finance: Reverse Engineering Warren Buffett’s Portfolio Strategy (EP5)". Stay tuned as you get a snippet of the python code that built the automative and efficient DCF in excel. 👏 💻 🔗 : https://lnkd.in/d4v-Qz85 #datascience #aiinfinance #python #DCF #warrenbuffett #valuationmethods #investmentmethods #optimisationmethods #dkaw #lecture #tutorial #educational #statistics #dataanalysis #datavisualisation #data