Python Lists vs NumPy Performance Boost

When I first started using Pandas, I wrote code the same way I wrote normal Python. Lots of loops. Lots of step-by-step logic. And it worked… at first. But then datasets got bigger. And things slowed down quickly. That’s when I learned something important: 👉 Pandas works best when you think in vectorized operations. Instead of: looping through rows You start thinking in columns. Example mindset shift: Instead of processing each row individually, you transform entire columns at once. This small change made my code: ✔ faster ✔ simpler ✔ easier to read Still learning, but it's one of those small mental shifts that really changes how you work with data. #DataEngineering #Python #Pandas

𝐐𝐮𝐢𝐜𝐤 𝐭𝐡𝐢𝐧𝐠 𝐈 𝐥𝐞𝐚𝐫𝐧𝐞𝐝 𝐭𝐨𝐝𝐚𝐲 𝐰𝐡𝐢𝐥𝐞 𝐮𝐬𝐢𝐧𝐠 𝐩𝐚𝐧𝐝𝐚𝐬 While practicing data analysis in Python, I realized how useful groupby() in pandas actually is. At first it looked confusing, but once it clicked, it felt like one of the most practical tools for analysis. You can quickly split data by categories and calculate things like averages, counts, or totals for each group. For example, instead of manually filtering a dataset multiple times, a simple groupby() can show things like average sales per city or number of users per category in seconds. Small function, but it really shows how powerful pandas can be when working with real datasets. #Python #Pandas #DataAnalytics #LearningByDoing

Machine Learning Graph Data using pygal #machinelearning #datascience #graphdata #pygal Pygal is a simple yet powerful Python library for generating beautiful SVG charts. It allows users to create a wide variety of static and animated visualizations, including bar charts, pie charts, line charts, and radar charts. https://lnkd.in/gn8-hBUW

AI video editing with Python 🐍 is here 🎬 https://lnkd.in/ed3Nqf-t This tutorial shows how to automatically turn long videos into short, captioned clips in a few lines of code Simple setup. Real results. ⚡ #Python #AI #VideoEditing #Automation

Python Functions Cheat Sheet | Everything You Need at One Place Python functions are the backbone of clean, reusable, and scalable code. This Python Functions Cheat Sheet covers all the essentials—from basics to interview-ready concepts. What you’ll find inside: • Function definition & calling • Parameters vs arguments • Default & keyword arguments • *args and **kwargs • Lambda (anonymous) functions • Return statements • Scope (local vs global) • Docstrings & best practices Perfect for beginners, revision before interviews, and daily coding reference for your next AI project. Save it, revise it, and code smarter. #Python #PythonProgramming #PythonFunctions #CodingCheatSheet

Python A/B Test Tools Compared: tea-tasting Pingouin vs statsmodels and SciPy 📌 Python’s A/B testing scene just got more organized-tea-tasting stands out with a sleek, experiment-focused API that auto-calculates effect sizes, confidence intervals, and p-values in one go. While statsmodels and SciPy offer deep stats power, tea-tasting cuts through the noise for teams craving speed and clarity. No more manual variance calculations or boilerplate code-just clean, production-ready insights. 🔗 Read more: https://lnkd.in/d88Xhtxe #TeaTasting #Pingouin #Statsmodels #Scipy #ABtesting

Most people learn Python by staring at output. I tried something different. 👇 This is what actually happens when Python executes a basic function — step by step, visually. No theory. No slides. Just execution in real time. Still early in my journey — but this is how I'm learning. If you're learning Python too, drop a 👋 below. 🔧 Tool: pythontutor / staying.fun 🐍 Concept: Functions — how they're called, executed & returned #Python #LearningInPublic #DataAnalytics #BBA #100DaysOfCode #PythonBeginners

Day 4/120 – I finally understood how Python actually “thinks” 🤯 For the past 3 days, I was learning concepts… But today, things started making sense. Because I learned this 👇 👉 Operators in Python Operators are what make your code “do something” Without them, Python is just… variables sitting idle 😅 Here’s what I explored today 👇 ➕ Arithmetic Operators → +, -, *, / Example: 10 + 5 = 15 📊 Comparison Operators → ==, !=, >, < Example: 10 > 5 → True 🧠 Logical Operators → and, or, not Example: (10 > 5) and (5 > 2) → True This is where logic begins 🔥 Now I can actually: ✔ Make decisions ✔ Compare values ✔ Build logic Feels like I unlocked a new level 🎮 Consistency > Perfection 💪 If you're learning, comment “LEVEL UP” 🚀 #Day4 #Python #DataAnalytics #LearningInPublic #CodingJourney #Consistency #Beginners

Python Series — Day 3 🧠 Let’s level it up a bit 👇 What will be the output of this code? def modify_list(lst): lst.append(4) a = [1, 2, 3] modify_list(a) print(a) Options: A. [1, 2, 3] B. [1, 2, 3, 4] C. Error D. None Think carefully 👀 (Hint: It’s not about functions… it’s about how Python handles data) Drop your answer 👇 Answer tomorrow 🚀 #Python #CodingChallenge #LearningInPublic #DataEngineering #Tech