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

Hi, friends! I just built my first AI-powered stock prediction app from scratch — as a complete Python beginner. It uses Machine Learning to predict stock price direction for any ticker in the world. Built with Python, scikit-learn, yfinance and Streamlit. Check it out https://lnkd.in/eXAZW4U8 #Python #MachineLearning #DataScience #100DaysOfCode #PythonProgramming

Hi, friends! I just built my first AI-powered stock prediction app from scratch — as a complete Python beginner. It uses Machine Learning to predict stock price direction for any ticker in the world. Built with Python, scikit-learn, yfinance and Streamlit. Check it out https://lnkd.in/eXAZW4U8 #Python #MachineLearning #DataScience #100DaysOfCode #PythonProgramming

Hi, friends! I just built my first AI-powered stock prediction app from scratch — as a complete Python beginner. It uses Machine Learning to predict stock price direction for any ticker in the world. Built with Python, scikit-learn, yfinance and Streamlit. Check it out https://lnkd.in/eXAZW4U8 #Python #MachineLearning #DataScience #100DaysOfCode #PythonProgramming

Python is powerful but libraries are what make it truly practical. From data analysis to machine learning, libraries help us build faster and smarter without starting from scratch. Still learning, still exploring. #Python #DataScience #MachineLearning

Python is the base of growth in today’s tech world. From data analysis to AI, automation to web development—everything starts with Python. If you want to grow faster, start with the foundation. Start with Python. #Python #CareerGrowth #TechSkills #AI #DataScience #day71

Let’s get back to the Python series Today, let’s talk about something powerful in NumPy that beginners often ignore 💡 Vectorization in NumPy Instead of using loops in Python, NumPy allows you to perform operations on entire arrays at once. 🔹 Traditional Python (loop-based) = slower 🔹 NumPy (vectorized operations) = faster + cleaner Example: Instead of writing loops to add two lists, NumPy does it in one line. 🔷 Why this matters? Because in real-world data analysis, performance and efficiency are everything. This is one of the reasons why NumPy is widely used in data science and machine learning. My learning: Writing less code but getting faster results is a game changer. #Python #NumPy #DataAnalytics #MachineLearning #LearningInPublic #CodingJourney

🚀 The Fundamentals of 'for' Loops (Python) A 'for' loop in Python is used to iterate over a sequence (like a list, tuple, or string). It executes a block of code for each element in the sequence. The loop variable takes on the value of each element in turn. 'for' loops are essential for processing collections of data, performing repetitive tasks, and implementing algorithms that require iterating through data structures. Understanding 'for' loops is crucial for efficient data manipulation. #Python #PythonDev #DataScience #WebDev #professional #career #development

All you need is Python basics and 90 minutes. I put together a free hands-on guide to build your first AI agent using Python + LangChain — from zero to a working agent with tools and memory. 📎 PDF attached — open it, follow along, build it yourself. #Python #LangChain #AIAgents #MachineLearning #OpenAI #PythonProgramming #ArtificialIntelligence

Learn in Public — Day 15 Today I studied Selection Sort and implemented it in Python. 🔹 Key Idea: Selection Sort repeatedly finds the minimum element from the unsorted part of the array and places it at the correct position in the sorted part. 🔹 How it works: 1️⃣ Start from the first element 2️⃣ Find the smallest element in the remaining array 3️⃣ Swap it with the current position 4️⃣ Repeat for the rest of the array 🔹 Complexity: Time Complexity: O(n²) Space Complexity: O(1) (in-place sorting) Even though Selection Sort is not efficient for large datasets, it’s a great algorithm for understanding how sorting works internally. Every small step builds the foundation for mastering algorithms. #LearnInPublic #100DaysOfCode #Python #Algorithms #DataStructures