🔢 Creation of Arrays using NumPy In this practical, I explored how to create and manipulate arrays efficiently using NumPy in Python. Learned different methods to create arrays such as array(), arange(), zeros(), ones(), and linspace() — essential for numerical computing and data manipulation tasks. 📘 Guided by: Ashish Sawant 💻 GitHub: 👉 [https://lnkd.in/dFff8cPb] #DataScience #NumPy #Python #MachineLearning #Coding #Array #PracticalLearning #DataScienceLab

🔢 Experiment 5: Creating of Dataframe using NumPy ⚙️ In this lab, I explored the core concepts of Data Frame creation and manipulation using NumPy, one of the most essential Python libraries for numerical computing. 🔍 Key learning outcomes:  • Creating 1D, 2D, and multi-dimensional arrays  • Understanding array attributes and indexing  • Leveraging NumPy for efficient mathematical and statistical computations This practical helped me understand how NumPy arrays form the foundation for most data manipulation, analysis and machine learning tasks in Python. 📁 Explore the repository here : 👉https://lnkd.in/epWys7e7 #DataScience  #Python  #NumPy  #MachineLearning  #DataAnalysis  #DataScienceLearning  #JupyterNotebook  #LearningJourney Ashish Sawant sir

🚀Excited to share my 5th Python practical! 💻 This practical focused on the creation of arrays using NumPy, one of the most powerful libraries in Python for numerical computing. I learned how to efficiently create, manipulate, and explore different types of arrays — an essential step toward mastering data analysis and scientific computation. 📁 Here's the Google drive : linkhttps://lnkd.in/gxfhQ8cB 🔗GitHub account : https://lnkd.in/gcCiRDfS #Python #DataAnalysis #NumPy #LearningJourney #CentralTendency

Today I explored NumPy, one of the most powerful library of Python for numerical and scientific computing. Here’s what I practiced: Creating arrays with np.array() Using functions like zeros(), ones(), arange(), eye(), and linspace() Checking dimensions with .ndim Understanding array shapes using .shape I’m really enjoying how NumPy makes working with data so much easier and faster. #Python #NumPy #DataScience #LearningJourney #PythonForDataScience

📌 Day 8 of My #50DaysOfPython Challenge 🐍 🔹 Task: Fibonacci Series using Recursion Today’s challenge helped me dive deeper into recursion, one of the most powerful concepts in Python! I learned how a function can call itself to generate the Fibonacci sequence — a great exercise for understanding base cases and recursive logic flow. 🧠 Key Takeaways: How recursion works with base and recursive cases The difference between iteration and recursion Importance of function calls and return values 🧪 Example: Input: 7 Output: 0 1 1 2 3 5 8 This challenge helped me strengthen my problem-solving and logical thinking skills 🚀 #Python #CodingChallenge #LearningJourney #Fibonacci #Recursion #100DaysOfCode #PythonProgramming

All NumPy essentials in one place! In this checklist, you’ll find: - How to create and initialize arrays - How to perform elementwise computations - How to stack and split arrays - How to apply linear algebra functions - How to index, slice, and manipulate arrays and much more! If I missed something - let me know in the comments! ♻️ Repost if you found this useful! #NumPy #Python #DataEngineer #DataScience #DataAnalytics

🔢 Creating Arrays using NumPy I recently explored how to create and work with NumPy arrays, which are the foundation of numerical computing in Python. This task helped me understand how arrays make data handling faster and more efficient compared to traditional Python lists. Key concepts I practiced: 🔹 Creating 1D, 2D arrays 🔹 Using built-in NumPy functions 🔹 Performing element-wise operations and reshaping arrays This was a great learning experience that strengthened my fundamentals in scientific computing and data analysis. 📎 Check out the uploaded PDF to see my implementation and outputs! Guided by : Ashish Sawant sir 🔗GitHub Link : https://lnkd.in/ew4musav 📁Google drive : https://lnkd.in/eY5hyc43 #NumPy #Python #DataScience #Array #MachineLearning #DataAnalysis #LearningJourney

Welcome back, everyone 👋 Today we’re diving into one of the most powerful and frequently used data types in Python — Strings! 💡 Strings handle text, messages, inputs, and even hidden data behind the scenes. Let’s understand how they work, how to manipulate them, and some cool tricks you can use every day. Swipe through 👉 to master Python Strings step by step. #Python #Coding #LearnWithMe #Programming #DataScience #LinkedInLearning #PythonTips

In Python, strings are one of the most commonly used data types. They come with a variety of built-in methods that make text manipulation super easy and powerful. Here are a few commonly used String Methods 👇 String methods never change the original string; they return a new string. #Python #Coding #Learning #Programming #DataScience #LinkedInLearning #PythonBasics Yash Wadpalliwar Fireblaze AI School - Training and Placement Cell Harish K Namita Hedaoo