🔢 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

📉 Experiment 5 – Creation of Arrays using NumPy In this practical, I learned how to create and manipulate arrays using Python’s NumPy library. Created 1D, 2D, and matrix arrays to understand how NumPy helps in handling numerical data efficiently. This experiment gave me a clear idea of how arrays form the foundation for data analysis and scientific computing in Python. 📁 GitHub:https://lnkd.in/eTtC53qu 🎓 Guided by: Ashish Sawant #Python #NumPy #Array #DataScience #MachineLearning #Coding #Learning #JupyterNotebook #CSE#PRMCEAM

🔢 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

🔢 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

 Creation of Arrays – Data Science / Python / NumPy In this practical, I learned how to create different types of arrays using Python and NumPy. I explored one-dimensional, two-dimensional, and multi-dimensional arrays, along with functions like array(), zeros(), ones(), arange(), and linspace(). Array creation is an essential step in data handling and numerical computation, as arrays allow fast mathematical operations and efficient data storage. ✅ #ArrayCreation #Python #NumPy #DataScience #Practical #ProgrammingBasics #MachineLearning #LearningJourney https://lnkd.in/dZGwNpEy

I’ve been trying out different Python libraries for number crunching, and the right choice can make a big difference. NumPy – solid for general calculations on the CPU. JAX – works on GPU and can handle automatic calculations for machine learning. CuPy – best for heavy calculations that need GPU speed. They all look similar, but each works best in different situations. Which one do you use most? #Python #DataScience #MachineLearning #NumericalComputing

Diving deep into Python and unlocking its true power! 💡 Just wrapped up learning about loops, functions, and NumPy in Python with Digital Skola. Such a fundamental and powerful set of tools! - Loops: Harnessing for and while loops to automate repetitive tasks, from processing data to cleaning datasets. - Functions: Writing reusable functions for better code modularity and readability. - NumPy: Exploring the power of NumPy for lightning-fast numerical computation. Handling large arrays for data analysis and machine learning just became a whole lot easier and more efficient. This is a powerful combination for anyone looking to level up their Python skills. Wanna know more about these topics, let's check this slide below 👇 #DigitalSkola #LearningProgressReview #DataScience

🚀 Exploring Data Structures in Python! Recently, I implemented a Stack using Python functions — one of the most fundamental data structures in Computer Science. This program performs all major stack operations: ✅ Push – Add an element to the stack ✅ Pop – Remove the top element ✅ Peek – View the top element without removing it ✅ Display – Show all elements in the stack ✅ Check Empty – Verify if the stack is empty 💡 The project helped me understand how stacks work internally and how easily they can be implemented using Python lists and basic functions. #Python #DataStructures #Coding #Learning #Stack #Programming #PythonProjects #TechLearning #ComputerScience