📉 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

🚀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

Day 15 of #30DaysOfCode with Educative 🟦 Challenge: Similar String Groups Approach: Union-Find (Disjoint Set) Insight: Utilizing union-find simplifies the task to identifying connected components in a similarity graph, enhancing group enumeration and eliminating repetitive verifications. Reflection: The application of union-find is common in graph and clustering scenarios, showcasing the power of fundamental data structures in delivering efficient solutions. #Educative #Python #SoftwareEngineering #ContinuousLearning #ProblemSolving

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

🔢 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

📌 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

Day 13: #100DaysOfCodingChallenge 🚀 Focusing today on in-place array transformation patterns in Python, specifically replacing each element with the greatest value among those to its right. 📌 Today's Progress: 1️⃣ Replace Elements with Greatest Element on Right Side (LeetCode 1299) 👉Problem: Modify the array so each element is replaced by the greatest element to its right (final element set to -1). 👉Approach: Scanned the array from right to left, tracking the current maximum 'max_right' and updating each value in-place. Achieves O(n) time and O(1) space with a clean backwards pass. Practising these in-place update patterns helps build critical skills needed for real-world coding and interview scenarios. Thanks as always to Nandan Kumar Mishra Sir and #KRMangalamUniversity for their continuous encouragement! #DSA #100DaysOfCodingChallenge #CodingChallenge #ProblemSolving #Python #Array #InPlace #LeetCode #LearningTogether #100DaysOfCode

 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

🚀 **Day 38 of my 50 Days Code Challenge!** Today, I explored **Nested Loops** in Python — loops inside loops! 🔁 Understanding how outer and inner loops work together helped me see how powerful and flexible iteration can be. 🧠 **Key learnings:** * Outer loops control the main iteration. * Inner loops repeat completely for each iteration of the outer loop. * Useful in tasks like working with matrices, patterns, and combinations. 💻 Example: Using nested `for` loops to print values from two different ranges. Each outer value runs the entire inner loop — a great way to understand structured repetition in Python. #100DaysOfCode #Python #CodingChallenge #LearningJourney #NestedLoops #CodeEveryday #WomenInTech