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which is faster ? -> Python vs NumPy Performance Comparison I recently tested the execution speed of a simple operation (squaring numbers) using a Python list vs a NumPy array on 1,000,000 elements. 🔹 Results: Python List Time: ~0.098 seconds NumPy Array Time: ~0.019 seconds ⚡ Conclusion: NumPy is significantly faster than traditional Python lists for numerical computations due to its optimized, vectorized operations. 📌 Key Takeaway: If you're working with large datasets or performing mathematical operations, always prefer NumPy for better performance and efficiency. #Python #NumPy #DataScience #Performance #Coding #Learning

Headline: Making Health Data Actionable with Python! 🐍 I recently worked on a calorie requirement calculator that uses List Comprehension to streamline data processing. By combining conditional logic with Python’s concise syntax, I was able to map out nutritional needs across different age brackets and genders effectively. Key takeaways from this build: Using range() and list comprehension for efficient iterations. Implementing multi-level if-elif statements for precise data output. Practicing clean, readable code in Jupyter Notebook. Always looking for ways to make complex data simpler through code! Muhammad Rafay Shaikh #Python #DataScience #Coding #ListComprehension #JupyterNotebook #WebDevelopment

🚀 Python Practice – Function Examples Taking my Python learning a step further by practicing real-world function-based problems 🐍 In this session, I worked on: ✔️ Temperature Conversion (Celsius ↔ Fahrenheit) ✔️ Password Strength Checker ✔️ Shopping Cart Total Cost Calculator ✔️ Palindrome Checker ✔️ Factorial using Recursion These examples helped me understand how functions can be used to solve practical problems and write reusable, structured code. A big thanks to Krish Naik Sir for his amazing teaching and clear explanations 🙌 Documented all my practice in a Jupyter Notebook and shared it as a PDF to track my progress. Learning by building real logic step by step 📊 #Python #Functions #Practice #LearningJourney #DataAnalytics #Coding

In this tutorial, we will show how to use Python in Excel and when and how it’s useful. Instead of switching to other tools like Jupyter or VS Code, you can use Python directly in Excel. https://lnkd.in/e_QdRFzk

🚀 Learning Something New Every Day! Today I learned an important Python concept — *args and **kwargs. 🔹 *args allows a function to take multiple positional arguments (stored as a tuple) 🔹 **kwargs allows a function to take multiple keyword arguments (stored as a dictionary) 💡 This makes functions more flexible and reusable in real-world scenarios. Here’s a simple example: def demo(*args, **kwargs): print(args) print(kwargs) Step by step, I’m strengthening my Python fundamentals and building a strong base for data analytics. #Python #LearningJourney #Coding #DataAnalytics

🌟 New Blog Just Published! 🌟 📌 Docker Made Easy for Python Data Projects: A Beginner’s Step-by-Step Guide 🚀 📖 Ever heard the phrase “It works on my machine” and felt your blood pressure spike? One missing library, a mismatched Python version, or a hidden system package can turn a tidy notebook into a...... 🔗 Read more: https://lnkd.in/dYRvhQPi 🚀✨ #dockerpython #datascience #beginnerguide

🔁 Python Fundamentals Revision Continuing my Python revision to strengthen core concepts 🐍 In this session, I focused on: ✔️ Syntax & Semantics ✔️ Variables ✔️ Data Types ✔️ Operators Revisiting these fundamentals is helping me improve code clarity and avoid common mistakes. I’ve documented my practice in a Jupyter Notebook and shared it as a PDF to keep track of my learning journey. Strong basics = Better problem solving 💡 Next step: diving deeper into control flow and real-world problem solving 🚀 #Python #Revision #Programming #DataAnalytics #LearningJourney #Coding

Machine Learning Time Series Data using tsaug #machinelearning #datascience #timeseriesdata #tsaug tsaug is a Python package for time series augmentation. It offers a set of augmentation methods for time series, as well as a simple API to connect multiple augmenters into a pipeline. https://lnkd.in/gURVDkPv

Newsflash: Python is the new Excel. Don't be the only one stuck with 1,048,576 rows. So to avoid this fate, here's a 7-day crash course to help you finally quit the green icon: https://lnkd.in/d7neSJXZ

🐍 Your All-in-One Python Syntax Cheat Sheet When I started learning Python, I kept Googling the same small syntax again and again That’s when I realized how powerful a well-structured cheat sheet can be. This Python Syntax Cheat Sheet covers everything you need to build a strong foundation: ☑️ Basic Syntax — print, variables, type casting ☑️ Data Structures — lists, tuples, sets, dictionaries ☑️ Control Flow — if-else, loops, break & continue ☑️ Functions & Lambdas 📌 Whether you're a beginner or a data professional, this is a must-have reference. Follow Rohit Kushwaha for more valuable content #Python #DataScience #DataEngineering #CheatSheet #Pandas