Python Performance: NumPy Arrays Outperform Lists

Working with messy real-world datasets taught me one thing: The cleaning step takes longer than the actual analysis. So I spent the last few weeks building dfdoctor - an open-source Python library that audits your DataFrame, tells you what’s wrong, and helps you fix it systematically instead of manually. It helps you quickly understand what’s broken and what to fix first. The part I'm most proud of: 5 correlation methods (including Kendall τ and Phi-k) implemented from scratch in pure numpy - no scipy dependency anywhere. 164 tests. CI passing across Python 3.9–3.12. Try it: pip install dfdoctor https://lnkd.in/e-ChV6mE #Python #OpenSource #DataEngineering #Pandas #EDA #DataScience

I always heard: “NumPy is faster than Python lists.” But today, I tested it myself 👇 Day 8 of my Data Science Journey 🚀: I added 1,000,000 elements using: 🔹 Python lists 🔹 NumPy arrays 📊 Result? NumPy was significantly faster. 💡 Why this happens: NumPy uses vectorized operations and runs on optimized C code, avoiding slow Python loops. 👉 This is why NumPy is the backbone of Data Science & Machine Learning. Small step today, but building real understanding. #DataScience #Python #NumPy #LearningInPublic #Day8

🚀 Day 16/30 – Python Challenge Exploring file handling in Python in more depth! 🐍📂 🔹 Key Concepts Covered: * File operations: read and write * File modes ("w" for writing, "r" for reading) * Managing file content programmatically * Importance of closing files after use 💻 Mini Task: Created a file, wrote custom content into it, and then read the same content back to display it in the console. 🎯 Learning Outcome: Gained a better understanding of how Python interacts with external files, which is essential for data storage, logging, and real-world applications. From basic coding to real-world concepts step by step 🚀 #Python #CodingChallenge #LearningJourney #FileHandling #BuildInPublic #StudentDeveloper #Day16

Day 2/100: Logic & Math in Python! 🐍💸 Day 2 of my #100DaysOfCode challenge is in the books! Today’s focus was on handling user input, data types, and mathematical operations. I built a Tip Calculator project that handles bill splitting and percentage calculations—a simple but essential exercise in ensuring data accuracy and clean logic. What I practiced today: ->Type conversion (String to Float/Int) ->F-strings for clean output ->Floating-point precision 🔗 GitHub: https://lnkd.in/gWWzMYdn Small wins every day lead to big victories! Onward to Day 3. ⚔️ #Python #100DaysOfCode #LogicBuilding #CodingChallenge #GrowthMindset #DevCommunity

#Day2 of my #30dayscodingchallenge What I learned today: • Variables and data types in Python • Taking user input • Basic arithmetic operations Mini Project: I built a simple calculator using Python that can perform addition, subtraction, multiplication, and division. Key takeaway: Every big skill starts with small basics. Understanding fundamentals clearly is the real game changer. I am committed to showing up every single day and improving step by step. If you are also learning or planning to start, let’s connect and grow together #30daysofcode #pythondeveloper #codingjourney #learnpublic #fullstackdeveloper

Week 4 Digital Skola! To put it simply, with Python, I Learned about: • Data structures (list, dictionary, tuple): organizing data • Conditionals & loops: controlling program flow • Functions: writing reusable and structured code • Lambda, modules, packages: simpler & organized code • NumPy: library with arrays more efficiently and more operations for manipulating data #DigitalSkola #LearningProgressReview #DataScience #ProfessionalBranding

🚀 Day 3 — Python Journey Today’s focus was on float operations in Python (working with decimal numbers). 📌 What I learned: Float declaration Addition, subtraction, multiplication, division Rounding values using round() Scientific notation Precision handling in floats 💡 What I found interesting: Float values are not always 100% accurate due to precision limitations. Even simple calculations can sometimes give unexpected results. Understanding this early is important, especially for real-world applications like finance or data science. Step by step, trying to build a strong foundation. #Day3 #Python #CodingJourney #LearnInPublic #Consistency

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

🐍 Exploring NumPy Basics in Python Today I practiced core NumPy operations to understand how numerical computing works in Python. ✔ Converted Python lists into NumPy arrays ✔ Created arrays using np.array() ✔ Generated sequences with np.arange() and np.linspace() ✔ Built matrices using np.zeros(), np.ones(), and np.eye() ✔ Worked with random numbers using np.random.rand() and np.random.randint() ✔ Performed basic array operations like max(), min(), and reshape() 💡 Key takeaway: NumPy is powerful for handling large datasets and is the foundation for Data Science and Machine Learning in Python. 📌 Full code available here: 👉https://lnkd.in/dCMhYQey Next step: I will explore array indexing, slicing, and basic statistical operations. #Python #NumPy #DataScience #MachineLearning #100DaysOfCode #LearningJourney

🔁 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