I added 1 million numbers. Python list: 0.21 seconds. NumPy array: 0.001 seconds. Same result. 200× faster. NumPy replaces Python lists when you’re working with numerical data. Instead of this: [x * 2 for x in my_list] ← loop needed You write this: arr * 2 ← no loop, instant That’s called vectorisation — NumPy operates on the entire array at once. This is why Pandas, scikit-learn, and TensorFlow are all built on NumPy underneath. When you’re working with millions of rows, this speed difference decides whether your model trains in 1 second or 3 minutes. Day 12 · Libraries series starts now. Did you know NumPy was this much faster? #NumPy #Python #DataScience #LearnPython #MachineLearning PythonLibraries aartii_py DataScienceIndia