Complete Python NumPy Tutorial in Hindi (With Notes)

Currently Playing: NumPy Vectorization: Stop Slow Python Loops (1.2M Rows Tutorial) | Sharad Khare

Your Python for loop works — but on 1.2 million sales rows it is painfully slow. In this tutorial you will learn NumPy vectorization: how to replace row-by-row loops with fast array operations, time both approaches, and see a real 150× speedup on the same data. What you will learn Why Python loops are slow on large data NumPy arrays vs Python lists (memory layout) Vectorization: qty * unit_price on the whole file Timing with time.perf_counter() — loop vs NumPy Universal functions, broadcasting, boolean masking Common traps: np.vectorize, iterrows, tolist() + loop Full VS Code project: notebook + revenue_race.py Chapters 0:00 Intro 0:26 The problem 0:59 Why vectorization 1:17 NumPy docs 2:02 Vectorization & broadcasting 2:35 Open project in VS Code 3:04 Load data 3:32 Memory layout 3:59 Python loop timing 4:19 NumPy timing 4:49 Key lines recap 5:41 Terminal script 5:54 Universal functions 6:28 Broadcasting 7:01 Boolean masking 7:39 More examples 8:24 VS Code speed test 8:50 Traps 9:30 When loops are OK 9:54 Memory recap 10:19 Views vs copies 10:39 Summary Links Website: https://www.sharadkhare.in Free notes: https://www.sharadkhare.in/notes NumPy docs: https://numpy.org/doc/stable/ #Python #NumPy #DataScience #Programming #Vectorization