Data & Math › 8 of 14
NumPy Essentials
Vectorized arrays — the foundation of ML in Python.
python
import numpy as np
X = np.random.randn(100, 3)
w = np.array([0.5, -1.0, 2.0])
y = X @ w # matrix multiply
X_norm = (X - X.mean(axis=0)) / X.std(axis=0)
mask = y > 0
print(X.shape, y[mask].shape)# output
(100, 3) (52,)- Broadcasting aligns shapes from the right
- axis=0 → down columns, axis=1 → across rows
- Avoid Python loops; vectorize
⚡ Quick check
- ✓ @ for matmul
- ✓ arr.reshape(-1, 1)
- ✓ Boolean masks: a[a > 0]