python 17 lines · 1 tab

NumPy broadcasting for vectorized feature engineering

Dr. Elena Vasquez Apr 2026

1 tab

import numpy as np

features = np.array([
    [120.0, 3.0, 10.0],
    [90.0, 5.0, 7.0],
    [150.0, 2.0, 14.0],
])

mean = features.mean(axis=0)
std = features.std(axis=0)
standardized = (features - mean) / std

weights = np.array([0.5, 1.5, 2.0])
weighted_score = standardized * weights
final_score = weighted_score.sum(axis=1)

print(final_score)

1 file · python Explain with highlit

Good NumPy code replaces Python loops with array semantics that are easier to optimize and easier to benchmark. Broadcasting is the feature that makes those transformations elegant. I rely on it for normalization, distance calculations, and matrix-friendly preprocessing when raw speed matters.

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