Vectorized Universal Functions (ufuncs)

Element-wise arithmetic, trigonometry, exponentials, and logarithms

« NumPy Broadcasting Rules & Dimensional Expansion« Previous Aggregations & Axis Operations: sum, mean, std, argmax »Next »

Universal functions (ufuncs) are compiled C functions that execute element-wise operations on ndarrays with zero Python overhead.

Fast Mathematical Transformations

Math ufuncs: np.add, np.subtract, np.multiply, np.divide, np.power, np.sqrt.

Exponential and Logarithmic: np.exp, np.log, np.log2, np.log10.

Trigonometric: np.sin, np.cos, np.tan, np.arctan2.

Comparison: np.greater, np.less, np.equal, np.isclose (safe floating point equality).

Vectorized Sigmoid and Math Pipeline

Try It Yourself »

import numpy as np

# Vectorized raw logit scores
logits = np.array([-2.5, 0.0, 1.2, 4.5])

# Vectorized sigmoid function: 1 / (1 + exp(-x))
probabilities = 1.0 / (1.0 + np.exp(-logits))

print("Raw logits:    ", logits)
print("Probabilities: ", np.round(probabilities, 4))

np.exp executes across all 4 elements simultaneously in compiled C.

Google AdSense Slot (inContentBanner)

Set your Publisher ID in src/config/ads.ts to display live ads.

Test Yourself With Exercise

+15 XP

Which NumPy function should you use to test if two floating-point arrays are equal within a numerical tolerance?

« Previous: NumPy Broadcasting Rules & Dimensional Expansion Next: Aggregations & Axis Operations: sum, mean, std, argmax »