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🔢 NumPy

Math Functions

NumPy provides optimized mathematical functions (ufuncs) for trigonometry, logarithms, rounding, and statistics. These operate element-wise on arrays and are orders of magnitude faster than Python loops.

● Intermediate 📖 Based on: numpy-user manual

📋 Table of Contents

  1. Universal Functions (ufuncs)
  2. Statistical Functions
  3. Rounding & Comparison

1 · Universal Functions

CategoryFunctions
Trigonometricnp.sin, cos, tan, arcsin, arccos, arctan
Exponentialnp.exp, exp2, log, log2, log10
Powernp.sqrt, square, power, cbrt
Arithmeticnp.add, subtract, multiply, divide, mod
Absolutenp.abs, fabs, sign
Python
import numpy as np
x = np.array([0, np.pi/4, np.pi/2, np.pi])
print(np.sin(x))      # [0, 0.707, 1, 0]
print(np.exp([0,1,2]))  # [1, 2.718, 7.389]
print(np.log([1, np.e, np.e**2]))  # [0, 1, 2]
▶ Output
[0. 0.707 1. 0.]
[1. 2.718 7.389]
[0. 1. 2.]

2 · Statistical Functions

Python
data = np.array([14,18,11,13,16,15,12,17])
print(f"Mean:   {np.mean(data):.2f}")      # 14.50
print(f"Median: {np.median(data):.2f}")    # 14.50
print(f"Std:    {np.std(data):.2f}")       # 2.29
print(f"Var:    {np.var(data):.2f}")       # 5.25
print(f"25th %: {np.percentile(data, 25)}") # 12.25
print(f"Corr:   {np.corrcoef([1,2,3],[2,4,6])}")
▶ Output
Mean: 14.50
Median: 14.50
Std: 2.29
Var: 5.25

3 · Rounding & Comparison

Python
x = np.array([1.23, 2.78, 3.45])
print(np.round(x, 1))   # [1.2, 2.8, 3.5]
print(np.floor(x))     # [1., 2., 3.]
print(np.ceil(x))      # [2., 3., 4.]
print(np.clip(x, 2, 3))  # [2., 2.78, 3.] (clamp)

# Float comparison (avoid == for floats)
a = np.array([0.1+0.2])
print(np.isclose(a, 0.3))  # [True]
▶ Output
[1.2 2.8 3.5] · [1. 2. 3.] · [2. 3. 4.] · [True]
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