Constants and Elementary Functions in math

The math module ships with Python's standard library and exposes fundamental mathematical constants alongside a broad set of floating-point operations. Access constants like π and e directly:

from math import pi, e

circle_radius = 5
area = pi * (circle_radius ** 2)
print(f"Area: {area}")          # Uses pi constant
print(f"Euler's number: {e}")   # Direct constant access

Trigonometric calculations operate on radians. Pair them with math.radians() when working in degrees:

from math import sin, cos, radians

angle_deg = 60
sine_val = sin(radians(angle_deg))
cosine_val = cos(radians(angle_deg))
print(sine_val, cosine_val)

Power and lgoarithmic functions cover common scientific needs:

from math import sqrt, log, log10

base_value = 49
root = sqrt(base_value)          # 7.0
natural_log = log(20)            # ln(20)
common_log = log10(20)           # log10(20)
print(root, natural_log, common_log)

Rounding and Absolute Values

Precision control is handled by dedicated rounding and truncation functions:

from math import floor, ceil, fabs

raw = -7.45
absolute = fabs(raw)             # 7.45
rounded_down = floor(absolute)   # 7
rounded_up = ceil(absolute)      # 8

Note that standard round() is a built-in, not part of math:

from math import floor

value = 3.7
nearest_int = round(value)       # built-in: 4
floored = floor(value)           # 3

Random Selection with random

The random module (distinct from math) provides stochastic utilities. Generate uniform floats and select random elements from sequences:

import random

rnd_float = random.random()      # value in [0.0, 1.0)
choices = ['alpha', 'beta', 'gamma', 'delta']
picked = random.choice(choices)
print(rnd_float, picked)

For reproducibility, seed the generator:

random.seed(42)
print(random.random())

Introducing NumPy for Array-Oriented Computation

NumPy extends Python's numerical capabilities to multidimensional arrays. After installing it (pip install numpy), import the library conventionally as np:

import numpy as np

vector = np.array([2, 4, 6, 8])
mean_val = np.mean(vector)       # 5.0
variance = np.var(vector)        # population variance
print(mean_val, variance)

Matrix operations become straightforward:

matrix_a = np.array([[1, 2], [3, 4]])
matrix_b = np.array([[5, 6], [7, 8]])
product = np.dot(matrix_a, matrix_b)
print(product)

NumPy also includes an extensive set of statistical and linear algebra routines, making it well suited for large-scale numerical computation, data analysis, and scientific workflows. By using math for scalar operations and numpy for vectorized tasks, you can handle diverse mathematical workloads efficiently.