Numerical Data — Discrete vs Continuous

Think of discrete data like stairs — you step from one whole number to the next. Continuous data is like a ramp — you can stop at any point, including 3.7 or 3.14159.

Discrete data: countable, whole numbers

# Number of cars passing by — you cannot have 3.5 cars
cars_per_hour = [12, 15, 8, 22, 17, 9]
print(sum(cars_per_hour))
# Output: 83
# These are always integers

Continuous data: measurable, any value

# Temperature can be ANY value on the scale
temperatures = [36.6, 37.2, 36.8, 38.1, 36.5]
print(round(sum(temperatures) / len(temperatures), 2))
# Output: 37.04
# Price can have decimals
prices = [9.99, 24.50, 3.75, 149.99]
print(min(prices))
# Output: 3.75

Quick reference:

Pro Tip: This distinction matters in ML because different algorithms handle discrete and continuous data differently. Classification algorithms predict discrete labels (spam/not spam). Regression algorithms predict continuous values (price, temperature).