math and statistics — Single-Value Math and Data Aggregation

This article focuses on Python's numeric modules for the everyday case where regular `float` is good enough. math handles operations on a single value (square roots, logarithms, trig functions); statistics handles aggregating a sequence of numbers (mean, median, standard deviation). When you need to avoid floating-point error strictly, the next article on decimal / fractions is the right place.

math — Operations on a Single Number

math is the standard library for operations on a single number. math.sqrt for square roots, math.floor / math.ceil for rounding down / up, math.log for logarithms, math.sin / math.cos for trig — the textbook math functions are all there. It also exposes constants as attributes like math.pi and math.e (pi and the base of the natural log).

import math

print(math.sqrt(16))         # 4.0  (float)
print(math.floor(3.7))       # 3    (int)
print(math.ceil(3.2))        # 4    (int)
print(math.log(100, 10))     # 2.0
print(math.pi)               # 3.141592653589793
print(round(math.pi, 4))     # 3.1416  (rounded to 4 decimal places)

Try the most common math functions and constants.

① Import math.

② Compute the square root of 16 with math.sqrt and print it as sqrt(16): ◯.◯.

③ Compute the floor of 3.7 and the ceil of 3.2 with math.floor / math.ceil, and print them as floor: ◯ ceil: ◯.

④ Round math.pi to 4 decimal places and print it as pi (4 dp): ◯.◯◯◯◯.

(If your code runs correctly, the explanation will appear.)

statistics — Aggregating and Summarizing Data

statistics is the standard library for aggregating a group of values. statistics.mean returns the arithmetic mean, statistics.median the median, statistics.mode the most frequent value, and statistics.stdev the sample standard deviation — all in a single call. You can also see classic statistical observations in action, like median being a more stable summary than the mean when outliers are present, with just a few lines of code.

import statistics

prices = [1200, 1500, 1100, 1400]

print(statistics.mean(prices))    # 1300       (mean)
print(statistics.median(prices))  # 1300.0     (median)
print(statistics.stdev(prices))   # ~182.57    (standard deviation)

# When there's an outlier, mean and median diverge
with_outlier = [1200, 1500, 1100, 1400, 100000]
print(statistics.mean(with_outlier))    # 21040  (pulled by the outlier)
print(statistics.median(with_outlier))  # 1400   (resists the outlier)

Compute the mean, median, and standard deviation of four product prices.

① Import statistics and put the four prices [1200, 1500, 1100, 1400] in a list called prices.

② Compute the mean with statistics.mean, round it to an integer, and print it as mean: ◯.

③ Compute the median with statistics.median, round it to an integer, and print it as median: ◯.

④ Compute the standard deviation with statistics.stdev, round it to 1 decimal place, and print it as stdev: ◯.◯.