A tour of the standard library

Batteries included

The modules that come with every Python are the standard librarystandard libraryThe modules that come with every Python install, ready to import: math, random, json, csv, datetime, collections, and many more.. You've used some already: random (lesson 3.5), json and csv (this module). Each is one import away, with nothing to install.

math has the functions a calculator has. Three friends split a 1,000 birr bill. Each share is 333.33..., and nobody pays in fractions of a santim, so round each share up to whole birr with math.ceil (ceiling), or down with math.floor:

import math

share = 1000 / 3
print(share)
print(math.ceil(share))
print(math.floor(share))
print(math.sqrt(144))

Rounding up means the bill is covered: 3 × 334 is 1,002. Change the 3 to 4 and run it again: 250 divides evenly, so ceil and floor agree.

random picks for you. random.seed() (lesson 3.5) makes the picks the same on every run, so you can check them; delete it for real randomness:

import random

random.seed(2026)
print(random.choice(["Bole", "Piassa", "Kazanchis", "Merkato"]))
names = ["Hana", "Abel", "Selam", "Dawit"]
random.shuffle(names)
print(names)

choice picks one item; shuffle reorders the list itself and returns None, like sort() in lesson 4.2.

Describing numbers

statistics describes a list of numbers. The mean is the average; the median is the middle value once they're sorted. Four exam results, an even count. Predict the median:

import statistics

print(statistics.median([72, 85, 90, 64]))

78.5

If you picked 87.5, you took the two middle values as written, 85 and 90; the median sorts first. If you picked 77.75, that's the mean, the average. With an even count, the median is halfway between the two middle values, 72 and 85:

import statistics

results = [72, 85, 90, 64]
print(statistics.mean(results))
print(statistics.median(results))
print(max(results))

max(), from lesson 4.1, is the best-so-far plan from lesson 3.3 in one call. The median matters when one value is far from the rest: a single 1,000 birr payment drags the mean up but barely moves the median. You'll see that in the exercise.

Counting and grouping

The tally plan from lesson 4.4 counts with a dict. collections.Counter is that plan in one line. Three coffee rounds, abol, tona, and bereka, and Selam's record of which ones she drank:

from collections import Counter

rounds = ["abol", "tona", "abol", "bereka"]
counts = {}
for name in rounds:
    counts[name] = counts.get(name, 0) + 1
print(counts)

print(Counter(rounds))
print(Counter(rounds).most_common(1))

The loop and Counter count the same way. most_common(1) gives the top one as a list of (item, count) tuples.

defaultdict(list) groups: every new key starts with an empty list, so you can append without checking first, the collect plan (lesson 4.6) once per key:

from collections import defaultdict

expenses = [("transport", 120), ("food", 250), ("transport", 60)]
groups = defaultdict(list)
for category, amount in expenses:
    groups[category].append(amount)
print(dict(groups))

Dates

datetime knows the calendar: month lengths, leap years, weekdays. Subtracting one date from another gives a timedelta, a length of time, whose .days is a number:

from datetime import date

start = date(2026, 10, 1)
exam = date(2027, 6, 15)
print((exam - start).days)
print(exam.strftime("%A %d %B %Y"))

strftime formats a date with codes: %A the weekday, %d the day, %B the month, %Y the year. date.today() gives today, so its output is different for you than for anyone else, and different tomorrow. That's why lessons use fixed dates like these:

from datetime import date

print(date.today())

The module and the thing you want from it can share a name, which trips up everyone once:

datetime uses the Gregorian calendar; converting to the Ethiopian calendar needs more than this module gives you.

There's much more: the standard library index lists every module. Before writing a common job yourself, check whether it's there.