Lecture VI - Modules and the Standard Library

Programming with Python

Dr. Tobias Vlćek

Kühne Logistics University Hamburg - Fall 2026

📋 Checkpoint 3 — Sessions I–V

The first 40 minutes are the checkpoint. It starts now, before the investor sits down.

Individual work: no AI, no neighbors, no chat
The link and QR are handed out in class. Open it and start
~6 short tasks: write code, trace code, fix a bug, answer a multiple choice
It sweeps everything from Sessions I–V: variables, control flow, functions, data structures, errors

When you’re done: menu → Download → Download Python code → upload the .py to the “Checkpoint 3” assignment on Moodle. No retakes: one sitting.

The green ✅ live checks are provisional. The final grading runs on our side. And take a breath: everything in it was rehearsed in the labs.

Episode 6: Due Diligence Week

The investor walks in

Pens down. The checkpoint is behind you. Now the reason today matters.

An hour ago the investor walked into the shop unannounced. It’s due-diligence week: before she signs anything, she wants to see how this place actually runs. Kevin offered her a coffee and a spreadsheet he “mostly trusts.”

She didn’t drink the coffee. She walked to the whiteboard, uncapped a marker, and wrote one question:

“Why is everything built from scratch?”

Part II: the rules change

For five sessions you built everything by hand, on purpose. From today, that changes.

AI is now allowed and taught. We work with it, deliberately, starting in tonight’s lab.
The disclosure habit: every submission that used AI carries a one-line note saying what you used it for. Not a confession. A professional reflex.
The course chatbot (sidebar widget) now gives you full code on request, not just hints.

Full details on the AI Tools page.

You spent five sessions learning to think without a co-pilot. Now you get one — and you’ll be the pilot.

Don’t build it, import it

What’s a module?

The investor’s question has an answer: you shouldn’t build it from scratch. Most of what you need is already written.

A module is a toolbox of code someone already wrote and tested
Python ships with a whole shelf of them: the standard library
You import a module, then reach for the tools inside it with a dot: math.ceil(...)

Kevin has been hand-rolling arithmetic for months. The standard library did most of it before he was born.

`import math`: stop rounding by hand

130 pastries need to ship. They go in crates of 48. How many crates? You need to round up: a half-full crate still ships as a whole one.

import math

pastries = 130
per_crate = 48
crates = math.ceil(pastries / per_crate)   # ceil = round UP
print(crates)

130 / 48 is 2.7…; math.ceil bumps it to 3. No fiddling with “if there’s a remainder, add one.” The tool already knows.

`from statistics import ...`

Sometimes you only want a couple of tools, not the whole box. Import them by name and use them directly, no statistics. prefix:

from statistics import mean, median

ratings = [4.5, 4.8, 1.0, 5.0, 4.2]
print(mean(ratings))     # the average
print(median(ratings))   # the middle value

3.9
4.5

One furious review (a 1.0) drags the mean down to 3.9. The investor asked for the typical rating: median sorts the values and hands back the middle one (4.5), unmoved by one angry customer.

Aliases: a shorter name

Some module names are long, or you’ll type them fifty times. import ... as gives a module a nickname for the rest of the file:

import statistics as stats

print(stats.median([4.5, 4.8, 1.0, 5.0, 4.2]))

4.5

stats.median is the same tool as statistics.median, just less to type. You’ll meet fixed conventions soon (import pandas as pd); using the community’s nickname makes your code instantly readable to everyone else.

Looking inside a module

You don’t have to memorize a module. Python will tell you what’s in it and what each tool does:

import math

dir(math)          # lists every name in the module
help(math.ceil)    # prints what ceil does, and how to call it

dir() is the drawer of tools; help() is the little instruction card taped to each one. Between them you can explore any module without leaving your editor.

Predict: which way does floor go?

math.ceil rounds up. Its partner math.floor rounds down, but down from a negative number is the tricky part. What does the last line print?

import math
print(math.floor(-2.5))

a) -3 b) -2 c) an error

Predict first. Commit to an answer before the next slide.

Answer: floor goes down, not toward zero

a) -3 — floor always heads down the number line, toward more negative. From -2.5, down is -3, not the -2 you’d get by rounding toward zero:

import math
print(math.floor(-2.5))   # down the number line → -3

-3

“Down” means smaller, and -3 is smaller than -2. Keep the number line in your head, not the distance to zero.

⚡ Your turn — 10 minutes

Open the exercise (scan the QR or type the link):

beyondsimulations.github.io/Introduction-to-Python/notebooks/ex_06_a/

First predict what happens, then run it.

Rehearsing luck

The `random` toolbox

The investor wants to see how the shop copes with a busy day, but the busy day hasn’t happened yet. So we rehearse it with made-up numbers. The random module deals them:

import random

print(random.random())                       # a float in [0.0, 1.0)
print(random.randint(1, 20))                  # an integer 1–20, ends included
print(random.choice(["latte", "mocha", "tea"]))  # one item, picked at random

queue = [1, 2, 3, 4, 5]
random.shuffle(queue)                         # reorders the list in place
print(queue)

0.9252263675891648
20
latte
[4, 2, 3, 5, 1]

Four tools, four flavors of luck: a raw float, a bounded integer, a pick from a list, and a reshuffle.

“Run it again”

The investor leans over and says: “Run it again.” Kevin does, and gets completely different numbers:

import random

print([random.randint(1, 20) for _ in range(5)])   # one run
print([random.randint(1, 20) for _ in range(5)])   # ...and again — different!

[4, 9, 13, 8, 17]
[5, 11, 15, 17, 7]

Two runs, two answers. That’s exactly what random is supposed to do, but it’s useless for due diligence. A projection nobody can reproduce is a projection nobody can trust.

`random.seed` makes luck repeatable

random.seed(n) fixes the starting point of the number stream. Same seed → same sequence, every time:

import random

random.seed(7)
print([random.randint(1, 20) for _ in range(5)])   # → [11, 5, 13, 2, 3]

random.seed(7)
print([random.randint(1, 20) for _ in range(5)])   # same seed → same list

[11, 5, 13, 2, 3]
[11, 5, 13, 2, 3]

Both lines print [11, 5, 13, 2, 3]. The numbers still look random, but now the investor can run it herself and land on the identical result.

Predict: seeded once, built twice

Kevin seeds once, then builds two lists the same way, without touching the seed in between. Are first and second equal?

import random

random.seed(42)
first  = [random.randint(1, 20) for _ in range(3)]
second = [random.randint(1, 20) for _ in range(3)]

print(first)
print(second)

a) different: the second list continues where the first stopped b) equal: the seed is set, so both come out the same c) an error: the stream is empty after three draws

Predict first. Commit to an answer before the next slide.

Answer: different

a) different — a seed doesn’t freeze random, it fixes the whole sequence. The first list eats the first three numbers of the stream; the second list simply continues from number four:

import random

random.seed(42)
first  = [random.randint(1, 20) for _ in range(3)]
second = [random.randint(1, 20) for _ in range(3)]

print(first)    # [4, 1, 9]  — the stream's first three numbers
print(second)   # [8, 8, 5]  — the stream carries on

[4, 1, 9]
[8, 8, 5]

To get the same list twice, you re-seed before each run, and that rewinds the stream to the start. One seed, one fixed sequence: that’s the entire job of a seed.

⚡ Your turn — 10 minutes

Open the exercise (scan the QR or type the link):

beyondsimulations.github.io/Introduction-to-Python/notebooks/ex_06_b/

First predict what happens, then run it.

To the Lab

Tonight’s episode

Head to the lab notebook: Episode 6 — Due Diligence Week
You’ll import math and statistics for investor-grade counts and averages, then use random (with and without a seed) to rehearse a busy day she can reproduce
It’s the first lab where AI is allowed, so try the chatbot, and add your one-line disclosure note
It runs entirely in your browser: no setup, just click and code

Download your .py before you leave. Closing the tab without downloading loses your work, and downloading is exactly how you handed in the checkpoint this morning.

Wrap-up

Three things to remember

Don’t build it, import it. A module is a toolbox someone already wrote: import math, from statistics import median, import ... as for a nickname. dir() and help() show you what’s inside.
random deals the luck (random(), randint, choice, shuffle), perfect for rehearsing a day that hasn’t happened yet.
random.seed(n) makes luck repeatable. Same seed → same sequence, every run. A projection you can reproduce is a projection an investor can trust.

Next episode: one array to rule a thousand orders. The shop’s data has outgrown plain lists, and NumPy turns a thousand numbers into a single, fast object.

Literature

Books to start with

Downey, A. B. (2024). Think Python: How to think like a computer scientist (Third edition). O’Reilly. Link to free online version
Elter, S. (2021). Schrödinger programmiert Python: Das etwas andere Fachbuch (1. Auflage). Rheinwerk Verlag.

New this session: the AI Tools page: how and when to use AI in Part II, and the one-line disclosure habit that goes on every submission from here on.

For more, see the literature list of this course.

Lecture VI - Modules and the Standard Library

📋 Checkpoint 3 — Sessions I–V

Episode 6: Due Diligence Week

The investor walks in

Part II: the rules change

Don’t build it, import it

What’s a module?

import math: stop rounding by hand

from statistics import ...

Aliases: a shorter name

Looking inside a module

Predict: which way does floor go?

Answer: floor goes down, not toward zero

⚡ Your turn — 10 minutes

Rehearsing luck

The random toolbox

“Run it again”

random.seed makes luck repeatable

Predict: seeded once, built twice

Answer: different

⚡ Your turn — 10 minutes

To the Lab

Tonight’s episode

Wrap-up

Three things to remember

Literature

Books to start with

`import math`: stop rounding by hand

`from statistics import ...`

The `random` toolbox

`random.seed` makes luck repeatable