import math
pastries = 130
per_crate = 48
crates = math.ceil(pastries / per_crate) # ceil = round UP
print(crates)3Lecture VI - Modules and the Standard Library
Programming with Python
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. Tobi 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 today’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, where it used to stop at hints.
. . .
You spent five sessions learning to think without a co-pilot. Now you get one, and you’ll be the pilot.
Two accounts this week
Both are free, both are on the AI Tools page, together about ten minutes:
- Mistral: a Le Chat account for questions, plus an API key. The same key later powers the AI agent inside your editor
- Zed student plan: sign in with the GitHub account you made in Session I (it’s old enough now) and apply with your KLU e-mail. Verification takes up to 72 hours, so the free year of Zed Pro is ready long before Session X
. . .
Turn off the training-data switches in Mistral’s privacy settings before you paste coursework: one for Le Chat, one for the API (details on the AI Tools page). Nothing here needs a credit card.
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
importa module, then reach for the tools inside it with a dot:math.ceil(...)
. . .
Tobi 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.
. . .
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 value3.9
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. Pick a letter, then I reveal the answer.
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):
python.tobiasvlcek.com/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)0.45347989140954614
6
latte
[2, 3, 1, 4, 5]. . .
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.” Tobi does, and gets completely different numbers:
import random
print([random.randint(1, 20) for _ in range(5)]) # _ : a loop name we never use
print([random.randint(1, 20) for _ in range(5)]) # ...and again, different![20, 8, 18, 20, 16]
[10, 14, 20, 4, 2]. . .
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
Tobi 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. Pick a letter, then I reveal the answer.
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):
python.tobiasvlcek.com/notebooks/ex_06_b/
First predict what happens, then run it.
To the Lab
After the break: the lab
- Head to the lab notebook: Episode 6 — Due Diligence Week
- You’ll
import mathandstatisticsfor investor-grade counts and averages, then userandomwith aseedto rehearse a busy day she can reproduce - It’s the first lab where AI is allowed, so try the chatbot
- This week’s homework: the Mistral account and the Zed student plan from the AI Tools page
- 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 at the start of the session.
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 ... asfor a nickname.dir()andhelp()show you what’s inside. randomdeals 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: the numbers deck. 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.