Lecture IX - Data Visualization
Programming with Python
Matplotlib in four lines
The library everyone plots with is matplotlib; import matplotlib.pyplot as plt is the nickname. Four lines turn a list of numbers into a picture:
Two habits for every chart cell: open with plt.figure() (a clean canvas) and end with plt.gca(). In marimo and scripts there’s no plt.show(); the figure appears as the cell’s last expression, and plt.plot(...) alone returns line objects, not a picture. Same frame around every chart in tonight’s lab.
The parts of a chart
Every honest chart names its axes. xlabel, ylabel, title, and (when there’s more than one line) a legend:
import matplotlib.pyplot as plt
days = [1, 2, 3, 4, 5]
revenue = [120, 90, 140, 160, 130]
plt.figure()
plt.plot(days, revenue, label="Revenue") # label feeds the legend
plt.xlabel("Day") # what the x-axis counts
plt.ylabel("Revenue (€)") # what the y-axis measures
plt.title("This week's revenue") # what the picture is about
plt.legend() # the little key, one entry per label
plt.gca()
A little styling — and only a little
Two keywords cover almost everything: color and linestyle. Restraint is the professional move. One clear line beats a rainbow:
Answer: one chart, two lines
a) one chart with two lines — matplotlib keeps drawing on the current figure until you start a new one with plt.figure():
That’s exactly why every chart cell opens with plt.figure(): it’s how you say “new picture, start clean.”
⚡ Your turn — 10 minutes
Open the exercise (scan the QR or type the link):
beyondsimulations.github.io/Introduction-to-Python/notebooks/ex_09_a/
First predict what happens, then run it.
Categories → bar
“Which dish sells best?” Categories to compare — that’s a bar:
The tallest bar answers the question at a glance: Pizza.
A distribution → histogram
“How are our delivery times spread out?” One column of numbers, dropped into buckets — that’s a histogram:
Most deliveries land in the mid-20s, with one lonely slow one far right. A histogram shows shape, not individual values.
Two numbers → scatter
“Do bigger orders take longer?” Two numbers per order, one on each axis — that’s a scatter:
Just a cloud. No upward drift. Sometimes the honest answer is “there’s no pattern here.”
Answer: bar counts categories, hist counts ranges
c) about 2–3 lumps — the histogram groups the numbers into ranges and counts how many fall in each. 8 and 9 land in the same bucket, so that bar is two tall:
Bar counts CATEGORIES; histogram counts RANGES. Same numbers, different question.
⚡ Your turn — 10 minutes
Open the exercise (scan the QR or type the link):
beyondsimulations.github.io/Introduction-to-Python/notebooks/ex_09_b/
First predict what happens, then run it.
Take the break after this one: five minutes, then honest charts.
The same data, told two ways
Four weeks of revenue: [96, 98, 97, 99]. Barely moving. But where the y-axis starts decides whether the room sees a rocket or the truth:
import matplotlib.pyplot as plt
weeks = [1, 2, 3, 4]
revenue = [96, 98, 97, 99]
plt.figure()
plt.subplot(1, 2, 1)
plt.plot(weeks, revenue, marker="o")
plt.ylim(96, 99) # tight — DRAMA
plt.title("Rocket (ylim 96–99)")
plt.subplot(1, 2, 2)
plt.plot(weeks, revenue, marker="o")
plt.ylim(0, 100) # from zero — TRUTH
plt.title("Honest (from 0)")
plt.gca()
Same numbers. The left one gets you a term sheet; the right one gets you trusted.
⚡ Your turn — 10 minutes
Open the exercise (scan the QR or type the link):
beyondsimulations.github.io/Introduction-to-Python/notebooks/ex_09_c/
First predict what happens, then run it.