Programming with Python
It’s Friday. The boardroom. The investor slides a pencil out of her jacket and sharpens it while you set up, the sound louder than it has any right to be.
. . .
Last week you handed her honest numbers, a table she could trust. She read exactly one row, looked up, and said the thing this whole session is about:
“Tables are homework. Charts are arguments.”
. . .
A number convinces the careful. A picture convinces the room. Today: draw the right chart for the question, and refuse to draw a dishonest one.
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:
import matplotlib.pyplot as plt
days = [1, 2, 3, 4, 5]
revenue = [120, 90, 140, 160, 130]
plt.figure() # OPEN: a fresh canvas, so this chart
# doesn't draw on the last one
plt.plot(days, revenue) # x, then y
plt.title("This week's revenue")
plt.gca() # SHOW: "get current axes" — the chart
. . .
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.
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()
Two keywords cover almost everything: color and linestyle. Restraint is the professional move. One clear line beats a rainbow:
import matplotlib.pyplot as plt
days = [1, 2, 3, 4, 5]
revenue = [120, 90, 140, 160, 130]
plt.figure()
plt.plot(days, revenue, color="crimson", linestyle="--")
plt.title("Styled, not decorated")
plt.gca()
Kevin runs two plot calls, back to back, with no plt.figure() between them:
plt.plot([3, 1, 2])
plt.plot([1, 2, 3])a) one chart with two lines b) two separate charts, one per call c) an error: the figure is already in use
. . .
Predict first. Commit to an answer before the next slide.
a) one chart with two lines — matplotlib keeps drawing on the current figure until you start a new one with plt.figure():
import matplotlib.pyplot as plt
plt.figure()
plt.plot([3, 1, 2])
plt.plot([1, 2, 3]) # same canvas — a second line, not a second chart
plt.gca()
. . .
That’s exactly why every chart cell opens with plt.figure(): it’s how you say “new picture, start clean.”
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.
The wrong chart is its own kind of lie. Start from the question, not the chart:
| The question | The chart | The call |
|---|---|---|
| Compare categories? | bar | plt.bar(labels, heights) |
| See a distribution? | histogram | plt.hist(values) |
| Two numbers per order? | scatter | plt.scatter(x, y) |
| Change over time? | line | plt.plot(x, y) |
. . .
plt.plot from block one, time on the x-axis. The other three get a slide each.. . .
Notice what’s missing: the pie chart. It’s usually the wrong answer: the eye can’t compare slice sizes well. When in doubt, a bar chart is clearer.
“Which dish sells best?” Categories to compare — that’s a bar:
import matplotlib.pyplot as plt
dishes = ["Pizza", "Pasta", "Salad", "Wrap"]
sold = [12, 9, 5, 7]
plt.figure()
plt.bar(dishes, sold)
plt.title("Orders per dish")
plt.gca()
The tallest bar answers the question at a glance: Pizza.
“How are our delivery times spread out?” One column of numbers, dropped into buckets — that’s a histogram:
import matplotlib.pyplot as plt
minutes = [19, 22, 23, 24, 24, 25, 26, 28, 31, 44]
plt.figure()
plt.hist(minutes, bins=5)
plt.title("Delivery times")
plt.gca()
Most deliveries land in the mid-20s, with one lonely slow one far right. A histogram shows shape, not individual values.
“Do bigger orders take longer?” Two numbers per order, one on each axis — that’s a scatter:
import matplotlib.pyplot as plt
minutes = [19, 22, 23, 24, 25, 26, 28, 31, 40, 44]
euros = [24, 9, 31, 12, 8, 30, 15, 22, 11, 19]
plt.figure()
plt.scatter(minutes, euros)
plt.xlabel("Delivery time (min)")
plt.ylabel("Order value (€)")
plt.gca()
Just a cloud. No upward drift. Sometimes the honest answer is “there’s no pattern here.”
The same four numbers, two ways. On the left, a bar per number; on the right, a histogram:
values = [8, 12, 9, 15]
plt.bar(range(4), values) # left
plt.hist(values, bins=3) # rightThe bar chart shows 4 bars. How many bars does the histogram show?
a) 4: one bar per number b) 15: one bar per unit up to the max c) about 2–3 lumps: it counts ranges
. . .
Predict first. Commit to an answer before the next slide.
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:
import matplotlib.pyplot as plt
values = [8, 12, 9, 15]
plt.figure()
plt.subplot(1, 2, 1)
plt.bar(range(4), values) # one bar per NUMBER
plt.title("bar: 4 categories")
plt.subplot(1, 2, 2)
plt.hist(values, bins=3) # counts how many per RANGE
plt.title("hist: 3 ranges")
plt.gca()
. . .
Bar counts CATEGORIES; histogram counts RANGES. Same numbers, different question.
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.
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.
. . .
She’s the type who reads Formular 27b/6 for fun. A stretched axis is the first thing she catches, and the last thing you want to explain.
AI is genuinely good at plotting code, if you drive it like a co-pilot:
orders with columns zone (text) and total_eur (float). Bar chart of total revenue per zone.”. . .
The pilot flies; the co-pilot advises. You still land the plane.
orders["revenue"], but your column is total_eur: a KeyError, exactly the confident-nonsense you caught in Episode 8. Read the code before you run it.. . .
Both threads, one rule: an AI chart runs long before it’s true. You verify the columns and the axis, every time.
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.
plt.figure() and closes with plt.gca(). . .
Download your .py before you leave. It’s the appendix of your pitch: the file that proves every chart came from the real data.
plt.figure(), close with plt.gca(); label the axes and title it. A chart nobody can read argues nothing.. . .
Season finale next: git, real files, and the project kickoff. We leave the browser for your own machine: the toolchain you install this week is the price of admission.
. . .
Building charts with AI this session? Revisit the AI Tools page: describe-then-verify is the whole workflow. Matplotlib’s own pyplot tutorial is a friendly next step.
. . .
For more, see the literature list of this course.