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GeezerVison is showing flypython.com — Data Visualization with an Agent — a challenge course — FlyPython

Challenge course · Any coding agent + matplotlib

Data Visualization with an Agent

A challenge course — turn a clean sales dataset into three spec'd charts plus a summary.json whose numbers are verified against the data.

Learn with Coach P, an AI coach inside your own agent, using your model. Ask for guided learning: first a question, then a small hint. verify.py checks objective gates; reflections need your own answers and confirmation.

Intermediate5 challenges1–2 hoursBeginner; pandas + matplotlibFreeCourse badge: Visualization ChallengerReviewed 2026-11-07

Start it with your agent — paste this one sentence:

Read https://flypython.com/skills/flypython/SKILL.md and start the FlyPython course `da-visualization`.

Needs a coding agent that can run commands and reach the network (chat-only web AIs cannot). Your agent authorizes you with a one-time link, fetches this course's files itself, and you never download anything. Haven't installed the FlyPython Skill? Pick your agent's tool course →

Browse the challenges

At a glance

What you learnA matplotlib script producing three valid PNG charts and a summary.json — the machine-checkable half of “here are my charts”, with an honest statement of what verification cannot see.

Who it's forBeginner; pandas + matplotlib · Python 3.11 or newer available on your PATH

Duration & cost1–2 hours · Free

Challenges

The challenges — read them online

No clone, no account — click a checkpoint to read the full challenge on its own page.

A chart is packaged evidence

State the claim behind each required chart before writing code — a wrong claim makes a pretty chart worse than none.

Checkpoint l01 · 10 pts · read online →02

Spec first, chart second

Map each output file to its source aggregation before letting the agent render anything.

Checkpoint l02 · 10 pts · read online →03

Three charts and a summary

Implement the contract in starter/charts.py — Agg backend, parsed dates, real PNG output.

Checkpoint l03 · 10 pts · read online →04

Numbers match the charts

summary.json is the auditable layer — verified against dataset ground truth.

Checkpoint l04 · 10 pts · read online →05

What verification cannot see

Write the sentence stating what was and was not verified — the suite does not judge aesthetics or honest scales.

Checkpoint l05 · 10 pts · read online →

Start

How the run works

Your agent drives; the files arrive through the Skill — no download, no clone.

SkillAny coding agent + matplotlib

The Skill does the fetching

The sentence above starts the course: your agent walks you through the one-time authorization link (compare the code, click Allow), fetches this course's files, and drives the challenges with you. Agents without the Skill installed can work from this brief instead:

Work on the FlyPython challenge "Data Visualization with an Agent" (course id course-da-visualization).
Machine-readable brief: https://flypython.com/api/challenges/da-visualization
Open the course folder and read TASK.md first — it is the contract.
Rules: smallest change, no new dependencies, never edit tests/ or solution/.
Check starter/ with python verify.py; ask me the lesson questions before using --attest ID for any self-reported checkpoint.

Your agent works under the task contract: smallest change, no new dependencies, never edit solution/. Optional guided mode: COURSE.md.

Files

Files come to you

Course files live at /api/challenges/da-visualization/files — your agent fetches the manifest and writes each file at its path. There is nothing to download by hand.

How "done" is decided →

Scenario skins

Same skills, a domain you recognize

All three skins run through the same code and the same tests. Pick the one closest to your job before lesson 1.

scenario/sales-clean/sales_daily.csv

Clean daily sales

135 rows of long-format daily revenue across three regions.

Verification

How “done” is decided

Five-test contract suite: PNG magic bytes and file sizes, summary.json numbers against ground truth. The starter must reproduce the unfinished state.

python verify.py  # from the course folder: checkpoint status + claim codes

Apply this to your own project

Lesson 5 ports the workflow — not the code — to one script you actually own: a three-line task contract, one new failing test made to pass, and a written record of what the tests do not prove. The same loop is described in the AI coding workflow guide and practiced by therunnable examples.

Claim your evidence

When a checkpoint passes, python verify.py prints a deterministic claim code. Your authorized agent submits them for you in one batch; by hand, paste them on yourprogress page — worth 10 points per checkpoint and theVisualization Challenger course badge at five. Claim codes are self-reported evidence, not certificates.

What verification does not prove

Passing the course suite proves the pinned behaviors on the tested inputs — not correctness on tomorrow’s data, not production performance, and not anything about your own project until you write its contract. Repository tests are evidence, not your outcome.

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