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id course-da-eda
type course
title Exploratory Data Analysis with an Agent
summary A challenge course — point your agent at a messy shop export and produce a verified EDA summary (results.json) whose numbers are checked against ground truth. First non-stdlib course in the catalog; pandas is pinned in requirements.txt.
lang en-US
content_version 2
status reviewed
reviewed_on 2026-11-07
badge
id name_en name_zh requires
course-da-eda
EDA Challenger
探索分析挑战者
All five checkpoints claimed (L01–L05)
course_id course-da-eda

Exploratory Data Analysis with an Agent

Coach P — guided learning

When the learner asks to start a lesson or requests guided help, act as Coach P. At your first introduction in that session, say:

I'm Coach P, an AI coach running inside your own agent, using your model. I'll help you reason through the task; verify.py checks the objective gates. Reflection checkpoints still need your own answers and explicit confirmation.

Ask what the learner tried and what they expect before suggesting a change. Give the smallest useful hint, then wait for their answer. Name the failing test and explain which contract it checks; do not paste a solution or edit solution/. Follow this course's lesson order and file boundaries. If the learner cannot explain a result, return to that test and ask them to predict the next run. Before --attest or a claim submission, obtain the learner's own answers and confirmation for those checkpoints; never answer or attest on their behalf. The Skill's authorization and human-confirmation gates still apply. For a verification-only or progress request, perform that workflow without forcing a lesson. Coach P is an AI teaching style, not a human teacher or a certification; do not promise learning or career outcomes. FlyPython does not run the model or receive the chat conversation through this workflow; course-file, account, and claim requests are separate API data.

TL;DR: this is a challenge course — you solve it, your agent is the tool. Point it at scenario/shop-export/transactions.csv (142 messy rows), implement the contract in starter/eda.py, and finish with a results.json whose numbers are objectively checked against ground truth. Optional guided mode: COURSE.md + lessons/ still work as an agent-taught path if you want a tour instead of a challenge.

What you build

A pandas script that loads a real-world-messy transactions export, cleans it (duplicates, unparseable amounts, bad dates), and writes a verified summary: clean row counts, total revenue, revenue by region, top category, date range.

Setup — lesson zero is the environment

This is the first course in the catalog that leaves the standard library:

cd courses/da-eda
python -m venv .venv               # or: uv venv
source .venv/bin/activate          # Windows: .venv\Scripts\activate
pip install -r requirements.txt    # or: uv pip install -r requirements.txt

verify.py checks for pandas and prints this hint if it is missing.

Challenges (checkpoints)

# Challenge Gate
L01 What messy data looks like — profile the CSV before touching code self-attested
L02 Define the answer first — write down the expected results.json shape self-attested
L03 Clean and summarize — implement clean_transactions + summarize objective (starter suite)
L04 Check against ground truth — both suites green objective (both suites)
L05 Run it on your own data — swap in an export you actually have self-attested

Badge contract

  • Badge: EDA Challenger (badge id da-eda) — earned by claiming all five checkpoints.
  • Challenges: L01–L05, 10 points each; +50 course-badge bonus when all five are claimed on flypython.com.
  • Evidence: python verify.py — L03 and L04 are objectively gated by the suite; L01/L02/L05 are learner-attested.
  • Submission: each test-passed checkpoint prints a deterministic claim code; a reflection checkpoint prints one only after you answer its questions and run python verify.py --attest ID; record it on flypython.com against your account. Self-reported evidence, never a certificate.

What this course does NOT cover

Visualization (see da-visualization), reporting (see da-report), statistics theory, SQL, or notebooks. The dataset is synthetic but the quality issues are the ones you will meet in real exports.

Folder map

COURSE.md / COURSE_cn.md   this file (EN / 中文)
lessons/L01.md … L05.md    challenge notes (each has an _cn.md pair)
scenario/shop-export/      transactions.csv (messy input data)
TASK.md / TASK_cn.md       the contract your code must satisfy
starter/eda.py             the deliberately unfinished implementation
solution/eda.py            the reviewed solution
tests/test_eda.py          the contract suite (read-only)
verify.py                  objective pass/fail + claim codes
requirements.txt           pinned pandas dependency
REVIEW.md                  maintainer run-through record

Evidence and licensing

Reviewed content: REVIEW.md records the run-through. Code is MIT-licensed; lesson prose is CC BY 4.0 (see repository LICENSE).