Week 01: Exercises & Checklist

Graded exercises

1. Easy: Run notebooks/01-environment-and-tools.ipynb end-to-end. It prints a readiness score at the end (a number from 0 to 6). Record that number in the Week 1 sheet of the Excel tracker. If any check fails, fix the environment and re-run, the goal is a score of 6, not a screenshot of an error.

2. Standard: Open notebooks/02-zorologistics-data-generator.ipynb and change two things: the seed (from 42 to something of your own) and the shipment count (from 100_000 to 5_000). Re-run. Confirm that (a) the row counts change, (b) the column schema is identical, and (c) the planted data issues (duplicate rows, NaN weights, NaN distances) still appear. Write one sentence in a markdown cell explaining why the schema is stable but the rows differ.

3. Stretch: Write a standalone script generate_small.py in your own scratch space (not committed to the curriculum folder) that imports zoro.data, generates one of each table (carriers, lanes, shipments, support_tickets) at a reduced size with an explicit seed, and prints a one-line summary per table (name + row count). Run it from a fresh terminal with python generate_small.py to prove your environment is portable outside Jupyter.

4. Portfolio: Begin the ZoroLogistics data generator + silver dataset milestone (tracked in curriculum/projects/README.md). Commit the output of save_all() to your fork's data/ directory together with data/data-dictionary.md, and add a short README block stating: the seed used, the exact row counts per table, and the schema. This is the first artifact of the program that a stranger can inspect.

Hints

1. Easy: If the score is not 6, read each FAIL line in order from the top; the very first failure (usually a missing zoro import or not being inside a Git work tree) is usually what pulls the score down.
2. Standard: Change only the seed and n arguments you pass to data.shipments(...); do not touch zoro/data.py. Then confirm the column list (not the values) is identical by printing df.columns before and after.
3. Stretch: Reuse the walk-up path loop from the notebooks (while not (p / "zoro").is_dir() ...) so your script finds zoro regardless of which folder you launch it from.
4. Portfolio: In your README block, record the four row counts and the seed in one place; a stranger should be able to regenerate and see the same numbers without asking you.

Checklist (mirrors manifest.json + Excel tracker)

• [ ] Tue: Complete the Python refresher exercises in the environment notebook.
• [ ] Wed: Run the environment check notebook end-to-end (versions, imports, deterministic seed).
• [ ] Thu: Build the ZoroLogistics data generator: shipments, carriers, lanes, deterministic seeds.
• [ ] Fri: Use case: generate the 100k-row dataset, save to CSV/Parquet, commit it with a data dictionary.
• [ ] Sat: Take the Week 1 quiz (quiz.md), pass with 8/10; record the score in Notes.
• [ ] Milestone: Update the Excel tracker: Week 1 checked off; push your fork to GitHub.