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Level 2 / Project 12 - CSV to JSON Converter

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Concept This project — Quiz Flashcards Diagram Browser

Estimated time: 40 minutes

Focus

  • format conversion and schema checks

Why this project exists

This project gives you level-appropriate practice in a realistic operations context. Goal: run the baseline, alter behavior, break one assumption, recover safely, and explain the fix.

Run (copy/paste)

Use <repo-root> as the folder containing this repository's README.md.

cd <repo-root>/projects/level-2/12-csv-to-json-converter
python project.py data/sample_input.txt --pretty
python project.py data/sample_input.txt --format columns
python project.py data/sample_input.txt --no-types
python project.py data/sample_input.txt --output data/output.json
pytest -q

Expected terminal output

[{"name": "Alice Johnson", "age": 30, "department": "Engineering", ...}, ...]
11 passed

Expected artifacts

  • JSON output on stdout or file
  • Passing tests
  • Updated notes.md

Checkpoint: Baseline code runs and all tests pass. Commit your work before continuing.

Alter it (required) — Extension

  1. Add a --columns flag to select only specific columns: --columns name,age.
  2. Add row-count validation — log rows with mismatched column counts.
  3. Add a --schema output that shows detected types per column.

Break it (required) — Core

  1. Feed a CSV with quoted fields containing commas (e.g. "Smith, Jr").
  2. Feed a file with inconsistent column counts per row.
  3. Feed a value like "123abc" — does type inference misfire?

Fix it (required) — Core

  1. Add basic quoted-field handling for CSV values.
  2. Pad short rows and truncate long rows to match header length.
  3. Ensure infer_type only converts when the entire value is numeric.

Checkpoint: All modifications done, tests still pass. Good time to review your changes.

Explain it (teach-back)

  1. Why does type inference try int before float?
  2. What is the difference between "array of objects" and "columnar" JSON formats?
  3. How does zip(headers, values) elegantly build record dicts?
  4. When would you choose JSON over CSV for data storage?

Mastery check

You can move on when you can: - explain how zip pairs two lists element-by-element, - implement type inference logic from memory, - convert between objects and columnar JSON formats, - describe edge cases in CSV parsing (quotes, escapes, encodings).

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