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Level 1 / Project 05 - CSV First Reader

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Try in Browser: Run this exercise online — no installation needed!

Before You Start

Recall these prerequisites before diving in: - Can you open a file and read its contents line by line? - Can you use a dictionary to store key-value pairs?

Estimated time: 25 minutes

Learn Your Way

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Concept This project Walkthrough Quiz Flashcards — Browser

Focus

  • read csv rows into dictionaries

Why this project exists

Load a CSV file using Python's csv module, display the data as a formatted table, and compute basic column statistics. This is your first encounter with structured tabular data and auto-detecting numeric columns.

Run (copy/paste)

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

cd <repo-root>/projects/level-1/05-csv-first-reader
python project.py --input data/sample_input.txt
pytest -q

Expected terminal output

=== CSV Table ===

  name             department    salary  years
  ──────────────── ──────────── ─────── ──────
  Alice Johnson    Engineering   85000      5
  Bob Smith        Marketing     62000      3

  Numeric columns: salary (avg: 73500.0), years (avg: 4.0)
4 passed

Expected artifacts

  • data/output.json
  • 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 that selects which columns to display (comma-separated names).
  2. Add row numbering to the formatted table output.
  3. Re-run script and tests.

Break it (required) — Core

  1. Use a CSV file with no data rows (just headers) -- does column_stats() crash on empty data?
  2. Use a CSV with inconsistent column counts (some rows have extra commas) -- what happens?
  3. Use a column with mixed numeric/text values like "10, N/A, 30" -- does detect_numeric_columns() handle it?

Fix it (required) — Core

  1. Handle empty-data CSVs by returning zero stats without crashing.
  2. Ensure detect_numeric_columns() treats columns with any non-numeric values as text.
  3. Add a test for the headers-only CSV case.

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

Explain it (teach-back)

  1. Why does load_csv() use csv.DictReader instead of splitting lines on commas?
  2. What does detect_numeric_columns() do and why is auto-detection useful?
  3. Why does format_table() truncate long values instead of letting them overflow?
  4. Where would CSV table display appear in real software (data explorers, admin dashboards, report generators)?

Mastery check

You can move on when you can: - run baseline without docs, - explain one core function line-by-line, - break and recover in one session, - keep tests passing after your change.

Stuck? Ask AI

If you are stuck after trying for 20 minutes, use one of these prompts:

  • "I am working on CSV First Reader. I got this error: [paste error]. Can you explain what this error means without giving me the fix?"
  • "I am trying to format a table with aligned columns. Can you explain how Python string formatting with :>10 or :<20 works?"
  • "Can you explain the difference between csv.reader and csv.DictReader with a simple example?"
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