Working with tables and CSVs (small-scale)

Safely transform small datasets with clear instructions

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Learning goal

• Give column-aware instructions to reshape data.
• Ask for validation steps and highlight missing values.

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Why it matters

• Specific column/format instructions avoid broken tables.
• Highlighting missing values prevents silent errors.

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Explanation

• Specify columns, expected output format (markdown table), and operations (filter/sort/add column).
• Forbid adding rows; ask to flag missing/invalid data.
• Request a short “checks” line: counts, missing fields.

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Examples

• Prompt: “You have columns A,B,C. Filter where status=Active, sort by date, add total=price*qty. Return markdown table; flag missing values; do not invent rows.”
• Weak: “Fix this CSV.”

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Guided exercise (10–15 min)

1. Provide 5–10 rows of sample data.
2. Ask for a filtered/sorted/derived-column table with checks.

Independent exercise (5–10 min)

Add a “validation” line: row count before/after, missing field list.

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Self-check

• Output table matches requested columns/ops.
• No invented data.
• Validation line present.

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Optional deepening

• Data prompts: prompt guide