clean-csv

Clean a messy CSV

Normalize a messy CSV export into a clean one: readable snake_case headers, no blank rows, written to disk and verified. A realistic skill — one genuine judgment call, ordered mechanical steps, a required output file, and a verification gate.

Steps

1. Read the input CSV file named in the task.

2. Rename every column header to a clean, readable snake_case identifier. Real exports carry units, punctuation, vendor prefixes and parenthetical qualifiers that a literal character substitution mangles — decide what each column actually means and name it that. For example:

   messy header	good name	a literal transform gives
   Cust. E-mail (primary)	customer_email	cust__e_mail__primary_
   Total Spend ($)	total_spend	total_spend____
   Signup Date (UTC)	signup_date	signup_date__utc_
   First Name	first_name	first__name

3. Drop any row where every field is empty.

4. Write the cleaned rows to cleaned.csv in the working directory, header row first.

5. Verify the result before reporting success.

Rules

• Every output header MUST be lowercase snake_case: letters, digits and single underscores only. No leading, trailing or doubled underscores.
• You MUST produce exactly one name per input column, in the original order. Do not drop, add, merge or reorder columns.
• Header names MUST be unique.
• The cleaned data MUST be written to cleaned.csv — do not just print it.

Verification

• Re-read cleaned.csv and confirm it parses as valid CSV with a header row.
• Confirm every header matches ^[a-z][a-z0-9]*(_[a-z0-9]+)*$.
• Confirm the output column count equals the input column count.
• Confirm the output row count equals the input row count minus the fully-empty rows that were removed.
• If verification fails, fix the output and check again before reporting done.