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CSV Quality Auditor

Audit CSV and delimiter-separated text files for blank or duplicate headers, inconsistent row widths, and fully empty data rows. Use when validating CSV structure before import, debugging malformed tabular exports, checking data handoffs, or producing a machine-readable quality report without third-party packages.

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Data & Analytics

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CSV Quality Auditor

Audit a delimited file with the bundled standard-library CLI and interpret its exit status before using the data downstream.

Workflow

  1. Choose the delimiter. Omit --delimiter for comma-separated input; otherwise pass comma, semicolon, tab, a literal \t, or one custom character.

  2. Run:

    python scripts/audit_csv.py INPUT [--delimiter DELIM] [--json]

  3. Treat exit 0 as clean, exit 1 as completed with quality issues, and exit 2 as a usage, read, decoding, or CSV parse failure.

  4. Use --json for automation. Read issues rather than scraping human output.

  5. Report issue codes, logical row numbers, and messages. Do not claim the file is clean when the command failed with exit 2.

Interpretation

  • row_count counts data records and excludes the header.
  • column_count is the header width.
  • Row 1 is the header; data records begin at row 2.
  • Empty rows are classified as empty_row and are not also reported as width mismatches.

Read references/rules.md when exact rule semantics or JSON behavior matter.

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