csvnorm 1.2.14

A command-line utility to validate and normalize CSV files

csvnorm

A command-line utility to validate and normalize CSV files for initial exploration.

Version 1.0 Breaking Change

If upgrading from v0.x: The default output has changed from file to stdout for better Unix composability.

# v0.x behavior
csvnorm data.csv              # Created data.csv in current directory

# v1.0 behavior (NEW)
csvnorm data.csv              # Outputs to stdout
csvnorm data.csv -o data.csv  # Explicitly save to file
csvnorm data.csv > data.csv   # Or use shell redirect

This follows the Unix philosophy and matches tools like jq, csvkit, and xsv.

Installation

Recommended (uv):

uv tool install csvnorm

Or with pip:

pip install csvnorm

Purpose

This tool prepares CSV files for basic exploratory data analysis (EDA), not for complex transformations. It focuses on achieving a clean, standardized baseline format that allows you to quickly assess data quality and structure before designing more sophisticated ETL pipelines.

What it does:

• Validates CSV structure and reports errors
• Normalizes encoding to UTF-8 when needed
• Normalizes delimiters and field names
• Creates a consistent starting point for data exploration

What it doesn't do:

• Complex data transformations or business logic
• Type inference or data validation beyond structure
• Heavy processing or aggregations

Features

• CSV Validation: Checks for common CSV errors and inconsistencies using DuckDB
• Delimiter Normalization: Converts all field separators to standard commas (,)
• Field Name Normalization: Converts column headers to snake_case format
• Encoding Normalization: Auto-detects encoding and converts to UTF-8 when needed (ASCII is already UTF-8 compatible)
• Processing Summary: Displays comprehensive statistics (rows, columns, file sizes) and error details
• Error Reporting: Exports detailed error file for invalid rows with summary panel
• Remote URL Support: Process CSV files directly from HTTP/HTTPS URLs without downloading (unless --fix-mojibake is used)

Usage

csvnorm input.csv [options]

By default, csvnorm writes to stdout for easy piping and composability with other Unix tools.

Options

Option	Description
-o, --output-file PATH	Write to file instead of stdout
-f, --force	Force overwrite of existing output file (when -o is specified)
-k, --keep-names	Keep original column names (disable snake_case)
-d, --delimiter CHAR	Set custom output delimiter (default: ,)
-s, --skip-rows N	Skip first N rows of input file (useful for metadata/comments)
--fix-mojibake [N]	Fix mojibake using ftfy (optional sample size N; use 0 to force repair)
--strict	Exit with error code 1 if any validation errors occur (fail-fast mode)
--check	Validate CSV without processing or normalizing (exit code 0=valid, 1=invalid)
--download-remote	Download remote CSV locally before processing (needed for remote .zip/.gz)
-V, --verbose	Enable verbose output for debugging
-v, --version	Show version number
-h, --help	Show help message

Examples

# Default: output to stdout
csvnorm data.csv

# Preview first rows
csvnorm data.csv | head -20

# Pipe to other tools
csvnorm data.csv | csvcut -c name,age | csvstat

# Save to file
csvnorm data.csv -o output.csv

# Shell redirect
csvnorm data.csv > output.csv

# Process remote CSV from URL
csvnorm "https://raw.githubusercontent.com/aborruso/csvnorm/refs/heads/main/test/Trasporto%20Pubblico%20Locale%20Settore%20Pubblico%20Allargato%20-%20Indicatore%202000-2020%20Trasferimenti%20Correnti%20su%20Entrate%20Correnti.csv" -o output.csv

# Process remote compressed CSV (download first, then handle gzip/zip locally)
csvnorm "https://example.com/data.csv.gz" --download-remote -o output.csv

# Custom delimiter
csvnorm data.csv -d ';' -o output.csv

# Keep original headers
csvnorm data.csv --keep-names -o output.csv

# Skip first 2 rows (metadata or comments)
csvnorm data.csv --skip-rows 2 -o output.csv

# Force overwrite with verbose output
csvnorm data.csv -f -V -o processed.csv

# Fix mojibake using ftfy (default sample size)
csvnorm data.csv --fix-mojibake -o fixed.csv

# Fix mojibake with custom sample size
csvnorm data.csv --fix-mojibake 4000 -o fixed.csv

# Force mojibake repair even with low badness score
csvnorm data.csv --fix-mojibake 0 -o fixed.csv

# Fail-fast mode: exit with error if validation errors occur
csvnorm data.csv --strict > output.csv || echo "Validation failed!"

# Use in pipelines where data quality is critical
csvnorm remote_data.csv --strict | other_tool || handle_error

# Quick validation check (no processing or output)
csvnorm data.csv --check && echo "Valid CSV" || echo "Invalid CSV"

# Check remote CSV for validity
csvnorm https://example.com/data.csv --check

# Use in CI/CD pipelines for validation
csvnorm raw_data.csv --check || exit 1

Output

Default behavior (stdout):

• Writes normalized CSV to stdout
• Progress and errors go to stderr
• Validation errors (if any) are shown to stderr before the output data
• Reject file saved to ./reject_errors.csv in current working directory
• Perfect for piping to other tools or shell redirection

File output (with -o):

• Creates a normalized CSV file at the specified path with:
  • UTF-8 encoding
  • Consistent field delimiters
  • Normalized column names (unless --keep-names is specified)
• Error report if any invalid rows are found (saved as {output_name}_reject_errors.csv in the same directory)
• Shows success table with statistics (rows, columns, file sizes)
• Supports absolute and relative paths
• Any file extension is allowed (not limited to .csv)

Input file protection:

• csvnorm will never overwrite the input file, even with --force
• If you try to use the same path for input and output, you'll get an error
• Use -o to specify a different output path

Remote URLs:

• Encoding is handled automatically by DuckDB
• If --fix-mojibake is enabled, the URL is downloaded to a temp file first

Mojibake repair (--fix-mojibake [N]):

• Mojibake is garbled text produced by decoding bytes with the wrong character encoding (e.g., Città instead of Città).
• Enables optional mojibake repair using ftfy (for already-misdecoded text).
• N is the sample size (number of characters) used by the detector; default is 5000.
• The repair runs only when ftfy's badness heuristic flags the sample as "bad."
• Use N=0 to force repair without detection (useful for files with low badness scores but visible mojibake).
• Note: ftfy cannot recover bytes that were irreversibly lost in the original encoding. Replacement characters (�) may remain where data was corrupted beyond repair.
• HTTP timeout is set to 30 seconds
• Only public URLs are supported (no authentication)

The tool provides modern terminal output (shown only when using -o to write to a file) with:

• Progress indicators for multi-step processing
• Color-coded error messages with panels
• Success summary table with statistics (rows, columns, file sizes)
• Encoding conversion status (converted/no conversion/remote; ASCII is already UTF-8 compatible)
• Error summary panel with reject count and error types when validation fails
• ASCII art banner with --version and -V verbose mode

Success Example: (shown only when using -o)

 ✓ Success
 Input:        test/utf8_basic.csv
 Output:       output/utf8_basic.csv
 Encoding:     ascii (ASCII is UTF-8 compatible; no conversion needed)
 Rows:         2
 Columns:      3
 Input size:   42 B
 Output size:  43 B
 Headers:      normalized to snake_case

Error Example: (shown only when using -o)

 ✓ Success
 Input:        test/malformed_rows.csv
 Output:       output/malformed_rows.csv
 Encoding:     ascii (ASCII is UTF-8 compatible; no conversion needed)
 Rows:         1
 Columns:      4
 Input size:   24 B
 Output size:  40 B
 Headers:      normalized to snake_case

╭──────────────────────────── ! Validation Failed ─────────────────────────────╮
│ Validation Errors:                                                           │
│                                                                              │
│ Rejected rows: 2                                                             │
│                                                                              │
│ Error types:                                                                 │
│   • Expected Number of Columns: 3 Found: 2                                   │
│   • Expected Number of Columns: 3 Found: 4                                   │
│                                                                              │
│ Details: output/malformed_rows_reject_errors.csv                             │
╰──────────────────────────────────────────────────────────────────────────────╯

Exit Codes

Code	Meaning
0	Success
1	Error (validation failed, file not found, etc.)

Requirements

• Python 3.9+
• Dependencies (automatically installed):
  • charset-normalizer>=3.0.0 - Encoding detection
  • duckdb>=0.9.0 - CSV validation and normalization
  • ftfy>=6.3.1 - Mojibake repair
  • rich>=13.0.0 - Modern terminal output formatting
  • rich-argparse>=1.0.0 - Enhanced CLI help formatting

Optional extras:

• [dev] - Development dependencies (pytest>=7.0.0, pytest-cov>=4.0.0, ruff>=0.1.0)

Development

Setup

git clone https://github.com/aborruso/csvnorm
cd csvnorm

# Create and activate venv with uv (recommended)
uv venv
source .venv/bin/activate
uv pip install -e ".[dev]"

# Or with pip
pip install -e ".[dev]"

Testing

pytest tests/ -v

Project Structure

csvnorm/
├── src/csvnorm/
│   ├── __init__.py      # Package version
│   ├── __main__.py      # python -m support
│   ├── cli.py           # CLI argument parsing
│   ├── core.py          # Main processing pipeline
│   ├── encoding.py      # Encoding detection/conversion
│   ├── validation.py    # DuckDB validation
│   └── utils.py         # Helper functions
├── tests/               # Test suite
├── test/                # CSV fixtures
└── pyproject.toml       # Package configuration

Stay Updated

Get notified of new releases

Watch → Custom → ✓ Releases to receive notifications for all new versions.

Get notified of breaking changes only

Subscribe to Announcements to be notified only about:

• Breaking changes (major version bumps)
• Security updates
• Important deprecation notices

We follow Semantic Versioning:

• MAJOR (e.g., 1.0.0 → 2.0.0): Breaking changes
• MINOR (e.g., 1.0.0 → 1.1.0): New features, backward compatible
• PATCH (e.g., 1.0.0 → 1.0.1): Bug fixes only

See docs/COMMUNICATION.md for details.

License

MIT License (c) 2026 [e-mail] - See LICENSE file for details