CSV Refine

CSV Refine is a Python CLI for cleaning, filtering, and normalizing CSV files.

It lets you define exactly how each column should be validated, cleaned, and normalized through a YAML contract. CSV Refine then separates valid and invalid rows and generates reports to assess the overall health of the dataset.

Features

• Clean and normalize messy CSV data
• Filter out invalid rows while preserving valid data
• Detect missing values, duplicates, invalid formats, and rule violations
• Separate valid and invalid rows into dedicated CSV files
• Generate JSON and HTML reports to assess CSV data quality

Installation

Install CSV Refine from PyPI:

pip install csv-refine

Check the CLI:

csv-refine --help

Quick start

csv-refine \
  --contract examples/01-input/example-contract.yaml \
  --csv examples/01-input/example.csv \
  --mode permissive

By default, generated files are written to output/.

A custom output directory can be provided with --output.

YAML contract

CSV Refine is configured through a YAML contract that defines exactly how each column should be validated and normalized.

A contract can define data types, nullability, uniqueness, validation rules, transformations, delimiter, and encoding.

Example:

columns:
  Transaction ID:
    type: str
    nullable: false
    unique: true
    rules:
      starts_with: "TXN_"

For the complete contract syntax and all supported options, see the contract README.

Validation modes

Permissive

Invalid rows are collected while processing continues.

Strict

Structural, type conversion, nullability, and uniqueness failures can stop processing.

Validation-rule violations still act as row filters.

Outputs

CSV Refine generates:

output/
├── validated_example.csv
├── errors_example.csv
├── example.json
└── example.html

• Validated CSV — valid rows with transformations applied
• Error CSV — invalid rows with their detected errors
• JSON report — machine-readable summary
• HTML report — human-readable overview of CSV health

The original CSV is never modified.

Real-world example

The repository includes a 200-row retail dataset containing missing values, invalid categories, malformed identifiers, duplicate values, invalid dates, and conversion errors.

examples/
├── 01-input/
│   ├── example.csv
│   └── example-contract.yaml
└── 02-expected-output/
    ├── validated_example.csv
    ├── errors_example.csv
    ├── example.json
    └── example.html

Run the example with:

csv-refine \
  --contract examples/01-input/example-contract.yaml \
  --csv examples/01-input/example.csv \
  --mode permissive

Example result:

Total rows:    200
Valid rows:    133
Invalid rows:   67
Total errors:  117

Preview

Raw CSV

The original dataset contains missing values, invalid formats, duplicates, and values that do not comply with the configured data contract.

Validated rows

Rows that successfully pass the contract validation are cleaned, normalized, and written to a dedicated CSV file.

Invalid rows and detected errors

Invalid rows are separated from the validated dataset and include the errors detected during processing.

Data quality report

Development

Clone the repository:

git clone https://github.com/alexiskuypers/csv-refine.git
cd csv-refine

python3 -m venv .venv
source .venv/bin/activate

pip install -e ".[dev]"

Tests

Run the test suite with:

python3 -m pytest

Stack

Python 3.12+, PyYAML, email-validator, pytest, argparse, pathlib, logging.

Status

Version 0.1.2 is available on PyPI.