Tabalyst

Tabalyst is a local-first CSV profiling library and command-line tool. It reads a CSV file once, produces a structured JSON profile, and renders the same result as an interactive HTML report.

Version 0.1.0 is the first packaged public API. The analysis format is still young, so larger schema changes are reserved for future minor releases.

Requirements and installation

Tabalyst supports Python 3.11, 3.12, 3.13, and 3.14.

pip install tabalyst

For local development from a clone:

python -m venv .venv
python -m pip install -e ".[dev]"

Command line

Pass the CSV source and the complete HTML report path as positional arguments:

tabalyst data.csv reports/report.html

The report path must end in .html. Parent directories are created automatically, and existing artifacts are replaced. A successful run creates:

reports/
|-- report.html
|-- report.json
`-- executions.json

The JSON profile always shares the HTML filename stem. executions.json keeps a cumulative history for successful analyses in the same report directory, so several named reports can coexist there.

Common options are:

tabalyst data.csv reports/report.html --separator ";" --encoding cp1252
tabalyst data.csv reports/report.html --config tabalyst.json
tabalyst --help
tabalyst --version

python -m tabalyst accepts the same arguments.

Python API

import tabalyst

result = tabalyst.analyze(
    "data.csv",
    "reports/report.html",
    separator=";",
    encoding="cp1252",
    config_path="tabalyst.json",
)

print(tabalyst.__version__)
print(result["summary"]["row_count"])

analyze() returns a JSON-serializable dictionary whose logical content matches the adjacent JSON file. The HTML is rendered from that canonical JSON profile. Expected failures derive from tabalyst.TabalystError; callers may distinguish InputError, ConfigurationError, and ReportError.

Configuration

Configuration files are strict JSON. Unknown names and invalid values are errors. The existing analysis settings remain available; the smallest useful file is:

{
  "csv": {
    "delimiter": ";",
    "encoding": "cp1252"
  }
}

Resolution order is:

1. an explicit separator or encoding argument;
2. the file passed with config_path or --config;
3. Tabalyst defaults: comma and utf-8-sig.

utf-8-sig accepts ordinary UTF-8 and UTF-8 with a byte-order mark. See the configuration reference for analysis, normalization, date, type-inference, sampling, and enum settings.

Public examples

The repository contains two reproducible examples:

• basic: five rows covering common CSV values and one duplicate row.
• insurance-customers: 3,000 synthetic customer and contract records across 34 columns. Names and contact details are fictional, and email addresses use reserved .example domains.

Each dataset under examples/input/ has a matching generated report under examples/output/. Regenerate both with:

tabalyst examples/input/basic.csv examples/output/basic/report.html --config examples/config.json
tabalyst examples/input/insurance-customers.csv examples/output/insurance-customers/report.html --config examples/config.json

See the complete layout and maintenance notes in the examples README.

What the report contains

• Dataset dimensions, missing cells, duplicate rows, and quality observations.
• Physical and semantic type inference with explicit confidence and error rates.
• Numeric, date, string-length, normalization, and value-distribution profiles.
• A bounded raw-data preview while all records are analyzed.
• Sortable and filterable HTML tables.

Raw strings are preserved. The complete CSV is currently loaded into memory. Report data remains local and Tabalyst adds no telemetry or remote analysis. Bootstrap, DataTables, and Google Fonts are loaded from pinned CDNs for the full interactive presentation; the Tabalyst template, theme, and report JavaScript are included in the Python package.

Compatibility with alpha commands

The earlier commands remain available during the 0.1.x transition:

tabalyst analyze data.csv -o reports/report.html
tabalyst render reports/report.json -o reports/regenerated.html

The alpha analyze form also retains automatic tabalyst.json discovery, multiple --config overrides, and --preview-rows. New integrations should use the direct command or tabalyst.analyze().

Development and packaging

python -m pytest
python -m ruff check .
python -m build

The build creates a wheel and source distribution under dist/. Release steps, including the clean-wheel smoke test and PyPI Trusted Publishing setup, are in RELEASING.md. The packaging architecture and automated publication flow are documented in docs/python-package-and-release.md.

Report format details and internal boundaries are documented in docs/architecture.md. Please report defects through the GitHub issue tracker.

License

Tabalyst is released under the MIT License.

Created by Gregory Borelli - Catalyseur Numérique.