Tabalyst

Tools for unfamiliar data.

Tabalyst is an open-source, local-first toolkit for understanding and working with structured data.

Its first tool is Tabalyst Report. The current CSV implementation, Tabalyst CSV Report, analyzes a CSV file and produces both a structured JSON profile and a self-contained interactive HTML report.

Tabalyst is in active alpha development. Its interfaces may still change while the shared toolkit architecture is being established.

Install

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

pip install --upgrade tabalyst

The same command installs Tabalyst or updates it. Tabalyst is in alpha and changes often: update it before each new test.

Tabalyst Report

The simplest command keeps the source filename:

tabalyst report customers.csv

It creates these files beside the source:

customers.csv
customers.html
customers.json
executions.json

Use -o to choose a different HTML filename for one source:

tabalyst report customers.csv -o customer-analysis.html

JSON files

A JSON file gets a report too, named <stem>.report.html so its profile <stem>.report.json never replaces the source. Each collection of records, such as the customers array of {"customers": [...]}, is a dataset of the report, and nested fields are columns named by their path, such as address.city:

tabalyst report orders.json

Multiple files

Report several CSV files at once:

tabalyst report *.csv

Each report is created beside its source and keeps the source stem:

customers.csv → customers.html
orders.csv    → orders.html
products.csv  → products.html

Use -d to place all reports in one directory:

tabalyst report *.csv -d reports/

This produces:

reports/
├── customers.html
├── customers.json
├── orders.html
├── orders.json
├── products.html
├── products.json
└── executions.json

Both -d reports/ and -d reports are accepted. Quotes are only needed when a path contains spaces.

-o always names one output file and therefore accepts only one input. -d always names an output directory and accepts one or many inputs.

This is invalid because several inputs cannot share one output file:

tabalyst report *.csv -o report.html

Tabalyst rejects the command before processing any file. Use -d reports/ instead.

Safe batch behavior

Before processing begins, Tabalyst resolves every input and planned output. It stops the entire batch if output names collide or if an output already exists. Use --force only when replacing all matching report artifacts is intentional:

tabalyst report *.csv -d reports/ --force

If one CSV is malformed during analysis, Tabalyst reports that error, continues with the remaining files, and returns a non-zero exit code at the end.

Interactive terminals show accurate file and phase progress:

[2/8] orders.csv - Analyzing

Progress and diagnostics use standard error. Progress is disabled automatically outside a terminal and can be disabled explicitly with --no-progress or --quiet.

Useful options

tabalyst report data.csv --delimiter ";"
tabalyst report data.csv --encoding cp1252
tabalyst report data.csv --config tabalyst.json
tabalyst report data.csv --verbose
tabalyst report data.csv --workers 4
tabalyst report --help
tabalyst --version

python -m tabalyst accepts the same commands.

Tabalyst Sample

Create a smaller CSV without modifying the source:

tabalyst sample customers.csv --sample-method random --rows 1000 --seed 42

The default output is customers.sample.csv beside the source. Use -o to name the output for one input, or -d to sample several files into one directory:

tabalyst sample customers.csv --sample-method first --rows 100 -o test.csv
tabalyst sample *.csv --sample-method random --percent 5 -d samples/

Available methods are first, last, random and stratified. Stratified sampling approximately preserves the distribution of a selected field:

tabalyst sample customers.csv --sample-method stratified --field province --rows 1000 --seed 42

Sampling reads CSV records as a stream. Random and stratified sampling keep only the requested sample, plus stratum counts, in memory. Existing outputs require --force, and an input file is never overwritten.

Tabalyst Scan

Describe every field of a CSV or JSON file in one JSON document:

tabalyst scan customers.csv
tabalyst scan orders.json --collection "$.customers[]"

The default output is customers.scan.json beside the source; -o, -d and --force work as for the other commands. Tabalyst Scan reads the file once as a stream, with memory bounded by configurable limits, and records for each field its presence, native types, missing values, frequencies, exact statistics, normalization variants, technical type and the result of every detector: numbers with decimal commas, dates, booleans, enumerations, email addresses, URLs, phone numbers, postal codes, currency amounts, percentages, quantities, UUIDs and IP addresses. Values of sensitive fields, such as email addresses, are masked by default. Results are written atomically: an interrupted scan never leaves a partial file. Files of 16 MiB or more are analyzed by several worker processes, with the same result; --workers chooses their number, --workers 1 keeps one process.

Build the report from a scan document without reading the source again:

tabalyst report --scan customers.scan.json

Tabalyst refuses a scan whose source changed since it was written, or whose settings differ from the scan settings of --config.

See Scan CSV and JSON files and the scan format.

What the report analyzes

• Dataset dimensions, missing cells, duplicates, and quality observations.
• Physical and semantic types with confidence and error rates.
• Numeric, date, string-length, normalization, and value distributions.
• Distinct values, representative examples, date formats, and semantic types such as enumerations, email addresses, phone numbers, and postal codes.
• CSV record widths, quoting, encoding, and delimiter configuration.
• A bounded raw-data preview while every record is analyzed.

The report is built on Tabalyst Scan: it reads the CSV once as a stream and masks values of sensitive columns, such as email addresses, by default. Ambiguous dates stay ambiguous; the report shows the evidence of the column without applying it. The HTML report is self-contained and works without a CDN or network connection. The JSON profile contains the same canonical analysis result for scripts and future Tabalyst tools.

Python API

The same operations are available without the CLI:

import tabalyst

result = tabalyst.analyze(
    "customers.csv",
    "customers.html",
    separator=";",
)

batch = tabalyst.generate_reports(
    ["*.csv"],
    output_dir="reports",
)

sample = tabalyst.sample_csv(
    "customers.csv",
    method="random",
    rows=1000,
    seed=42,
)

scan = tabalyst.scan("orders.json")
scans = tabalyst.generate_scans(["data/*.json"], output_dir="scans")
reports = tabalyst.generate_reports(["scans/*.scan.json"], from_scan=True)

analyze() returns the JSON-serializable profile for one report. generate_reports() returns the complete batch plan, successes, and failures. scan() returns a scan result without writing anything; generate_scans() writes one .scan.json document per source; from_scan=True builds reports from such documents. Expected failures derive from tabalyst.TabalystError.

Existing artifacts are never replaced silently. Pass force=True when replacement is intentional.

Configuration

Configuration files are strict JSON. A minimal file is:

{
  "scan": {
    "csv": {
      "delimiter": ";",
      "encoding": "cp1252"
    },
    "values": {"null_markers": ["N/A"]}
  }
}

Analysis settings, CSV reading included, go in the scan object, shared by tabalyst report and tabalyst scan. Top-level settings shape the report presentation, and csv configures tabalyst sample. Explicit CLI or Python arguments override the configuration file, which overrides Tabalyst defaults. No configuration file is loaded unless it is passed with --config. See the configuration reference for all analysis settings.

Local-first behavior and current limits

Tabalyst performs analysis locally and adds no telemetry or remote processing. Generated JSON and HTML may contain source values and should be shared accordingly.

Reports, samples, and scans read files as a stream, with memory bounded by configurable limits, including duplicate-row detection (scan.limits.max_tracked_records). Reports and scans show the share of the file read; throughput estimates, recursive directory input, and parallel batch execution will require later work.

Examples and development

The repository includes small and synthetic public examples under examples/. See the examples README for regeneration commands.

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

Additional documentation:

• Architecture
• Configuration
• Release procedure

Please report defects and feature requests through the GitHub issue tracker.

License

Tabalyst is released under the MIT License.

Created by Gregory Borelli — Catalyseur Numérique.