---
id: rdt
version: "1.22.0"
license: BUSL-1.1
license_treatment: unclear
maintenance: active
---

# rdt — Reversible Data Transforms

License: unclear · Maintenance: active · Downloads: 196.1K/mo

## What it is and what it does

RDT is a data transformation library that automatically detects the semantic type of each column in your dataset (datetime, boolean, categorical, numerical) and applies an appropriate reversible encoder to convert it to numerical form. Once transformed, your data is ready for machine learning pipelines. The key feature is reversibility: you can transform data back to its original format after processing, which is useful for validation, inspection, or final output generation.

The library works through a HyperTransformer that learns column statistics during a fit phase, then applies the learned transformations consistently. It depends on numpy, pandas, scipy, and scikit-learn for numerical operations, Faker for synthetic value generation, and python-dateutil for temporal handling. The library supports Python 3.9 through 3.14 and is actively maintained.

Use it for:

- Prepare mixed-type tabular data for machine learning models that require numerical input
- Generate synthetic data by transforming real data, training generative models, then reversing to realistic format
- Handle missing values and categorical encoding automatically across multiple columns at once
- Validate data transformations by round-tripping: transform and reverse to check fidelity
- Normalize heterogeneous datasets from databases or CSVs into a uniform numerical representation

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

RDT converts raw, mixed-type data into fully numerical form via reversible transformations, then converts it back to the original format.

Yes, with conditions. RDT is actively maintained, has low install friction, and solves a real problem in data preprocessing. However, the BUSL-1.1 license restricts commercial use until a future date—verify the license terms match your use case before committing. No known security vulnerabilities. Suitable for research, internal tools, and open-source projects where the license permits.

## Install

pip install rdt
uv add rdt
poetry add rdt

## Installing rdt

Before you install: Low friction: pure Python wheel with six standard scientific dependencies (numpy, pandas, scipy, scikit-learn, Faker, python-dateutil). Active maintenance with a release 7 days ago.

License in practice: Licensed under BUSL-1.1 (Business Source License), a source-available license that restricts commercial use until a future date; verify terms apply to your use case.

Quickstart:

pip install rdt

from rdt import HyperTransformer, get_demo

data = get_demo()
ht = HyperTransformer()
ht.detect_initial_config(data=data)
ht.fit(data)
transformed = ht.transform(data)
original = ht.reverse_transform(transformed)

Requires Python 3.9 or later (supports up to 3.14). BUSL-1.1 license may restrict commercial use.

Verify before relying:

- Whether BUSL-1.1 restrictions affect your intended use (commercial, internal, or open-source context)
- Performance characteristics with large datasets or high-dimensional data
- Reversibility accuracy for edge cases (missing values, rare categories, outliers)

## Package facts

- License: BUSL-1.1 (unclear)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 196.1K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

reversible data transformation, convert data to numerical, data preprocessing pipeline, synthetic data preparation, mixed-type column encoding, data-preprocessing, reversible-transforms, synthetic-data

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