rdt
Reversible Data Transforms
Decision gist · record as of 2026-08-14
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.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.9 or later (supports up to 3.14).
- BUSL-1.1 license may restrict commercial use.
- Low friction: pure Python wheel with six standard scientific dependencies (numpy, pandas, scipy, scikit-learn, Faker, python-dateutil).
License · maintenance · safety
BUSL-1.1 (unclear) — 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.
last release 2026-08-07 (7 days) · last repo commit 2026-08-10 · 135 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 196,091 downloads/mo, #9,798 on PyPI
Alternatives
Verify before relying
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)- 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)
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 on this page. Verify before relying on it.
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
rdt on PyPI
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.
Requires Python 3.9 or later (supports up to 3.14). BUSL-1.1 license may restrict commercial use.
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)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 the current Python release <3.15,>=3.9 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 6 packagesnumpypandasscipyscikit-learnFakerpython-dateutil |
| Maintenance | Actively maintained 7 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 196,091 / month, #9,798 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableIntended Audience :: DevelopersNatural Language :: EnglishProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Programming Language :: Python :: 3.9Topic :: Scientific/Engineering :: Artificial Intelligence |
Evidence: rdt-1.22.0-py3-none-any.whl
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