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Reversible Data Transforms

Project description

DAI-Lab An Open Source Project from the Data to AI Lab, at MIT

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RDT is a Python library used to transform data for data science libraries and preserve the transformations in order to revert them as needed.


RDT is part of the SDV project and is automatically installed alongside it. For details about this process please visit the SDV Installation Guide

Optionally, RDT can also be installed as a standalone library using the following commands:

Using pip:

pip install rdt

Using conda:

conda install -c sdv-dev -c conda-forge rdt

For more installation options please visit the RDT installation Guide


In this short series of tutorials we will guide you through a series of steps that will help you getting started using RDT to transform columns, tables and datasets.

Transforming a column

In this first guide, you will learn how to use RDT in its simplest form, transforming a single column loaded as a pandas.DataFrame object.

1. Load the demo data

You can load some demo data using the rdt.get_demo function, which will return some random data for you to play with.

from rdt import get_demo

data = get_demo()

This will return a pandas.DataFrame with 10 rows and 4 columns, one of each data type supported:

   0_int    1_float 2_str          3_datetime
0   38.0  46.872441     b 2021-02-10 21:50:00
1   77.0  13.150228   NaN 2021-07-19 21:14:00
2   21.0        NaN     b                 NaT
3   10.0  37.128869     c 2019-10-15 21:39:00
4   91.0  41.341214     a 2020-10-31 11:57:00
5   67.0  92.237335     a                 NaT
6    NaN  51.598682   NaN 2020-04-01 01:56:00
7    NaN  42.204396     c 2020-03-12 22:12:00
8   68.0        NaN     c 2021-02-25 16:04:00
9    7.0  31.542918     a 2020-07-12 03:12:00

Notice how the data is random, so your output might look a bit different. Also notice how RDT introduced some null values randomly.

2. Load the transformer

In this example we will use the datetime column, so let's load a DatetimeTransformer.

from rdt.transformers import DatetimeTransformer

transformer = DatetimeTransformer()

3. Fit the Transformer

Before being able to transform the data, we need the transformer to learn from it.

We will do this by calling its fit method passing the column that we want to transform.['3_datetime'])

4. Transform the data

Once the transformer is fitted, we can pass the data again to its transform method in order to get the transformed version of the data.

transformed = transformer.transform(data['3_datetime'])

The output will be a numpy.ndarray with two columns, one with the datetimes transformed to integer timestamps, and another one indicating with 1s which values were null in the original data.

array([[1.61299380e+18, 0.00000000e+00],
       [1.62672924e+18, 0.00000000e+00],
       [1.59919923e+18, 1.00000000e+00],
       [1.57117554e+18, 0.00000000e+00],
       [1.60414542e+18, 0.00000000e+00],
       [1.59919923e+18, 1.00000000e+00],
       [1.58570616e+18, 0.00000000e+00],
       [1.58405112e+18, 0.00000000e+00],
       [1.61426904e+18, 0.00000000e+00],
       [1.59452352e+18, 0.00000000e+00]])

5. Revert the column transformation

In order to revert the previous transformation, the transformed data can be passed to the reverse_transform method of the transformer:

reversed_data = transformer.reverse_transform(transformed)

The output will be a pandas.Series containing the reverted values, which should be exactly like the original ones.

0   2021-02-10 21:50:00
1   2021-07-19 21:14:00
2                   NaT
3   2019-10-15 21:39:00
4   2020-10-31 11:57:00
5                   NaT
6   2020-04-01 01:56:00
7   2020-03-12 22:12:00
8   2021-02-25 16:04:00
9   2020-07-12 03:12:00
dtype: datetime64[ns]

Transforming a table

Once we know how to transform a single column, we can try to go the next level and transform a table with multiple columns.

1. Load the HyperTransformer

In order to manuipulate a complete table we will need to load a rdt.HyperTransformer.

from rdt import HyperTransformer

ht = HyperTransformer()

2. Fit the HyperTransformer

Just like the transfomer, the HyperTransformer needs to be fitted before being able to transform data.

This is done by calling its fit method passing the data DataFrame.

3. Transform the table data

Once the HyperTransformer is fitted, we can pass the data again to its transform method in order to get the transformed version of the data.

transformed = ht.transform(data)

The output, will now be another pandas.DataFrame with the numerical representation of our data.

    0_int  0_int#1    1_float  1_float#1  2_str    3_datetime  3_datetime#1
0  38.000      0.0  46.872441        0.0   0.70  1.612994e+18           0.0
1  77.000      0.0  13.150228        0.0   0.90  1.626729e+18           0.0
2  21.000      0.0  44.509511        1.0   0.70  1.599199e+18           1.0
3  10.000      0.0  37.128869        0.0   0.15  1.571176e+18           0.0
4  91.000      0.0  41.341214        0.0   0.45  1.604145e+18           0.0
5  67.000      0.0  92.237335        0.0   0.45  1.599199e+18           1.0
6  47.375      1.0  51.598682        0.0   0.90  1.585706e+18           0.0
7  47.375      1.0  42.204396        0.0   0.15  1.584051e+18           0.0
8  68.000      0.0  44.509511        1.0   0.15  1.614269e+18           0.0
9   7.000      0.0  31.542918        0.0   0.45  1.594524e+18           0.0

4. Revert the table transformation

In order to revert the transformation and recover the original data from the transformed one, we need to call reverse_transform method of the HyperTransformer instance passing it the transformed data.

reversed_data = ht.reverse_transform(transformed)

Which should output, again, a table that looks exactly like the original one.

   0_int    1_float 2_str          3_datetime
0   38.0  46.872441     b 2021-02-10 21:50:00
1   77.0  13.150228   NaN 2021-07-19 21:14:00
2   21.0        NaN     b                 NaT
3   10.0  37.128869     c 2019-10-15 21:39:00
4   91.0  41.341214     a 2020-10-31 11:57:00
5   67.0  92.237335     a                 NaT
6    NaN  51.598682   NaN 2020-04-01 01:56:00
7    NaN  42.204396     c 2020-03-12 22:12:00
8   68.0        NaN     c 2021-02-25 16:04:00
9    7.0  31.542918     a 2020-07-12 03:12:00

The Synthetic Data Vault

This repository is part of The Synthetic Data Vault Project


0.4.1 - 2021-03-29

This release improves the HyperTransformer memory usage when working with a high number of columns or a high number of categorical values when using one hot encoding.

Issues closed

  • Boolean, Datetime and LabelEncoding transformers fail with 2D ndarray - Issue #160 by @pvk-developer
  • HyperTransformer: Memory usage increase when reverse_transform is called - Issue #156 by @pvk-developer and @AnupamaGangadhar

0.4.0 - 2021-02-24

In this release a change in the HyperTransformer allows using it to transform and reverse transform a subset of the columns seen during training.

The anonymization functionality which was deprecated and not being used has also been removed along with the Faker dependency.

Issues closed

  • Allow the HyperTransformer to be used on a subset of the columns - Issue #152 by @csala
  • Remove faker - Issue #150 by @csala

0.3.0 - 2021-01-27

This release changes the behavior of the HyperTransformer to prevent it from modifying any column in the given DataFrame if the transformers dictionary is passed empty.

Issues closed

  • If transformers is an empty dict, do nothing - Issue #149 by @csala

0.2.10 - 2020-12-18

This release adds a new argument to the HyperTransformer which gives control over which transformers to use by default for each dtype if no specific transformer has been specified for the field.

This is also the first version to be officially released on conda.

Issues closed

  • Add dtype_transformers argument to HyperTransformer - Issue #148 by @csala
  • Makes Copulas an optional dependency - Issue #144 by @fealho

0.2.9 - 2020-11-27

This release fixes a bug that prevented the CategoricalTransformer from working properly when being passed data that contained numerical data only, without any strings, but also contained None or NaN values.

Issues closed

  • KeyError: nan - CategoricalTransformer fails on numerical + nan data only - Issue #142 by @csala

0.2.8 - 2020-11-20

This release fixes a few minor bugs, including some which prevented RDT from fully working on Windows systems.

Thanks to this fixes, as well as a new testing infrastructure that has been set up, from now on RDT is officially supported on Windows systems, as well as on the Linux and macOS systems which were previously supported.

Issues closed

  • TypeError: unsupported operand type(s) for: 'NoneType' and 'int' - Issue #132 by @csala
  • Example does not work on Windows - Issue #114 by @csala
  • OneHotEncodingTransformer producing all zeros - Issue #135 by @fealho
  • OneHotEncodingTransformer support for lists and lists of lists - Issue #137 by @fealho

0.2.7 - 2020-10-16

In this release we drop the support for the now officially dead Python 3.5 and introduce a new feature in the DatetimeTransformer which reduces the dimensionality of the generated numerical values while also ensuring that the reverted datetimes maintain the same level as time unit precision as the original ones.

  • Drop Py35 support - Issue #129 by @csala
  • Add option to drop constant parts of the datetimes - Issue #130 by @csala

0.2.6 - 2020-10-05

  • Add GaussianCopulaTransformer - Issue #125 by @csala
  • dtype category error - Issue #124 by @csala

0.2.5 - 2020-09-18

Miunor bugfixing release.

Bugs Fixed

  • Handle NaNs in OneHotEncodingTransformer - Issue #118 by @csala
  • OneHotEncodingTransformer fails if there is only one category - Issue #119 by @csala
  • All NaN column produces NaN values enhancement - Issue #121 by @csala
  • Make the CategoricalTransformer learn the column dtype and restore it back - Issue #122 by @csala

0.2.4 - 2020-08-08

General Improvements

  • Support Python 3.8 - Issue #117 by @csala
  • Support pandas >1 - Issue #116 by @csala

0.2.3 - 2020-07-09

  • Implement OneHot and Label encoding as transformers - Issue #112 by @csala

0.2.2 - 2020-06-26

Bugs Fixed

  • Escape column_name in hypertransformer - Issue #110 by @csala

0.2.1 - 2020-01-17

Bugs Fixed

  • Boolean Transformer fails to revert when there are NO nulls - Issue #103 by @JDTheRipperPC

0.2.0 - 2019-10-15

This version comes with a brand new API and internal implementation, removing the old metadata JSON from the user provided arguments, and making each transformer work only with pandas.Series of their corresponding data type.

As part of this change, several transformer names have been changed and a new BooleanTransformer and a feature to automatically decide which transformers to use based on dtypes have been added.

Unit test coverage has also been increased to 100%.

Special thanks to @JDTheRipperPC and @csala for the big efforts put in making this release possible.


  • Drop the usage of meta - Issue #72 by @JDTheRipperPC
  • Make CatTransformer.probability_map deterministic - Issue #25 by @csala

0.1.3 - 2019-09-24

New Features

  • Add attributes NullTransformer and col_meta - Issue #30 by @ManuelAlvarezC

General Improvements

  • Integrate with CodeCov - Issue #89 by @csala
  • Remake Sphinx Documentation - Issue #96 by @JDTheRipperPC
  • Improve README - Issue #92 by @JDTheRipperPC
  • Document RELEASE workflow - Issue #93 by @JDTheRipperPC
  • Add support to Python 3.7 - Issue #38 by @ManuelAlvarezC
  • Create way to pass HyperTransformer table dict - Issue #45 by @ManuelAlvarezC


  • Add a numerical transformer for positive numbers.
  • Add option to anonymize data on categorical transformer.
  • Move the col_meta argument from method-level to class-level.
  • Move the logic for missing values from the transformers into the HyperTransformer.
  • Removed unreacheble lines in NullTransformer.
  • Numbertransfomer to set default value to 0 when the column is null.
  • Add a CLA for collaborators.
  • Refactor performance-wise the transformers.


  • Improve handling of NaN in NumberTransformer and CatTransformer.
  • Add unittests for HyperTransformer.
  • Remove unused methods get_types and impute_table from HyperTransformer.
  • Make NumberTransformer enforce dtype int on integer data.
  • Make DTTransformer check data format before transforming.
  • Add minimal API Reference.
  • Merge rdt.utils into HyperTransformer class.


  • First release on PyPI.

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