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Conditional GAN for generating synthetic tabular data.
| Date | Stars |
|---|---|
| 2026-07-24 | 1559 |
| 2026-07-25 | 1559 |
| 2026-07-28 | 1559 |
| 2026-07-30 | 1559 |
| 2026-08-06 | 1559 |
Today
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Momentum
5.0
growth rate 0.00%/day
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<i>This repository is part of <a href="https://sdv.dev">The Synthetic Data Vault Project</a>, a project from <a href="https://datacebo.com">DataCebo</a>.</i>
</p>
[](https://pypi.org/search/?c=Development+Status+%3A%3A+2+-+Pre-Alpha)
[](https://pypi.python.org/pypi/ctgan)
[](https://github.com/sdv-dev/CTGAN/actions/workflows/unit.yml)
[](https://pepy.tech/project/ctgan)
[](https://codecov.io/gh/sdv-dev/CTGAN)
[](https://forum.datacebo.com)
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<a href="https://github.com/sdv-dev/CTGAN">
<img align="center" width=40% src="https://github.com/sdv-dev/SDV/blob/stable/docs/images/CTGAN-DataCebo.png"></img>
</a>
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# Overview
CTGAN is a collection of Deep Learning based synthetic data generators for single table data, which are able to learn from real data and generate synthetic data with high fidelity.
| Important Links | |
| --------------------------------------------- | ------------------------------------------------------------------------------------------|
| :computer: **[Website]** | Check out the SDV Website for more information about our overall synthetic data ecosystem.|
| :orange_book: **[Blog]** | A deeper look at open source, synthetic data creation and evaluation. |
| :book: **[Documentation]** | Quickstarts, User and Development Guides, and API Reference. |
| :octocat: **[Repository]** | The link to the Github Repository of this library. |
| :keyboard: **[Development Status]** | This software is in its Pre-Alpha stage. |
| :busts_in_silhouette: **[DataCebo Forum]** | Discuss CTGAN features, ask questions, and receive help. |
[Website]: https://sdv.dev
[Blog]: https://datacebo.com/blog
[Documentation]: https://bit.ly/sdv-docs
[Repository]: https://github.com/sdv-dev/CTGAN
[License]: https://github.com/sdv-dev/CTGAN/blob/main/LICENSE
[Development Status]: https://pypi.org/search/?c=Development+Status+%3A%3A+2+-+Pre-Alpha
[DataCebo Forum]: https://forum.datacebo.com
Currently, this library implements the **CTGAN** and **TVAE** models described in the [Modeling Tabular data using Conditional GAN](https://arxiv.org/abs/1907.00503) paper, presented at the 2019 NeurIPS conference.
# Install
## Use CTGAN through the SDV library
:warning: If you're just getting started with synthetic data, we recommend installing the SDV library which provides user-friendly APIs for accessing CTGAN. :warning:
The SDV library provides wrappers for preprocessing your data as well as additional usability features like constraints. See the [SDV documentation](https://bit.ly/sdv-docs) to get started.
## Use the CTGAN standalone library
Alternatively, you can also install and use **CTGAN** directly, as a standalone library:
**Using `pip`:**
```bash
pip install ctgan
```
**Using `conda`:**
```bash
conda install -c pytorch -c conda-forge ctgan
```
When using the CTGAN library directly, you may need to manually preprocess your data into the correct format, for example:
* Continuous data must be represented as floats
* Discrete data must be represented as ints or strings
* The data should not contain any missing values
# Usage ExampExcerpt of 7,318 characters
Read on GitHub
Carles Sala · @precognit
89
Andrew Montanez
85
SDV Team · @sdv-dev · United States
61
Felipe Alex Hofmann · @datacebo · United States
59
Plamen Valentinov Kolev · @sdv-dev
43
30
Katharine Xiao
22
17
Frances Hartwell
13
10
Roy Wedge
10
9
Gaurav Sheni · @datacebo · United States
8
4
3
Jose David Pérez Cañellas
2
1
Tim Vink · Netherlands
1
Romain Egele · France
1
1
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:13198f27fbf662d3, topic:synthetic-data, readme:synthetic data