#4848th of 50 in Data and scraping for AI
fg-data-synthetic
Python library for synthetic tabular and time-series data using GANs and Gaussian Mixture
- Stars
- 1.7k
- License
- MIT
- Last commit
- Apr 2026
- Last release
- Apr 2026
- Language
- Jupyter Notebook
1,618 on 21 Apr 20261,664 on 10 Nov 2026
From GitHub's daily star counts.
Checked on GitHub.
Overview
fg-data-synthetic is a Python package, the renamed successor to ydata-synthetic, that trains generative models on tabular or sequential data and samples synthetic records. It includes GAN variants (CGAN, WGAN, WGAN-GP, DRAGAN, Cramer, CTGAN), a Gaussian Mixture model that needs no GPU, and TimeGAN and DoppelGANger for time series. A Streamlit app offers a low-code flow to train a synthesizer and profile the output.
Who it is for: Data scientists generating synthetic tabular and time-series datasets
Strengths
- Covers both tabular (CTGAN, WGAN-GP, Gaussian Mixture) and time-series (TimeGAN, DoppelGANger) synthesis
- Gaussian Mixture model lets you start without a GPU
- Optional Streamlit UI for training and profiling without writing code
- Colab notebooks for adult census, stock and FCC MBA datasets
Weaknesses
- Deep learning models are built on TensorFlow 2, not PyTorch
- README lists Python 3.9 and 3.10 only
- Streamlit app does not run inside Jupyter notebooks
- Streamlit app covers tabular data only; time-series models are code-only
What it needs
- GPU optional
- Needs TensorFlow 2, Streamlit (optional extra)
- Models: CGAN, WGAN, WGAN-GP, DRAGAN, Cramer GAN
Quick answers
Is fg-data-synthetic open source?
Yes. Its code is on GitHub under the MIT license.
Does fg-data-synthetic need a GPU?
No. It runs on a CPU and can use a GPU when you have one.
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