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Fg Data Synthetic

Fg Data Synthetic is an open-source Python package of models and examples for generating synthetic tabular and time-series data, with a Streamlit interface for tabular synthesis.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Fg Data Synthetic — OpenViST project profile in AI Data & Analytics.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it when a Python workflow needs generated tabular or time-series data, or when exploring the documented Streamlit route for training and profiling tabular samples.

Who it is for

  • Python developers who need synthetic structured data for development or testing.
  • Practitioners exploring GAN-based generation for tables or time series.
  • Users who want a guided interface for training tabular synthesizers and profiling generated samples.

Documented uses

  • Generate tabular samples using models such as CTGAN.
  • Create synthetic time-series data using examples based on TimeGAN or DoppelGANger.
  • Train a tabular synthesizer and generate and profile samples through the Streamlit interface.
Before you choose

Requirements & limits

The repository identifies the project as open source and lists an MIT license. The README gives PyPI installation instructions and specifies Python 3.9 or 3.10.

  • The Streamlit interface is documented for tabular generation, while time-series workflows are shown in Jupyter notebook examples.
  • The README says its Gaussian Mixture model can be used without a GPU; deep-learning models are described as implemented with TensorFlow 2.0.
  • The interface supports CGAN, WGAN, WGANGP, DRAGAN, CRAMER and CTGAN.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Install the package and launch its Streamlit interface

Set up the documented UI entry point for training a tabular synthesizer and generating and profiling samples.

Before you start

  • Python 3.9 or 3.10, as listed in the repository README.
  • Run the quickstart in a Python file; Jupyter notebooks are not supported for this interface.

Steps

  1. Install the Streamlit extra with the command below.

    Documented command
    pip install fg-data-synthetic[streamlit]

  2. In a Python file, import streamlit_app from data_synthetic.

  3. Call streamlit_app.run().

Expected outcome

The README presents this interface for training a synthesizer and generating and profiling synthetic tabular samples.

Guide limitations

  • The documented UI flows concern structured tabular data.
  • The quickstart explicitly excludes Jupyter notebooks.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
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Sources checked
12 October 2026
Original website title
Data-Centric-AI-Community/fg-data-synthetic · Official homepage title · retained in its source language
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