# Data-Centric-AI-Community/fg-data-synthetic

Synthetic data generators for tabular and time-series data

Repository: https://github.com/Data-Centric-AI-Community/fg-data-synthetic
Canonical: https://ross.abutalabs.com/products/fg-data-synthetic
Homepage: https://docs.sdk.ydata.ai
Language: Jupyter Notebook
License: MIT
License Family: permissive
Topics: gan-architectures, gan, deep-learning, synthetic-data, tensorflow2, machine-learning, training-data, python3, datagenerator, datageneration, timeseries, generative-adversarial-network, gans, pytorch, time-series
Last push: 2026-04-23T12:34:53+00:00

## Health v2 (maintenance only)
Score: 72/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 78, release rhythm 48, longevity 100
- inputs: {"age_days": 2312, "days_push": 132, "days_rel": 132, "gap_med": 294.5, "n_releases_24m": 3}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1654, forks 257 (observed 2026-08-28T04:05:17.257359+00:00)

## What it is
A Python library for generating synthetic tabular and time-series data using state-of-the-art generative models such as GANs (including CTGAN) and Gaussian Mixture models. It is the community-maintained continuation of ydata-synthetic, available on PyPI as fg-data-synthetic.

## Use cases
- generate synthetic tabular data for machine learning training
- create synthetic time-series data
- augment or balance imbalanced datasets
- share data while preserving privacy compliance
- remove bias from training datasets
- generate synthetic data without a GPU using Gaussian Mixture models
- generate synthetic data via a low-code Streamlit UI

## When to avoid
- you need an end-to-end enterprise UI for data preparation and evaluation
- you need synthetic data for unstructured data like images or text
- you require strict formal privacy guarantees rather than statistical replication

## Facets
- artifact type: library
- maturity: active
- function: data-generation, machine-learning, deep-learning
- domain: data-science, machine-learning, privacy
- platform: python, cross-platform
- tags: synthetic-data, gan, ctgan, time-series, tabular-data, generative-models, tensorflow, pytorch

## Member repositories
- Data-Centric-AI-Community/fg-data-synthetic (main) score 72

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:17.257359+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T03:44:54.471159+00:00, confidence not recorded.
  - readme: https://github.com/Data-Centric-AI-Community/fg-data-synthetic (fetched 2026-08-28T04:05:17.257359+00:00, sha 4d844fa5f152)
  - homepage: https://docs.sdk.ydata.ai (fetched 2026-08-29T11:17:48.586573+00:00, sha 36c6c3c2e4f9)
  - registry_pypi: https://pypi.org/pypi/fg-data-synthetic/json (fetched 2026-08-29T11:17:48.595841+00:00, sha b8d7275d8da1)
- Data as of 2026-08-30T08:39:29.467469+00:00.