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ydata-synthetic is the go-to Python package for synthetic data generation for tabular and time-series data. It uses the latest Generative AI models to learn the properties of real data and create realistic synthetic data. This project was created to educate the community about synthetic data and its applications in real-world domains, such as data augmentation, bias mitigation, data sharing, and privacy engineering. To learn more about Synthetic Data and its applications, check this article.

Current Functionality

  • 🤖 Create Realistic Synthetic Data using Generative AI Models: ydata-synthetic supports the state-of-the-art generative adversarial networks for data generation, namely Vanilla GAN, CGAN, WGAN, WGAN-GP, DRAGAN, Cramer GAN, CWGAN-GP, CTGAN, and TimeGAN. Learn more about the use of GANs for Synthetic Data generation.

  • 📀 Synthetic Data Generation for Tabular and Time-Series Data: The package supports the synthesization of tabular and time-series data, covering a wide range of real-world applications. Learn how to leverage ydata-synthetic for tabular and time-series data.

  • 💻 Best Generation Experience in Open Source: Including a guided UI experience for the generation of synthetic data, from reading the data to visualization of synthetic data. All served by a slick Streamlit app. Here's a quick overview 1min


Looking for an end-to-end solution to Synthetic Data Generation?

YData Fabric enables the generation of high-quality datasets within a full UI experience, from data preparation to synthetic data generation and evaluation. Check out the Community Version.

Supported Data Types

Tabular data does not have a temporal dependence, and can be structured and organized in a table-like format, where features are represented in columns, whereas observations correspond to the rows.

Additionally, tabular data usually comprises both numeric and categorical features. Numeric features are those that encode quantitative values, whereas categorical represent qualitative measurements. Categorical features can further divided in ordinal, binary or boolean, and nominal features.

Learn more about synthesizing tabular data in this article, or check the quickstart guide to get started with the synthesization of tabular datasets.

Time-series data exhibit a sequencial, temporal dependency between records, and may present a wide range of patterns and trends, including seasonality (patterns that repeat at calendar periods -- days, weeks, months -- such as holiday sales, for instance) or periodicity (patterns that repeat over time).

Read more about generating time-series data in this article and check this quickstart guide to get started with time-series data synthesization.

Supported Generative AI Models

The following architectures are currently supported:

  • GAN
  • CGAN (Conditional GAN)
  • WGAN (Wasserstein GAN)
  • WGAN-GP (Wassertein GAN with Gradient Penalty)
  • DRAGAN (Deep Regret Analytic GAN)
  • Cramer GAN (Cramer Distance Solution to Biased Wasserstein Gradients)
  • CWGAN-GP (Conditional Wassertein GAN with Gradient Penalty)
  • CTGAN (Conditional Tabular GAN)
  • TimeGAN (specifically for time-series data)
  • DoppelGANger (specifically for time-series data)