The Synthetic Data Vault (SDV) is a Python library designed to be your one-stop shop for creating tabular synthetic data. The SDV uses a variety of machine learning algorithms to learn patterns from your real data and emulate them in synthetic data.

🧠 Create synthetic data using machine learning. The SDV offers multiple models, ranging from classical statistical methods (GaussianCopula) to deep learning methods (CTGAN). Generate data for single tables, multiple connected tables or sequential tables.

📊 Evaluate and visualize data. Compare the synthetic data to the real data against a variety of measures. Diagnose problems and generate a quality report to get more insights.

🔄 Preprocess, anonymize and define constraints. Control data processing to improve the quality of synthetic data, choose from different types of anonymization and define business rules in the form of logical constraints.

Important Links
Tutorials	Get some hands-on experience with the SDV. Launch the tutorial notebooks and run the code yourself.
📖 Docs	Learn how to use the SDV library with user guides and API references.
📙 Blog	Get more insights about using the SDV, deploying models and our synthetic data community.
👥 DataCebo Forum	Discuss SDV features, ask questions, and receive help .
💻 Website	Check out the SDV website for more information about the project.

The SDV is publicly available under the Business Source License. Install SDV using pip or conda. We recommend using a virtual environment to avoid conflicts with other software on your device.

pip install sdv

conda install -c pytorch -c conda-forge sdv

Load a demo dataset to get started. This dataset is a single table describing guests staying at a fictional hotel.

from sdv.datasets.demo import download_demo

real_data, metadata = download_demo(modality='single_table'