SDV supports modeling single table datasets. It provides unique features for making it easy for the user to learn models and synthesize datasets. Some important features of sdv.tabular include:
sdv.tabular
Support for tables with primary key
Support to anonymize certain fields like addresses, emails, phone numbers, names and other PII information.
Support for a number of different data types - categorical, numerical, discrete-ordinal and datetimes.
Support multiple types of statistical and deep learning models:
GaussianCopula Model: A tool to model multivariate distributions using copula functions.
CTGAN Model: A GAN-based Deep Learning data synthesizer that can generate synthetic tabular data with high fidelity.