Diyago/Tabular-data-generation
We well know GANs for success in the realistic image generation. However, they can be applied in tabular data generation. We will review and examine some recent papers about tabular GANs in action.
This tool helps data professionals create realistic, artificial tabular datasets when real data is scarce, sensitive, or difficult to obtain. You provide your existing dataset, and it generates a new, larger dataset that mimics the original's statistical properties and relationships between columns. This is useful for data scientists, analysts, or anyone working with structured data who needs more examples for training models or testing analyses.
564 stars. Actively maintained with 18 commits in the last 30 days.
Use this if you need to expand a limited dataset, anonymize sensitive information, or generate synthetic data that closely resembles your original tables for various analytical or machine learning tasks.
Not ideal if your primary need is simply to anonymize data without requiring new synthetic entries, or if you need to generate entirely random data without any underlying distribution from an existing dataset.
Stars
564
Forks
84
Language
Python
License
Apache-2.0
Category
Last pushed
Mar 07, 2026
Commits (30d)
18
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/Diyago/Tabular-data-generation"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Related frameworks
meta-llama/synthetic-data-kit
Tool for generating high quality Synthetic datasets
Data-Centric-AI-Community/ydata-synthetic
Synthetic data generators for tabular and time-series data
tdspora/syngen
Open-source version of the TDspora synthetic data generation algorithm.
vanderschaarlab/synthcity
A library for generating and evaluating synthetic tabular data for privacy, fairness and data...
always-further/deepfabric
Generate High-Quality Synthetics, Train, Measure, and Evaluate in a Single Pipeline