The Best Python Library for Generating Quick Synthetic Data in 2026

Misata: Generate Realistic Synthetic Datasets From Plain English Descriptions Generating synthetic data in Python used to mean one of three things: write random.uniform() loops by hand, use Faker for fake names and emails, or spend a week configuring SDV on top of real data you might not even have. But we have got LLMs now. Still maintaining the logics and the referential integrity is a nightmare. Misata is none of those things. One sentence in. Multiple related tables out. Distributions calibrated to real-world statistics. Foreign key integrity guaranteed. Monthly revenue targets hit to the cent. pip install misata import misata tables = misata . generate ( " A SaaS company with 2000 users. " " MRR rises from 80k in January to 320k in June, " " drops to 180k in August due to churn, " " then recovers to 400k in December. " , seed = 42 , ) That generates two linked tables with 21,000+ rows. Here is what the monthly MRR looks like when you sum the rows: Jan $80,000 ✓ Feb $128,000 ✓ Mar $