Generate Marketplace Synthetic Data in Python

Online marketplaces have a distinctive data shape: a small number of power sellers drive most of the volume, buyer ratings cluster high, and listing prices follow a long-tailed distribution. If you generate seller, buyer, and listing data independently you lose all of this structure, and your marketplace analytics tool will look wrong from the first dashboard query. Misata generates a fully wired marketplace dataset where seller volume follows a power-law, ratings are beta-distributed toward 4–5 stars, and every order references a real buyer and listing.

import misata

tables = misata.generate("A freelance marketplace with 500 sellers and 2000 buyers", rows=2000, seed=42)
print(list(tables.keys()))   # ['sellers', 'buyers', 'listings', 'orders']
print(tables["orders"][["amount", "status"]].describe())

What Misata generates

Four tables: sellers, buyers, listings (linked to sellers), and orders (linked to buyers and listings). Order completion rate is ~85% by default.

Tables and columns

TableKey columns
sellersseller_id, name, email, rating, total_sales, joined_at, country
buyersbuyer_id, name, email, total_spent, joined_at
listingslisting_id, seller_id, title, category, price, status, created_at
ordersorder_id, buyer_id, listing_id, amount, status, created_at, completed_at

Realistic distributions

  • Seller volume follows a power-law, top 10% of sellers account for ~60% of total_sales
  • Seller ratings beta-distributed (skewed toward 4–5 stars), matching real platform rating inflation
  • Listing prices lognormal, affordable commodities plus premium service listings
  • Order completion rate ~85%, remaining 15% in pending, disputed, or cancelled states
  • completed_at is always after created_at for completed orders

Quick start

import misata

tables = misata.generate("A freelance marketplace with 500 sellers and 2000 buyers", rows=2000, seed=42)

# Power-law seller distribution
import numpy as np
total_sales = tables["sellers"]["total_sales"].sort_values(ascending=False)
top10_pct = total_sales.head(len(total_sales) // 10).sum() / total_sales.sum()
print(f"Top 10% sellers: {top10_pct:.0%} of total sales")

# Order completion rate
print(tables["orders"]["status"].value_counts(normalize=True))

Common use cases

  • Marketplace trust and safety: generate seller profiles with varied rating histories for testing fraud detection and account suspension workflows
  • Search ranking model training: use listings with price, category, and seller rating features to train relevance ranking models
  • Commission and fee calculation testing: validate fee structures against thousands of orders with varied amounts and statuses
  • Seller analytics dashboard development: build GMV, conversion rate, and listing performance reports on realistic power-law distributed seller data
  • Buyer recommendation systems: use orders history to prototype collaborative filtering before real purchase data exists
  • Dispute resolution workflow testing: generate orders in all status states including disputed for testing escalation logic

Advanced: GMV narrative curves

tables = misata.generate(
    "Marketplace with 1k sellers — Black Friday GMV spike, slow January, growing through the year",
    rows=5000,
    seed=42,
)

Advanced: locale-aware generation

# Latin American marketplace — BRL/MXN pricing, regional product categories
tables = misata.generate("Brazilian ecommerce marketplace with 500 sellers", rows=2000)

# Southeast Asian gig marketplace — SGD pricing, regional skills
tables = misata.generate("Freelance platform in Southeast Asia with 300 sellers", rows=1000)

Advanced: quality-guaranteed generation

tables = misata.generate(
    "Freelance marketplace with 500 sellers",
    min_quality_score=85,
    smart_correlations=True,
    rows=2000,
    seed=42,
)

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