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# 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.

```python
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

| Table | Key columns |
|:--|:--|
| `sellers` | `seller_id`, `name`, `email`, `rating`, `total_sales`, `joined_at`, `country` |
| `buyers` | `buyer_id`, `name`, `email`, `total_spent`, `joined_at` |
| `listings` | `listing_id`, `seller_id`, `title`, `category`, `price`, `status`, `created_at` |
| `orders` | `order_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

```python
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

```python
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

```python
# 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

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

## Related guides

- [Multi-table Synthetic Data](../guides/multi-table-synthetic-data.md)
- [Narrative Patterns](../guides/narrative-patterns.md)
- [Database Seeding in Python](../guides/database-seeding-python.md)
- [Faker vs SDV vs Misata](../guides/faker-vs-sdv-vs-misata.md)