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Generate Ecommerce Synthetic Data in Python

Ecommerce data is inherently relational: customers place orders, orders contain items, items reference products. If you generate these tables independently you get orphaned foreign keys, negative delivery times, and order amounts that don't match line-item totals. Misata generates a fully wired ecommerce dataset, customers, products, orders, and order_items, where every FK relationship is valid, every delivered_at is after shipped_at, and order amounts follow realistic lognormal distributions.

Whether you're building a Shopify analytics tool, training a product recommendation model, or demoing a warehouse management system, you can have a realistic ecommerce dataset running in under 10 seconds.

Python
import misata

tables = misata.generate("An ecommerce store with 10k customers", rows=10_000, seed=42)
print(list(tables.keys()))   # ['customers', 'products', 'orders', 'order_items']
print(tables["orders"][["amount", "status", "ordered_at"]].describe())

What Misata generates#

Four tables with complete referential integrity: customers → orders → order_items → products. Temporal columns respect real-world constraints: shipped_at is always after ordered_at, delivered_at is always after shipped_at.

Tables and columns#

TableKey columns
customerscustomer_id, name, email, city, country, signup_date, lifetime_value
productsproduct_id, name, category, price, cost, stock_count, rating
ordersorder_id, customer_id, amount, status, ordered_at, shipped_at, delivered_at
order_itemsitem_id, order_id, product_id, quantity, unit_price, discount

Realistic distributions#

  • Order amounts are lognormal with median ~$85, matching real consumer ecommerce spend patterns
  • Order status distribution: ~88% delivered, ~8% returned, ~4% in other states (pending, shipped, cancelled)
  • Temporal coherence: shipped_at is always 1–5 days after ordered_at; delivered_at is always after shipped_at
  • Product prices are lognormal with heavy right tail (cheap commodities + premium items)
  • Customer lifetime value correlates with order history, high-CLV customers have more orders

Quick start#

Python
import misata

tables = misata.generate(
    "Ecommerce store with 10k customers and 50k orders",
    rows=10_000,
    seed=42,
)

# Verify temporal coherence
orders = tables["orders"].copy()
orders["ordered_at"] = orders["ordered_at"].astype("datetime64[ns]")
orders["shipped_at"] = orders["shipped_at"].astype("datetime64[ns]")
assert (orders["shipped_at"] >= orders["ordered_at"]).all()

# Revenue by category via order_items join
items = tables["order_items"].merge(tables["products"], on="product_id")
print(items.groupby("category")["unit_price"].sum().sort_values(ascending=False))

Common use cases#

  • Product recommendation ML: generate customer purchase histories with realistic category affinities for training collaborative filtering models
  • Shopify analytics development: build revenue dashboards, cohort analyses, and funnel reports before your store has enough real sales
  • Warehouse / fulfillment system testing: validate shipment tracking logic against thousands of orders in all status states
  • Fraud detection training data: combine with anomaly_rate to inject outlier transactions for classifier training
  • A/B test simulation: generate two cohorts with different discount rates and measure the impact on order volume
  • GDPR-safe data exports: replace real customer PII with synthetic equivalents that preserve statistical properties for analytics

Advanced: seasonal narrative curves#

Ecommerce revenue is highly seasonal, Black Friday, Christmas, and post-holiday slumps are the defining patterns. Misata extracts these from your story and generates row-level data that rolls up to your target monthly revenue.

Python
tables = misata.generate(
    "Ecommerce store with 10k customers — "
    "Black Friday spike in November, Christmas peak in December, Q1 slump, "
    "steady growth through the year from $500k to $2M",
    rows=10_000,
    seed=42,
)

# Monthly revenue follows the narrative
monthly_revenue = tables["orders"].groupby(
    tables["orders"]["ordered_at"].str[:7]
)["amount"].sum()
print(monthly_revenue)

Advanced: locale-aware generation#

Python
# Brazilian ecommerce — BRL currency, Portuguese names, São Paulo/Rio cities
tables = misata.generate("Brazilian ecommerce store with 5k customers", rows=5000)

# UK ecommerce — GBP, British names and addresses
tables = misata.generate("UK online shop with 3k customers", rows=3000)

Advanced: quality-guaranteed generation#

Python
tables = misata.generate(
    "Ecommerce store with 10k customers",
    min_quality_score=85,
    smart_correlations=True,  # auto-correlates price↔quantity sold
    rows=10_000,
    seed=42,
)

Export and seed#

Python
# Seed a test Postgres database
misata.seed_database(tables, "postgresql://user:pass@localhost/shop_test")

# Export to CSV for dbt seeds
misata.to_parquet(tables, output_dir="./ecommerce_data/")

Related guides#

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