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Generate Food Delivery Synthetic Data in Python

Food delivery platforms have a five-entity data model: restaurants, customers, couriers, orders, and the individual items within each order. Misata generates all five tables in one call, with every delivered_at timestamp guaranteed to be after placed_at, cuisine types drawn from realistic distributions, and delivery fees and order amounts matching real-world food app economics.

The data is built for immediate use, no orphaned order_items, no couriers assigned to non-existent restaurants, no negative delivery times.

Python
import misata

tables = misata.generate(
    "A food delivery app with 500 restaurants, 2k customers, and 1k couriers",
    rows=2000,
    seed=42,
)
print(list(tables.keys()))   # ['restaurants', 'customers', 'couriers', 'orders', 'order_items']
print(tables["orders"][["total_amount", "delivery_fee", "status"]].describe())

What Misata generates#

Five tables: restaurants, customers, couriers, orders (linking all three), and order_items (line items per order). Complete referential integrity throughout.

Tables and columns#

TableKey columns
restaurantsrestaurant_id, name, cuisine, city, rating, avg_prep_time, is_active
customerscustomer_id, name, email, phone, city, joined_at
courierscourier_id, name, vehicle_type, rating, deliveries_completed
ordersorder_id, customer_id, restaurant_id, courier_id, total_amount, delivery_fee, status, placed_at, delivered_at
order_itemsitem_id, order_id, name, quantity, unit_price

Realistic distributions#

  • delivered_at is always after placed_at, enforced, not probabilistic
  • Cuisine types drawn from realistic distribution: pizza, sushi, burgers, Indian, Chinese, Mexican, Thai, and more
  • Delivery fees lognormal, consistent with platform fee structures
  • Courier ratings beta-distributed toward 4–5 stars
  • Order totals match the sum of order_items with realistic variation for fees and promotions

Quick start#

Python
import misata
import pandas as pd

tables = misata.generate(
    "Food delivery app in a major city with 300 restaurants and 1k orders",
    rows=1000,
    seed=42,
)

# Average order value by cuisine
merged = tables["orders"].merge(tables["restaurants"][["restaurant_id", "cuisine"]], on="restaurant_id")
print(merged.groupby("cuisine")["total_amount"].mean().sort_values(ascending=False))

# Delivery time distribution
orders = tables["orders"].copy()
orders["placed_at"] = pd.to_datetime(orders["placed_at"])
orders["delivered_at"] = pd.to_datetime(orders["delivered_at"])
delivered = orders.dropna(subset=["delivered_at"])
delivered["delivery_minutes"] = (delivered["delivered_at"] - delivered["placed_at"]).dt.seconds / 60
print(delivered["delivery_minutes"].describe())

Common use cases#

  • Delivery routing algorithm testing: generate orders with restaurant locations and courier positions to validate dispatch and routing logic
  • Food tech platform development: seed test databases with full order histories before your app has real restaurant partners
  • Demand forecasting models: generate order volumes with hour-of-day and day-of-week patterns to train surge prediction models
  • Courier performance analytics: build delivery time, rating, and completion rate dashboards on realistic courier histories
  • Restaurant analytics tools: develop order volume, revenue, and rating trend reports before real restaurant data is available
  • Customer LTV and retention models: generate customer order histories with realistic reorder rates and recency patterns

Advanced: peak hour curves#

Python
tables = misata.generate(
    "Food delivery app with lunchtime and dinner peaks, low overnight volume, "
    "Friday and Saturday surge",
    rows=3000,
    seed=42,
)

Advanced: locale-aware generation#

Python
# Indian food delivery — Indian cuisines, INR pricing, Indian cities
tables = misata.generate("Indian food delivery app with 200 restaurants in Mumbai and Delhi", rows=1000)

# UK platform — British and international cuisines, GBP pricing, UK cities
tables = misata.generate("UK food delivery platform with 150 restaurants in London", rows=800)

Advanced: quality-guaranteed generation#

Python
tables = misata.generate(
    "Food delivery platform with 500 restaurants",
    min_quality_score=85,
    smart_correlations=True,
    rows=2000,
    seed=42,
)

Related guides#

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