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import pandas as pd
import random
from faker import Faker
fake = Faker()
# Fixed customers
customers = [
{"customer_id": 1, "customer_name": "John", "customer_email": "john@example.com"},
{"customer_id": 2, "customer_name": "Alice", "customer_email": "alice@example.com"},
{"customer_id": 3, "customer_name": "Bob", "customer_email": "bob@example.com"},
{"customer_id": 4, "customer_name": "Mark", "customer_email": "mark@example.com"},
{"customer_id": 5, "customer_name": "Maxwell", "customer_email": "maxwell@example.com"},
]
# Product catalog
products = ["Laptop", "Smartphone", "Headphones", "Tablet", "Camera", "Smartwatch", "Monitor"]
# Order statuses
statuses = ["In Progress", "Delivered", "Cancelled", "Returned", "Refunded"]
def generate_orders(num_orders, start_order_id=1000):
data = []
for i in range(num_orders):
order_id = start_order_id + i
order_date = fake.date_between_dates(date_start=pd.to_datetime("2025-01-01"),
date_end=pd.to_datetime("2025-08-31"))
customer = random.choice(customers)
ordered_product = random.choice(products)
quantity = random.randint(1, 5)
price_per_unit = round(random.uniform(50, 2000), 2) # product price
price = round(price_per_unit * quantity, 2)
status = random.choice(statuses)
data.append({
"order_id": order_id,
"order_date": order_date,
"customer_id": customer["customer_id"],
"customer_name": customer["customer_name"],
"customer_email": customer["customer_email"],
"ordered_products": ordered_product,
"order_quantity": 1, # since we keep one product per row
"quantity": quantity,
"price": price,
"status": status,
})
return pd.DataFrame(data)
# Generate datasets
df_small = generate_orders(100)
df_large = generate_orders(10000)
# Save to CSV
df_small.to_csv("orders_100.csv", index=False)
df_large.to_csv("orders_10000.csv", index=False)
print("Generated orders_100.csv and orders_10000.csv ✅")
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