This notebook guides you in creating a complete and realistic dataset for an online clothing store. We will generate 10,000 simulated orders, each with numerous details (price, customer, logistics), and save everything in a CSV file.

Step 1: Installation and import of libraries

We start by installing Faker and importing all the necessary libraries.

  • pandas: To create and manipulate our data table.
  • Faker: To generate fake data (names, addresses, etc.).
  • random, datetime, hashlib: Python utilities for randomness, date management, and creating unique identifiers.

Python

# Installation de la bibliothèque Faker pour générer des données factices !pip install Faker

import pandas as pd import random import hashlib from datetime import datetime from faker import Faker

Step 2: Product Configuration

This is where we define the universe of our store. We replace watches with clothing that has its own characteristics, prices, and images. We maintain the logic of seasonal periods to simulate sales or winter/summer collections.

Python

# Initialisation de Faker fake = Faker()

PRODUCTS = { 'T-shirt Classique Blanc': { 'sku': 'TS-WH-CL-01', 'price': 25.00, 'price_ex_vat': 20.00, 'price_chf': 22.50, 'price_ex_vat_chf': 18.00, 'image': 'https://images.pexels.com/photos/428338/pexels-photo-428338.jpeg' }, 'Pull en Laine Hivernal': { 'sku': 'SW-GR-WO-01', 'price': 80.00, 'price_ex_vat': 64.00, 'price_chf': 72.00, 'price_ex_vat_chf': 57.60, 'image': 'https://images.pexels.com/photos/6770141/pexels-photo-6770141.jpeg' }, 'Jean Slim Noir - Premium': { 'sku': 'JN-BK-SL-PR', 'price': 120.00, 'price_ex_vat': 96.00, 'price_chf': 108.00, 'price_ex_vat_chf': 86.40, 'image': 'https://images.pexels.com/photos/1082529/pexels-photo-1082529.jpeg' }, 'Jean Slim Noir - Standard': { 'sku': 'JN-BK-SL-ST', 'price': 90.00, 'price_ex_vat': 72.00, 'price_chf': 81.00, 'price_ex_vat_chf': 64.80, 'image': 'https://images.pexels.com/photos/1598507/pexels-photo-1597507.jpeg' }, 'Robe d\'Été Fleurie': { 'sku': 'DR-FL-SU-01', 'price': 75.00, 'price_ex_vat': 60.00, 'price_chf': 67.50, 'price_ex_vat_chf': 54.00, 'image': 'https://images.pexels.com/photos/1755428/pexels-photo-1755428.jpeg' }, } PRODUCT_NAMES = list(PRODUCTS.keys())

CITIES = { 'Asia': ['Tokyo', 'Seoul', 'Shanghai', 'Beijing', 'Singapore'], 'Europe': ['Paris', 'London', 'Berlin', 'Rome', 'Madrid'], 'North America': ['New York', 'Los Angeles', 'Toronto'], 'Others': ['Sydney', 'Cairo', 'Moscow'] } COUNTRIES = ['CN', 'JP', 'KR', 'FR', 'UK', 'DE', 'IT', 'ES', 'US', 'CA', 'AU', 'EG', 'RU']

SEASONAL_PERIODS = [ (datetime(2023, 11, 15), datetime(2024, 1, 15)), (datetime(2024, 6, 20), datetime(2024, 8, 20)), (datetime(2024, 11, 15), datetime(2025, 1, 15)), ]

Step 3: Business logic to improve data quality

These functions add realism. calculate_price_order adjusts the final selling price based on the product and the period. For example, the Wool Sweater will be more expensive during the holiday seasons, simulating high winter demand.

Python

def generate_seasonal_date(): """Génère une date, avec une probabilité accrue d'être dans une période saisonnière.""" if random.random() < 0.5: start_date, end_date = random.choice(SEASONAL_PERIODS) return fake.date_time_between(start_date=start_date, end_date=end_date) return fake.date_time_between(start_date='-3y', end_date='now')

def calculate_price_order(name, product, creation_date): """Calcule le prix de vente final en fonction de règles métier.""" if 'T-shirt' in name: time_factor = (datetime.now() - creation_date).days / 365 sales_multiplier = 1 + random.uniform(0.2, 0.8) * time_factor return product['price'] * sales_multiplier * random.uniform(0.9, 1.1)

elif 'Pull en Laine' in name:
    if any(start <= creation\_date <= end for start, end in SEASONAL\_PERIODS):
        return product\['price'\] \* random.uniform(1.1, 1.3) # Plus cher en hiver
    else:
        return product\['price'\] \* random.uniform(0.9, 1.1)

elif 'Jean Slim' in name:
    return product\['price'\] \* random.uniform(0.9, 1.1)

else: # Pour la robe et autres futurs articles
    time\_factor = (datetime.now() - creation\_date).days / 365
    sales\_reduction = max(1 - random.uniform(0.1, 0.5) \* time\_factor, 0.7)
    return product\['price'\] \* sales\_reduction \* random.uniform(0.9, 1.0)

Step 4: Final assembly of the database

The generate_row function is the heart of the script. It builds a complete order by drawing from all our configurations and generating dozens of fields to make each line unique and detailed: customer information, payment, logistics, etc.

Python

def generate_row(): """Génère un dictionnaire complet représentant une commande.""" creation_date = generate_seasonal_date() name = random.choice(PRODUCT_NAMES) product = PRODUCTS[name]

price\_order = calculate\_price\_order(name, product, creation\_date)

# Logique pour la localisation
region = 'Europe' if 'Jean' in name and random.random() < 0.5 else random.choice(list(CITIES.keys()))
city = random.choice(CITIES\[region\])
country\_code = random.choice(COUNTRIES)

row = {
    'id': fake.unique.random\_int(min=1000000000, max=9999999999),
    'creation\_date': creation\_date,
    'update\_date': fake.date\_time\_between(start\_date=creation\_date, end\_date='now'),
    'status': random.choices(\['complete', 'canceled', 'deleted'\], \[0.85, 0.1, 0.05\])\[0\],
    'price\_order': round(price\_order, 2),
    'traffic': random.randint(50, 500),
    'city': city,
    'country\_code': country\_code,
    'total\_quantity': random.randint(1, 5),
    'sku': product\['sku'\],
    'name': name.capitalize(),
    'price\_ex\_vat': product\['price\_ex\_vat'\],
    'store\_name': fake.company(),
    'price': product\['price'\],
    'payment\_cc\_type': random.choice(\['VISA', 'MASTERCARD', 'AMEX'\]),
    'quantity': random.randint(1, 5),
    'quantity\_refunded': random.randint(0, 2),
    'quantity\_invoiced': random.randint(1, 5),
    'quantity\_returned': random.randint(0, 2),
    'black\_friday': any(p\[0\].month == 11 and 20 < p\[0\].day < 30 for p in SEASONAL\_PERIODS if p\[0\] <= creation\_date <= p\[1\]),
    'is\_customized': random.choices(\[True, False\], \[0.1, 0.9\])\[0\],
    'discount': round(random.uniform(0, 25), 2),
    'vat\_amount': round(price\_order \* 0.2, 2),
    'options': random.choice(\[None, 'Emballage cadeau', 'Retouches simples'\]),
    'subtotal\_invoiced': round(price\_order \* 0.8, 2),
    'discount\_amount': round(price\_order \* random.uniform(0, 0.15), 2),
    'tax\_invoiced': round(price\_order \* 0.2, 2),
    'options\_id': random.choice(\['men', 'women', 'unisex'\]),
    'payment\_base\_shipping\_amount': round(random.uniform(5, 15), 2),
    'payment\_amount\_paid': round(price\_order, 2),
    'order\_currency\_code': random.choice(\['USD', 'EUR', 'GBP'\]),
    'coupon\_code': random.choice(\[None, 'SUMMER24', 'WINTER24', 'WELCOME10'\]),
    'payment\_method': random.choice(\['paypal\_express', 'credit\_card'\]),
    'shipping\_description': fake.country(),
    'mail\_ID': fake.sha256(),
    'shipping': random.choice(\['GLS', 'DHL', 'UPS'\]),
    'payment': random.choice(\['paypal', 'credit card'\]),
    'image': product\['image'\],
    'price\_ex\_vat\_chf': product\['price\_ex\_vat\_chf'\],
    'is\_combo': random.choices(\[True, False\], \[0.2, 0.8\])\[0\],
    'discount\_chf': round(random.uniform(0, 20), 2),
    'vat\_amount\_chf': round(product\['price\_chf'\] \* 0.077, 2),
    'creation\_date\_local': fake.date\_time\_this\_year(),
    'price\_chf': product\['price\_chf'\]
}
return row

print(generate_row())

Step 5: Export CSV

This is the final step! We execute the generate_row function 10,000 times to create our dataset, we transform it into a pandas DataFrame, and we save it as a CSV file.

Python

# Nombre de lignes à générer NUM_ROWS = 10000 print(f"Génération de {NUM_ROWS} lignes de données de commandes...")

rows = [generate_row() for _ in range(NUM_ROWS)]

df = pd.DataFrame(rows)

print("DataFrame généré avec succès !") print(df.head())

output_csv_path = 'fake_clothing_store_database.csv' df.to_csv(output_csv_path, index=False)

print(f"\nLes données ont été sauvegardées dans '{output_csv_path}'.") print("Vous pouvez trouver ce fichier dans le panneau de gauche de Colab (icône dossier).")

The dataset generated by this script is available for viewing and download via this Google Sheets link.

➡️ **E-commerce Database (Clothing)

**You can import this data into a Power BI Desktop or Looker Studio environment for free to practice data visualization.

Complete code:

Python

# ==============================================================================

import pandas as pd import random import hashlib from datetime import datetime from faker import Faker

fake = Faker()

PRODUCTS = { 'T-shirt Classique Blanc': { 'sku': 'TS-WH-CL-01', 'price': 25.00, 'price_ex_vat': 20.00, 'price_chf': 22.50, 'price_ex_vat_chf': 18.00, 'image': 'https://images.pexels.com/photos/428338/pexels-photo-428338.jpeg' }, 'Pull en Laine Hivernal': { 'sku': 'SW-GR-WO-01', 'price': 80.00, 'price_ex_vat': 64.00, 'price_chf': 72.00, 'price_ex_vat_chf': 57.60, 'image': 'https://images.pexels.com/photos/6770141/pexels-photo-6770141.jpeg' }, 'Jean Slim Noir - Premium': { 'sku': 'JN-BK-SL-PR', 'price': 120.00, 'price_ex_vat': 96.00, 'price_chf': 108.00, 'price_ex_vat_chf': 86.40, 'image': 'https://images.pexels.com/photos/1082529/pexels-photo-1082529.jpeg' }, 'Jean Slim Noir - Standard': { 'sku': 'JN-BK-SL-ST', 'price': 90.00, 'price_ex_vat': 72.00, 'price_chf': 81.00, 'price_ex_vat_chf': 64.80, 'image': 'https://images.pexels.com/photos/1598507/pexels-photo-1597507.jpeg' }, 'Robe d\'Été Fleurie': { 'sku': 'DR-FL-SU-01', 'price': 75.00, 'price_ex_vat': 60.00, 'price_chf': 67.50, 'price_ex_vat_chf': 54.00, 'image': 'https://images.pexels.com/photos/1755428/pexels-photo-1755428.jpeg' }, } PRODUCT_NAMES = list(PRODUCTS.keys())

CITIES = { 'Asia': ['Tokyo', 'Seoul', 'Shanghai', 'Beijing', 'Singapore'], 'Europe': ['Paris', 'London', 'Berlin', 'Rome', 'Madrid'], 'North America': ['New York', 'Los Angeles', 'Toronto'], 'Others': ['Sydney', 'Cairo', 'Moscow'] } COUNTRIES = ['CN', 'JP', 'KR', 'FR', 'UK', 'DE', 'IT', 'ES', 'US', 'CA', 'AU', 'EG', 'RU']

SEASONAL_PERIODS = [ (datetime(2023, 11, 15), datetime(2024, 1, 15)), (datetime(2024, 6, 20), datetime(2024, 8, 20)), (datetime(2024, 11, 15), datetime(2025, 1, 15)), ]

def generate_seasonal_date(): """Génère une date, avec une probabilité accrue d'être dans une période saisonnière.""" if random.random() < 0.5: start_date, end_date = random.choice(SEASONAL_PERIODS) return fake.date_time_between(start_date=start_date, end_date=end_date) return fake.date_time_between(start_date='-3y', end_date='now')

def calculate_price_order(name, product, creation_date): """Calcule le prix de vente final en fonction de règles métier.""" if 'T-shirt' in name: time_factor = (datetime.now() - creation_date).days / 365 sales_multiplier = 1 + random.uniform(0.2, 0.8) * time_factor return product['price'] * sales_multiplier * random.uniform(0.9, 1.1)

elif 'Pull en Laine' in name:
    if any(start <= creation\_date <= end for start, end in SEASONAL\_PERIODS):
        return product\['price'\] \* random.uniform(1.1, 1.3) # Plus cher en hiver
    else:
        return product\['price'\] \* random.uniform(0.9, 1.1)

elif 'Jean Slim' in name:
    return product\['price'\] \* random.uniform(0.9, 1.1)

else: # Pour la robe et autres futurs articles
    time\_factor = (datetime.now() - creation\_date).days / 365
    sales\_reduction = max(1 - random.uniform(0.1, 0.5) \* time\_factor, 0.7)
    return product\['price'\] \* sales\_reduction \* random.uniform(0.9, 1.0)

def generate_row(): """Génère un dictionnaire complet représentant une commande.""" creation_date = generate_seasonal_date() name = random.choice(PRODUCT_NAMES) product = PRODUCTS[name]

price\_order = calculate\_price\_order(name, product, creation\_date)

region = 'Europe' if 'Jean' in name and random.random() < 0.5 else random.choice(list(CITIES.keys()))
city = random.choice(CITIES\[region\])
country\_code = random.choice(COUNTRIES)

row = {
    'id': fake.unique.random\_int(min=1000000000, max=9999999999),
    'creation\_date': creation\_date,
    'update\_date': fake.date\_time\_between(start\_date=creation\_date, end\_date='now'),
    'status': random.choices(\['complete', 'canceled', 'deleted'\], \[0.85, 0.1, 0.05\])\[0\],
    'price\_order': round(price\_order, 2),
    'traffic': random.randint(50, 500),
    'city': city,
    'country\_code': country\_code,
    'total\_quantity': random.randint(1, 5),
    'sku': product\['sku'\],
    'name': name.capitalize(),
    'price\_ex\_vat': product\['price\_ex\_vat'\],
    'store\_name': fake.company(),
    'price': product\['price'\],
    'payment\_cc\_type': random.choice(\['VISA', 'MASTERCARD', 'AMEX'\]),
    'quantity': random.randint(1, 5),
    'quantity\_refunded': random.randint(0, 2),
    'quantity\_invoiced': random.randint(1, 5),
    'quantity\_returned': random.randint(0, 2),
    'black\_friday': any(p\[0\].month == 11 and 20 < p\[0\].day < 30 for p in SEASONAL\_PERIODS if p\[0\] <= creation\_date <= p\[1\]),
    'is\_customized': random.choices(\[True, False\], \[0.1, 0.9\])\[0\],
    'discount': round(random.uniform(0, 25), 2),
    'vat\_amount': round(price\_order \* 0.2, 2),
    'options': random.choice(\[None, 'Emballage cadeau', 'Retouches simples'\]),
    'subtotal\_invoiced': round(price\_order \* 0.8, 2),
    'discount\_amount': round(price\_order \* random.uniform(0, 0.15), 2),
    'tax\_invoiced': round(price\_order \* 0.2, 2),
    'options\_id': random.choice(\['men', 'women', 'unisex'\]),
    'payment\_base\_shipping\_amount': round(random.uniform(5, 15), 2),
    'payment\_amount\_paid': round(price\_order, 2),
    'order\_currency\_code': random.choice(\['USD', 'EUR', 'GBP'\]),
    'coupon\_code': random.choice(\[None, 'SUMMER24', 'WINTER24', 'WELCOME10'\]),
    'payment\_method': random.choice(\['paypal\_express', 'credit\_card'\]),
    'shipping\_description': fake.country(),
    'mail\_ID': fake.sha256(),
    'shipping': random.choice(\['GLS', 'DHL', 'UPS'\]),
    'payment': random.choice(\['paypal', 'credit card'\]),
    'image': product\['image'\],
    'price\_ex\_vat\_chf': product\['price\_ex\_vat\_chf'\],
    'is\_combo': random.choices(\[True, False\], \[0.2, 0.8\])\[0\],
    'discount\_chf': round(random.uniform(0, 20), 2),
    'vat\_amount\_chf': round(product\['price\_chf'\] \* 0.077, 2),
    'creation\_date\_local': fake.date\_time\_this\_year(),
    'price\_chf': product\['price\_chf'\]
}
return row

NUM_ROWS = 10000 print(f"Génération de {NUM_ROWS} lignes de données de commandes...")

rows = [generate_row() for _ in range(NUM_ROWS)]

df = pd.DataFrame(rows)

print("\nDataFrame généré avec succès ! Voici les 5 premières lignes :") print(df.head())

output_csv_path = 'fake_clothing_store_database.csv' df.to_csv(output_csv_path, index=False)

print(f"\nLes données ont été sauvegardées dans le fichier '{output_csv_path}'.")

in Faire une fake base de données