Dpcito/nigerian_retail_and_ecommerce_stock_and_inventory_levels

Stock And Inventory Levels

Dataset Description

Comprehensive stock and inventory levels for Nigerian retail and e-commerce analysis

Dataset Information

• Category: Product and Inventory
• Industry: Retail & E-Commerce
• Country: Nigeria
• Format: CSV, Parquet
• Rows: 500,000
• Columns: 10
• Date Generated: 2025-10-06
• Location: data/stock_and_inventory_levels/
• License: GPL

Schema

Column	Type	Sample Values
inventory_id	String	INV0000000
product_id	String	PRD45195
warehouse_location	String	Lagos
current_stock	Integer	275
reorder_point	Integer	14
reorder_quantity	Integer	352
stock_status	String	in_stock
last_restock_date	String	2024-08-13
shelf_location	String	Aisle-6-Shelf-3
expiry_date	String	2026-11-14

Sample Data

inventory_id product_id warehouse_location  current_stock  reorder_point  reorder_quantity stock_status last_restock_date   shelf_location expiry_date
  INV0000000   PRD45195              Lagos            275             14               352     in_stock        2024-08-13  Aisle-6-Shelf-3        None
  INV0000001   PRD48058         Benin City            606             56               386     in_stock        2024-07-02  Aisle-9-Shelf-6        None
  INV0000002   PRD72766              Lagos            293             45               444     in_stock        2024-09-25 Aisle-15-Shelf-6  2026-11-14

Use Cases

• Data analysis and insights
• Machine learning model training
• Business intelligence
• Research and education
• Predictive analytics

Nigerian Context

This dataset incorporates authentic Nigerian retail and e-commerce characteristics:

E-Commerce Platforms

• Jumia (35% market share) - Leading marketplace
• Konga (25% market share) - Major competitor
• Jiji (20% market share) - Classifieds platform
• PayPorte, Slot, and other platforms

Physical Retail

• Shoprite, Spar, Game - Major supermarket chains
• Slot, Pointek - Electronics retailers
• Mr Price - Fashion retail
• Traditional markets: Balogun Market, Computer Village

Payment Methods

• Cash on Delivery (45%) - Most popular
• Bank Transfer (25%)
• Debit Card (15%)
• USSD (8%)
• Mobile Money (5%)
• Credit Card (2%)

Logistics & Delivery

• GIG Logistics - Nationwide coverage
• Kwik Delivery - Fast urban delivery
• DHL, FedEx - International and express
• Red Star Express - Nationwide courier
• Local dispatch riders

Geographic Coverage

Major Nigerian cities including:

• Lagos - Commercial capital, highest retail density
• Abuja - Federal capital, high e-commerce penetration
• Kano - Northern commercial hub
• Port Harcourt - Oil city, strong purchasing power
• Ibadan - Large urban market
• Plus 10+ other major cities

Products & Categories

• Electronics: Tecno, Infinix, Samsung phones; laptops, TVs
• Fashion: Ankara fabric, Agbada, Kaftan, sneakers
• Groceries: Rice (50kg bags), Garri, Palm Oil, Indomie
• Beauty: Shea butter, Black soap, hair extensions
• Home: Generators, inverters, solar panels

Currency & Pricing

• Currency: Nigerian Naira (NGN, ₦)
• Exchange Rate: ~₦1,500/USD
• Price Ranges: Realistic Nigerian market prices
• Time Zone: West Africa Time (WAT, UTC+1)

File Formats

CSV

data/stock_and_inventory_levels/nigerian_retail_and_ecommerce_stock_and_inventory_levels.csv

Parquet (Recommended)

data/stock_and_inventory_levels/nigerian_retail_and_ecommerce_stock_and_inventory_levels.parquet

Nigerian Retail and E-Commerce - Loading the Dataset

Hugging Face Datasets

from datasets import load_dataset

# Load dataset
dataset = load_dataset("electricsheepafrica/nigerian_retail_and_ecommerce_stock_and_inventory_levels")

# Convert to pandas
df = dataset['train'].to_pandas()

print(f"Loaded {len(df):,} rows")

Pandas (Direct)

import pandas as pd

# Load CSV
df = pd.read_csv('data/stock_and_inventory_levels/nigerian_retail_and_ecommerce_stock_and_inventory_levels.csv')

# Load Parquet (recommended for large datasets)
df = pd.read_parquet('data/stock_and_inventory_levels/nigerian_retail_and_ecommerce_stock_and_inventory_levels.parquet')

PyArrow

import pyarrow.parquet as pq

# Load Parquet
table = pq.read_table('data/stock_and_inventory_levels/nigerian_retail_and_ecommerce_stock_and_inventory_levels.parquet')
df = table.to_pandas()

Data Quality

• ✅ Realistic Distributions: Based on Nigerian retail patterns
• ✅ No Missing Critical Fields: Complete core data
• ✅ Proper Data Types: Appropriate types for each column
• ✅ Consistent Naming: Clear, descriptive column names
• ✅ Nigerian Context: Authentic local characteristics
• ✅ Production Scale: Suitable for real-world applications

Ethical Considerations

• This is synthetic data generated for research and development
• No real customer data or personally identifiable information
• Designed to reflect realistic patterns without privacy concerns
• Safe for public use, testing, and education

License

GPL License - General Public License

This dataset is free to use for:

• Research and academic purposes
• Commercial applications
• Educational projects
• Open source development

Citation

@dataset{nigerian_retail_stock_and_inventory_levels_2025,
  title={Stock And Inventory Levels},
  author={Electric Sheep Africa},
  year={2025},
  publisher={Hugging Face},
  howpublished={\url{https://huggingface.co/datasets/electricsheepafrica/nigerian-retail-stock-and-inventory-levels}}
}

Related Datasets

This dataset is part of the Nigerian Retail & E-Commerce Datasets collection, which includes 42 datasets covering:

• Customer & Shopper Data
• Sales & Transactions
• Product & Inventory
• Marketing & Engagement
• Operations & Workforce
• Pricing & Revenue
• Customer Support
• Emerging & Advanced Technologies

Browse all datasets: https://huggingface.co/electricsheepafrica

Updates & Maintenance

• Version: 1.0
• Last Updated: 2025-10-06
• Maintenance: Active
• Issues: Report via Hugging Face discussions

Contact

For questions, feedback, or collaboration:

• Hugging Face: electricsheepafrica
• Issues: Open a discussion on the dataset page
• General Inquiries: Via Hugging Face profile

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Part of the Nigerian Industry Datasets Initiative
Building comprehensive, authentic datasets for African markets.