AhmadBabajoytk/nigerian_retail_and_ecommerce_point_of_sale_records

Point Of Sale Records

Dataset Description

Comprehensive point of sale records for Nigerian retail and e-commerce analysis

Dataset Information

• Category: Sales and Transactions
• Industry: Retail & E-Commerce
• Country: Nigeria
• Format: CSV, Parquet
• Rows: 800,000
• Columns: 11
• Date Generated: 2025-10-06
• Location: data/point_of_sale_records/
• License: GPL

Schema

Column	Type	Sample Values
transaction_id	String	POS00000000
store_name	String	Balogun Market
city	String	Kano
transaction_date	String	2024-08-03 16:00:00
cashier_id	String	CASH990
items_count	Integer	13
total_amount_ngn	Float	33837.28
payment_method	String	cash
discount_applied	Boolean	False
loyalty_points_earned	Integer	662
receipt_number	String	RCP0000000000

Sample Data

transaction_id     store_name          city    transaction_date cashier_id  items_count  total_amount_ngn payment_method  discount_applied  loyalty_points_earned receipt_number
   POS00000000 Balogun Market          Kano 2024-08-03 16:00:00    CASH990           13          33837.28           cash             False                    662  RCP0000000000
   POS00000001           Game          Kano 2024-09-07 20:00:00    CASH840            2          23883.91           cash             False                    531  RCP0000000001
   POS00000002           Game Port Harcourt 2024-07-04 09:00:00    CASH148           19          35400.49   mobile_money             False                    840  RCP0000000002

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/point_of_sale_records/nigerian_retail_and_ecommerce_point_of_sale_records.csv

Parquet (Recommended)

data/point_of_sale_records/nigerian_retail_and_ecommerce_point_of_sale_records.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_point_of_sale_records")

# 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/point_of_sale_records/nigerian_retail_and_ecommerce_point_of_sale_records.csv')

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

PyArrow

import pyarrow.parquet as pq

# Load Parquet
table = pq.read_table('data/point_of_sale_records/nigerian_retail_and_ecommerce_point_of_sale_records.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_point_of_sale_records_2025,
  title={Point Of Sale Records},
  author={Electric Sheep Africa},
  year={2025},
  publisher={Hugging Face},
  howpublished={\url{https://huggingface.co/datasets/electricsheepafrica/nigerian-retail-point-of-sale-records}}
}

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.