electricsheepafrica/africa-synth-retail-and-ecommerce-point-of-sale-records-nigeria

⚠️ Synthetic dataset — Parameterized from published SSA literature, not real observations. Not suitable for empirical analysis or policy inference.

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.