electricsheepafrica/nigerian_retail_and_ecommerce_staff_scheduling_data

Staff Scheduling Data

Dataset Description

Comprehensive staff scheduling data for Nigerian retail and e-commerce analysis

Dataset Information

• Category: Operations and Workforce
• Industry: Retail & E-Commerce
• Country: Nigeria
• Format: CSV, Parquet
• Rows: 200,000
• Columns: 12
• Date Generated: 2025-10-06
• Location: data/staff_scheduling_data/
• License: GPL

Schema

Column	Type	Sample Values
schedule_id	String	SCHED0000000
employee_id	String	EMP9709
employee_name	String	Fatima Williams
store_location	String	Shoprite
city	String	Abuja
shift_date	String	2024-07-27
shift_start	String	11:00
shift_end	String	16:00
shift_type	String	evening
hours_worked	Integer	11
overtime_hours	Integer	0
role	String	sales_associate

Sample Data

 schedule_id employee_id   employee_name store_location       city shift_date shift_start shift_end shift_type  hours_worked  overtime_hours            role
SCHED0000000     EMP9709 Fatima Williams       Shoprite      Abuja 2024-07-27       11:00     16:00    evening            11               0 sales_associate
SCHED0000001     EMP7323  Adamu Williams       Mr Price    Onitsha 2024-10-16       12:00     22:00    morning             5               0     stock_clerk
SCHED0000002     EMP6209     Chioma Udoh         Ebeano Benin City 2024-06-25       07:00     19:00    morning             6               0         cashier

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/staff_scheduling_data/nigerian_retail_and_ecommerce_staff_scheduling_data.csv

Parquet (Recommended)

data/staff_scheduling_data/nigerian_retail_and_ecommerce_staff_scheduling_data.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_staff_scheduling_data")

# 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/staff_scheduling_data/nigerian_retail_and_ecommerce_staff_scheduling_data.csv')

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

PyArrow

import pyarrow.parquet as pq

# Load Parquet
table = pq.read_table('data/staff_scheduling_data/nigerian_retail_and_ecommerce_staff_scheduling_data.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_staff_scheduling_data_2025,
  title={Staff Scheduling Data},
  author={Electric Sheep Africa},
  year={2025},
  publisher={Hugging Face},
  howpublished={\url{https://huggingface.co/datasets/electricsheepafrica/nigerian-retail-staff-scheduling-data}}
}

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

---

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