AhmadBabajoytk/nigerian_retail_and_ecommerce_point_of_sale_records

---
license: gpl
task_categories:
- tabular-regression
- time-series-forecasting
language:
- en
tags:
- retail
- ecommerce
- nigeria
- synthetic-data
- sales-analytics
- transactions
size_categories:
- 100K<n<1M
pretty_name: 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

```python
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)

```python
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

```python
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

```bibtex
@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

---

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

---
许可证：GPL
任务类别：
- 表格回归（tabular-regression）
- 时间序列预测（time-series-forecasting）
语言：
- 英语
标签：
- 零售
- 电子商务
- 尼日利亚
- 合成数据（synthetic-data）
- 销售分析（sales-analytics）
- 交易记录（transactions）
规模类别：
- 100K<数据量<1M
展示名称：销售点（Point Of Sale）记录
---

# 销售点记录

## 数据集描述
面向尼日利亚零售与电子商务分析的全量销售点交易记录

## 数据集详情
- **数据集类别**：销售与交易记录
- **所属行业**：零售与电子商务
- **覆盖国家**：尼日利亚
- **存储格式**：CSV、Parquet
- **数据行数**：800,000
- **字段数量**：11
- **生成日期**：2025-10-06
- **存储路径**：`data/point_of_sale_records/`
- **许可证**：GPL

## 字段结构
| 字段名 | 数据类型 | 示例值 |
|--------|----------|--------|
| `transaction_id` | 字符串 | POS00000000 |
| `store_name` | 字符串 | Balogun Market |
| `city` | 字符串 | Kano |
| `transaction_date` | 字符串 | 2024-08-03 16:00:00 |
| `cashier_id` | 字符串 | CASH990 |
| `items_count` | 整数 | 13 |
| `total_amount_ngn` | 浮点数 | 33837.28 |
| `payment_method` | 字符串 | cash |
| `discount_applied` | 布尔值 | False |
| `loyalty_points_earned` | 整数 | 662 |
| `receipt_number` | 字符串 | RCP0000000000 |

## 示例数据

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

## 应用场景
- 数据分析与洞察挖掘
- 机器学习模型训练
- 商业智能分析
- 研究与教学用途
- 预测性分析

## 尼日利亚本土适配说明
本数据集融入了尼日利亚零售与电子商务的真实特征：

### 电子商务平台
- **Jumia**（市场份额35%）：头部电商平台
- **Konga**（市场份额25%）：主要竞品平台
- **Jiji**（市场份额20%）：分类信息平台
- PayPorte、Slot等其他平台

### 实体零售
- **Shoprite**、**Spar**、**Game**：大型连锁超市
- **Slot**、**Pointek**：电子产品零售商
- **Mr Price**：时尚零售品牌
- 传统市场：巴洛贡市场、电脑村

### 支付方式
- 货到付款（占比45%）：最主流支付方式
- 银行转账（25%）
- 借记卡支付（15%）
- USSD支付（8%）
- 移动支付（5%）
- 信用卡支付（2%）

### 物流配送
- **GIG Logistics**：全国覆盖配送
- **Kwik Delivery**：快速城市配送
- **DHL**、**FedEx**：国际与加急配送服务
- **Red Star Express**：全国 Courier 配送服务
- 本地配送骑手

### 地理覆盖范围
尼日利亚主要城市包括：
- **拉各斯**：商业首都，零售密度最高
- **阿布贾**：联邦首都，电子商务渗透率最高
- **卡诺**：北部商业枢纽
- **哈科特港**：石油城市，购买力强劲
- **伊巴丹**：大型城市市场
- 以及其他10余个主要城市

### 商品品类
- **电子产品**：Tecno、Infinix、三星手机、笔记本电脑、电视等
- **时尚服饰**：安卡拉面料、阿巴达长袍、卡夫坦长衫、运动鞋等
- **食品杂货**：50公斤装大米、加里木薯粉、棕榈油、Indomie方便面等
- **美妆产品**：乳木果油、黑皂、接发片等
- **家居用品**：发电机、逆变器、太阳能板等

### 货币与定价规则
- **货币**：尼日利亚奈拉（NGN，₦）
- **汇率**：约1美元兑换1500奈拉
- **价格区间**：贴合尼日利亚本土市场的真实定价
- **时区**：西非时间（WAT，UTC+1）

## 文件格式
### CSV格式

data/point_of_sale_records/nigerian_retail_and_ecommerce_point_of_sale_records.csv

### Parquet格式（推荐）

data/point_of_sale_records/nigerian_retail_and_ecommerce_point_of_sale_records.parquet

## 尼日利亚零售与电子商务数据集加载方法
### Hugging Face Datasets 加载方式
python
from datasets import load_dataset

# 加载数据集
dataset = load_dataset("electricsheepafrica/nigerian_retail_and_ecommerce_point_of_sale_records")

# 转换为pandas数据框
df = dataset['train'].to_pandas()

print(f"已加载 {len(df):,} 行数据")

### 直接使用Pandas加载
python
import pandas as pd

# 加载CSV文件
df = pd.read_csv('data/point_of_sale_records/nigerian_retail_and_ecommerce_point_of_sale_records.csv')

# 加载Parquet文件（推荐用于大型数据集）
df = pd.read_parquet('data/point_of_sale_records/nigerian_retail_and_ecommerce_point_of_sale_records.parquet')

### PyArrow加载方式
python
import pyarrow.parquet as pq

# 加载Parquet文件
table = pq.read_table('data/point_of_sale_records/nigerian_retail_and_ecommerce_point_of_sale_records.parquet')
df = table.to_pandas()

## 数据质量保障
- ✅ **真实分布贴合市场**：基于尼日利亚零售市场的真实交易模式
- ✅ **无关键字段缺失**：核心交易字段完整无缺失
- ✅ **数据类型规范统一**：各字段匹配适配的数据类型
- ✅ **命名清晰一致**：字段命名清晰且具备强描述性
- ✅ **本土特征完整**：融入尼日利亚本地零售与电商的真实特征
- ✅ **生产级规模**：适配真实世界的业务应用场景

## 伦理考量
本数据集为**合成数据**，仅用于研究与开发用途；未包含任何真实客户数据或个人可识别信息；旨在还原真实交易模式的同时规避隐私风险；可安全用于公开使用、测试与教学场景。

## 许可证
**GPL许可证**（通用公共许可证）
本数据集可免费用于以下场景：
- 研究与学术用途
- 商业应用
- 教育项目
- 开源开发

## 引用格式
bibtex
@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}}
}

## 相关数据集
本数据集隶属于**尼日利亚零售与电子商务数据集合集**，该合集包含42个数据集，覆盖以下领域：
- 客户与购物者数据
- 销售与交易记录
- 商品与库存数据
- 营销与用户互动数据
- 运营与劳动力数据
- 定价与营收数据
- 客户支持数据
- 新兴与前沿技术相关数据

**浏览全部数据集**：https://huggingface.co/electricsheepafrica

## 更新与维护
- **版本号**：1.0
- **最后更新日期**：2025-10-06
- **维护状态**：活跃
- **问题反馈**：通过Hugging Face讨论区提交

## 联系方式
如有疑问、反馈或合作意向：
- **Hugging Face账号**：electricsheepafrica
- **问题反馈**：在数据集页面开启讨论
- **通用咨询**：通过Hugging Face个人主页联系

---

**隶属于尼日利亚行业数据集倡议项目**
本项目旨在为非洲市场打造全面且真实的行业数据集。