Datasets:

electricsheepafrica
/

africa-synth-retail-and-ecommerce-supply-chain-logistics-data-nigeria

@@ -1,249 +1,144 @@
 ---
-license: gpl
-task_categories:
-- tabular-classification
-- text-classification
 language:
 - en
-tags:
-- retail
-- ecommerce
-- nigeria
-- synthetic-data
-- inventory
-- supply-chain
-- synthetic
 size_categories:
 - 100K<n<1M
-pretty_name: Supply Chain Logistics Data
-data_type: synthetic
 ---
-> ⚠️ **Synthetic dataset** — Parameterized from published SSA literature, not real observations. Not suitable for empirical analysis or policy inference.
-# Supply Chain Logistics Data
-## Dataset Description
-Comprehensive supply chain logistics data for Nigerian retail and e-commerce analysis
-## Dataset Information
-- **Category**: Product and Inventory
-- **Industry**: Retail & E-Commerce
-- **Country**: Nigeria
-- **Format**: CSV, Parquet
-- **Rows**: 400,000
-- **Columns**: 12
-- **Date Generated**: 2025-10-06
-- **Location**: `data/supply_chain_logistics_data/`
-- **License**: GPL
-## Schema
-| Column | Type | Sample Values |
-|--------|------|---------------|
-| `shipment_id` | String | SHIP0000000 |
-| `product_id` | String | PRD81347 |
-| `supplier_name` | String | Supplier_B |
-| `origin_city` | String | Abuja |
-| `destination_city` | String | Warri |
-| `ship_date` | String | 2024-03-23 |
-| `expected_delivery_date` | String | 2024-03-29 |
-| `actual_delivery_date` | String | 2024-03-27 |
-| `quantity` | Integer | 828 |
-| `shipping_cost_ngn` | Float | 18379.93 |
-| `logistics_company` | String | DHL Nigeria |
-| `delivery_status` | String | delayed |
-## Sample Data
-```
-shipment_id product_id supplier_name origin_city destination_city  ship_date expected_delivery_date actual_delivery_date  quantity  shipping_cost_ngn logistics_company delivery_status
-SHIP0000000   PRD81347    Supplier_B       Abuja            Warri 2024-03-23             2024-03-29           2024-03-27       828           18379.93       DHL Nigeria         delayed
-SHIP0000001   PRD68757    Supplier_D        Kano              Jos 2024-06-10             2024-06-21           2024-06-20       329           91632.07     Fedex Nigeria       delivered
-SHIP0000002   PRD34311    Supplier_E       Lagos    Port Harcourt 2024-10-15             2024-10-19           2024-10-18       203           77141.08      Ace Couriers       delivered
-```
-## 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/supply_chain_logistics_data/nigerian_retail_and_ecommerce_supply_chain_logistics_data.csv
-```
-### Parquet (Recommended)
-```
-data/supply_chain_logistics_data/nigerian_retail_and_ecommerce_supply_chain_logistics_data.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_supply_chain_logistics_data")
-# 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/supply_chain_logistics_data/nigerian_retail_and_ecommerce_supply_chain_logistics_data.csv')
-# Load Parquet (recommended for large datasets)
-df = pd.read_parquet('data/supply_chain_logistics_data/nigerian_retail_and_ecommerce_supply_chain_logistics_data.parquet')
 ```
-### PyArrow
-```python
-import pyarrow.parquet as pq
-# Load Parquet
-table = pq.read_table('data/supply_chain_logistics_data/nigerian_retail_and_ecommerce_supply_chain_logistics_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
 ```bibtex
-@dataset{nigerian_retail_supply_chain_logistics_data_2025,
-  title={Supply Chain Logistics Data},
-  author={Electric Sheep Africa},
-  year={2025},
-  publisher={Hugging Face},
-  howpublished={\url{https://huggingface.co/datasets/electricsheepafrica/nigerian-retail-supply-chain-logistics-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.

 ---
+license: other
 language:
 - en
+task_categories:
+- tabular-classification
+- tabular-regression
+multilinguality: monolingual
 size_categories:
 - 100K<n<1M
+tags:
+- "africa"
+- "electric-sheep-africa"
+- "open-data"
+- "metadata-backed"
+- "culture-language"
+- "parquet"
+- "tabular"
+- "text"
+- "retail"
+- "ecommerce"
+- "nigeria"
+- "synthetic-data"
+- "inventory"
+- "supply-chain"
+- "synthetic"
+- "literature"
+pretty_name: "Supply Chain Logistics Data | Africa (Electric Sheep Africa metadata inventory)"
 ---
+# Supply Chain Logistics Data | Africa (Electric Sheep Africa metadata inventory)
+**Size category:** `100K<n<1M` - **Formats:** `parquet` - **Sector:** culture_language - *Engineered by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*
+![size](https://img.shields.io/badge/size-100K%3Cn%3C1M-blue)
+![sector](https://img.shields.io/badge/sector-culture_language-green)
+![downloads](https://img.shields.io/badge/HF_downloads-35-orange)
+![license](https://img.shields.io/badge/license-other-lightgrey)
+## TL;DR
+This dataset is part of the Electric Sheep Africa catalog on Hugging Face. It is indexed for African data discovery with standardized metadata, loading guidance, provenance notes, and analyst-oriented context.
+## What This Dataset Covers
+Public datasets help analysts inspect structured evidence, build reproducible workflows, and compare patterns across domains.
+Dataset context from the existing Hugging Face card: ⚠️ Synthetic dataset — Parameterized from published SSA literature, not real observations. Not suitable for empirical analysis or policy inference. Supply Chain Logistics Data Dataset Description Comprehensive supply chain logistics data for Nigerian retail and e-commerce analysis Dataset Information Category: Product and Inventory Industry: Retail & E-Commerce Country: Nigeria Format: CSV, Parquet Rows: 400,000 Columns: 12 Date Generated: 2025-10-06… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/africa-synth-retail-and-ecommerce-supply-chain-logistics-data-nigeria.
+## Dataset Profile
+| Field | Value |
+|---|---|
+| Hugging Face repo | [`electricsheepafrica/africa-synth-retail-and-ecommerce-supply-chain-logistics-data-nigeria`](https://huggingface.co/datasets/electricsheepafrica/africa-synth-retail-and-ecommerce-supply-chain-logistics-data-nigeria) |
+| Sector | culture_language |
+| Topic tags | retail, ecommerce, nigeria, synthetic-data, inventory, supply-chain, synthetic |
+| Modalities | `tabular`, `text` |
+| Formats | `parquet` |
+| Size category | `100K<n<1M` |
+| Countries | Nigeria |
+| ISO3 coverage | `NGA` |
+| Last modified on HF | `2026-04-14 22:19:19+00:00` |
+| Inventory snapshot | `2026-07-16T16:00:34Z` |
+## How To Read This Dataset
+- Start from the repository files and the dataset viewer when available.
+- Treat the README context as a fast orientation layer; confirm variable definitions and units in the data files before modeling.
+- Use explicit country columns when present. When geography is only implied by the title or source metadata, document that assumption in downstream analysis.
+- Preserve missing values until you have a defensible imputation rule.
+## Usage
 ```python
 from datasets import load_dataset
+ds = load_dataset("electricsheepafrica/africa-synth-retail-and-ecommerce-supply-chain-logistics-data-nigeria")
+print(ds)
+split_name = next(iter(ds))
+table = ds[split_name]
+print(table.features)
+print(table[:3])
 ```
+### Convert To Pandas When Tabular
 ```python
+from datasets import Dataset
+first_split = ds[next(iter(ds))]
+if isinstance(first_split, Dataset):
+    df = first_split.to_pandas()
+    print(df.head())
 ```
+## Data Quality Notes
+- This card was standardized from the Electric Sheep Africa Hugging Face metadata inventory.
+- Exact schema, row counts, and source files should be inspected in the repository data files.
+- Metadata gaps from the inventory: upstream_publisher.
+- Do not infer policy meaning from labels alone; confirm definitions, units, and methods in the source material.
+## Source And Provenance
+- **Source context:** Electric Sheep Africa metadata inventory
+- **Publisher/source attribution:** Public dataset metadata
+- **License:** gpl
+- **Hugging Face URL:** [https://huggingface.co/datasets/electricsheepafrica/africa-synth-retail-and-ecommerce-supply-chain-logistics-data-nigeria](https://huggingface.co/datasets/electricsheepafrica/africa-synth-retail-and-ecommerce-supply-chain-logistics-data-nigeria)
+- **Inventory retrieved at:** `2026-07-16T16:00:34Z`
+## Suggested Analyses
+- Inspect schema and missingness before modeling.
+- Profile variables by geography, time, and subgroup columns where present.
+- Join with other Electric Sheep Africa datasets using explicit country, year, and indicator fields when available.
+- Build reproducible notebooks that cite both the original source context and the Electric Sheep Africa Hugging Face repo.
 ## Citation
 ```bibtex
+@misc{electric_sheep_africa_africa_synth_retail_and_ecommerce_supply_chain_logistics_data_nigeria_2026,
+  title        = {Supply Chain Logistics Data | Africa (Electric Sheep Africa metadata inventory)},
+  author       = {Public dataset metadata},
+  year         = {2026},
+  url          = {https://huggingface.co/datasets/electricsheepafrica/africa-synth-retail-and-ecommerce-supply-chain-logistics-data-nigeria},
+  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
+  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-synth-retail-and-ecommerce-supply-chain-logistics-data-nigeria}}
 }
 ```
+## License
+Released under gpl.
+Original source rights remain with the original publisher or data provider. Electric Sheep Africa engineering standardizes discovery metadata, documentation, and usage guidance for analysis on Hugging Face.
+## About Electric Sheep Africa
+Electric Sheep Africa publishes ML-ready African public datasets on Hugging Face.
 ---
+Provenance: metadata-backed README standardized 2026-08-12 by the Electric Sheep Africa README system. Inventory source: `catalog/esa_metadata_inventory/master_metadata.jsonl`.