Add README.md

README.md

@@ -1,36 +1,146 @@
 ---
+annotations_creators:
+- no-annotation
+language_creators:
+- found
+language:
+- en
+license: cc-by-4.0
+multilinguality:
+- monolingual
+size_categories:
+- 1K<n<10K
+source_datasets:
+- original
+task_categories:
+- tabular-regression
+task_ids: []
+tags:
+- africa
+- humanitarian
+- hdx
+- electric-sheep-africa
+- economics
+- indicators
+- stp
+pretty_name: "Sao Tome and Principe - Economy and Growth"
 dataset_info:
-  features:
-  - name: country_name
-    dtype: string
-  - name: country_iso3
-    dtype: string
-  - name: year
-    dtype: int64
-  - name: indicator_name
-    dtype: string
-  - name: indicator_code
-    dtype: string
-  - name: value
-    dtype: float64
-  - name: esa_source
-    dtype: string
-  - name: esa_processed
-    dtype: string
   splits:
-  - name: train
-    num_bytes: 553415
-    num_examples: 4062
-  - name: test
-    num_bytes: 138550
-    num_examples: 1016
-  download_size: 98961
-  dataset_size: 691965
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: test
-    path: data/test-*
+    - name: train
+      num_examples: 4062
+    - name: test
+      num_examples: 1015
 ---
+
+# Sao Tome and Principe - Economy and Growth
+
+**Publisher:** World Bank Group · **Source:** [HDX](https://data.humdata.org/dataset/world-bank-economy-and-growth-indicators-for-sao-tome-and-principe) · **License:** `cc-by` · **Updated:** 2026-03-27
+
+---
+
+## Abstract
+
+Contains data from the World Bank's [data portal](http://data.worldbank.org/). There is also a [consolidated country dataset](https://data.humdata.org/dataset/world-bank-combined-indicators-for-sao-tome-and-principe) on HDX.
+
+Economic growth is central to economic development. When national income grows, real people benefit. While there is no known formula for stimulating economic growth, data can help policy-makers better understand their countries' economic situations and guide any work toward improvement. Data here covers measures of economic growth, such as gross domestic product (GDP) and gross national income (GNI). It also includes indicators representing factors known to be relevant to economic growth, such as capital stock, employment, investment, savings, consumption, government spending, imports, and exports.
+
+Each row in this dataset represents country-level aggregates. Data was last updated on HDX on 2026-03-27. Geographic scope: **STP**.
+
+*Curated into ML-ready Parquet format by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica).*
+
+---
+
+## Dataset Characteristics
+
+| | |
+|---|---|
+| **Domain** | Humanitarian and development data |
+| **Unit of observation** | Country-level aggregates |
+| **Rows (total)** | 5,078 |
+| **Columns** | 8 (2 numeric, 6 categorical, 0 datetime) |
+| **Train split** | 4,062 rows |
+| **Test split** | 1,015 rows |
+| **Geographic scope** | STP |
+| **Publisher** | World Bank Group |
+| **HDX last updated** | 2026-03-27 |
+
+---
+
+## Variables
+
+**Geographic** — `country_name` (Sao Tome and Principe), `country_iso3` (STP), `year` (range 1960.0–2024.0).
+
+**Outcome / Measurement** — `value` (range -231353595.9032–18756000000.0).
+
+**Identifier / Metadata** — `indicator_name` (DEC alternative conversion factor (LCU per US$), GDP deflator (base year varies by country), GDP (constant LCU)), `indicator_code` (PA.NUS.ATLS, NY.GDP.DEFL.ZS, NY.GDP.MKTP.KN), `esa_source` (HDX), `esa_processed` (2026-04-13).
+
+---
+
+## Quick Start
+
+```python
+from datasets import load_dataset
+
+ds    = load_dataset("electricsheepafrica/africa-world-bank-economy-and-growth-indicators-for-sao-tome-and-principe")
+train = ds["train"].to_pandas()
+test  = ds["test"].to_pandas()
+
+print(train.shape)
+train.head()
+```
+
+---
+
+## Schema
+
+| Column | Type | Null % | Range / Sample Values |
+|---|---|---|---|
+| `country_name` | object | 0.0% | Sao Tome and Principe |
+| `country_iso3` | object | 0.0% | STP |
+| `year` | int64 | 0.0% | 1960.0 – 2024.0 (mean 2002.999) |
+| `indicator_name` | object | 0.0% | DEC alternative conversion factor (LCU per US$), GDP deflator (base year varies by country), GDP (constant LCU) |
+| `indicator_code` | object | 0.0% | PA.NUS.ATLS, NY.GDP.DEFL.ZS, NY.GDP.MKTP.KN |
+| `value` | float64 | 0.0% | -231353595.9032 – 18756000000.0 (mean 251318199.0517) |
+| `esa_source` | object | 0.0% | HDX |
+| `esa_processed` | object | 0.0% | 2026-04-13 |
+
+---
+
+## Numeric Summary
+
+| Column | Min | Max | Mean | Median |
+|---|---|---|---|---|
+| `year` | 1960.0 | 2024.0 | 2002.999 | 2006.0 |
+| `value` | -231353595.9032 | 18756000000.0 | 251318199.0517 | 11736.323 |
+
+---
+
+## Curation
+
+Raw data was downloaded from HDX via the CKAN API and converted to Parquet. Column names were lowercased and standardised to snake_case. Common missing-value markers (`N/A`, `null`, `none`, `-`, `unknown`, `no data`, `#N/A`) were unified to `NaN`. The dataset was split 80/20 into train and test partitions using a fixed random seed (42) and saved as Snappy-compressed Parquet.
+
+---
+
+## Limitations
+
+- Data originates from World Bank Group and has not been independently validated by ESA.
+- Automated cleaning cannot correct for misreported values, definitional inconsistencies, or sampling bias in the original collection.
+- Refer to the [original HDX dataset page](https://data.humdata.org/dataset/world-bank-economy-and-growth-indicators-for-sao-tome-and-principe) for the publisher's own methodology notes and caveats.
+
+---
+
+## Citation
+
+```bibtex
+@dataset{hdx_africa_world_bank_economy_and_growth_indicators_for_sao_tome_and_principe,
+  title     = {Sao Tome and Principe - Economy and Growth},
+  author    = {World Bank Group},
+  year      = {2026},
+  url       = {https://data.humdata.org/dataset/world-bank-economy-and-growth-indicators-for-sao-tome-and-principe},
+  note      = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
+}
+```
+
+---
+
+*[Electric Sheep Africa](https://huggingface.co/electricsheepafrica) — Africa's ML dataset infrastructure. Lagos, Nigeria.*