Datasets:

electricsheepafrica
/

africa-settlements-ethiopia

@@ -1,154 +1,142 @@
 ---
-annotations_creators:
-- no-annotation
-language_creators:
-- found
 language:
 - en
-license: other
-multilinguality:
-- monolingual
 size_categories:
 - n<1K
-source_datasets:
-- original
-task_categories:
-- other
-task_ids: []
 tags:
-- africa
-- humanitarian
-- hdx
-- electric-sheep-africa
-- geodata
-- hxl
-- populated-places-settlements
-- eth
-pretty_name: "Ethiopia: Settlements"
-dataset_info:
-  splits:
-    - name: train
-      num_examples: 650
-    - name: test
-      num_examples: 162
 ---
-# Ethiopia: Settlements
-**Publisher:** OCHA Ethiopia · **Source:** [HDX](https://data.humdata.org/dataset/ethiopia-settlements) · **License:** `hdx-other` · **Updated:** 2025-05-05
----
-## Abstract
-The settlements dataset contains the location of cities, towns and villages in Ethiopia.
-Populated places dataset for Ethiopia endorsed by the Inter-Cluster Information Management Working group (ICMWG) after cleaning and processing done by [Information Technology Outreach Services (ITOS)](https://www.uga.edu/). Source: Multiple sources
-Each row in this dataset represents tabular records. Data was last updated on HDX on 2025-05-05. Geographic scope: **ETH**.
-*Curated into ML-ready Parquet format by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica).*
----
-## Dataset Characteristics
-| | |
 |---|---|
-| **Domain** | Humanitarian and development data |
-| **Unit of observation** | Tabular records |
-| **Rows (total)** | 813 |
-| **Columns** | 15 (2 numeric, 13 categorical, 0 datetime) |
-| **Train split** | 650 rows |
-| **Test split** | 162 rows |
-| **Geographic scope** | ETH |
-| **Publisher** | OCHA Ethiopia |
-| **HDX last updated** | 2025-05-05 |
----
-## Variables
-**Geographic** — `point_x` (range 33.4451–47.1179), `point_y` (range 3.4485–14.5207).
-**Identifier / Metadata** — `t_name` (Soyoma, Arb Gebeya, Nunu), `adm3_pcode` (#adm3+code, ET030411, ET030907), `adm2_pcode` (ET0408, ET1600, ET0305), `adm1_pcode` (ET04, ET07, ET03), `esa_source` and 1 others.
-**Other** — `admt_pcod` (#adm5+code, ET03041101, ET03090701), `t_status` (Wereda Capital, Zonal Capital, Regional Capital), `adm3_en` (#adm3+name, Sayint, Guagusa Shikudad), `adm2_en` (Arsi, Sidama, North Shewa (AM)), `adm1_en` (Oromia, SNNP, Amhara) and 2 others.
----
-## Quick Start
 ```python
 from datasets import load_dataset
-ds    = load_dataset("electricsheepafrica/africa-settlements-ethiopia")
-train = ds["train"].to_pandas()
-test  = ds["test"].to_pandas()
-print(train.shape)
-train.head()
 ```
----
-## Schema
-| Column | Type | Null % | Range / Sample Values |
-|---|---|---|---|
-| `t_name` | object | 0.0% | Soyoma, Arb Gebeya, Nunu |
-| `admt_pcod` | object | 0.0% | #adm5+code, ET03041101, ET03090701 |
-| `t_status` | object | 0.1% | Wereda Capital, Zonal Capital, Regional Capital |
-| `adm3_en` | object | 0.0% | #adm3+name, Sayint, Guagusa Shikudad |
-| `adm3_pcode` | object | 0.0% | #adm3+code, ET030411, ET030907 |
-| `adm2_en` | object | 0.0% | Arsi, Sidama, North Shewa (AM) |
-| `adm2_pcode` | object | 0.0% | ET0408, ET1600, ET0305 |
-| `adm1_en` | object | 0.0% | Oromia, SNNP, Amhara |
-| `adm1_pcode` | object | 0.0% | ET04, ET07, ET03 |
-| `adm0_en` | object | 0.0% | Ethiopia, #adm0+name |
-| `adm0_pcod` | object | 0.0% |  |
-| `point_x` | float64 | 0.1% | 33.4451 – 47.1179 (mean 38.5972) |
-| `point_y` | float64 | 0.1% | 3.4485 – 14.5207 (mean 8.9522) |
-| `esa_source` | object | 0.0% |  |
-| `esa_processed` | object | 0.0% |  |
----
-## Numeric Summary
-| Column | Min | Max | Mean | Median |
-|---|---|---|---|---|
-| `point_x` | 33.4451 | 47.1179 | 38.5972 | 38.3648 |
-| `point_y` | 3.4485 | 14.5207 | 8.9522 | 8.6891 |
----
-## 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 OCHA Ethiopia 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/ethiopia-settlements) for the publisher's own methodology notes and caveats.
----
 ## Citation
 ```bibtex
-@dataset{hdx_africa_settlements_ethiopia,
-  title     = {Ethiopia: Settlements},
-  author    = {OCHA Ethiopia},
-  year      = {2025},
-  url       = {https://data.humdata.org/dataset/ethiopia-settlements},
-  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.*

 ---
+license: other
 language:
 - en
+task_categories:
+- tabular-classification
+- tabular-regression
+multilinguality: monolingual
 size_categories:
 - n<1K
 tags:
+- "africa"
+- "electric-sheep-africa"
+- "open-data"
+- "metadata-backed"
+- "humanitarian-development"
+- "parquet"
+- "tabular"
+- "text"
+- "humanitarian"
+- "hdx"
+- "geodata"
+- "hxl"
+- "populated-places-settlements"
+- "eth"
+pretty_name: "Ethiopia: Settlements | Africa (original)"
 ---
+# Ethiopia: Settlements | Africa (original)
+**Size category:** `n<1K` - **Formats:** `parquet` - **Sector:** humanitarian_development - *Engineered by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*
+![size](https://img.shields.io/badge/size-n%3C1K-blue)
+![sector](https://img.shields.io/badge/sector-humanitarian_development-green)
+![downloads](https://img.shields.io/badge/HF_downloads-21-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: Ethiopia: Settlements Publisher: OCHA Ethiopia · Source: HDX · License: hdx-other · Updated: 2025-05-05 Abstract The settlements dataset contains the location of cities, towns and villages in Ethiopia. Populated places dataset for Ethiopia endorsed by the Inter-Cluster Information Management Working group (ICMWG) after cleaning and processing done by Information Technology Outreach Services (ITOS). Source: Multiple sources Each row in this dataset represents tabular… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/africa-settlements-ethiopia.
+## Dataset Profile
+| Field | Value |
 |---|---|
+| Hugging Face repo | [`electricsheepafrica/africa-settlements-ethiopia`](https://huggingface.co/datasets/electricsheepafrica/africa-settlements-ethiopia) |
+| Sector | humanitarian_development |
+| Topic tags | humanitarian, hdx, electric-sheep-africa, geodata, hxl, populated-places-settlements, eth |
+| Modalities | `tabular`, `text` |
+| Formats | `parquet` |
+| Size category | `n<1K` |
+| Countries | Ethiopia |
+| ISO3 coverage | `ETH` |
+| Last modified on HF | `2026-04-28 09:23:40+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-settlements-ethiopia")
+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:** original
+- **Publisher/source attribution:** original
+- **License:** Source-specific or other license
+- **Hugging Face URL:** [https://huggingface.co/datasets/electricsheepafrica/africa-settlements-ethiopia](https://huggingface.co/datasets/electricsheepafrica/africa-settlements-ethiopia)
+- **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_settlements_ethiopia_2026,
+  title        = {Ethiopia: Settlements | Africa (original)},
+  author       = {original},
+  year         = {2026},
+  url          = {https://huggingface.co/datasets/electricsheepafrica/africa-settlements-ethiopia},
+  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
+  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-settlements-ethiopia}}
 }
 ```
+## License
+Released under Source-specific or other license.
+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`.