Datasets:

electricsheepafrica
/

africa-namibia-mpi

@@ -1,166 +1,146 @@
 ---
-annotations_creators:
-- no-annotation
-language_creators:
-- found
 language:
 - en
-license: other
-multilinguality:
-- monolingual
-size_categories:
-- n<1K
-source_datasets:
-- original
 task_categories:
 - tabular-classification
 - tabular-regression
-- other
-task_ids: []
 tags:
-- africa
-- humanitarian
-- hdx
-- electric-sheep-africa
-- development
-- education
-- health
-- indicators
-- mortality
-- nutrition
-- poverty
-- socioeconomics
-- nam
-pretty_name: "Namibia Multidimensional Poverty Index"
-dataset_info:
-  splits:
-    - name: train
-      num_examples: 11
-    - name: test
-      num_examples: 2
 ---
-# Namibia Multidimensional Poverty Index
-**Publisher:** Oxford Poverty & Human Development Initiative · **Source:** [HDX](https://data.humdata.org/dataset/namibia-mpi) · **License:** `other-pd-nr` · **Updated:** 2026-03-05
----
-## Abstract
-The global Multidimensional Poverty Index provides the only comprehensive measure available for non-income poverty, which has become a critical underpinning of the SDGs. The global Multidimensional Poverty Index (MPI) measures multidimensional poverty in over 100 developing countries, using internationally comparable datasets and is updated annually. The measure captures the acute deprivations that each person faces at the same time using information from 10 indicators, which are grouped into three equally weighted dimensions: health, education, and living standards. Critically, the MPI comprises variables that are already reported under the Demographic Health Surveys (DHS), the Multi-Indicator Cluster Surveys (MICS) and in some cases, national surveys.
-The subnational multidimensional poverty data from the [data tables](https://ophi.org.uk/global-mpi-archive) are published by the Oxford Poverty and Human Development Initiative (OPHI), University of Oxford. For the details of the global MPI methodology, please see the latest Methodological Notes [found here](https://ophi.org.uk/publications-table?title=&field_authors_value=&field_publication_type_target_id=11&publication_year_filter=All&field_keywords_value=&field_country_target_id=All&field_region_target_id=All).
-Each row in this dataset represents country-level aggregates. Data was last updated on HDX on 2026-03-05. Geographic scope: **NAM**.
-*Curated into ML-ready Parquet format by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica).*
----
-## Dataset Characteristics
-| | |
 |---|---|
-| **Domain** | Public health |
-| **Unit of observation** | Country-level aggregates |
-| **Rows (total)** | 14 |
-| **Columns** | 13 (5 numeric, 6 categorical, 0 datetime) |
-| **Train split** | 11 rows |
-| **Test split** | 2 rows |
-| **Geographic scope** | NAM |
-| **Publisher** | Oxford Poverty & Human Development Initiative |
-| **HDX last updated** | 2026-03-05 |
----
-## Variables
-**Geographic** — `country_iso3` (NAM), `admin_1_pcode` (NA01, NA02, NA03), `admin_1_name` (Zambezi, Erongo, Hardap), `intensity_of_deprivation` (range 38.4089–49.9575), `vulnerable_to_poverty` (range 11.9064–33.1402) and 2 others.
-**Temporal** — `start_date`, `end_date`.
-**Outcome / Measurement** — `headcount_ratio` (range 11.1518–72.8493).
-**Identifier / Metadata** — `esa_source` (HDX), `esa_processed` (2026-04-04).
-**Other** — `mpi` (range 0.0432–0.3508).
----
-## Quick Start
 ```python
 from datasets import load_dataset
-ds    = load_dataset("electricsheepafrica/africa-namibia-mpi")
-train = ds["train"].to_pandas()
-test  = ds["test"].to_pandas()
-print(train.shape)
-train.head()
 ```
----
-## Schema
-| Column | Type | Null % | Range / Sample Values |
-|---|---|---|---|
-| `country_iso3` | object | 0.0% | NAM |
-| `admin_1_pcode` | object | 7.1% | NA01, NA02, NA03 |
-| `admin_1_name` | object | 7.1% | Zambezi, Erongo, Hardap |
-| `mpi` | float64 | 0.0% | 0.0432 – 0.3508 (mean 0.1817) |
-| `headcount_ratio` | float64 | 0.0% | 11.1518 – 72.8493 (mean 39.913) |
-| `intensity_of_deprivation` | float64 | 0.0% | 38.4089 – 49.9575 (mean 44.248) |
-| `vulnerable_to_poverty` | float64 | 0.0% | 11.9064 – 33.1402 (mean 20.2978) |
-| `in_severe_poverty` | float64 | 0.0% | 0.3509 – 34.943 (mean 13.1534) |
-| `survey` | object | 0.0% | DHS |
-| `start_date` | datetime64[ns, UTC] | 0.0% |  |
-| `end_date` | datetime64[ns, UTC] | 0.0% |  |
-| `esa_source` | object | 0.0% | HDX |
-| `esa_processed` | object | 0.0% | 2026-04-04 |
----
-## Numeric Summary
-| Column | Min | Max | Mean | Median |
-|---|---|---|---|---|
-| `mpi` | 0.0432 | 0.3508 | 0.1817 | 0.1907 |
-| `headcount_ratio` | 11.1518 | 72.8493 | 39.913 | 42.7672 |
-| `intensity_of_deprivation` | 38.4089 | 49.9575 | 44.248 | 44.4403 |
-| `vulnerable_to_poverty` | 11.9064 | 33.1402 | 20.2978 | 20.6386 |
-| `in_severe_poverty` | 0.3509 | 34.943 | 13.1534 | 11.762 |
----
-## 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`. 2 column(s) were cast from string to numeric or datetime based on parse-success rate (>85% threshold). 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 Oxford Poverty & Human Development Initiative 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/namibia-mpi) for the publisher's own methodology notes and caveats.
----
 ## Citation
 ```bibtex
-@dataset{hdx_africa_namibia_mpi,
-  title     = {Namibia Multidimensional Poverty Index},
-  author    = {Oxford Poverty & Human Development Initiative},
-  year      = {2026},
-  url       = {https://data.humdata.org/dataset/namibia-mpi},
-  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"
+- "health"
+- "parquet"
+- "tabular"
+- "text"
+- "humanitarian"
+- "hdx"
+- "development"
+- "education"
+- "indicators"
+- "mortality"
+- "nutrition"
+- "poverty"
+- "socioeconomics"
+- "nam"
+pretty_name: "Namibia Multidimensional Poverty Index | Africa (original)"
 ---
+# Namibia Multidimensional Poverty Index | Africa (original)
+**Size category:** `n<1K` - **Formats:** `parquet` - **Sector:** health - *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-health-green)
+![downloads](https://img.shields.io/badge/HF_downloads-32-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
+Health datasets help researchers examine disease burden, service delivery, risk factors, outcomes, and public-health program performance.
+Dataset context from the existing Hugging Face card: Namibia Multidimensional Poverty Index Publisher: Oxford Poverty & Human Development Initiative · Source: HDX · License: other-pd-nr · Updated: 2026-03-05 Abstract The global Multidimensional Poverty Index provides the only comprehensive measure available for non-income poverty, which has become a critical underpinning of the SDGs. The global Multidimensional Poverty Index (MPI) measures multidimensional poverty in over 100 developing countries, using internationally… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/africa-namibia-mpi.
+## Dataset Profile
+| Field | Value |
 |---|---|
+| Hugging Face repo | [`electricsheepafrica/africa-namibia-mpi`](https://huggingface.co/datasets/electricsheepafrica/africa-namibia-mpi) |
+| Sector | health |
+| Topic tags | humanitarian, hdx, electric-sheep-africa, development, education, health, indicators, mortality, nutrition, poverty, socioeconomics, nam |
+| Modalities | `tabular`, `text` |
+| Formats | `parquet` |
+| Size category | `n<1K` |
+| Countries | Namibia |
+| ISO3 coverage | `NAM` |
+| Last modified on HF | `2026-04-04 13:48:29+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-namibia-mpi")
+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-namibia-mpi](https://huggingface.co/datasets/electricsheepafrica/africa-namibia-mpi)
+- **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_namibia_mpi_2026,
+  title        = {Namibia Multidimensional Poverty Index | Africa (original)},
+  author       = {original},
+  year         = {2026},
+  url          = {https://huggingface.co/datasets/electricsheepafrica/africa-namibia-mpi},
+  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
+  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-namibia-mpi}}
 }
 ```
+## 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`.