Datasets:

electricsheepafrica
/

africa-world-bank-public-sector-indicators-for-namibia

@@ -1,146 +1,143 @@
 ---
-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-classification
-task_ids: []
 tags:
-- africa
-- humanitarian
-- hdx
-- electric-sheep-africa
-- economics
-- indicators
-- nam
-pretty_name: "Namibia - Public Sector"
-dataset_info:
-  splits:
-    - name: train
-      num_examples: 1989
-    - name: test
-      num_examples: 497
 ---
-# Namibia - Public Sector
-**Publisher:** World Bank Group · **Source:** [HDX](https://data.humdata.org/dataset/world-bank-public-sector-indicators-for-namibia) · **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-namibia) on HDX.
-Effective governments improve people's standard of living by ensuring access to essential services –  health, education, water and sanitation, electricity, transport – and the opportunity to live and work in peace and security. Data here includes World Bank staff assessments of country performance in economic management, structural policies, policies for social inclusion and equity, and public sector management and institutions for the poorest countries. Also included are indicators on revenues and expenses from the International Monetary Fund's Government Finance Statistics, and on tax policies from various sources.
-Each row in this dataset represents country-level aggregates. Data was last updated on HDX on 2026-03-27. 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)** | 2,487 |
-| **Columns** | 8 (2 numeric, 6 categorical, 0 datetime) |
-| **Train split** | 1,989 rows |
-| **Test split** | 497 rows |
-| **Geographic scope** | NAM |
-| **Publisher** | World Bank Group |
-| **HDX last updated** | 2026-03-27 |
----
-## Variables
-**Geographic** — `country_name` (Namibia), `country_iso3` (NAM), `year` (range 1990.0–2024.0).
-**Outcome / Measurement** — `value` (range -28165538740.0–83533496231.1569).
-**Identifier / Metadata** — `indicator_name` (Military expenditure (current USD), Military expenditure (current LCU), Military expenditure (% of GDP)), `indicator_code` (MS.MIL.XPND.CD, MS.MIL.XPND.CN, MS.MIL.XPND.GD.ZS), `esa_source` (HDX), `esa_processed` (2026-04-12).
----
-## Quick Start
 ```python
 from datasets import load_dataset
-ds    = load_dataset("electricsheepafrica/africa-world-bank-public-sector-indicators-for-namibia")
-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% | Namibia |
-| `country_iso3` | object | 0.0% | NAM |
-| `year` | int64 | 0.0% | 1990.0 – 2024.0 (mean 2009.5424) |
-| `indicator_name` | object | 0.0% | Military expenditure (current USD), Military expenditure (current LCU), Military expenditure (% of GDP) |
-| `indicator_code` | object | 0.0% | MS.MIL.XPND.CD, MS.MIL.XPND.CN, MS.MIL.XPND.GD.ZS |
-| `value` | float64 | 0.0% | -28165538740.0 – 83533496231.1569 (mean 2034242993.3187) |
-| `esa_source` | object | 0.0% | HDX |
-| `esa_processed` | object | 0.0% | 2026-04-12 |
----
-## Numeric Summary
-| Column | Min | Max | Mean | Median |
-|---|---|---|---|---|
-| `year` | 1990.0 | 2024.0 | 2009.5424 | 2010.0 |
-| `value` | -28165538740.0 | 83533496231.1569 | 2034242993.3187 | 38.229 |
----
-## 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-public-sector-indicators-for-namibia) for the publisher's own methodology notes and caveats.
----
 ## Citation
 ```bibtex
-@dataset{hdx_africa_world_bank_public_sector_indicators_for_namibia,
-  title     = {Namibia - Public Sector},
-  author    = {World Bank Group},
-  year      = {2026},
-  url       = {https://data.humdata.org/dataset/world-bank-public-sector-indicators-for-namibia},
-  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: cc-by-4.0
 language:
 - en
 task_categories:
 - tabular-classification
+- tabular-regression
+multilinguality: monolingual
+size_categories:
+- 1K<n<10K
 tags:
+- "africa"
+- "electric-sheep-africa"
+- "open-data"
+- "metadata-backed"
+- "governance-security"
+- "parquet"
+- "tabular"
+- "text"
+- "humanitarian"
+- "hdx"
+- "economics"
+- "indicators"
+- "nam"
+- "public-sector"
+- "security"
+pretty_name: "Namibia - Public Sector | Africa (original)"
 ---
+# Namibia - Public Sector | Africa (original)
+**Size category:** `1K<n<10K` - **Formats:** `parquet` - **Sector:** governance_security - *Engineered by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*
+![size](https://img.shields.io/badge/size-1K%3Cn%3C10K-blue)
+![sector](https://img.shields.io/badge/sector-governance_security-green)
+![downloads](https://img.shields.io/badge/HF_downloads-9-orange)
+![license](https://img.shields.io/badge/license-cc--by--4.0-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: Namibia - Public Sector Publisher: World Bank Group · Source: HDX · License: cc-by · Updated: 2026-03-27 Abstract Contains data from the World Bank's data portal. There is also a consolidated country dataset on HDX. Effective governments improve people's standard of living by ensuring access to essential services – health, education, water and sanitation, electricity, transport – and the opportunity to live and work in peace and security. Data here includes World Bank… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/africa-world-bank-public-sector-indicators-for-namibia.
+## Dataset Profile
+| Field | Value |
 |---|---|
+| Hugging Face repo | [`electricsheepafrica/africa-world-bank-public-sector-indicators-for-namibia`](https://huggingface.co/datasets/electricsheepafrica/africa-world-bank-public-sector-indicators-for-namibia) |
+| Sector | governance_security |
+| Topic tags | humanitarian, hdx, electric-sheep-africa, economics, indicators, nam |
+| Modalities | `tabular`, `text` |
+| Formats | `parquet` |
+| Size category | `1K<n<10K` |
+| Countries | Namibia |
+| ISO3 coverage | `NAM` |
+| Last modified on HF | `2026-04-12 14:52:42+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-world-bank-public-sector-indicators-for-namibia")
+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:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)
+- **Hugging Face URL:** [https://huggingface.co/datasets/electricsheepafrica/africa-world-bank-public-sector-indicators-for-namibia](https://huggingface.co/datasets/electricsheepafrica/africa-world-bank-public-sector-indicators-for-namibia)
+- **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_world_bank_public_sector_indicators_for_namibia_2026,
+  title        = {Namibia - Public Sector | Africa (original)},
+  author       = {original},
+  year         = {2026},
+  url          = {https://huggingface.co/datasets/electricsheepafrica/africa-world-bank-public-sector-indicators-for-namibia},
+  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
+  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-world-bank-public-sector-indicators-for-namibia}}
 }
 ```
+## License
+Released under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/).
+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`.