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README.md

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 ---
-dataset_info:
-  features:
-  - name: country_name
-    dtype: string
-  - name: country_iso3
-    dtype: string
-  - name: year
-    dtype: int64
-  - name: Nurses and midwives (per 1,000 people)
-    dtype: float64
-  splits:
-  - name: train
-    num_bytes: 16894
-    num_examples: 476
-  - name: test
-    num_bytes: 4240
-    num_examples: 120
-  download_size: 9799
-  dataset_size: 21134
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: test
-    path: data/test-*
+license: cc-by-4.0
+language:
+- en
+task_categories:
+- tabular-classification
+- tabular-regression
+- time-series-forecasting
+multilinguality: monolingual
+size_categories:
+- n<1K
+tags:
+- tabular
+- africa
+- our-world-in-data
+- nurses-and-midwives-per-1000-people
+- owid
+- long-run-series
+- time-series
+pretty_name: "Nurses And Midwives Per 1000 People | Africa (Our World in Data)"
 ---
+
+# Nurses And Midwives Per 1000 People | Africa (Our World in Data)
+
+🌍 **596 observations** · **54 Africa countries** · **1990–2023** · *Repackaged by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*
+
+![rows](https://img.shields.io/badge/rows-596-blue) ![countries](https://img.shields.io/badge/countries-54-green) ![years](https://img.shields.io/badge/years-1990–2023-orange) ![license](https://img.shields.io/badge/license-cc-by-4.0-lightgrey)
+
+## TL;DR
+
+This dataset contains **596 observations** of `Nurses And Midwives Per 1000 People` data across **54 Africa countries**, spanning **1990–2023**.
+
+## About the source
+
+- **Source:** [Our World in Data](https://ourworldindata.org/grapher/nurses-and-midwives-per-1000-people)
+- **Publisher:** Our World in Data
+- **License:** [cc-by-4.0](https://creativecommons.org/licenses/by/4.0/)
+- **Topic:** Nurses And Midwives Per 1000 People
+
+## Geographic coverage
+
+54 Africa countries · top rows shown below, sorted by row count:
+
+| Country | Rows | First year | Last year |
+|---------|-----:|-----------:|----------:|
+| `ZAF` | 24 | 1998 | 2022 |
+| `MOZ` | 24 | 1999 | 2022 |
+| `BWA` | 22 | 1998 | 2023 |
+| `TCD` | 18 | 1997 | 2023 |
+| `CPV` | 16 | 2004 | 2023 |
+| `BFA` | 16 | 2004 | 2023 |
+| `KEN` | 16 | 2004 | 2023 |
+| `MUS` | 16 | 2003 | 2022 |
+| `GMB` | 15 | 2003 | 2023 |
+| `CIV` | 15 | 2004 | 2023 |
+| `GHA` | 14 | 2004 | 2023 |
+| `NGA` | 14 | 2003 | 2022 |
+| `SYC` | 14 | 2004 | 2023 |
+| `ZWE` | 14 | 1990 | 2022 |
+| `MDG` | 14 | 2002 | 2022 |
+| ... | _39 more countries_ | | |
+
+## Schema
+
+| Column | Type | Description | Example |
+|--------|------|-------------|---------|
+| `country_name` | `string` | — | `Algeria` |
+| `country_iso3` | `string` | — | `DZA` |
+| `year` | `int64` | — | `2002` |
+| `Nurses and midwives (per 1,000 people)` | `float64` | — | `2.197` |
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("electricsheepafrica/africa-owid-nurses-and-midwives-per-1000-people")
+df = ds["train"].to_pandas()
+print(df.head())
+```
+
+### Filter to one country
+
+```python
+kenya = df[df["country_iso3"] == "KEN"]
+```
+
+### Time-series for a single indicator
+
+```python
+sample = df.sort_values("year")
+sample.plot(x="year", y="Nurses and midwives (per 1,000 people)")
+```
+
+## Citation
+
+```bibtex
+@misc{africa_owid_nurses_and_midwives_per_1000_people_2023,
+  title        = {Nurses And Midwives Per 1000 People | Africa (Our World in Data)},
+  author       = {Our World in Data},
+  year         = {2023},
+  url          = {https://ourworldindata.org/grapher/nurses-and-midwives-per-1000-people},
+  publisher    = {HuggingFace Datasets, repackaged by Electric Sheep Africa},
+  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-owid-nurses-and-midwives-per-1000-people}}
+}
+```
+
+## License
+
+Released under [cc-by-4.0](https://creativecommons.org/licenses/by/4.0/).
+
+Original data © Our World in Data. When using this dataset, please cite both the original source above and the Electric Sheep Africa repackaging.
+
+## About Electric Sheep
+
+Electric Sheep Africa is part of the Electric Sheep mission: a unified, ML-ready data layer for Africa on HuggingFace. We pull data from authoritative open sources, normalize the schemas, package as Parquet, and publish with consistent dataset cards so researchers and developers can use `load_dataset()` to start working in seconds.
+
+Browse the full collection: [huggingface.co/electricsheepafrica](https://huggingface.co/electricsheepafrica)
+
+---
+
+_Provenance: ingested 2026-06-06 via the Electric Sheep pipeline. Source URL: https://ourworldindata.org/grapher/nurses-and-midwives-per-1000-people_