---
license: cc-by-4.0
task_categories:
  - tabular-classification
  - tabular-regression
language:
  - en
tags:
  - africa
  - health
  - who
  - gho
  - "hwf_0002"
pretty_name: "Africa — WHO GHO: Medical doctors (number)"
size_categories:
  - n<1K
---

# Africa — WHO GHO: Medical doctors (number)

**Indicator code:** `HWF_0002`
**HuggingFace slug:** `electricsheepafrica/africa-who-medical-doctors-hwf0002`
**Source:** [WHO Global Health Observatory](https://www.who.int/data/gho/data/indicators/indicator-details/GHO/HWF_0002)
**License:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) — WHO Open Data

---

## Dataset Description

This dataset contains country-level observations for the WHO GHO indicator **"Medical doctors (number)"** (`HWF_0002`) across African nations, spanning 1985–2024. It is part of the [Electric Sheep Africa](https://huggingface.co/electricsheepafrica) collection — a unified, ML-ready repository of African data.

Data is sourced directly from the WHO Global Health Observatory OData API and repackaged as Parquet files with a consistent schema. All values are drawn from `NumericValue` (the float-precision field), not the display string. Confidence interval bounds (`value_low`, `value_high`) are included where available.

---

## Coverage

| | |
|---|---|
| **Countries** | 47 African nations |
| **Years** | 1985 – 2024 |
| **Total rows** | 586 |
| **Region filter** | WHO AFRO (`ParentLocationCode = 'AFR'`) |

**Countries included:** AGO, BDI, BEN, BFA, BWA, CAF, CIV, CMR, COD, COG, COM, CPV, DZA, ERI, ETH, GAB, GHA, GIN, GMB, GNB … and 27 more

---

## Sub-dimensions

_No sub-dimensions (single value per country/year)_

When an indicator is stratified (e.g., by sex or age group), each unique combination of country × year × dimension produces a separate row. Filter on `dim1` / `dim2` for the stratum you need, or aggregate across strata.

---

## Schema

| Column | Type | Description |
|--------|------|-------------|
| `indicator_code` | string | GHO indicator code (e.g., `HWF_0002`) |
| `country_iso3` | string | ISO 3166-1 alpha-3 country code |
| `who_region` | string | WHO region code (always `AFR` here) |
| `year` | int | Observation year |
| `value_numeric` | float | Point estimate (primary ML target) |
| `value_low` | float | Lower confidence bound (if available) |
| `value_high` | float | Upper confidence bound (if available) |
| `value_display` | string | Formatted display string, e.g. `"58.3 [57.7–59.0]"` |
| `dim1_type` | string | Dimension 1 type, e.g. `SEX`, `RESIDENCEAREATYPE` |
| `dim1` | string | Dimension 1 value, e.g. `SEX_BTSX`, `RURAL` |
| `dim2_type` | string | Dimension 2 type (if present) |
| `dim2` | string | Dimension 2 value (if present) |
| `last_updated` | string | WHO data last-updated timestamp |

---

## Usage

```python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-who-medical-doctors-hwf0002")
df = ds["train"].to_pandas()

# Both-sexes, national level only
national = df[df.get("dim1", "").str.endswith("_BTSX") | df.get("dim1", pd.Series()).isna()]

# Time series for one country
kenya = df[df["country_iso3"] == "KEN"].sort_values("year")
```

---

## Citation

```bibtex
@misc{who_gho_hwf_0002,
  title     = {WHO Global Health Observatory: Medical doctors (number)},
  author    = {World Health Organization},
  year      = {2024},
  url       = {https://www.who.int/data/gho/data/indicators/indicator-details/GHO/HWF_0002},
  note      = {Repackaged by Electric Sheep Africa}
}
```

---

_Repackaged by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica) from WHO GHO open data. Original data © World Health Organization, licensed CC BY 4.0._