Datasets:

electricsheepafrica
/

africa-who-medical-doctors-hwf0002

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 License: CC 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 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

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

@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 from WHO GHO open data. Original data © World Health Organization, licensed CC BY 4.0.