electricsheepafrica/africa-physician-density

Physician Density in Kenya by counties

Publisher: OCHA Regional Office for Southern and Eastern Africa (ROSEA) · Source: HDX · License: other-pd-nr · Updated: 2023-09-28

---

Abstract

The data was sourced from the Kenya County Health Fact Sheets(2013)

Each row in this dataset represents country-level aggregates. Data was last updated on HDX on 2023-09-28. Geographic scope: KEN.

Curated into ML-ready Parquet format by Electric Sheep Africa.

---

Dataset Characteristics

Domain	Public health
Unit of observation	Country-level aggregates
Rows (total)	48
Columns	5 (1 numeric, 4 categorical, 0 datetime)
Train split	38 rows
Test split	9 rows
Geographic scope	KEN
Publisher	OCHA Regional Office for Southern and Eastern Africa (ROSEA)
HDX last updated	2023-09-28

---

Variables

Geographic — country (Kenya), county (National Average, Baringo, Meru), physicians_density (range 0.0–20.0).

Identifier / Metadata — esa_source (HDX), esa_processed (2026-04-04).

---

Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-physician-density")
train = ds["train"].to_pandas()
test  = ds["test"].to_pandas()

print(train.shape)
train.head()

---

Schema

Column	Type	Null %	Range / Sample Values
country	object	0.0%	Kenya
county	object	0.0%	National Average, Baringo, Meru
physicians_density	float64	0.0%	0.0 – 20.0 (mean 5.6577)
esa_source	object	0.0%	HDX
esa_processed	object	0.0%	2026-04-04

---

Numeric Summary

Column	Min	Max	Mean	Median
physicians_density	0.0	20.0	5.6577	5.0

---

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 OCHA Regional Office for Southern and Eastern Africa (ROSEA) 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 for the publisher's own methodology notes and caveats.

---

Citation

@dataset{hdx_africa_physician_density,
  title     = {Physician Density in Kenya by counties},
  author    = {OCHA Regional Office for Southern and Eastern Africa (ROSEA)},
  year      = {2023},
  url       = {https://data.humdata.org/dataset/physician-density},
  note      = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}

---

Electric Sheep Africa — Africa's ML dataset infrastructure. Lagos, Nigeria.