Datasets:

electricsheepafrica
/

africa-zimbabwe-health-facilities

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
  - other
task_ids: []
tags:
  - africa
  - humanitarian
  - hdx
  - electric-sheep-africa
  - facilities-infrastructure
  - health
  - health-facilities
  - hxl
  - zwe
pretty_name: 'Zimbabwe: Health facilities'
dataset_info:
  splits:
    - name: train
      num_examples: 1352
    - name: test
      num_examples: 338

Zimbabwe: Health facilities

Publisher: OCHA Regional Office for Southern and Eastern Africa (ROSEA) · Source: HDX · License: cc-by · Updated: 2025-11-13

Abstract

List of Health facilities in Zimbabwe

Each row in this dataset represents subnational administrative unit observations. Data was last updated on HDX on 2025-11-13. Geographic scope: ZWE.

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

Dataset Characteristics


Domain	Public health
Unit of observation	Subnational administrative unit observations
Rows (total)	1,690
Columns	43 (37 numeric, 6 categorical, 0 datetime)
Train split	1,352 rows
Test split	338 rows
Geographic scope	ZWE
Publisher	OCHA Regional Office for Southern and Eastern Africa (ROSEA)
HDX last updated	2025-11-13

Variables

Geographic — province (Manicaland, Mashonaland East, Midlands), district (Mutasa, Chipinge, Mutare), longitude (range 25.8258–33.0345), latitude (range -22.3173–-15.7006), yearbuilt (range 0.0–2011.0) and 2 others.

Temporal — updated (range 1998.0–2013.0).

Identifier / Metadata — id1 (range 1.0–1686.0), id (range 0.0–1685.0), nameoffaci (ZRP, Chivi, Shamva), esa_source (HDX), esa_processed (2026-04-18).

Other — elevation (range 0.0–1569.0), ownership (range 0.0–9.0), numofdocto (range 0.0–3.0), numofnurse (range 0.0–41.0), numofnur_1 (range 0.0–21.0) and 25 others.

Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-zimbabwe-health-facilities")
train = ds["train"].to_pandas()
test  = ds["test"].to_pandas()

print(train.shape)
train.head()

Schema

Column	Type	Null %	Range / Sample Values
`id1`	float64	0.1%	1.0 – 1686.0 (mean 845.1445)
`id`	float64	2.0%	0.0 – 1685.0 (mean 662.1087)
`province`	object	0.0%	Manicaland, Mashonaland East, Midlands
`district`	object	0.0%	Mutasa, Chipinge, Mutare
`longitude`	float64	0.1%	25.8258 – 33.0345 (mean 30.5756)
`latitude`	float64	0.1%	-22.3173 – -15.7006 (mean -18.8716)
`elevation`	float64	0.9%	0.0 – 1569.0 (mean 87.397)
`updated`	float64	0.9%	1998.0 – 2013.0 (mean 2001.2042)
`nameoffaci`	object	0.1%	ZRP, Chivi, Shamva
`ownership`	float64	10.7%	0.0 – 9.0 (mean 5.0199)
`yearbuilt`	float64	4.7%	0.0 – 2011.0 (mean 297.0981)
`typeoffaci`	object	1.0%	Clinic, Rural Health Centre, Council Clinic
`numofdocto`	float64	4.0%	0.0 – 3.0 (mean 0.0265)
`numofnurse`	float64	4.0%	0.0 – 41.0 (mean 0.4418)
`numofnur_1`	float64	4.0%	0.0 – 21.0 (mean 0.244)
`numofpcn`	float64	4.0%	0.0 – 39.0 (mean 0.1041)
`numofehts`	float64	4.0%	0.0 – 4.0 (mean 0.0444)
`numofpharm`	float64	4.0%	0.0 – 2.0 (mean 0.0105)
`numoflabte`	float64	4.0%	0.0 – 5.0 (mean 0.008)
`numofbeds`	float64	4.0%	0.0 – 2500.0 (mean 2.6402)
`numofmater`	float64	4.0%	0.0 – 20.0 (mean 0.1763)
`numofgener`	float64	4.0%	0.0 – 140.0 (mean 0.6245)
`cathmentpo`	float64	4.0%	0.0 – 1616300.0 (mean 2154.8712)
`distneares`	float64	4.0%	0.0 – 565.0 (mean 5.4855)
`hascommuni`	float64	4.0%
`hascommu_1`	float64	4.0%
`haswaterpi`	float64	4.0%
`haswaterun`	float64	4.0%
`haselectri`	float64	4.0%
`haselect_1`	float64	4.0%
`distnear_1`	float64	4.0%
`hassanitat`	float64	4.0%
`hassanit_1`	float64	4.0%
`hassanit_2`	float64	4.0%
`hassecurit`	float64	4.0%
`hassecur_1`	float64	4.0%
`hasroadtar`	float64	4.0%
`hasroadgra`	float64	4.0%
`hasinciner`	float64	4.0%
`hasautoway`	float64	4.1%
`hasdental`	float64	4.0%
`esa_source`	object	0.0%	HDX
`esa_processed`	object	0.0%	2026-04-18

Numeric Summary

Column	Min	Max	Mean	Median
`id1`	1.0	1686.0	845.1445	845.0
`id`	0.0	1685.0	662.1087	652.5
`longitude`	25.8258	33.0345	30.5756	30.8722
`latitude`	-22.3173	-15.7006	-18.8716	-18.7445
`elevation`	0.0	1569.0	87.397	0.0
`updated`	1998.0	2013.0	2001.2042	1998.0
`ownership`	0.0	9.0	5.0199	5.0
`yearbuilt`	0.0	2011.0	297.0981	0.0
`numofdocto`	0.0	3.0	0.0265	0.0
`numofnurse`	0.0	41.0	0.4418	0.0
`numofnur_1`	0.0	21.0	0.244	0.0
`numofpcn`	0.0	39.0	0.1041	0.0
`numofehts`	0.0	4.0	0.0444	0.0
`numofpharm`	0.0	2.0	0.0105	0.0
`numoflabte`	0.0	5.0	0.008	0.0

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. 2 column(s) with >80% missing values were removed: comments, type_edite. 4 column(s) were cast from string to numeric or datetime based on parse-success rate (>85% threshold). 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_zimbabwe_health_facilities,
  title     = {Zimbabwe: Health facilities},
  author    = {OCHA Regional Office for Southern and Eastern Africa (ROSEA)},
  year      = {2025},
  url       = {https://data.humdata.org/dataset/zimbabwe-health-facilities},
  note      = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}

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