electricsheepafrica/africa-disability-zimbabwe

Zimbabwe - Health Indicators

Publisher: World Health Organization · Source: HDX · License: hdx-other · Updated: 2026-04-15

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Abstract

This dataset contains data from WHO's data portal covering the following categories:

Air pollution, Child mortality, Dementia diagnosis, treatment and care, Environment and health, Food safety, Global Dementia Observatory (GDO), Global Health Estimates: Life expectancy and leading causes of death and disability, Global Information System on Alcohol and Health, Global Patient Safety Observatory, HIV, Health financing, Health systems, Health taxes, Health workforce, Hepatitis, Immunization coverage and vaccine-preventable diseases, Malaria, Maternal and reproductive health, Mental health, Neglected tropical diseases, Noncommunicable diseases, Nutrition, Oral Health, Priority health technologies, Resources for Substance Use Disorders, Road Safety, Sexually Transmitted Infections, Substance use disorders service coverage, Tobacco control, Tuberculosis, Universal health coverage (UHC), SDG Target 3.8, Vaccine-preventable communicable diseases, Violence prevention, Water, sanitation and hygiene (WASH), World Health Statistics.

For links to individual indicator metadata, see resource descriptions.

Each row in this dataset represents first-level administrative unit observations. Data was last updated on HDX on 2026-04-15. Geographic scope: ZWE.

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

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Dataset Characteristics

Domain	Food security and nutrition
Unit of observation	First-level administrative unit observations
Rows (total)	25,012
Columns	19 (6 numeric, 13 categorical, 0 datetime)
Train split	20,009 rows
Test split	5,002 rows
Geographic scope	ZWE
Publisher	World Health Organization
HDX last updated	2026-04-15

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Variables

Geographic — gho_display (Stunting prevalence among children under 5 years of age (% height-for-age <-2 SD), survey-based estimates, Number of deaths in children aged <5 years, by cause, Underweight prevalence among children under 5 years of age (% weight-for-age <-2 SD), survey-based estimates), year_display (range 1954.0–2030.0), startyear (range 1954.0–2030.0), endyear (range 1954.0–2030.0), region_code (AFR) and 4 others.

Outcome / Measurement — value.

Identifier / Metadata — gho_code (NUTRITION_ANT_HAZ_NE2, MORT_100, NUTRITION_WA_2), dimension_code (SEX_BTSX, SEX_FMLE, SEX_MLE), dimension_name (Both sexes, Female, Male), esa_source, esa_processed.

Other — gho_url (https://www.who.int/data/gho/data/indicators/indicator-details/GHO/gho-jme-country-children-aged-5-years-stunted-%28-height-for-age--2-sd%29, https://www.who.int/data/gho/data/indicators/indicator-details/GHO/number-of-deaths, https://www.who.int/data/gho/data/indicators/indicator-details/GHO/gho-jme-country-children-aged-5-years-underweight-%28-weight-for-age--2-sd%29-%28-%29), numeric (range -4.89–54056742.92), low (range -0.1438–1700000.0), high (range 0.0–2000000.0).

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Quick Start

from datasets import load_dataset

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

print(train.shape)
train.head()

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Schema

Column	Type	Null %	Range / Sample Values
gho_code	object	0.0%	NUTRITION_ANT_HAZ_NE2, MORT_100, NUTRITION_WA_2
gho_display	object	0.0%	Stunting prevalence among children under 5 years of age (% height-for-age <-2 SD), survey-based estimates, Number of deaths in children aged <5 years, by cause, Underweight prevalence among children under 5 years of age (% weight-for-age <-2 SD), survey-based estimates
gho_url	object	0.0%	https://www.who.int/data/gho/data/indicators/indicator-details/GHO/gho-jme-country-children-aged-5-years-stunted-%28-height-for-age--2-sd%29, https://www.who.int/data/gho/data/indicators/indicator-details/GHO/number-of-deaths, https://www.who.int/data/gho/data/indicators/indicator-details/GHO/gho-jme-country-children-aged-5-years-underweight-%28-weight-for-age--2-sd%29-%28-%29
year_display	int64	0.0%	1954.0 – 2030.0 (mean 2009.0284)
startyear	int64	0.0%	1954.0 – 2030.0 (mean 2009.0241)
endyear	int64	0.0%	1954.0 – 2030.0 (mean 2009.0284)
region_code	object	0.0%	AFR
region_display	object	0.0%	Africa
country_code	object	0.0%	ZWE
country_display	object	0.0%	Zimbabwe
dimension_type	object	9.7%	SEX, AGEGROUP, RESIDENCEAREATYPE
dimension_code	object	9.7%	SEX_BTSX, SEX_FMLE, SEX_MLE
dimension_name	object	9.9%	Both sexes, Female, Male
numeric	float64	8.6%	-4.89 – 54056742.92 (mean 17414.6167)
value	object	0.1%
low	float64	31.1%	-0.1438 – 1700000.0 (mean 5171.9878)
high	float64	31.1%	0.0 – 2000000.0 (mean 7534.2009)
esa_source	object	0.0%
esa_processed	object	0.0%

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Numeric Summary

Column	Min	Max	Mean	Median
year_display	1954.0	2030.0	2009.0284	2011.0
startyear	1954.0	2030.0	2009.0241	2011.0
endyear	1954.0	2030.0	2009.0284	2011.0
numeric	-4.89	54056742.92	17414.6167	15.2055
low	-0.1438	1700000.0	5171.9878	11.2229
high	0.0	2000000.0	7534.2009	22.2

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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. 713 exact duplicate rows were removed. The dataset was split 80/20 into train and test partitions using a fixed random seed (42) and saved as Snappy-compressed Parquet.

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Limitations

• Data originates from World Health Organization and has not been independently validated by ESA.
• Automated cleaning cannot correct for misreported values, definitional inconsistencies, or sampling bias in the original collection.
• The following columns have >20% missing values and should be treated with caution in modelling: low, high.
• Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

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Citation

@dataset{hdx_africa_disability_zimbabwe,
  title     = {Zimbabwe - Health Indicators},
  author    = {World Health Organization},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/who-data-for-zwe},
  note      = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}

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