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africa-demographics-zambia

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metadata 
annotations_creators:
  - no-annotation
language_creators:
  - found
language:
  - en
license: other
multilinguality:
  - monolingual
size_categories:
  - 1K<n<10K
source_datasets:
  - original
task_categories:
  - tabular-classification
  - other
task_ids: []
tags:
  - africa
  - humanitarian
  - hdx
  - electric-sheep-africa
  - demographics
  - health
  - zmb
pretty_name: Zambia - Subnational Demographic and Health Data
dataset_info:
  splits:
    - name: train
      num_examples: 1394
    - name: test
      num_examples: 348

Zambia - Subnational Demographic and Health Data

Publisher: The DHS Program · Source: HDX · License: hdx-other · Updated: 2026-04-20

Abstract

Contains data from the DHS data portal. There is also a dataset containing Zambia - National Demographic and Health Data on HDX.

The DHS Program Application Programming Interface (API) provides software developers access to aggregated indicator data from The Demographic and Health Surveys (DHS) Program. The API can be used to create various applications to help analyze, visualize, explore and disseminate data on population, health, HIV, and nutrition from more than 90 countries.

Each row in this dataset represents first-level administrative unit observations. Data was last updated on HDX on 2026-04-20. Geographic scope: ZMB.

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

Dataset Characteristics

Domain Public health
Unit of observation First-level administrative unit observations
Rows (total) 1,743
Columns 30 (14 numeric, 16 categorical, 0 datetime)
Train split 1,394 rows
Test split 348 rows
Geographic scope ZMB
Publisher The DHS Program
HDX last updated 2026-04-20

Variables

Geographic — iso3 (ZMB), location (Central, Copperbelt, Luapula), dhs_countrycode (ZM), countryname (Zambia), surveyyear (range 1992.0–2024.0) and 8 others.

Outcome / Measurement — value (range 0.0–254.0), istotal (range 0.0–0.0).

Identifier / Metadata — dataid (range 584.0–7980700.0), indicatorid (RH_DELP_C_DHF, CH_DIAT_C_ORT, FE_FRTR_W_TFR), characteristicid (range 456001.0–456012.0), characteristiclabel (Central, Copperbelt, Luapula), ispreferred (range 0.0–1.0) and 3 others.

Other — indicator (Place of delivery: Health facility, Treatment of diarrhea: Either ORS or RHF, Total fertility rate 15-49), precision (range 0.0–1.0), indicatororder (range 11763080.0–260321010.0), characteristicorder (range 1456001.0–1456012.0), denominatorweighted (range 24.0–5683.0) and 2 others.

Quick Start 

from datasets import load_dataset

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

print(train.shape)
train.head()

Schema

Column Type Null % Range / Sample Values
iso3 object 0.0% ZMB
location object 0.0% Central, Copperbelt, Luapula
dataid int64 0.0% 584.0 – 7980700.0 (mean 4302204.7522)
indicator object 0.0% Place of delivery: Health facility, Treatment of diarrhea: Either ORS or RHF, Total fertility rate 15-49
value float64 0.0% 0.0 – 254.0 (mean 39.2798)
precision int64 0.0% 0.0 – 1.0 (mean 0.9243)
dhs_countrycode object 0.0% ZM
countryname object 0.0% Zambia
surveyyear int64 0.0% 1992.0 – 2024.0 (mean 2009.1664)
surveyid object 0.0% ZM2013DHS, ZM2018DHS, ZM2024DHS
indicatorid object 0.0% RH_DELP_C_DHF, CH_DIAT_C_ORT, FE_FRTR_W_TFR
indicatororder int64 0.0% 11763080.0 – 260321010.0 (mean 100486077.8026)
indicatortype object 0.0% I
characteristicid int64 0.0% 456001.0 – 456012.0 (mean 456005.9484)
characteristicorder int64 0.0% 1456001.0 – 1456012.0 (mean 1456006.572)
characteristiccategory object 0.0% Region
characteristiclabel object 0.0% Central, Copperbelt, Luapula
byvariableid int64 0.0% 0.0 – 631002.0 (mean 21369.8847)
byvariablelabel object 71.3%
istotal int64 0.0% 0.0 – 0.0 (mean 0.0)
ispreferred int64 0.0% 0.0 – 1.0 (mean 0.8698)
sdrid object 0.0%
regionid object 0.0%
surveyyearlabel float64 30.1% 1992.0 – 2024.0 (mean 2009.63)
surveytype object 0.0%
denominatorweighted float64 22.7% 24.0 – 5683.0 (mean 738.317)
denominatorunweighted float64 22.7% 51.0 – 3620.0 (mean 741.9569)
levelrank float64 16.6% 1.0 – 1.0 (mean 1.0)
esa_source object 0.0%
esa_processed object 0.0%

Numeric Summary

Column Min Max Mean Median
dataid 584.0 7980700.0 4302204.7522 4315680.0
value 0.0 254.0 39.2798 32.6
precision 0.0 1.0 0.9243 1.0
surveyyear 1992.0 2024.0 2009.1664 2007.0
indicatororder 11763080.0 260321010.0 100486077.8026 94096040.0
characteristicid 456001.0 456012.0 456005.9484 456006.0
characteristicorder 1456001.0 1456012.0 1456006.572 1456006.0
byvariableid 0.0 631002.0 21369.8847 0.0
istotal 0.0 0.0 0.0 0.0
ispreferred 0.0 1.0 0.8698 1.0
surveyyearlabel 1992.0 2024.0 2009.63 2007.0
denominatorweighted 24.0 5683.0 738.317 615.0
denominatorunweighted 51.0 3620.0 741.9569 641.0
levelrank 1.0 1.0 1.0 1.0

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: cilow, cihigh. 1 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 The DHS Program 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: byvariablelabel, surveyyearlabel, denominatorweighted, denominatorunweighted.
  • Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

Citation 

@dataset{hdx_africa_demographics_zambia,
  title     = {Zambia - Subnational Demographic and Health Data},
  author    = {The DHS Program},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/dhs-subnational-data-for-zambia},
  note      = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}

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