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africa-demographics-sudan

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metadata 
annotations_creators:
  - no-annotation
language_creators:
  - found
language:
  - en
license: other
multilinguality:
  - monolingual
size_categories:
  - n<1K
source_datasets:
  - original
task_categories:
  - tabular-classification
  - other
task_ids: []
tags:
  - africa
  - humanitarian
  - hdx
  - electric-sheep-africa
  - demographics
  - health
  - sdn
pretty_name: Sudan - Subnational Demographic and Health Data
dataset_info:
  splits:
    - name: train
      num_examples: 33
    - name: test
      num_examples: 8

Sudan - Subnational Demographic and Health Data

Publisher: The DHS Program · Source: HDX · License: hdx-other · Updated: 2026-02-24

Abstract

Contains data from the DHS data portal. There is also a dataset containing Sudan - 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 2026-02-24. Geographic scope: SDN.

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) 42
Columns 32 (15 numeric, 17 categorical, 0 datetime)
Train split 33 rows
Test split 8 rows
Geographic scope SDN
Publisher The DHS Program
HDX last updated 2026-02-24

Variables

Geographic — iso3 (SDN), location (Khartoum, Northern, Eastern), dhs_countrycode (SD), countryname (Sudan), surveyyear (range 1990.0–1990.0) and 8 others.

Outcome / Measurement — value (range 0.2–178.0), istotal (range 0.0–0.0).

Identifier / Metadata — dataid (range 117286.0–7925878.0), indicatorid (FE_FRTR_W_TFR, FP_CUSM_W_ANY, FP_CUSM_W_MOD), characteristicid (range 443001.0–443006.0), characteristiclabel (Khartoum, Northern, Eastern), ispreferred (range 1.0–1.0) and 3 others.

Other — indicator (Total fertility rate 15-49, Married women currently using any method of contraception, Married women currently using any modern method of contraception), precision (range 0.0–1.0), indicatororder (range 11763080.0–104336020.0), characteristicorder (range 1443001.0–1443006.0), denominatorweighted (range 365.0–1480.0) and 4 others.

Quick Start 

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-demographics-sudan")
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% SDN
location object 0.0% Khartoum, Northern, Eastern
dataid int64 0.0% 117286.0 – 7925878.0 (mean 3119772.7381)
indicator object 0.0% Total fertility rate 15-49, Married women currently using any method of contraception, Married women currently using any modern method of contraception
value float64 0.0% 0.2 – 178.0 (mean 35.8429)
precision int64 0.0% 0.0 – 1.0 (mean 0.7143)
dhs_countrycode object 0.0% SD
countryname object 0.0% Sudan
surveyyear int64 0.0% 1990.0 – 1990.0 (mean 1990.0)
surveyid object 0.0% SD1990DHS
indicatorid object 0.0% FE_FRTR_W_TFR, FP_CUSM_W_ANY, FP_CUSM_W_MOD
indicatororder int64 0.0% 11763080.0 – 104336020.0 (mean 49915757.1429)
indicatortype object 0.0% I
characteristicid int64 0.0% 443001.0 – 443006.0 (mean 443003.5)
characteristicorder int64 0.0% 1443001.0 – 1443006.0 (mean 1443003.5)
characteristiccategory object 0.0% Region
characteristiclabel object 0.0% Khartoum, Northern, Eastern
byvariableid int64 0.0% 0.0 – 14003.0 (mean 4000.8571)
byvariablelabel object 71.4%
istotal int64 0.0% 0.0 – 0.0 (mean 0.0)
ispreferred int64 0.0% 1.0 – 1.0 (mean 1.0)
sdrid object 0.0%
regionid object 0.0%
surveyyearlabel object 0.0%
surveytype object 0.0%
denominatorweighted float64 71.4% 365.0 – 1480.0 (mean 900.0)
denominatorunweighted float64 71.4% 365.0 – 1480.0 (mean 900.0)
cilow float64 71.4% 49.0 – 144.0 (mean 87.75)
cihigh float64 71.4% 71.0 – 212.0 (mean 126.25)
levelrank int64 0.0% 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 117286.0 7925878.0 3119772.7381 1137980.0
value 0.2 178.0 35.8429 10.15
precision 0.0 1.0 0.7143 1.0
surveyyear 1990.0 1990.0 1990.0 1990.0
indicatororder 11763080.0 104336020.0 49915757.1429 41633090.0
characteristicid 443001.0 443006.0 443003.5 443003.5
characteristicorder 1443001.0 1443006.0 1443003.5 1443003.5
byvariableid 0.0 14003.0 4000.8571 0.0
istotal 0.0 0.0 0.0 0.0
ispreferred 1.0 1.0 1.0 1.0
denominatorweighted 365.0 1480.0 900.0 901.5
denominatorunweighted 365.0 1480.0 900.0 901.5
cilow 49.0 144.0 87.75 76.5
cihigh 71.0 212.0 126.25 121.0
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. 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, denominatorweighted, denominatorunweighted, cilow, cihigh.
  • Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

Citation 

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

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