README.md

---
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](https://data.humdata.org/dataset/dhs-subnational-data-for-sudan) · **License:** `hdx-other` · **Updated:** 2026-02-24

---

## Abstract

Contains data from the [DHS data portal](https://api.dhsprogram.com/). There is also a dataset containing [Sudan - National Demographic and Health Data](https://data.humdata.org/dataset/dhs-data-for-sudan) 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](https://huggingface.co/electricsheepafrica).*

---

## 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

```python
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](https://data.humdata.org/dataset/dhs-subnational-data-for-sudan) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@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](https://huggingface.co/electricsheepafrica) — Africa's ML dataset infrastructure. Lagos, Nigeria.*