electricsheepafrica/africa-demographics-tunisia

---
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
- tun
pretty_name: "Tunisia - Subnational Demographic and Health Data"
dataset_info:
splits:
- name: train
num_examples: 33
- name: test
num_examples: 8
---

# Tunisia - Subnational Demographic and Health Data

**Publisher:** The DHS Program · **Source:** [HDX](https://data.humdata.org/dataset/dhs-subnational-data-for-tunisia) · **License:** `hdx-other` · **Updated:** 2026-04-20

---

## Abstract

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

*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 (16 numeric, 16 categorical, 0 datetime) |
| **Train split** | 33 rows |
| **Test split** | 8 rows |
| **Geographic scope** | TUN |
| **Publisher** | The DHS Program |
| **HDX last updated** | 2026-04-20 |

---

## Variables

**Geographic** — `iso3` (TUN), `location` (Tunis, Nord Est, Nord Ouest), `dhs_countrycode` (TN), `countryname` (Tunisia), `surveyyear` (range 1988.0–1988.0) and 8 others.

**Outcome / Measurement** — `value` (range 0.5–107.0), `istotal` (range 0.0–0.0).

**Identifier / Metadata** — `dataid` (range 999142.0–7970284.0), `indicatorid` (FE_FRTR_W_TFR, FP_CUSM_W_ANY, FP_CUSM_W_MOD), `characteristicid` (range 449001.0–449006.0), `characteristiclabel` (Tunis, Nord Est, Nord Ouest), `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 1449001.0–1449006.0), `denominatorweighted` (range 587.0–748.0) and 4 others.

---

## Quick Start

```python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-demographics-tunisia")
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% | TUN |
| `location` | object | 0.0% | Tunis, Nord Est, Nord Ouest |
| `dataid` | int64 | 0.0% | 999142.0 – 7970284.0 (mean 3462838.0) |
| `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% | 0.5 – 107.0 (mean 34.7571) |
| `precision` | int64 | 0.0% | 0.0 – 1.0 (mean 0.7143) |
| `dhs_countrycode` | object | 0.0% | TN |
| `countryname` | object | 0.0% | Tunisia |
| `surveyyear` | int64 | 0.0% | 1988.0 – 1988.0 (mean 1988.0) |
| `surveyid` | object | 0.0% | TN1988DHS |
| `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% | 449001.0 – 449006.0 (mean 449003.5) |
| `characteristicorder` | int64 | 0.0% | 1449001.0 – 1449006.0 (mean 1449003.5) |
| `characteristiccategory` | object | 0.0% | Region |
| `characteristiclabel` | object | 0.0% | Tunis, Nord Est, Nord Ouest |
| `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` | int64 | 0.0% | 1988.0 – 1988.0 (mean 1988.0) |
| `surveytype` | object | 0.0% | |
| `denominatorweighted` | float64 | 71.4% | 587.0 – 748.0 (mean 668.6667) |
| `denominatorunweighted` | float64 | 71.4% | 587.0 – 748.0 (mean 668.6667) |
| `cilow` | float64 | 71.4% | 23.0 – 89.0 (mean 47.6667) |
| `cihigh` | float64 | 71.4% | 52.0 – 124.0 (mean 80.5833) |
| `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` | 999142.0 | 7970284.0 | 3462838.0 | 1776224.0 |
| `value` | 0.5 | 107.0 | 34.7571 | 35.3 |
| `precision` | 0.0 | 1.0 | 0.7143 | 1.0 |
| `surveyyear` | 1988.0 | 1988.0 | 1988.0 | 1988.0 |
| `indicatororder` | 11763080.0 | 104336020.0 | 49915757.1429 | 41633090.0 |
| `characteristicid` | 449001.0 | 449006.0 | 449003.5 | 449003.5 |
| `characteristicorder` | 1449001.0 | 1449006.0 | 1449003.5 | 1449003.5 |
| `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 |
| `surveyyearlabel` | 1988.0 | 1988.0 | 1988.0 | 1988.0 |
| `denominatorweighted` | 587.0 | 748.0 | 668.6667 | 661.5 |
| `denominatorunweighted` | 587.0 | 748.0 | 668.6667 | 661.5 |
| `cilow` | 23.0 | 89.0 | 47.6667 | 46.5 |
| `cihigh` | 52.0 | 124.0 | 80.5833 | 77.5 |

---

## 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-tunisia) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_demographics_tunisia,
title = {Tunisia - Subnational Demographic and Health Data},
author = {The DHS Program},
year = {2026},
url = {https://data.humdata.org/dataset/dhs-subnational-data-for-tunisia},
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.*