---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license: other
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- tabular-classification
task_ids: []
tags:
- africa
- humanitarian
- hdx
- electric-sheep-africa
- disability
- disease
- environment
- health
- indicators
- malaria
- maternity
- mental-health
- ner
pretty_name: "Niger - Health Indicators"
dataset_info:
  splits:
    - name: train
      num_examples: 20934
    - name: test
      num_examples: 5233
---

# Niger - Health Indicators

**Publisher:** World Health Organization · **Source:** [HDX](https://data.humdata.org/dataset/who-data-for-ner) · **License:** `hdx-other` · **Updated:** 2026-04-15

---

## Abstract

This dataset contains data from WHO's [data portal](https://www.who.int/gho/en/) 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: **NER**.

*Curated into ML-ready Parquet format by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica).*

---

## Dataset Characteristics

| | |
|---|---|
| **Domain** | Food security and nutrition |
| **Unit of observation** | First-level administrative unit observations |
| **Rows (total)** | 26,168 |
| **Columns** | 19 (6 numeric, 13 categorical, 0 datetime) |
| **Train split** | 20,934 rows |
| **Test split** | 5,233 rows |
| **Geographic scope** | NER |
| **Publisher** | World Health Organization |
| **HDX last updated** | 2026-04-15 |

---

## Variables

**Geographic** — `gho_display` (Stunting prevalence among children under 5 years of age (% height-for-age <-2 SD), survey-based estimates, Underweight prevalence among children under 5 years of age   (% weight-for-age <-2 SD), survey-based estimates, Wasting prevalence among children under 5 years of age (% weight-for-height <-2 SD), survey-based estimates), `year_display` (range 1961.0–2030.0), `startyear` (range 1961.0–2030.0), `endyear` (range 1961.0–2030.0), `region_code` (AFR) and 4 others.

**Outcome / Measurement** — `value`.

**Identifier / Metadata** — `gho_code` (NUTRITION_ANT_HAZ_NE2, NUTRITION_WA_2, NUTRITION_WH_2), `dimension_code` (SEX_FMLE, SEX_MLE, SEX_BTSX), `dimension_name` (Female, Male, Both sexes), `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/gho-jme-country-children-aged-5-years-underweight-%28-weight-for-age--2-sd%29-%28-%29, https://www.who.int/data/gho/data/indicators/indicator-details/GHO/gho-jme-country-children-aged-5-years-wasted-br-%28-weight-for-height--2-sd%29), `numeric` (range -3.95–54519429749.0), `low` (range 0.0–1288743.708), `high` (range 0.0–1705520.207).

---

## Quick Start

```python
from datasets import load_dataset

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

print(train.shape)
train.head()
```

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `gho_code` | object | 0.0% | NUTRITION_ANT_HAZ_NE2, NUTRITION_WA_2, NUTRITION_WH_2 |
| `gho_display` | object | 0.0% | Stunting prevalence among children under 5 years of age (% height-for-age <-2 SD), survey-based estimates, Underweight prevalence among children under 5 years of age   (% weight-for-age <-2 SD), survey-based estimates, Wasting prevalence among children under 5 years of age (% weight-for-height <-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/gho-jme-country-children-aged-5-years-underweight-%28-weight-for-age--2-sd%29-%28-%29, https://www.who.int/data/gho/data/indicators/indicator-details/GHO/gho-jme-country-children-aged-5-years-wasted-br-%28-weight-for-height--2-sd%29 |
| `year_display` | int64 | 0.0% | 1961.0 – 2030.0 (mean 2009.9305) |
| `startyear` | int64 | 0.0% | 1961.0 – 2030.0 (mean 2009.9275) |
| `endyear` | int64 | 0.0% | 1961.0 – 2030.0 (mean 2009.9305) |
| `region_code` | object | 0.0% | AFR |
| `region_display` | object | 0.0% | Africa |
| `country_code` | object | 0.0% | NER |
| `country_display` | object | 0.0% | Niger |
| `dimension_type` | object | 9.4% | SEX, AGEGROUP, RESIDENCEAREATYPE |
| `dimension_code` | object | 9.4% | SEX_FMLE, SEX_MLE, SEX_BTSX |
| `dimension_name` | object | 9.5% | Female, Male, Both sexes |
| `numeric` | float64 | 8.1% | -3.95 – 54519429749.0 (mean 7047401.0008) |
| `value` | object | 0.1% |  |
| `low` | float64 | 29.9% | 0.0 – 1288743.708 (mean 7098.1143) |
| `high` | float64 | 29.9% | 0.0 – 1705520.207 (mean 10995.2913) |
| `esa_source` | object | 0.0% |  |
| `esa_processed` | object | 0.0% |  |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `year_display` | 1961.0 | 2030.0 | 2009.9305 | 2012.0 |
| `startyear` | 1961.0 | 2030.0 | 2009.9275 | 2012.0 |
| `endyear` | 1961.0 | 2030.0 | 2009.9305 | 2012.0 |
| `numeric` | -3.95 | 54519429749.0 | 7047401.0008 | 17.6248 |
| `low` | 0.0 | 1288743.708 | 7098.1143 | 13.9739 |
| `high` | 0.0 | 1705520.207 | 10995.2913 | 23.7679 |

---

## 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`. 843 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.

---

## 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](https://data.humdata.org/dataset/who-data-for-ner) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_disability_niger,
  title     = {Niger - Health Indicators},
  author    = {World Health Organization},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/who-data-for-ner},
  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.*