electricsheepafrica/africa-who-historical-data-for-bdi

---
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
- tabular-regression
task_ids: []
tags:
- africa
- humanitarian
- hdx
- electric-sheep-africa
- hxl
- indicators
- bdi
pretty_name: "Burundi - Historical Health Indicators"
dataset_info:
splits:
- name: train
num_examples: 8835
- name: test
num_examples: 2208
---

# Burundi - Historical Health Indicators

**Publisher:** World Health Organization · **Source:** [HDX](https://data.humdata.org/dataset/who-historical-data-for-bdi) · **License:** `hdx-other` · **Updated:** 2025-02-07

---

## Abstract

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

Each row in this dataset represents first-level administrative unit observations. Data was last updated on HDX on 2025-02-07. Geographic scope: **BDI**.

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

---

## Dataset Characteristics

| | |
|---|---|
| **Domain** | Humanitarian and development data |
| **Unit of observation** | First-level administrative unit observations |
| **Rows (total)** | 11,044 |
| **Columns** | 18 (6 numeric, 12 categorical, 0 datetime) |
| **Train split** | 8,835 rows |
| **Test split** | 2,208 rows |
| **Geographic scope** | BDI |
| **Publisher** | World Health Organization |
| **HDX last updated** | 2025-02-07 |

---

## Variables

**Geographic** — `gho_display` (Mean BMI (kg/m&#xb2;) (crude estimate), Alcohol, recorded per capita (15+) consumption (in litres of pure alcohol), Adolescent mortality rate (per 1 000 age specific cohort)), `year_display` (range 1961.0–2025.0), `startyear` (range 1961.0–2025.0), `endyear` (range 1961.0–2025.0), `region_code` (AFR, #region+code) and 4 others.

**Outcome / Measurement** — `value` (No data, No, Not applicable).

**Identifier / Metadata** — `gho_code` (NCD_BMI_MEANC, SA_0000001400_ARCHIVED, CHILDMORT10TO19), `dimension_code` (SEX_FMLE, SEX_MLE, SEX_BTSX), `dimension_name` (Female, Male, Both sexes), `esa_source`, `esa_processed`.

**Other** — `numeric` (range 0.0–4724723237.0), `low` (range 0.0–2531000.0), `high` (range 0.0–5573000.0).

---

## Quick Start

```python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-who-historical-data-for-bdi")
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% | NCD_BMI_MEANC, SA_0000001400_ARCHIVED, CHILDMORT10TO19 |
| `gho_display` | object | 0.0% | Mean BMI (kg/m&#xb2;) (crude estimate), Alcohol, recorded per capita (15+) consumption (in litres of pure alcohol), Adolescent mortality rate (per 1 000 age specific cohort) |
| `year_display` | float64 | 0.0% | 1961.0 – 2025.0 (mean 2008.5624) |
| `startyear` | float64 | 0.0% | 1961.0 – 2025.0 (mean 2008.5472) |
| `endyear` | float64 | 0.0% | 1961.0 – 2025.0 (mean 2008.5624) |
| `region_code` | object | 0.0% | AFR, #region+code |
| `region_display` | object | 0.0% | Africa, #region+name |
| `country_code` | object | 0.0% | BDI, #country+code |
| `country_display` | object | 0.0% | Burundi, #country+name |
| `dimension_type` | object | 20.4% | SEX, WEALTHDECILE, DHSMICSGEOREGION |
| `dimension_code` | object | 20.4% | SEX_FMLE, SEX_MLE, SEX_BTSX |
| `dimension_name` | object | 21.7% | Female, Male, Both sexes |
| `numeric` | float64 | 24.2% | 0.0 – 4724723237.0 (mean 1081498.3017) |
| `value` | object | 1.1% | No data, No, Not applicable |
| `low` | float64 | 41.3% | 0.0 – 2531000.0 (mean 6267.9345) |
| `high` | float64 | 41.3% | 0.0 – 5573000.0 (mean 12263.4467) |
| `esa_source` | object | 0.0% | |
| `esa_processed` | object | 0.0% | |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `year_display` | 1961.0 | 2025.0 | 2008.5624 | 2010.0 |
| `startyear` | 1961.0 | 2025.0 | 2008.5472 | 2010.0 |
| `endyear` | 1961.0 | 2025.0 | 2008.5624 | 2010.0 |
| `numeric` | 0.0 | 4724723237.0 | 1081498.3017 | 34.3206 |
| `low` | 0.0 | 2531000.0 | 6267.9345 | 25.6516 |
| `high` | 0.0 | 5573000.0 | 12263.4467 | 45.0124 |

---

## 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`. 1 column(s) with >80% missing values were removed: `gho_url`. 50 exact duplicate rows were removed. 6 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 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: `dimension_type`, `dimension_code`, `dimension_name`, `numeric`, `low`, `high`.
- Refer to the [original HDX dataset page](https://data.humdata.org/dataset/who-historical-data-for-bdi) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_who_historical_data_for_bdi,
title = {Burundi - Historical Health Indicators},
author = {World Health Organization},
year = {2025},
url = {https://data.humdata.org/dataset/who-historical-data-for-bdi},
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.*