electricsheepafrica/africa-burundi-humanitarian-response-activities-by-sector-at-province-level-as-of-april-2015

---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license: cc-by-4.0
multilinguality:
- monolingual
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- tabular-classification
- tabular-regression
task_ids: []
tags:
- africa
- humanitarian
- hdx
- electric-sheep-africa
- education
- hxl
- who-is-doing-what-and-where-3w-4w-5w
- bdi
pretty_name: "Burundi Humanitarian Response Activities by Sector at Province Level as of April 2015"
dataset_info:
splits:
- name: train
num_examples: 375
- name: test
num_examples: 93
---

# Burundi Humanitarian Response Activities by Sector at Province Level as of April 2015

**Publisher:** OCHA Regional Office for Southern and Eastern Africa (ROSEA) · **Source:** [HDX](https://data.humdata.org/dataset/burundi-humanitarian-response-activities-by-sector-at-province-level-as-of-april-2015) · **License:** `cc-by-igo` · **Updated:** 2023-09-28

---

## Abstract

Burundi Humanitarian Response Activities by Sector at Province Level as of April 2015

Each row in this dataset represents first-level administrative unit observations. Data was last updated on HDX on 2023-09-28. Geographic scope: **BDI**.

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

---

## Dataset Characteristics

| | |
|---|---|
| **Domain** | Education |
| **Unit of observation** | First-level administrative unit observations |
| **Rows (total)** | 469 |
| **Columns** | 9 (0 numeric, 9 categorical, 0 datetime) |
| **Train split** | 375 rows |
| **Test split** | 93 rows |
| **Geographic scope** | BDI |
| **Publisher** | OCHA Regional Office for Southern and Eastern Africa (ROSEA) |
| **HDX last updated** | 2023-09-28 |

---

## Variables

**Geographic** — `province_name` (Bujumbura Mairie, Muyinga, Ruyigi), `hr_info_province_name` (Bujumbura Mairie, Muyinga, Ruyigi), `activity` (Regular and Emergency Programs, Multi-Sectorial Stand By agreement , Asylum seekers and refugees care and maintenance activities with JRS, AHA, IRC, PARESI, COPED.).

**Identifier / Metadata** — `id` (003BDI017, 003BDI012, 003BDI016), `globalid` (E4C62CAE-F69F-4335-B352-EB3940209304, B87BD42A-C642-4973-8457-CF784A976203, 03624F5F-F070-4CBD-90FD-44283BDCBCF8), `esa_source` (HDX), `esa_processed` (2026-04-08).

**Other** — `sector` (Education, Child Protection, WASH), `organisation` (Croix Rouge du Burundi, United Nations Children's Fund (UNICEF), World Food Program (WFP)).

---

## Quick Start

```python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-burundi-humanitarian-response-activities-by-sector-at-province-level-as-of-april-2015")
train = ds["train"].to_pandas()
test = ds["test"].to_pandas()

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

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `province_name` | object | 0.0% | Bujumbura Mairie, Muyinga, Ruyigi |
| `hr_info_province_name` | object | 0.2% | Bujumbura Mairie, Muyinga, Ruyigi |
| `id` | object | 0.2% | 003BDI017, 003BDI012, 003BDI016 |
| `globalid` | object | 0.2% | E4C62CAE-F69F-4335-B352-EB3940209304, B87BD42A-C642-4973-8457-CF784A976203, 03624F5F-F070-4CBD-90FD-44283BDCBCF8 |
| `sector` | object | 0.0% | Education, Child Protection, WASH |
| `organisation` | object | 0.0% | Croix Rouge du Burundi, United Nations Children's Fund (UNICEF), World Food Program (WFP) |
| `activity` | object | 38.8% | Regular and Emergency Programs, Multi-Sectorial Stand By agreement , Asylum seekers and refugees care and maintenance activities with JRS, AHA, IRC, PARESI, COPED. |
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% | 2026-04-08 |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
_No numeric columns._

---

## 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`. 4 column(s) with >80% missing values were removed: `district`, `activity_type`, `start_date`, `end_date`. 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 OCHA Regional Office for Southern and Eastern Africa (ROSEA) 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: `activity`.
- Refer to the [original HDX dataset page](https://data.humdata.org/dataset/burundi-humanitarian-response-activities-by-sector-at-province-level-as-of-april-2015) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_burundi_humanitarian_response_activities_by_sector_at_province_level_as_of_april_2015,
title = {Burundi Humanitarian Response Activities by Sector at Province Level as of April 2015},
author = {OCHA Regional Office for Southern and Eastern Africa (ROSEA)},
year = {2023},
url = {https://data.humdata.org/dataset/burundi-humanitarian-response-activities-by-sector-at-province-level-as-of-april-2015},
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.*