electricsheepafrica/africa-kenya-who-is-doing-what-and-where-2017
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---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license: cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- tabular-classification
task_ids: []
tags:
- africa
- humanitarian
- hdx
- electric-sheep-africa
- who-is-doing-what-and-where-3w-4w-5w
- ken
pretty_name: "Kenya - Who is doing what and where 2017"
dataset_info:
splits:
- name: train
num_examples: 806
- name: test
num_examples: 201
---
# Kenya - Who is doing what and where 2017
**Publisher:** OCHA Regional Office for Southern and Eastern Africa (ROSEA) · **Source:** [HDX](https://data.humdata.org/dataset/kenya-who-is-doing-what-and-where-2017) · **License:** `cc-by` · **Updated:** 2025-05-05
---
## Abstract
Dataset shows who is doing what and where (3W) in kenya. This data was collected for the Kenya drought response
Each row in this dataset represents tabular records. Data was last updated on HDX on 2025-05-05. Geographic scope: **KEN**.
*Curated into ML-ready Parquet format by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica).*
---
## Dataset Characteristics
| | |
|---|---|
| **Domain** | Natural hazards and disaster risk |
| **Unit of observation** | Tabular records |
| **Rows (total)** | 1,008 |
| **Columns** | 7 (0 numeric, 7 categorical, 0 datetime) |
| **Train split** | 806 rows |
| **Test split** | 201 rows |
| **Geographic scope** | KEN |
| **Publisher** | OCHA Regional Office for Southern and Eastern Africa (ROSEA) |
| **HDX last updated** | 2025-05-05 |
---
## Variables
**Geographic** — `county` (Garissa, Marsabit, Turkana), `type` (INGO, UN, NGO).
**Identifier / Metadata** — `name` (United Nations Childrens Fund, World Vision, Kenya Red Cross Society), `esa_source` (HDX), `esa_processed` (2026-04-18).
**Other** — `abbrev` (UNICEF, WV, KRCS ), `sector` (Nutrition, Health, Education).
---
## Quick Start
```python
from datasets import load_dataset
ds = load_dataset("electricsheepafrica/africa-kenya-who-is-doing-what-and-where-2017")
train = ds["train"].to_pandas()
test = ds["test"].to_pandas()
print(train.shape)
train.head()
```
---
## Schema
| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `county` | object | 0.0% | Garissa, Marsabit, Turkana |
| `name` | object | 0.0% | United Nations Childrens Fund, World Vision, Kenya Red Cross Society |
| `type` | object | 0.0% | INGO, UN, NGO |
| `abbrev` | object | 0.0% | UNICEF, WV, KRCS |
| `sector` | object | 0.0% | Nutrition, Health, Education |
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% | 2026-04-18 |
---
## 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`. 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.
- Refer to the [original HDX dataset page](https://data.humdata.org/dataset/kenya-who-is-doing-what-and-where-2017) for the publisher's own methodology notes and caveats.
---
## Citation
```bibtex
@dataset{hdx_africa_kenya_who_is_doing_what_and_where_2017,
title = {Kenya - Who is doing what and where 2017},
author = {OCHA Regional Office for Southern and Eastern Africa (ROSEA)},
year = {2025},
url = {https://data.humdata.org/dataset/kenya-who-is-doing-what-and-where-2017},
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.*
提供机构：
electricsheepafrica
