electricsheepafrica/africa-kenya-people-affected-by-elnino

---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license: other
multilinguality:
- monolingual
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- tabular-classification
task_ids: []
tags:
- africa
- humanitarian
- hdx
- electric-sheep-africa
- affected-population
- el-nino-el-nina
- flooding
- geodata
- ken
pretty_name: "Kenya - People affected by Elnino"
dataset_info:
splits:
- name: train
num_examples: 23
- name: test
num_examples: 5
---

# Kenya - People affected by Elnino

**Publisher:** Kenya Red Cross Society · **Source:** [HDX](https://data.humdata.org/dataset/kenya-people-affected-by-elnino) · **License:** `other-pd-nr` · **Updated:** 2025-09-08

---

## Abstract

This dataset shows the number of people affected by elnino rains per county

Each row in this dataset represents tabular records. Data was last updated on HDX on 2025-09-08. 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)** | 29 |
| **Columns** | 6 (2 numeric, 4 categorical, 0 datetime) |
| **Train split** | 23 rows |
| **Test split** | 5 rows |
| **Geographic scope** | KEN |
| **Publisher** | Kenya Red Cross Society |
| **HDX last updated** | 2025-09-08 |

---

## Variables

**Geographic** — `county` (Turkana, Meru, West Pokot), `displaced` (range 2.0–21349.0).

**Identifier / Metadata** — `esa_source` (HDX), `esa_processed` (2026-04-09).

**Other** — `unit` (HH, students, travelers), `dead` (range 1.0–8826.0).

---

## Quick Start

```python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-kenya-people-affected-by-elnino")
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% | Turkana, Meru, West Pokot |
| `unit` | object | 0.0% | HH, students, travelers |
| `displaced` | int64 | 0.0% | 2.0 – 21349.0 (mean 1409.6897) |
| `dead` | float64 | 13.8% | 1.0 – 8826.0 (mean 686.92) |
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% | 2026-04-09 |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `displaced` | 2.0 | 21349.0 | 1409.6897 | 216.0 |
| `dead` | 1.0 | 8826.0 | 686.92 | 66.0 |

---

## 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 Kenya Red Cross Society 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-people-affected-by-elnino) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_kenya_people_affected_by_elnino,
title = {Kenya - People affected by Elnino},
author = {Kenya Red Cross Society},
year = {2025},
url = {https://data.humdata.org/dataset/kenya-people-affected-by-elnino},
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.*