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

Kenya - People affected by Elnino

Publisher: Kenya Red Cross Society · Source: HDX · 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.

---

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

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 for the publisher's own methodology notes and caveats.

---

Citation

@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 — Africa's ML dataset infrastructure. Lagos, Nigeria.