electricsheepafrica/africa-percentage-of-population-with-access-to-improved-water-facilities-in-kenya

Percentage of population with access to improved water facilities in Kenya

Publisher: OCHA Regional Office for Southern and Eastern Africa (ROSEA) · Source: HDX · License: other-pd-nr · Updated: 2025-05-05

---

Abstract

The percentage of population with access to improved water sources in Kenya as reported in the Commission for Revenue Allocation report of 2011

Each row in this dataset represents country-level aggregates. Data was last updated on HDX on 2025-05-05. Geographic scope: KEN.

Curated into ML-ready Parquet format by Electric Sheep Africa.

---

Dataset Characteristics

Domain	Demographics and population
Unit of observation	Country-level aggregates
Rows (total)	48
Columns	5 (1 numeric, 4 categorical, 0 datetime)
Train split	38 rows
Test split	9 rows
Geographic scope	KEN
Publisher	OCHA Regional Office for Southern and Eastern Africa (ROSEA)
HDX last updated	2025-05-05

---

Variables

Geographic — country (Kenya), improved_water_source_of_population_with_access (range 33.6–89.3).

Outcome / Measurement — counties (National Average, Baringo, Meru).

Identifier / Metadata — esa_source (HDX), esa_processed (2026-04-05).

---

Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-percentage-of-population-with-access-to-improved-water-facilities-in-kenya")
train = ds["train"].to_pandas()
test  = ds["test"].to_pandas()

print(train.shape)
train.head()

---

Schema

Column	Type	Null %	Range / Sample Values
country	object	0.0%	Kenya
counties	object	0.0%	National Average, Baringo, Meru
improved_water_source_of_population_with_access	float64	0.0%	33.6 – 89.3 (mean 63.9812)
esa_source	object	0.0%	HDX
esa_processed	object	0.0%	2026-04-05

---

Numeric Summary

Column	Min	Max	Mean	Median
improved_water_source_of_population_with_access	33.6	89.3	63.9812	66.45

---

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 for the publisher's own methodology notes and caveats.

---

Citation

@dataset{hdx_africa_percentage_of_population_with_access_to_improved_water_facilities_in_kenya,
  title     = {Percentage of population with access to improved water facilities in Kenya},
  author    = {OCHA Regional Office for Southern and Eastern Africa (ROSEA)},
  year      = {2025},
  url       = {https://data.humdata.org/dataset/percentage-of-population-with-access-to-improved-water-facilities-in-kenya},
  note      = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}

---

Electric Sheep Africa — Africa's ML dataset infrastructure. Lagos, Nigeria.