electricsheepafrica/africa-households-paying-for-the-water-they-use-in-kenya

---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license: other
multilinguality:
- monolingual
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- tabular-regression
task_ids: []
tags:
- africa
- humanitarian
- hdx
- electric-sheep-africa
- water-sanitation-and-hygiene-wash
- ken
pretty_name: "Households paying for the water they use in Kenya"
dataset_info:
splits:
- name: train
num_examples: 82
- name: test
num_examples: 20
---

# Households paying for the water they use in Kenya

**Publisher:** Majidata (inactive) · **Source:** [HDX](https://data.humdata.org/dataset/households-paying-for-the-water-they-use-in-kenya) · **License:** `other-pd-nr` · **Updated:** 2023-05-16

---

## Abstract

This dataset shows the Households paying for the water they use in Kenya as collected by majidata organization (www.majidata.go.ke). MajiData is the pro-poor database covering all the urban low income areas of Kenya which has been prepared by the Ministry of Water and Irrigation (MWI) and the Water Services Trust Fund (WSTF) in cooperation with UN-Habitat, the German Development Bank (KfW), Google org. and GIZ.

Each row in this dataset represents tabular records. Data was last updated on HDX on 2023-05-16. Geographic scope: **KEN**.

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

---

## Dataset Characteristics

| | |
|---|---|
| **Domain** | Water, sanitation and hygiene (wash) |
| **Unit of observation** | Tabular records |
| **Rows (total)** | 103 |
| **Columns** | 10 (6 numeric, 4 categorical, 0 datetime) |
| **Train split** | 82 rows |
| **Test split** | 20 rows |
| **Geographic scope** | KEN |
| **Publisher** | Majidata (inactive) |
| **HDX last updated** | 2023-05-16 |

---

## Variables

**Geographic** — `countyid` (range 1.0–47.0), `countyname` (NAIROBI, NYANDARUA, UASIN GISHU).

**Identifier / Metadata** — `waterpaid` (No, Yes, Sometimes), `esa_source` (HDX), `esa_processed` (2026-04-07).

**Other** — `smpsrc` (range 1.0–22033.0), `smpdus` (range 178.0–23373.0), `totdus` (range 613.0–720300.0), `pcntusingsrc` (range 0.01–99.45), `nousingsrc` (range 5.0–679004.0).

---

## Quick Start

```python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-households-paying-for-the-water-they-use-in-kenya")
train = ds["train"].to_pandas()
test = ds["test"].to_pandas()

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

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `countyid` | int64 | 0.0% | 1.0 – 47.0 (mean 23.6019) |
| `countyname` | object | 0.0% | NAIROBI, NYANDARUA, UASIN GISHU |
| `waterpaid` | object | 0.0% | No, Yes, Sometimes |
| `smpsrc` | int64 | 0.0% | 1.0 – 22033.0 (mean 886.3689) |
| `smpdus` | int64 | 0.0% | 178.0 – 23373.0 (mean 2308.4563) |
| `totdus` | int64 | 0.0% | 613.0 – 720300.0 (mean 44110.3398) |
| `pcntusingsrc` | float64 | 0.0% | 0.01 – 99.45 (mean 45.6311) |
| `nousingsrc` | int64 | 0.0% | 5.0 – 679004.0 (mean 15978.0097) |
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% | 2026-04-07 |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `countyid` | 1.0 | 47.0 | 23.6019 | 24.0 |
| `smpsrc` | 1.0 | 22033.0 | 886.3689 | 311.0 |
| `smpdus` | 178.0 | 23373.0 | 2308.4563 | 873.0 |
| `totdus` | 613.0 | 720300.0 | 44110.3398 | 9149.0 |
| `pcntusingsrc` | 0.01 | 99.45 | 45.6311 | 42.03 |
| `nousingsrc` | 5.0 | 679004.0 | 15978.0097 | 1819.0 |

---

## 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`. 1 column(s) with >80% missing values were removed: `unnamed_8`. 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 Majidata (inactive) 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/households-paying-for-the-water-they-use-in-kenya) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_households_paying_for_the_water_they_use_in_kenya,
title = {Households paying for the water they use in Kenya},
author = {Majidata (inactive)},
year = {2023},
url = {https://data.humdata.org/dataset/households-paying-for-the-water-they-use-in-kenya},
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.*