electricsheepafrica/africa-liberia-water-point-data

Liberia Water Point Data

Publisher: Akvo Foundation (inactive) · Source: HDX · License: cc-by · Updated: 2024-02-06

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Abstract

This dataset contains data of a second national water point mapping in Liberia (2015). The first national water point inventory has been executed in 2013, and in 2017 all water points were mapped again using Akvo Flow: https://akvo.org/products/akvoflow/#overview.

Each row in this dataset represents tabular records. Data was last updated on HDX on 2024-02-06. Geographic scope: LBR.

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

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Dataset Characteristics

Domain	Water, sanitation and hygiene (wash)
Unit of observation	Tabular records
Rows (total)	34,282
Columns	2 (0 numeric, 2 categorical, 0 datetime)
Train split	27,425 rows
Test split	6,856 rows
Geographic scope	LBR
Publisher	Akvo Foundation (inactive)
HDX last updated	2024-02-06

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Variables

Identifier / Metadata — esa_source (HDX), esa_processed (2026-04-12).

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-liberia-water-point-data")
train = ds["train"].to_pandas()
test  = ds["test"].to_pandas()

print(train.shape)
train.head()

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Schema

Column	Type	Null %	Range / Sample Values
esa_source	object	0.0%	HDX
esa_processed	object	0.0%	2026-04-12

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Numeric Summary

Column	Min	Max	Mean	Median
No numeric columns.

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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. 2 column(s) with >80% missing values were removed: sep, unnamed_1. The dataset was split 80/20 into train and test partitions using a fixed random seed (42) and saved as Snappy-compressed Parquet.

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Limitations

• Data originates from Akvo Foundation (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 for the publisher's own methodology notes and caveats.

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Citation

@dataset{hdx_africa_liberia_water_point_data,
  title     = {Liberia Water Point Data},
  author    = {Akvo Foundation (inactive)},
  year      = {2024},
  url       = {https://data.humdata.org/dataset/liberia-water-point-data},
  note      = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}

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