electricsheepafrica/africa-access-to-clean-water-in-tanzania-by-2020

Access to clean water in Tanzania by 2020

Publisher: Rural Water Supply and Sanitation Agency (RUWASA) Tanzania · Source: OpenAfrica · License: cc-by · Updated: 2022-03-14

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Abstract

Water access by 2020

Each row in this dataset represents geolocated point observations. Data was last updated on OpenAfrica on 2022-03-14. Geographic scope: Africa (multiple countries).

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

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Dataset Characteristics

Domain	Demographics and population
Unit of observation	Geolocated point observations
Rows (total)	20
Columns	6 (1 numeric, 5 categorical, 0 datetime)
Train split	16 rows
Test split	4 rows
Geographic scope	Africa (multiple countries)
Publisher	Rural Water Supply and Sanitation Agency (RUWASA) Tanzania
OpenAfrica last updated	2022-03-14

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Variables

Geographic — ward (Rusumo, Kabanga, Keza), population_served_with_clean_water (8,892, 9,741, 184), percent_population_served_with_clean_water (range 2.06–90.45).

Outcome / Measurement — total_rural (Population, 14,378, 8944).

Identifier / Metadata — esa_source (HDX), esa_processed (2026-04-29).

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-access-to-clean-water-in-tanzania-by-2020")
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
ward	object	5.0%	Rusumo, Kabanga, Keza
total_rural	object	0.0%	Population, 14,378, 8944
population_served_with_clean_water	object	5.0%	8,892, 9,741, 184
percent_population_served_with_clean_water	float64	5.0%	2.06 – 90.45 (mean 52.0972)
esa_source	object	0.0%	HDX
esa_processed	object	0.0%	2026-04-29

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Numeric Summary

Column	Min	Max	Mean	Median
percent_population_served_with_clean_water	2.06	90.45	52.0972	51.3

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Curation

Raw data was downloaded from OpenAfrica 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.

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Limitations

• Data originates from Rural Water Supply and Sanitation Agency (RUWASA) Tanzania 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{openafrica_africa_access_to_clean_water_in_tanzania_by_2020,
  title     = {Access to clean water in Tanzania by 2020},
  author    = {Rural Water Supply and Sanitation Agency (RUWASA) Tanzania},
  year      = {2022},
  url       = {https://open.africa/dataset/access-to-clean-water-in-tanzania-by-2020},
  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.