Dataset Viewer
Auto-converted to Parquet Duplicate
country_name
stringclasses
24 values
country_iso3
stringclasses
24 values
year
int64
2k
2.02k
Number of people not using safely managed drinking water sources
float64
31.7k
163M
Algeria
DZA
2,000
9,427,816
Algeria
DZA
2,001
9,402,942
Algeria
DZA
2,002
9,375,910
Algeria
DZA
2,003
9,350,965
Algeria
DZA
2,004
9,334,514
Algeria
DZA
2,005
9,326,804
Algeria
DZA
2,006
9,328,776
Algeria
DZA
2,007
9,345,376
Algeria
DZA
2,008
9,378,683
Algeria
DZA
2,009
9,425,165
Algeria
DZA
2,010
9,478,401
Algeria
DZA
2,011
9,536,760
Algeria
DZA
2,012
9,602,785
Algeria
DZA
2,013
9,675,842
Algeria
DZA
2,014
9,755,129
Algeria
DZA
2,015
9,840,956
Algeria
DZA
2,016
10,301,945
Algeria
DZA
2,017
10,810,755
Algeria
DZA
2,018
11,337,561
Algeria
DZA
2,019
12,027,746
Algeria
DZA
2,020
12,981,335
Algeria
DZA
2,021
13,947,898
Algeria
DZA
2,022
14,141,417
Algeria
DZA
2,023
14,325,743
Algeria
DZA
2,024
14,498,774
Benin
BEN
2,000
6,195,509.5
Benin
BEN
2,001
6,379,925.5
Benin
BEN
2,002
6,567,717.5
Benin
BEN
2,003
6,759,648
Benin
BEN
2,004
6,958,001.5
Benin
BEN
2,005
7,173,541.5
Benin
BEN
2,006
7,388,329
Benin
BEN
2,007
7,596,895
Benin
BEN
2,008
7,816,344.5
Benin
BEN
2,009
8,040,475.5
Benin
BEN
2,010
8,269,763.5
Benin
BEN
2,011
8,504,994
Benin
BEN
2,012
8,745,978
Benin
BEN
2,013
8,992,093
Benin
BEN
2,014
9,245,529
Benin
BEN
2,015
9,505,874
Benin
BEN
2,016
9,770,552
Benin
BEN
2,017
10,038,102
Benin
BEN
2,018
10,305,872
Benin
BEN
2,019
10,572,365
Benin
BEN
2,020
10,837,706
Benin
BEN
2,021
11,101,805
Benin
BEN
2,022
11,367,078
Benin
BEN
2,023
11,635,688
Benin
BEN
2,024
11,903,280
Botswana
BWA
2,000
684,422.25
Botswana
BWA
2,001
691,701.75
Botswana
BWA
2,002
700,887.5
Botswana
BWA
2,003
709,792.9
Botswana
BWA
2,004
718,766.4
Botswana
BWA
2,005
728,185.3
Botswana
BWA
2,006
718,114.4
Botswana
BWA
2,007
697,416.94
Botswana
BWA
2,008
676,542.8
Botswana
BWA
2,009
655,466.4
Botswana
BWA
2,010
634,001.7
Botswana
BWA
2,011
611,023.94
Botswana
BWA
2,012
623,159.94
Botswana
BWA
2,013
644,871.1
Botswana
BWA
2,014
666,809.6
Botswana
BWA
2,015
689,148.1
Botswana
BWA
2,016
712,015.1
Botswana
BWA
2,017
735,489.7
Botswana
BWA
2,018
759,616.94
Botswana
BWA
2,019
784,443.7
Botswana
BWA
2,020
810,097.44
Botswana
BWA
2,021
836,903.4
Botswana
BWA
2,022
865,295.2
Botswana
BWA
2,023
894,977
Botswana
BWA
2,024
925,483.8
Central African Republic
CAF
2,000
3,506,392.5
Central African Republic
CAF
2,001
3,590,406.2
Central African Republic
CAF
2,002
3,675,106.8
Central African Republic
CAF
2,003
3,769,595.8
Central African Republic
CAF
2,004
3,857,640
Central African Republic
CAF
2,005
3,951,547.2
Central African Republic
CAF
2,006
4,038,922
Central African Republic
CAF
2,007
4,123,089.5
Central African Republic
CAF
2,008
4,217,548
Central African Republic
CAF
2,009
4,195,273
Central African Republic
CAF
2,010
4,167,144.5
Central African Republic
CAF
2,011
4,239,197
Central African Republic
CAF
2,012
4,286,956.5
Central African Republic
CAF
2,013
4,321,453
Central African Republic
CAF
2,014
4,308,585.5
Central African Republic
CAF
2,015
4,319,269.5
Central African Republic
CAF
2,016
4,402,937
Central African Republic
CAF
2,017
4,482,511
Central African Republic
CAF
2,018
4,567,145
Central African Republic
CAF
2,019
4,633,992.5
Central African Republic
CAF
2,020
4,715,815.5
Central African Republic
CAF
2,021
4,801,085
Central African Republic
CAF
2,022
4,785,710.5
Central African Republic
CAF
2,023
4,834,466.5
Central African Republic
CAF
2,024
4,999,255
End of preview. Expand in Data Studio

Number Without Safe Drinking Water | Africa (Our World in Data) | Africa (Electric Sheep Africa metadata inventory)

Size category: n<1K - Formats: parquet - Sector: other_unclassified - Engineered by Electric Sheep Africa

size sector downloads license

TL;DR

This dataset is part of the Electric Sheep Africa catalog on Hugging Face. It is indexed for African data discovery with standardized metadata, loading guidance, provenance notes, and analyst-oriented context.

What This Dataset Covers

Public datasets help analysts inspect structured evidence, build reproducible workflows, and compare patterns across domains.

Dataset context from the existing Hugging Face card: Number Without Safe Drinking Water | Africa (Our World in Data) 🌍 747 observations · 30 Africa countries · 2000–2024 · Repackaged by Electric Sheep Africa TL;DR This dataset contains 747 observations of Number Without Safe Drinking Water data across 30 Africa countries, spanning 2000–2024. About the source Source: Our World in Data Publisher: Our World in Data License: cc-by-4.0 Topic: Number Without Safe Drinking Water Geographic… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/africa-owid-number-without-safe-drinking-water.

Dataset Profile

Field Value
Hugging Face repo electricsheepafrica/africa-owid-number-without-safe-drinking-water
Sector other_unclassified
Topic tags tabular, our-world-in-data, number-without-safe-drinking-water, owid, long-run-series, time-series
Modalities tabular, text
Formats parquet
Size category n<1K
Countries Africa-wide or source-defined African coverage
ISO3 coverage not declared
Last modified on HF 2026-06-06 01:50:31+00:00
Inventory snapshot 2026-07-16T16:00:34Z

How To Read This Dataset

  • Start from the repository files and the dataset viewer when available.
  • Treat the README context as a fast orientation layer; confirm variable definitions and units in the data files before modeling.
  • Use explicit country columns when present. When geography is only implied by the title or source metadata, document that assumption in downstream analysis.
  • Preserve missing values until you have a defensible imputation rule.

Usage

from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-owid-number-without-safe-drinking-water")
print(ds)

split_name = next(iter(ds))
table = ds[split_name]
print(table.features)
print(table[:3])

Convert To Pandas When Tabular

from datasets import Dataset

first_split = ds[next(iter(ds))]
if isinstance(first_split, Dataset):
    df = first_split.to_pandas()
    print(df.head())

Data Quality Notes

  • This card was standardized from the Electric Sheep Africa Hugging Face metadata inventory.
  • Exact schema, row counts, and source files should be inspected in the repository data files.
  • Metadata gaps from the inventory: country, sector, upstream_publisher.
  • Do not infer policy meaning from labels alone; confirm definitions, units, and methods in the source material.

Source And Provenance

Suggested Analyses

  • Inspect schema and missingness before modeling.
  • Profile variables by geography, time, and subgroup columns where present.
  • Join with other Electric Sheep Africa datasets using explicit country, year, and indicator fields when available.
  • Build reproducible notebooks that cite both the original source context and the Electric Sheep Africa Hugging Face repo.

Citation

@misc{electric_sheep_africa_africa_owid_number_without_safe_drinking_water_2026,
  title        = {Number Without Safe Drinking Water | Africa (Our World in Data) | Africa (Electric Sheep Africa metadata inventory)},
  author       = {Public dataset metadata},
  year         = {2026},
  url          = {https://huggingface.co/datasets/electricsheepafrica/africa-owid-number-without-safe-drinking-water},
  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-owid-number-without-safe-drinking-water}}
}

License

Released under CC BY 4.0.

Original source rights remain with the original publisher or data provider. Electric Sheep Africa engineering standardizes discovery metadata, documentation, and usage guidance for analysis on Hugging Face.

About Electric Sheep Africa

Electric Sheep Africa publishes ML-ready African public datasets on Hugging Face.


Provenance: metadata-backed README standardized 2026-08-12 by the Electric Sheep Africa README system. Inventory source: catalog/esa_metadata_inventory/master_metadata.jsonl.

Downloads last month
20