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country_name
stringclasses
43 values
country_iso3
stringclasses
43 values
year
int64
2k
2.02k
Number of people not using improved sanitation facilities
float64
0
107M
Algeria
DZA
2,000
2,359,206.2
Algeria
DZA
2,001
2,306,637.8
Algeria
DZA
2,002
2,253,366.5
Algeria
DZA
2,003
2,200,503.2
Algeria
DZA
2,004
2,149,533.2
Algeria
DZA
2,005
2,100,469
Algeria
DZA
2,006
2,053,380.6
Algeria
DZA
2,007
2,009,277.6
Algeria
DZA
2,008
1,968,370.1
Algeria
DZA
2,009
1,930,055.6
Algeria
DZA
2,010
1,892,789.5
Algeria
DZA
2,011
1,856,275.9
Algeria
DZA
2,012
1,820,952.9
Algeria
DZA
2,013
1,786,664
Algeria
DZA
2,014
1,753,174.2
Algeria
DZA
2,015
1,720,550.9
Algeria
DZA
2,016
1,688,386.6
Algeria
DZA
2,017
1,656,379.4
Algeria
DZA
2,018
1,623,338.8
Algeria
DZA
2,019
1,589,341.8
Algeria
DZA
2,020
1,553,969.5
Algeria
DZA
2,021
1,517,896.1
Algeria
DZA
2,022
1,526,746.4
Algeria
DZA
2,023
1,534,589
Algeria
DZA
2,024
1,541,266.2
Angola
AGO
2,000
8,546,201
Angola
AGO
2,001
8,722,951
Angola
AGO
2,002
8,906,606
Angola
AGO
2,003
8,956,692
Angola
AGO
2,004
9,007,597
Angola
AGO
2,005
9,055,097
Angola
AGO
2,006
9,144,199
Angola
AGO
2,007
9,232,461
Angola
AGO
2,008
9,317,138
Angola
AGO
2,009
9,397,003
Angola
AGO
2,010
9,473,534
Angola
AGO
2,011
9,545,013
Angola
AGO
2,012
9,606,914
Angola
AGO
2,013
9,655,734
Angola
AGO
2,014
9,682,309
Angola
AGO
2,015
9,686,182
Angola
AGO
2,016
9,675,871
Angola
AGO
2,017
9,650,107
Angola
AGO
2,018
9,603,576
Angola
AGO
2,019
9,804,339
Angola
AGO
2,020
9,998,579
Angola
AGO
2,021
10,188,803
Angola
AGO
2,022
10,379,925
Benin
BEN
2,000
5,515,730.5
Benin
BEN
2,001
5,630,558.5
Benin
BEN
2,002
5,744,279
Benin
BEN
2,003
5,856,573.5
Benin
BEN
2,004
5,971,143
Benin
BEN
2,005
6,097,007
Benin
BEN
2,006
6,218,620
Benin
BEN
2,007
6,331,415.5
Benin
BEN
2,008
6,449,676
Benin
BEN
2,009
6,568,102.5
Benin
BEN
2,010
6,686,916
Benin
BEN
2,011
6,806,613.5
Benin
BEN
2,012
6,926,889
Benin
BEN
2,013
7,047,154.5
Benin
BEN
2,014
7,168,807.5
Benin
BEN
2,015
7,291,381.5
Benin
BEN
2,016
7,412,737
Benin
BEN
2,017
7,531,656.5
Benin
BEN
2,018
7,646,087.5
Benin
BEN
2,019
7,754,911
Benin
BEN
2,020
7,858,239
Benin
BEN
2,021
7,956,081.5
Benin
BEN
2,022
8,050,111.5
Benin
BEN
2,023
8,141,831
Benin
BEN
2,024
8,228,165.5
Botswana
BWA
2,000
671,585.7
Botswana
BWA
2,001
662,377.75
Botswana
BWA
2,002
653,493.4
Botswana
BWA
2,003
643,632.25
Botswana
BWA
2,004
633,252.4
Botswana
BWA
2,005
622,659.4
Botswana
BWA
2,006
611,983.44
Botswana
BWA
2,007
594,621.2
Botswana
BWA
2,008
577,099.1
Botswana
BWA
2,009
559,366.3
Botswana
BWA
2,010
541,270.94
Botswana
BWA
2,011
521,837.34
Botswana
BWA
2,012
503,701.28
Botswana
BWA
2,013
484,786.75
Botswana
BWA
2,014
465,137.75
Botswana
BWA
2,015
444,908.28
Botswana
BWA
2,016
424,186.78
Botswana
BWA
2,017
403,016.88
Botswana
BWA
2,018
381,442.7
Botswana
BWA
2,019
359,467.84
Botswana
BWA
2,020
337,130.16
Botswana
BWA
2,021
314,524.6
Botswana
BWA
2,022
291,718
Botswana
BWA
2,023
268,520.38
Botswana
BWA
2,024
269,065.5
Burkina Faso
BFA
2,000
9,813,482
Burkina Faso
BFA
2,001
10,051,691
End of preview. Expand in Data Studio

Number Without Access To Improved Sanitation | Africa (Our World in Data) | Africa (Electric Sheep Africa metadata inventory)

Size category: 1K<n<10K - Formats: parquet - Sector: infrastructure_transport - 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 Access To Improved Sanitation | Africa (Our World in Data) 🌍 1,318 observations · 54 Africa countries · 2000–2024 · Repackaged by Electric Sheep Africa TL;DR This dataset contains 1,318 observations of Number Without Access To Improved Sanitation data across 54 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 Access To Improved… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/africa-owid-number-without-access-to-improved-sanitation.

Dataset Profile

Field Value
Hugging Face repo electricsheepafrica/africa-owid-number-without-access-to-improved-sanitation
Sector infrastructure_transport
Topic tags tabular, our-world-in-data, number-without-access-to-improved-sanitation, owid, long-run-series, time-series
Modalities tabular, text
Formats parquet
Size category 1K<n<10K
Countries Africa-wide or source-defined African coverage
ISO3 coverage not declared
Last modified on HF 2026-06-06 01:49:17+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-access-to-improved-sanitation")
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, 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_access_to_improved_sanitation_2026,
  title        = {Number Without Access To Improved Sanitation | 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-access-to-improved-sanitation},
  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-owid-number-without-access-to-improved-sanitation}}
}

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.

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