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year
int64
1.96k
2.02k
Algeria
float64
13.2
30.8
Angola
float64
19.7
62.7
Benin
float64
64
71.9
Botswana
float64
39.6
58.7
Burkina Faso
float64
26.6
87.9
Burundi
float64
36.8
79.7
Cabo Verde
float64
46.4
70.9
Cameroon
float64
32.7
65.4
Central African Republic
float64
68.9
86
Chad
float64
82
91.6
Comoros
float64
48.5
68.6
Congo, Dem. Rep.
float64
71.9
78.4
Congo, Rep.
float64
44.2
87.8
Cote d'Ivoire
float64
48.3
67.1
Djibouti
float64
48.7
76.7
Egypt, Arab Rep.
float64
0.9
42.2
Equatorial Guinea
float64
52.9
64.9
Eritrea
float64
48.7
76.7
Eswatini
float64
10.8
55.4
Ethiopia
float64
64.3
92.2
Gabon
float64
38.8
57.4
Gambia, The
float64
37.1
56.9
Ghana
float64
33.5
60.4
Guinea
float64
40.7
49
Guinea-Bissau
float64
59
75.2
Kenya
float64
40.5
63.2
Lesotho
float64
25.6
62.7
Liberia
float64
60.5
75.5
Libya
float64
16.4
45.3
Madagascar
float64
65.7
91.4
Malawi
float64
38
81.4
Mali
float64
41.9
92.5
Mauritania
float64
56
84.7
Mauritius
float64
48.7
76.7
Morocco
float64
10.9
33.2
Mozambique
float64
55
90.1
Namibia
float64
41.4
42.6
Niger
float64
70
70.4
Nigeria
float64
48.5
74.1
Rwanda
float64
38.3
71.4
Sao Tome and Principe
float64
52.6
82.4
Senegal
float64
31.6
67.2
Seychelles
float64
48.7
76.7
Sierra Leone
float64
49.3
73.9
Somalia
float64
48.7
76.7
South Africa
float64
24.2
27.6
South Sudan
float64
94.2
99.8
Sudan
float64
21.8
99.1
Tanzania
float64
40.9
81.3
Togo
float64
38.5
69.3
Tunisia
float64
7.64
10.4
Uganda
float64
52.7
80.9
Zambia
float64
48.3
63.8
Zimbabwe
float64
21.6
54.9
1,960
30.8
19.7
71.86569
58.69239
82.21152
79.7
70.86701
65.3638
85.96516
91.58375
64.49449
71.91424
87.80583
67.07658
76.73784
42.22616
52.91641
76.73784
55.35217
92.17158
57.43
56.92053
60.36841
40.66534
75.2
63.18451
62.72729
75.48823
39.14517
91.43569
81.41073
83.87627
84.69319
76.73784
33.15472
90.09549
42.55683
70.03805
74.10729
71.42051
61.4
67.15485
76.73784
73.9
76.73784
27.60971
99.8
21.7636
81.32802
69.27204
10.4
80.90118
63.78455
27.52175
1,961
30.8
19.7
71.86569
58.69239
82.21152
79.7
70.86701
65.3638
85.96516
91.58375
64.49449
71.91424
87.80583
67.07658
76.73784
42.22616
52.91641
76.73784
55.35217
92.17158
57.43
56.92053
60.36841
40.66534
75.2
63.18451
62.72729
75.48823
39.14517
91.43569
81.41073
83.87627
84.69319
76.73784
33.15472
90.09549
42.55683
70.03805
74.10729
71.42051
61.4
67.15485
76.73784
73.9
76.73784
27.60971
99.8
21.7636
81.32802
69.27204
10.4
80.90118
63.78455
27.52175
1,962
30.8
19.7
71.86569
58.69239
82.21152
79.7
70.86701
65.3638
85.96516
91.58375
64.49449
71.91424
87.80583
67.07658
76.73784
42.22616
52.91641
76.73784
55.35217
92.17158
57.43
56.92053
60.36841
40.66534
75.2
63.18451
62.72729
75.48823
39.14517
91.43569
81.41073
83.87627
84.69319
76.73784
33.15472
90.09549
42.55683
70.03805
74.10729
71.42051
61.4
67.15485
76.73784
73.9
76.73784
27.60971
99.8
21.7636
81.32802
69.27204
10.4
80.90118
63.78455
27.52175
1,963
30.8
19.7
71.86569
58.69239
82.21152
79.7
70.86701
65.3638
85.96516
91.58375
64.49449
71.91424
87.80583
67.07658
76.73784
42.22616
52.91641
76.73784
55.35217
92.17158
57.43
56.92053
60.36841
40.66534
75.2
63.18451
62.72729
75.48823
39.14517
91.43569
81.41073
83.87627
84.69319
76.73784
33.15472
90.09549
42.55683
70.03805
74.10729
71.42051
61.4
67.15485
76.73784
73.9
76.73784
27.60971
99.8
21.7636
81.32802
69.27204
10.4
80.90118
63.78455
27.52175
1,964
30.8
19.7
71.86569
58.69239
82.21152
79.7
70.86701
65.3638
85.96516
91.58375
64.49449
71.91424
87.80583
67.07658
76.73784
42.22616
52.91641
76.73784
55.35217
92.17158
57.43
56.92053
60.36841
40.66534
75.2
63.18451
62.72729
75.48823
39.14517
91.43569
81.41073
83.87627
84.69319
76.73784
33.15472
90.09549
42.55683
70.03805
74.10729
71.42051
61.4
67.15485
76.73784
73.9
76.73784
27.60971
99.8
21.7636
81.32802
69.27204
10.4
80.90118
63.78455
27.52175
1,965
30.8
19.7
71.86569
58.69239
82.21152
79.7
70.86701
65.3638
85.96516
91.58375
64.49449
71.91424
87.80583
67.07658
76.73784
42.22616
52.91641
76.73784
55.35217
92.17158
57.43
56.92053
60.36841
40.66534
75.2
63.18451
62.72729
75.48823
39.14517
91.43569
81.41073
83.87627
84.69319
76.73784
33.15472
90.09549
42.55683
70.03805
74.10729
71.42051
61.4
67.15485
76.73784
73.9
76.73784
27.60971
99.8
21.7636
81.32802
69.27204
10.4
80.90118
63.78455
27.52175
1,966
30.8
19.7
71.86569
58.69239
82.21152
79.7
70.86701
65.3638
85.96516
91.58375
64.49449
71.91424
87.80583
67.07658
76.73784
42.22616
52.91641
76.73784
55.35217
92.17158
57.43
56.92053
60.36841
40.66534
75.2
63.18451
62.72729
75.48823
39.14517
91.43569
81.41073
83.87627
84.69319
76.73784
33.15472
90.09549
42.55683
70.03805
74.10729
71.42051
61.4
67.15485
76.73784
73.9
76.73784
27.60971
99.8
21.7636
81.32802
69.27204
10.4
80.90118
63.78455
27.52175
1,967
30.8
19.7
71.86569
58.69239
82.21152
79.7
70.86701
65.3638
85.96516
91.58375
64.49449
71.91424
87.80583
67.07658
76.73784
42.22616
52.91641
76.73784
55.35217
92.17158
57.43
56.92053
60.36841
40.66534
75.2
63.18451
62.72729
75.48823
39.14517
91.43569
81.41073
83.87627
84.69319
76.73784
33.15472
90.09549
42.55683
70.03805
74.10729
71.42051
61.4
67.15485
76.73784
73.9
76.73784
27.60971
99.8
21.7636
81.32802
69.27204
10.4
80.90118
63.78455
27.52175
1,968
30.8
19.7
71.86569
58.69239
82.21152
79.7
70.86701
65.3638
85.96516
91.58375
64.49449
71.91424
87.80583
67.07658
76.73784
42.22616
52.91641
76.73784
55.35217
92.17158
57.43
56.92053
60.36841
40.66534
75.2
63.18451
62.72729
75.48823
39.14517
91.43569
81.41073
83.87627
84.69319
76.73784
33.15472
90.09549
42.55683
70.03805
74.10729
71.42051
61.4
67.15485
76.73784
73.9
76.73784
27.60971
99.8
21.7636
81.32802
69.27204
10.4
80.90118
63.78455
27.52175
1,969
30.8
19.7
71.86569
58.69239
82.21152
79.7
70.86701
65.3638
85.96516
91.58375
64.49449
71.91424
87.80583
67.07658
76.73784
42.22616
52.91641
76.73784
55.35217
92.17158
57.43
56.92053
60.36841
40.66534
75.2
63.18451
62.72729
75.48823
39.14517
91.43569
81.41073
83.87627
84.69319
76.73784
33.15472
90.09549
42.55683
70.03805
74.10729
71.42051
61.4
67.15485
76.73784
73.9
76.73784
27.60971
99.8
21.7636
81.32802
69.27204
10.4
80.90118
63.78455
27.52175
1,970
30.8
19.7
71.86569
58.69239
82.21152
79.7
70.86701
65.3638
85.96516
91.58375
64.49449
71.91424
87.80583
67.07658
76.73784
42.22616
52.91641
76.73784
55.35217
92.17158
57.43
56.92053
60.36841
40.66534
75.2
63.18451
62.72729
75.48823
39.14517
91.43569
81.41073
83.87627
84.69319
76.73784
33.15472
90.09549
42.55683
70.03805
74.10729
71.42051
61.4
67.15485
76.73784
73.9
76.73784
27.60971
99.8
21.7636
81.32802
69.27204
10.4
80.90118
63.78455
27.52175
1,971
30.8
19.7
71.86569
58.69239
82.21152
79.7
70.86701
65.3638
85.96516
91.58375
64.49449
71.91424
87.80583
67.07658
76.73784
42.22616
52.91641
76.73784
55.35217
92.17158
57.43
56.92053
60.36841
40.66534
75.2
63.18451
62.72729
75.48823
39.14517
91.43569
81.41073
83.87627
84.69319
76.73784
33.15472
90.09549
42.55683
70.03805
74.10729
71.42051
61.4
67.15485
76.73784
73.9
76.73784
27.60971
99.8
21.7636
81.32802
69.27204
10.4
80.90118
63.78455
27.52175
1,972
30.8
19.7
71.86569
58.69239
82.21152
79.7
70.86701
65.3638
85.96516
91.58375
64.49449
71.91424
87.80583
67.07658
76.73784
42.22616
52.91641
76.73784
55.35217
92.17158
57.43
56.92053
60.36841
40.66534
75.2
63.18451
62.72729
75.48823
39.14517
91.43569
81.41073
83.87627
84.69319
76.73784
33.15472
90.09549
42.55683
70.03805
74.10729
71.42051
61.4
67.15485
76.73784
73.9
76.73784
27.60971
99.8
21.7636
81.32802
69.27204
10.4
80.90118
63.78455
27.52175
1,973
30.8
19.7
71.86569
58.69239
82.21152
79.7
70.86701
65.3638
85.96516
91.58375
64.49449
71.91424
87.80583
67.07658
76.73784
42.22616
52.91641
76.73784
55.35217
92.17158
57.43
56.92053
60.36841
40.66534
75.2
63.18451
62.72729
75.48823
39.14517
91.43569
81.41073
83.87627
84.69319
76.73784
33.15472
90.09549
42.55683
70.03805
74.10729
71.42051
61.4
67.15485
76.73784
73.9
76.73784
27.60971
99.8
21.7636
81.32802
69.27204
10.4
80.90118
63.78455
27.52175
1,974
30.8
19.7
71.86569
58.69239
82.21152
79.7
70.86701
65.3638
85.96516
91.58375
64.49449
71.91424
87.80583
67.07658
76.73784
42.22616
52.91641
76.73784
55.35217
92.17158
57.43
56.92053
60.36841
40.66534
75.2
63.18451
62.72729
75.48823
39.14517
91.43569
81.41073
83.87627
84.69319
76.73784
33.15472
90.09549
42.55683
70.03805
74.10729
71.42051
61.4
67.15485
76.73784
73.9
76.73784
27.60971
99.8
21.7636
81.32802
69.27204
10.4
80.90118
63.78455
27.52175
1,975
30.8
19.7
71.86569
58.69239
82.21152
79.7
70.86701
65.3638
85.96516
91.58375
64.49449
71.91424
87.80583
67.07658
76.73784
42.22616
52.91641
76.73784
55.35217
92.17158
57.43
56.92053
60.36841
40.66534
75.2
63.18451
62.72729
75.48823
39.14517
91.43569
81.41073
83.87627
84.69319
76.73784
33.15472
90.09549
42.55683
70.03805
74.10729
71.42051
61.4
67.15485
76.73784
73.9
76.73784
27.60971
99.8
21.7636
81.32802
69.27204
10.4
80.90118
63.78455
27.52175
1,976
30.8
19.7
71.86569
58.69239
82.21152
79.7
70.86701
65.3638
85.96516
91.58375
64.49449
71.91424
87.80583
67.07658
76.73784
42.22616
52.91641
76.73784
55.35217
92.17158
57.43
56.92053
60.36841
40.66534
75.2
63.18451
62.72729
75.48823
39.14517
91.43569
81.41073
83.87627
84.69319
76.73784
33.15472
90.09549
42.55683
70.03805
74.10729
71.42051
61.4
67.15485
76.73784
73.9
76.73784
27.60971
99.8
21.7636
81.32802
69.27204
10.4
80.90118
63.78455
27.52175
1,977
30.8
19.7
71.86569
58.69239
82.21152
79.7
70.86701
65.3638
85.96516
91.58375
64.49449
71.91424
87.80583
67.07658
76.73784
42.22616
52.91641
76.73784
55.35217
92.17158
57.43
56.92053
60.36841
40.66534
75.2
63.18451
62.72729
75.48823
39.14517
91.43569
81.41073
83.87627
84.69319
76.73784
33.15472
90.09549
42.55683
70.03805
74.10729
71.42051
61.4
67.15485
76.73784
73.9
76.73784
27.60971
99.8
21.7636
81.32802
69.27204
10.4
80.90118
63.78455
27.52175
1,978
30.8
19.7
71.86569
58.69239
82.21152
79.7
70.86701
65.3638
85.96516
91.58375
64.49449
71.91424
87.80583
67.07658
76.73784
42.22616
52.91641
76.73784
55.35217
92.17158
57.43
56.92053
60.36841
40.66534
75.2
63.18451
62.72729
75.48823
39.14517
91.43569
81.41073
83.87627
84.69319
76.73784
33.15472
90.09549
42.55683
70.03805
74.10729
71.42051
61.4
67.15485
76.73784
73.9
76.73784
27.60971
99.8
21.7636
81.32802
69.27204
10.4
80.90118
63.78455
27.52175
1,979
30.8
19.7
71.86569
58.69239
82.21152
79.7
70.86701
65.3638
85.96516
91.58375
64.49449
71.91424
87.80583
67.07658
76.73784
42.22616
52.91641
76.73784
55.35217
92.17158
57.43
56.92053
60.36841
40.66534
75.2
63.18451
62.72729
75.48823
39.14517
91.43569
81.41073
83.87627
84.69319
76.73784
33.15472
90.09549
42.55683
70.03805
74.10729
71.42051
61.4
67.15485
76.73784
73.9
76.73784
27.60971
99.8
21.7636
81.32802
69.27204
10.4
80.90118
63.78455
27.52175
1,980
30.8
19.7
71.86569
58.69239
82.21152
79.7
70.86701
65.3638
85.96516
91.58375
64.49449
71.91424
87.80583
67.07658
76.73784
42.22616
52.91641
76.73784
55.35217
92.17158
57.43
56.92053
60.36841
40.66534
75.2
63.18451
62.72729
75.48823
39.14517
91.43569
81.41073
83.87627
84.69319
76.73784
33.15472
90.09549
42.55683
70.03805
74.10729
71.42051
61.4
67.15485
76.73784
73.9
76.73784
27.60971
99.8
21.7636
81.32802
69.27204
10.4
80.90118
63.78455
27.52175
1,981
30.8
19.7
71.86569
58.69239
82.21152
79.7
70.86701
65.3638
85.96516
91.58375
64.49449
71.91424
87.80583
67.07658
76.73784
42.22616
52.91641
76.73784
55.35217
92.17158
57.43
56.92053
60.36841
40.66534
75.2
63.18451
62.72729
75.48823
39.14517
91.43569
81.41073
83.87627
84.69319
76.73784
33.15472
90.09549
42.55683
70.03805
74.10729
71.42051
61.4
67.15485
76.73784
73.9
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Population living in slums percentage of urban population africa | Africa (World Bank)

Size category: n<1K - Formats: csv - Sector: climate_environment - 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: Africa: Population living in slums (% of urban population) Dataset summary This dataset provides values for "Population living in slums (% of urban population)" across African countries, standardized and made ML-ready. Geographic scope: 54 African countries. Temporal coverage: 1960–2024 (annual). Units: As defined by the World Bank indicator. Source & licensing Source: World Bank – World Development Indicators (WDI), Indicator code: EN.POP.SLUM.UR.ZS.… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/Population-living-in-slums-percentage-of-urban-population-africa.

Dataset Profile

Field Value
Hugging Face repo electricsheepafrica/Population-living-in-slums-percentage-of-urban-population-africa
Sector climate_environment
Topic tags climate
Modalities tabular
Formats csv
Size category n<1K
Countries Africa-wide or source-defined African coverage
ISO3 coverage not declared
Last modified on HF 2025-08-16 16:48:29+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/Population-living-in-slums-percentage-of-urban-population-africa")
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_population_living_in_slums_percentage_of_urban_population_africa_2026,
  title        = {Population living in slums percentage of urban population africa | Africa (World Bank)},
  author       = {World Bank open data},
  year         = {2026},
  url          = {https://huggingface.co/datasets/electricsheepafrica/Population-living-in-slums-percentage-of-urban-population-africa},
  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/Population-living-in-slums-percentage-of-urban-population-africa}}
}

License

Released under gpl.

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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