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country_name
stringclasses
42 values
country_iso3
stringclasses
42 values
year
int64
1.98k
2.02k
Not in extreme poverty
int64
79.4k
137M
Living in extreme poverty
int64
0
94.7M
Algeria
DZA
1,988
21,152,984
2,956,552
Algeria
DZA
1,995
25,108,716
3,361,476
Algeria
DZA
2,011
37,029,652
0
Angola
AGO
2,000
11,914,327
4,396,533
Angola
AGO
2,008
17,139,931
4,856,783
Angola
AGO
2,018
19,110,328
12,370,168
Benin
BEN
2,003
2,337,740
5,575,330
Benin
BEN
2,011
3,220,182
6,973,772
Benin
BEN
2,015
3,882,979
7,477,702
Benin
BEN
2,018
7,887,518
4,667,533
Benin
BEN
2,021
9,950,728
3,722,252
Botswana
BWA
1,985
570,505
573,502
Botswana
BWA
1,993
829,557
603,728
Botswana
BWA
2,002
1,148,123
613,056
Botswana
BWA
2,009
1,579,594
421,688
Botswana
BWA
2,015
1,753,594
476,456
Burkina Faso
BFA
1,994
1,258,574
8,930,955
Burkina Faso
BFA
1,998
1,523,795
9,716,869
Burkina Faso
BFA
2,003
3,972,776
9,109,756
Burkina Faso
BFA
2,009
5,157,086
10,552,980
Burkina Faso
BFA
2,014
7,521,938
10,707,522
Burkina Faso
BFA
2,018
10,830,083
9,870,037
Burkina Faso
BFA
2,021
12,943,133
9,396,353
Burundi
BDI
1,992
880,108
4,978,299
Burundi
BDI
1,998
774,659
5,487,473
Burundi
BDI
2,006
1,454,885
6,410,662
Burundi
BDI
2,013
2,567,348
8,052,215
Burundi
BDI
2,020
3,268,785
9,400,518
Cameroon
CMR
1,996
5,838,198
7,576,559
Cameroon
CMR
2,001
10,591,575
4,717,915
Cameroon
CMR
2,007
11,549,155
6,500,741
Cameroon
CMR
2,014
15,656,803
6,403,089
Cameroon
CMR
2,021
20,083,629
7,312,527
Cape Verde
CPV
2,001
292,779
172,714
Cape Verde
CPV
2,007
367,655
130,239
Cape Verde
CPV
2,015
437,564
74,830
Central African Republic
CAF
1,992
472,964
2,628,440
Central African Republic
CAF
2,008
1,492,474
3,066,510
Central African Republic
CAF
2,021
1,450,950
3,661,150
Chad
TCD
2,003
3,159,430
6,328,403
Chad
TCD
2,011
6,990,038
5,764,959
Chad
TCD
2,018
9,546,859
6,874,018
Chad
TCD
2,022
11,170,011
7,285,305
Comoros
COM
2,004
439,718
140,986
Comoros
COM
2,014
488,397
223,106
Comoros
COM
2,020
730,505
71,658
Comoros
COM
2,024
822,717
43,911
Congo
COG
2,005
1,683,599
2,012,794
Congo
COG
2,011
2,855,171
1,842,139
Cote d'Ivoire
CIV
1,985
8,699,960
1,372,558
Cote d'Ivoire
CIV
1,986
9,409,826
1,063,936
Cote d'Ivoire
CIV
1,987
9,176,453
1,747,053
Cote d'Ivoire
CIV
1,988
8,846,828
2,575,073
Cote d'Ivoire
CIV
1,992
8,319,233
5,186,392
Cote d'Ivoire
CIV
1,995
8,939,896
5,915,669
Cote d'Ivoire
CIV
1,998
9,515,084
7,148,864
Cote d'Ivoire
CIV
2,008
11,406,692
10,107,748
Cote d'Ivoire
CIV
2,015
13,482,252
11,764,090
Cote d'Ivoire
CIV
2,018
21,797,837
6,030,755
Cote d'Ivoire
CIV
2,021
23,915,417
6,290,769
Democratic Republic of Congo
COD
2,004
2,993,589
55,586,555
Democratic Republic of Congo
COD
2,012
15,670,090
58,598,438
Democratic Republic of Congo
COD
2,020
14,093,567
81,896,433
Djibouti
DJI
2,002
538,366
257,208
Djibouti
DJI
2,012
711,766
254,981
Djibouti
DJI
2,013
677,075
307,749
Djibouti
DJI
2,017
787,324
267,517
Egypt
EGY
1,990
54,983,237
4,229,571
Egypt
EGY
1,995
63,963,477
2,720,715
Egypt
EGY
1,999
72,692,704
0
Egypt
EGY
2,004
76,963,485
3,309,499
Egypt
EGY
2,008
83,040,701
3,216,931
Egypt
EGY
2,010
88,734,308
1,391,284
Egypt
EGY
2,012
93,050,846
1,174,706
Egypt
EGY
2,015
98,393,497
1,203,847
Egypt
EGY
2,017
101,911,521
3,274,063
Egypt
EGY
2,019
106,160,707
2,150,093
Egypt
EGY
2,021
110,694,285
1,541,883
Equatorial Guinea
GNQ
2,022
1,669,420
160,968
Eswatini
SWZ
1,994
103,831
866,734
Eswatini
SWZ
2,000
367,692
682,600
Eswatini
SWZ
2,009
462,453
644,324
Eswatini
SWZ
2,016
638,440
512,544
Ethiopia
ETH
1,995
15,366,302
42,653,610
Ethiopia
ETH
1,999
22,647,630
43,745,398
Ethiopia
ETH
2,004
45,307,278
31,915,058
Ethiopia
ETH
2,010
57,486,043
34,358,197
Ethiopia
ETH
2,015
70,547,080
34,745,568
Ethiopia
ETH
2,021
74,948,985
47,189,607
Gabon
GAB
2,005
1,329,216
134,279
Gabon
GAB
2,017
2,074,712
82,188
Gambia
GMB
1,998
302,855
1,074,132
Gambia
GMB
2,003
712,886
877,398
Gambia
GMB
2,010
1,270,225
660,977
Gambia
GMB
2,015
1,859,464
383,430
Gambia
GMB
2,020
1,965,985
554,570
Ghana
GHA
1,987
3,150,150
11,312,688
Ghana
GHA
1,988
3,344,218
11,477,472
Ghana
GHA
1,991
3,157,254
12,920,314
Ghana
GHA
1,998
6,504,476
12,266,690
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Above Or Below Extreme Poverty Line World Bank | Africa (Our World in Data) | Africa (World Bank)

Size category: n<1K - Formats: parquet - Sector: economics_finance - 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: Above Or Below Extreme Poverty Line World Bank | Africa (Our World in Data) 🌍 269 observations · 51 Africa countries · 1980–2024 · Repackaged by Electric Sheep Africa TL;DR This dataset contains 269 observations of Above Or Below Extreme Poverty Line World Bank data across 51 Africa countries, spanning 1980–2024. About the source Source: Our World in Data Publisher: Our World in Data License: cc-by-4.0 Topic: Above Or Below Extreme Poverty… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/africa-owid-above-or-below-extreme-poverty-line-world-bank.

Dataset Profile

Field Value
Hugging Face repo electricsheepafrica/africa-owid-above-or-below-extreme-poverty-line-world-bank
Sector economics_finance
Topic tags tabular, our-world-in-data, above-or-below-extreme-poverty-line-world-bank, 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 2026-06-01 18:28:45+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-above-or-below-extreme-poverty-line-world-bank")
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_above_or_below_extreme_poverty_line_world_bank_2026,
  title        = {Above Or Below Extreme Poverty Line World Bank | Africa (Our World in Data) | Africa (World Bank)},
  author       = {World Bank open data},
  year         = {2026},
  url          = {https://huggingface.co/datasets/electricsheepafrica/africa-owid-above-or-below-extreme-poverty-line-world-bank},
  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-owid-above-or-below-extreme-poverty-line-world-bank}}
}

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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