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6 values
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year
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2.02k
Age-standardized deaths from liver cancer in both sexes in those aged all ages per 100,000 people
float64
0
15
Cape Verde
CPV
2,011
9.991155
Egypt
EGY
1,980
1.624266
Egypt
EGY
1,987
2.804248
Egypt
EGY
1,991
3.702296
Egypt
EGY
1,992
4.16562
Egypt
EGY
2,000
6.030124
Egypt
EGY
2,001
6.938183
Egypt
EGY
2,002
7.464301
Egypt
EGY
2,003
8.074411
Egypt
EGY
2,004
8.24268
Egypt
EGY
2,005
8.646093
Egypt
EGY
2,006
8.84131
Egypt
EGY
2,007
8.84021
Egypt
EGY
2,008
9.379258
Egypt
EGY
2,009
9.427692
Egypt
EGY
2,010
9.868873
Egypt
EGY
2,011
9.846841
Egypt
EGY
2,012
9.897708
Egypt
EGY
2,013
11.379632
Egypt
EGY
2,014
12.746107
Egypt
EGY
2,015
10.561598
Egypt
EGY
2,016
10.805192
Egypt
EGY
2,017
10.547023
Egypt
EGY
2,018
10.479657
Egypt
EGY
2,019
9.962385
Mauritius
MUS
1,981
0.236719
Mauritius
MUS
1,982
1.676344
Mauritius
MUS
1,983
0
Mauritius
MUS
1,984
0.077815
Mauritius
MUS
1,985
0.207534
Mauritius
MUS
1,986
0.542961
Mauritius
MUS
1,987
0.606353
Mauritius
MUS
1,988
0.622956
Mauritius
MUS
1,989
0.426181
Mauritius
MUS
1,990
0
Mauritius
MUS
1,991
0.116798
Mauritius
MUS
1,992
0.316876
Mauritius
MUS
1,993
0.346957
Mauritius
MUS
1,994
0
Mauritius
MUS
1,995
0.132021
Mauritius
MUS
1,996
0
Mauritius
MUS
1,997
0
Mauritius
MUS
1,998
0.13218
Mauritius
MUS
1,999
0.301488
Mauritius
MUS
2,000
0.648501
Mauritius
MUS
2,001
0.28451
Mauritius
MUS
2,002
0.665611
Mauritius
MUS
2,003
0.189833
Mauritius
MUS
2,004
0.202923
Mauritius
MUS
2,005
4.705041
Mauritius
MUS
2,006
4.40174
Mauritius
MUS
2,007
4.26298
Mauritius
MUS
2,008
4.709609
Mauritius
MUS
2,009
3.72484
Mauritius
MUS
2,010
3.874528
Mauritius
MUS
2,011
3.476465
Mauritius
MUS
2,012
3.212853
Mauritius
MUS
2,013
3.560075
Mauritius
MUS
2,014
2.71936
Mauritius
MUS
2,015
3.337021
Mauritius
MUS
2,016
3.116737
Mauritius
MUS
2,017
2.550473
Mauritius
MUS
2,018
3.199428
Mauritius
MUS
2,019
3.016125
Mauritius
MUS
2,020
3.101559
Mauritius
MUS
2,021
3.164496
Mauritius
MUS
2,022
3.187431
Mauritius
MUS
2,023
2.991406
Sao Tome and Principe
STP
1,984
7.548222
Sao Tome and Principe
STP
1,985
4.406231
Sao Tome and Principe
STP
1,987
3.466581
Seychelles
SYC
1,985
15.031635
Seychelles
SYC
1,986
2.110714
Seychelles
SYC
1,987
3.794048
Seychelles
SYC
2,001
4.879297
Seychelles
SYC
2,002
1.214334
Seychelles
SYC
2,003
2.288106
Seychelles
SYC
2,004
0
Seychelles
SYC
2,005
3.220369
Seychelles
SYC
2,006
7.826628
Seychelles
SYC
2,007
6.925547
Seychelles
SYC
2,008
12.7182
Seychelles
SYC
2,009
7.96919
Seychelles
SYC
2,010
4.73552
Seychelles
SYC
2,011
11.045321
Seychelles
SYC
2,012
7.057593
Seychelles
SYC
2,013
2.932697
Seychelles
SYC
2,014
7.412011
Seychelles
SYC
2,015
4.135332
Seychelles
SYC
2,016
11.021475
Seychelles
SYC
2,017
7.2468
Seychelles
SYC
2,019
4.942278
Seychelles
SYC
2,020
4.81073
Seychelles
SYC
2,021
0
South Africa
ZAF
1,996
6.184784

Liver Cancer Death Rate | Africa (Our World in Data)

🌍 119 observations · 6 Africa countries · 1980–2023 · Repackaged by Electric Sheep Africa

rows countries years license

TL;DR

This dataset contains 119 observations of Liver Cancer Death Rate data across 6 Africa countries, spanning 1980–2023.

About the source

Geographic coverage

6 Africa countries · top rows shown below, sorted by row count:

Country Rows First year Last year
MUS 43 1981 2023
ZAF 25 1996 2020
EGY 24 1980 2019
SYC 23 1985 2021
STP 3 1984 1987
CPV 1 2011 2011

Schema

Column Type Description Example
country_name string Cape Verde
country_iso3 string CPV
year int64 2011
Age-standardized deaths from liver cancer in both sexes in those aged all ages per 100,000 people float64 9.991155

Usage

from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-owid-liver-cancer-death-rate")
df = ds["train"].to_pandas()
print(df.head())

Filter to one country

kenya = df[df["country_iso3"] == "KEN"]

Time-series for a single indicator

sample = df.sort_values("year")
sample.plot(x="year", y="Age-standardized deaths from liver cancer in both sexes in those aged all ages per 100,000 people")

Citation

@misc{africa_owid_liver_cancer_death_rate_2023,
  title        = {Liver Cancer Death Rate | Africa (Our World in Data)},
  author       = {Our World in Data},
  year         = {2023},
  url          = {https://ourworldindata.org/grapher/liver-cancer-death-rate},
  publisher    = {HuggingFace Datasets, repackaged by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-owid-liver-cancer-death-rate}}
}

License

Released under cc-by-4.0.

Original data © Our World in Data. When using this dataset, please cite both the original source above and the Electric Sheep Africa repackaging.

About Electric Sheep

Electric Sheep Africa is part of the Electric Sheep mission: a unified, ML-ready data layer for Africa on HuggingFace. We pull data from authoritative open sources, normalize the schemas, package as Parquet, and publish with consistent dataset cards so researchers and developers can use load_dataset() to start working in seconds.

Browse the full collection: huggingface.co/electricsheepafrica


Provenance: ingested 2026-06-08 via the Electric Sheep pipeline. Source URL: https://ourworldindata.org/grapher/liver-cancer-death-rate

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