electricsheepafrica/africa-free-and-fair-election-1789-2021

Free and Fair Election (1789 – 2021)

Publisher: V-Dem Institute · Source: OpenAfrica · License: cc-by · Updated: 2023-01-26

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Abstract

"The variable denotes the best estimate of whether national elections were free and fair or not, based on the criteria of the classification by Lührmann et al. (2018) and the assessment by V-Dem’s experts. 1 if elections were free and fair and 0 if not."

Each row in this dataset represents tabular records. Data was last updated on OpenAfrica on 2023-01-26. Geographic scope: BENIN, BOTSWANA, CAPE-VERDE, ETHIOPIA, KENYA, NIGERIA, SENEGAL, SOUTH-AFRICA, and 4 others.

Curated into ML-ready Parquet format by Electric Sheep Africa.

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Dataset Characteristics

Domain	Humanitarian and development data
Unit of observation	Tabular records
Rows (total)	767
Columns	5 (2 numeric, 3 categorical, 0 datetime)
Train split	613 rows
Test split	153 rows
Geographic scope	BENIN, BOTSWANA, CAPE-VERDE, ETHIOPIA, KENYA, NIGERIA, SENEGAL, SOUTH-AFRICA, and 4 others
Publisher	V-Dem Institute
OpenAfrica last updated	2023-01-26

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Variables

Identifier / Metadata — unnamed_1 (range 1883.0–2021.0), unnamed_2 (range 0.0–1.0), esa_source (HDX), esa_processed (2026-04-28).

Other — free_and_fair_election_1789_2021 (South Africa, Kenya, Zambia).

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-free-and-fair-election-1789-2021")
train = ds["train"].to_pandas()
test  = ds["test"].to_pandas()

print(train.shape)
train.head()

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Schema

Column	Type	Null %	Range / Sample Values
free_and_fair_election_1789_2021	object	0.1%	South Africa, Kenya, Zambia
unnamed_1	float64	0.4%	1883.0 – 2021.0 (mean 1981.2304)
unnamed_2	float64	0.4%	0.0 – 1.0 (mean 0.6008)
esa_source	object	0.0%	HDX
esa_processed	object	0.0%	2026-04-28

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Numeric Summary

Column	Min	Max	Mean	Median
unnamed_1	1883.0	2021.0	1981.2304	1985.0
unnamed_2	0.0	1.0	0.6008	1.0

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Curation

Raw data was downloaded from OpenAfrica via the CKAN API and converted to Parquet. Column names were lowercased and standardised to snake_case. Common missing-value markers (N/A, null, none, -, unknown, no data, #N/A) were unified to NaN. 2 column(s) were cast from string to numeric or datetime based on parse-success rate (>85% threshold). The dataset was split 80/20 into train and test partitions using a fixed random seed (42) and saved as Snappy-compressed Parquet.

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Limitations

• Data originates from V-Dem Institute and has not been independently validated by ESA.
• Automated cleaning cannot correct for misreported values, definitional inconsistencies, or sampling bias in the original collection.
• This dataset spans 12 countries; geographic and methodological inconsistencies across national boundaries may affect cross-country comparability.
• Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

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Citation

@dataset{openafrica_africa_free_and_fair_election_1789_2021,
  title     = {Free and Fair Election (1789 – 2021)},
  author    = {V-Dem Institute},
  year      = {2023},
  url       = {https://open.africa/dataset/free-and-fair-election-1789-2021},
  note      = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}

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Electric Sheep Africa — Africa's ML dataset infrastructure. Lagos, Nigeria.