electricsheepafrica/africa-rule-of-law-index-2022-for-select-african-countries

Rule of law index, 2022 for select African countries

Publisher: The Charter Project · Source: OpenAfrica · License: cc-by · Updated: 2023-04-12

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Abstract

The 2022 WJP Rule of Law Index evaluates 140 countries and jurisdictions around the world. For the fifth year in a row, the rule of law has declined in most countries.

Each row in this dataset represents tabular records. Data was last updated on OpenAfrica on 2023-04-12. Geographic scope: Africa (multiple countries).

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)	82
Columns	57 (53 numeric, 4 categorical, 0 datetime)
Train split	65 rows
Test split	16 rows
Geographic scope	Africa (multiple countries)
Publisher	The Charter Project
OpenAfrica last updated	2023-04-12

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Variables

Geographic — rule_of_law_index_2012_2022 (Botswana, Ethiopia, Kenya).

Identifier / Metadata — unnamed_1 (2021, 2022, 2019), unnamed_2 (range 0.38–0.7), unnamed_3 (range 0.39–0.76), unnamed_4 (range 0.31–0.79), unnamed_5 (range 0.22–0.64) and 51 others.

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-rule-of-law-index-2022-for-select-african-countries")
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
rule_of_law_index_2012_2022	object	2.4%	Botswana, Ethiopia, Kenya
unnamed_1	object	3.7%	2021, 2022, 2019
unnamed_2	float64	4.9%	0.38 – 0.7 (mean 0.4909)
unnamed_3	float64	4.9%	0.39 – 0.76 (mean 0.5722)
unnamed_4	float64	4.9%	0.31 – 0.79 (mean 0.5068)
unnamed_5	float64	4.9%	0.22 – 0.64 (mean 0.4746)
unnamed_6	float64	4.9%	0.31 – 0.7 (mean 0.4787)
unnamed_7	float64	4.9%	0.22 – 0.78 (mean 0.525)
unnamed_8	float64	4.9%	0.25 – 0.83 (mean 0.5764)
unnamed_9	float64	4.9%	0.33 – 0.73 (mean 0.5223)
unnamed_10	float64	4.9%	0.26 – 0.73 (mean 0.4245)
unnamed_11	float64	4.9%	0.31 – 0.8 (mean 0.5174)
unnamed_12	float64	4.9%	0.21 – 0.77 (mean 0.4612)
unnamed_13	float64	4.9%	0.05 – 0.75 (mean 0.3278)
unnamed_14	float64	4.9%	0.25 – 0.75 (mean 0.4321)
unnamed_15	float64	4.9%	0.19 – 0.67 (mean 0.335)
unnamed_16	float64	4.9%	0.14 – 0.71 (mean 0.4476)
unnamed_17	float64	4.9%	0.2 – 0.86 (mean 0.5296)
unnamed_18	float64	4.9%	0.2 – 0.75 (mean 0.4709)
unnamed_19	float64	4.9%	0.27 – 0.67 (mean 0.4454)
unnamed_20	float64	4.9%	0.31 – 0.73 (mean 0.5308)
unnamed_21	float64	4.9%	0.19 – 0.66 (mean 0.4214)
unnamed_22	float64	4.9%
unnamed_23	float64	4.9%
unnamed_24	float64	4.9%
unnamed_25	float64	4.9%
unnamed_26	float64	4.9%
unnamed_27	float64	4.9%
unnamed_28	float64	4.9%
unnamed_29	float64	4.9%
unnamed_30	float64	4.9%
unnamed_31	float64	4.9%
unnamed_32	float64	4.9%
unnamed_33	float64	4.9%
unnamed_34	float64	4.9%
unnamed_35	float64	4.9%
unnamed_36	float64	4.9%
unnamed_37	float64	4.9%
unnamed_38	float64	4.9%
unnamed_39	float64	4.9%
unnamed_40	float64	4.9%
unnamed_41	float64	4.9%
unnamed_42	float64	4.9%
unnamed_43	float64	4.9%
unnamed_44	float64	4.9%
unnamed_45	float64	4.9%
unnamed_46	float64	4.9%
unnamed_47	float64	4.9%
unnamed_48	float64	4.9%
unnamed_49	float64	4.9%
unnamed_50	float64	4.9%
unnamed_51	float64	4.9%
unnamed_52	float64	4.9%
unnamed_53	float64	4.9%
unnamed_54	float64	4.9%
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_2	0.38	0.7	0.4909	0.47
unnamed_3	0.39	0.76	0.5722	0.57
unnamed_4	0.31	0.79	0.5068	0.51
unnamed_5	0.22	0.64	0.4746	0.47
unnamed_6	0.31	0.7	0.4787	0.49
unnamed_7	0.22	0.78	0.525	0.525
unnamed_8	0.25	0.83	0.5764	0.565
unnamed_9	0.33	0.73	0.5223	0.52
unnamed_10	0.26	0.73	0.4245	0.415
unnamed_11	0.31	0.8	0.5174	0.48
unnamed_12	0.21	0.77	0.4612	0.45
unnamed_13	0.05	0.75	0.3278	0.34
unnamed_14	0.25	0.75	0.4321	0.42
unnamed_15	0.19	0.67	0.335	0.305
unnamed_16	0.14	0.71	0.4476	0.455

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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. 53 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 The Charter Project and has not been independently validated by ESA.
• Automated cleaning cannot correct for misreported values, definitional inconsistencies, or sampling bias in the original collection.
• Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

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Citation

@dataset{openafrica_africa_rule_of_law_index_2022_for_select_african_countries,
  title     = {Rule of law index, 2022 for select African countries},
  author    = {The Charter Project},
  year      = {2023},
  url       = {https://open.africa/dataset/rule-of-law-index-2022-for-select-african-countries},
  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.