Datasets:
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license: cc-by-4.0
multilinguality:
- monolingual
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- tabular-regression
task_ids: []
tags:
- africa
- humanitarian
- hdx
- electric-sheep-africa
- rule-of-law
pretty_name: Rule of law index, 2022 for select African countries
dataset_info:
splits:
- name: train
num_examples: 65
- name: test
num_examples: 16
Rule of law index, 2022 for select African countries
Publisher: The Charter Project · Source: OpenAfrica · License: cc-by · Updated: 2023-04-12
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.
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 |
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.
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()
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 |
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 |
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.
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.
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)}
}
Electric Sheep Africa — Africa's ML dataset infrastructure. Lagos, Nigeria.