README.md

---
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](https://open.africa/dataset/rule-of-law-index-2022-for-select-african-countries) · **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](https://huggingface.co/electricsheepafrica).*

---

## 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

```python
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](https://open.africa/dataset/rule-of-law-index-2022-for-select-african-countries) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@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](https://huggingface.co/electricsheepafrica) — Africa's ML dataset infrastructure. Lagos, Nigeria.*