license: cc-by-4.0
language:
- en
task_categories:
- tabular-classification
- tabular-regression
- time-series-forecasting
multilinguality: monolingual
size_categories:
- n<1K
tags:
- tabular
- africa
- world-bank-—-gender-statistics
- gender-statistics
- world-bank
- worldbank
- development-indicators
- time-series
pretty_name: >-
WBL: Legal Framework, Assets, Score (scale 0-100) | Africa (World Bank —
Gender Statistics)
WBL: Legal Framework, Assets, Score (scale 0-100) | Africa (World Bank — Gender Statistics)
🌍 54 observations · 54 Africa countries · 2025–2025 · Repackaged by Electric Sheep Africa
TL;DR
This dataset contains 54 observations of Gender Statistics data across 54 Africa countries, spanning 2025–2025, covering 1 distinct indicators.
About the source
- Source: World Bank — Gender Statistics
- Publisher: World Bank
- License: cc-by-4.0
- Topic: Gender Statistics
Geographic coverage
54 Africa countries · top rows shown below, sorted by row count:
| Country | Rows | First year | Last year |
|---|---|---|---|
AGO |
1 | 2025 | 2025 |
BDI |
1 | 2025 | 2025 |
BEN |
1 | 2025 | 2025 |
BFA |
1 | 2025 | 2025 |
BWA |
1 | 2025 | 2025 |
CAF |
1 | 2025 | 2025 |
CIV |
1 | 2025 | 2025 |
CMR |
1 | 2025 | 2025 |
COD |
1 | 2025 | 2025 |
COG |
1 | 2025 | 2025 |
COM |
1 | 2025 | 2025 |
CPV |
1 | 2025 | 2025 |
DJI |
1 | 2025 | 2025 |
DZA |
1 | 2025 | 2025 |
EGY |
1 | 2025 | 2025 |
| ... | 39 more countries |
Indicators (sample)
GD_WBL_AST_LAW_T— WBL: Legal Framework, Assets, Score (scale 0-100)
Schema
| Column | Type | Description | Example |
|---|---|---|---|
indicator_id |
string |
— | GD_WBL_AST_LAW_T |
indicator_name |
string |
— | WBL: Legal Framework, Assets, Score (… |
country_iso3 |
string |
— | DZA |
country_name |
string |
— | Algeria |
year |
int64 |
— | 2025 |
value |
float64 |
— | 25.0 |
Usage
from datasets import load_dataset
ds = load_dataset("electricsheepafrica/africa-worldbank-wbl-legal-framework-assets-score-scale-0-100-gd-wbl-ast-law-t")
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[df["indicator_id"] == "GD_WBL_AST_LAW_T"]
.sort_values("year"))
sample.plot(x="year", y="value", title="GD_WBL_AST_LAW_T")
Pivot to country × year matrix
matrix = (df[df["indicator_id"] == "GD_WBL_AST_LAW_T"]
.pivot_table(index="year", columns="country_iso3", values="value"))
print(matrix.tail())
Citation
@misc{africa_worldbank_wbl_legal_framework_assets_score_scale_0_100_gd_wbl_ast_law_t_2025,
title = {WBL: Legal Framework, Assets, Score (scale 0-100) | Africa (World Bank — Gender Statistics)},
author = {World Bank},
year = {2025},
url = {https://databank.worldbank.org/},
publisher = {HuggingFace Datasets, repackaged by Electric Sheep Africa},
howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-worldbank-wbl-legal-framework-assets-score-scale-0-100-gd-wbl-ast-law-t}}
}
License
Released under cc-by-4.0.
Original data © World Bank. 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 2026-06-17 via the Electric Sheep pipeline. Source URL: https://databank.worldbank.org/