--- 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-β€”-education-statistics - education-statistics - world-bank - worldbank - development-indicators - time-series pretty_name: "Initial household funding per secondary student as a percentage of GDP per capita | Africa (World Bank β€” Education Statistics)" --- # Initial household funding per secondary student as a percentage of GDP per capita | Africa (World Bank β€” Education Statistics) 🌍 **84 observations** Β· **16 Africa countries** Β· **1999–2016** Β· *Repackaged by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)* ![rows](https://img.shields.io/badge/rows-84-blue) ![countries](https://img.shields.io/badge/countries-16-green) ![years](https://img.shields.io/badge/years-1999–2016-orange) ![indicators](https://img.shields.io/badge/indicators-1-purple) ![license](https://img.shields.io/badge/license-cc-by-4.0-lightgrey) ## TL;DR This dataset contains **84 observations** of `Education Statistics` data across **16 Africa countries**, spanning **1999–2016**, covering **1 distinct indicators**. ## About the source - **Source:** [World Bank β€” Education Statistics](https://databank.worldbank.org/) - **Publisher:** World Bank - **License:** [cc-by-4.0](https://creativecommons.org/licenses/by/4.0/) - **Topic:** Education Statistics ## Geographic coverage 16 Africa countries Β· top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `CPV` | 10 | 2003 | 2016 | | `MAR` | 9 | 2002 | 2010 | | `CMR` | 8 | 2004 | 2012 | | `BDI` | 8 | 2002 | 2010 | | `GHA` | 8 | 2006 | 2014 | | `MWI` | 8 | 2000 | 2008 | | `MLI` | 8 | 2003 | 2010 | | `BFA` | 8 | 1999 | 2007 | | `TCD` | 4 | 2009 | 2012 | | `GMB` | 3 | 2008 | 2010 | | `BEN` | 3 | 2011 | 2014 | | `CIV` | 2 | 2014 | 2015 | | `ETH` | 2 | 2011 | 2012 | | `GIN` | 1 | 2012 | 2012 | | `LBY` | 1 | 2006 | 2006 | | ... | _1 more countries_ | | | ## Indicators (sample) - `UIS.XUNIT.GDPCAP.23.FSHH` β€” Initial household funding per secondary student as a percentage of GDP per capita ## Schema | Column | Type | Description | Example | |--------|------|-------------|---------| | `indicator_id` | `string` | β€” | `UIS.XUNIT.GDPCAP.23.FSHH` | | `indicator_name` | `string` | β€” | `Initial household funding per seconda…` | | `country_iso3` | `string` | β€” | `BEN` | | `country_name` | `string` | β€” | `Benin` | | `year` | `int64` | β€” | `2014` | | `value` | `float64` | β€” | `13.95935` | ## Usage ```python from datasets import load_dataset ds = load_dataset("electricsheepafrica/africa-worldbank-initial-household-funding-per-secondary-student-as-a-percentage-of-gdp-per-capi") df = ds["train"].to_pandas() print(df.head()) ``` ### Filter to one country ```python kenya = df[df["country_iso3"] == "KEN"] ``` ### Time-series for a single indicator ```python sample = (df[df["indicator_id"] == "UIS.XUNIT.GDPCAP.23.FSHH"] .sort_values("year")) sample.plot(x="year", y="value", title="UIS.XUNIT.GDPCAP.23.FSHH") ``` ### Pivot to country Γ— year matrix ```python matrix = (df[df["indicator_id"] == "UIS.XUNIT.GDPCAP.23.FSHH"] .pivot_table(index="year", columns="country_iso3", values="value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{africa_worldbank_initial_household_funding_per_secondary_student_as_a_percentage_of_gdp_per_capi_2016, title = {Initial household funding per secondary student as a percentage of GDP per capita | Africa (World Bank β€” Education Statistics)}, author = {World Bank}, year = {2016}, url = {https://databank.worldbank.org/}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Africa}, howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-worldbank-initial-household-funding-per-secondary-student-as-a-percentage-of-gdp-per-capi}} } ``` ## License Released under [cc-by-4.0](https://creativecommons.org/licenses/by/4.0/). Original data Β© 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](https://huggingface.co/electricsheepafrica) --- _Provenance: ingested 2026-06-19 via the Electric Sheep pipeline. Source URL: https://databank.worldbank.org/_