electricsheepafrica/africa-unesco-data-for-liberia

Liberia - Education Indicators

Publisher: UNESCO · Source: HDX · License: cc-by-igo · Updated: 2026-03-02

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Abstract

Education indicators for Liberia.

Contains data from the UNESCO Institute for Statistics bulk data service covering the following categories: SDG 4 Global and Thematic (made 2026 February), Other Policy Relevant Indicators (made 2026 February), Demographic and Socio-economic (made 2026 February)

Each row in this dataset represents country-level aggregates. Data was last updated on HDX on 2026-03-02. Geographic scope: LBR.

Curated into ML-ready Parquet format by Electric Sheep Africa.

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Dataset Characteristics

Domain	Education
Unit of observation	Country-level aggregates
Rows (total)	4,821
Columns	6 (2 numeric, 4 categorical, 0 datetime)
Train split	3,856 rows
Test split	964 rows
Geographic scope	LBR
Publisher	UNESCO
HDX last updated	2026-03-02

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Variables

Geographic — country_id (LBR), year (range 1970.0–2025.0).

Outcome / Measurement — value (range 0.0–5788864.0).

Identifier / Metadata — indicator_id (CR.MOD.1.F, CR.MOD.1, CR.MOD.1.GPIA), esa_source (HDX), esa_processed (2026-04-05).

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-unesco-data-for-liberia")
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
indicator_id	object	0.0%	CR.MOD.1.F, CR.MOD.1, CR.MOD.1.GPIA
country_id	object	0.0%	LBR
year	int64	0.0%	1970.0 – 2025.0 (mean 2012.1104)
value	float64	0.0%	0.0 – 5788864.0 (mean 4609.7728)
esa_source	object	0.0%	HDX
esa_processed	object	0.0%	2026-04-05

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Numeric Summary

Column	Min	Max	Mean	Median
year	1970.0	2025.0	2012.1104	2013.0
value	0.0	5788864.0	4609.7728	11.9828

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Curation

Raw data was downloaded from HDX 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. 2 column(s) with >80% missing values were removed: magnitude, qualifier. 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 UNESCO 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{hdx_africa_unesco_data_for_liberia,
  title     = {Liberia - Education Indicators},
  author    = {UNESCO},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/unesco-data-for-liberia},
  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.