electricsheepasia/asia-education-eie-keyfigures-since2021

Education in Emergencies (EiE) Key Figures

Publisher: Global Education Cluster · Source: HDX · License: cc-by · Updated: 2025-05-05

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Abstract

The data shows key figures on Education in Emergencies (EiE) at country level and as reported by country Clusters/Sectors/Working Groups since 2021

Each row in this dataset represents country-level aggregates. Data was last updated on HDX on 2025-05-05. Geographic scope: AFG, BGD, BFA, BDI, CMR, CAF, TCD, COL, and 22 others.

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)	30
Columns	4 (1 numeric, 3 categorical, 0 datetime)
Train split	24 rows
Test split	6 rows
Geographic scope	AFG, BGD, BFA, BDI, CMR, CAF, TCD, COL, and 22 others
Publisher	Global Education Cluster
HDX last updated	2025-05-05

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Variables

Geographic — year (range 2021.0–2030.0), country (Afghanistan, Bangladesh, Burkina Faso).

Identifier / Metadata — esa_source (HDX), esa_processed (2026-05-05).

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/asia-education-eie-keyfigures-since2021")
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
year	float64	66.7%	2021.0 – 2030.0 (mean 2025.5)
country	object	0.0%	Afghanistan, Bangladesh, Burkina Faso
esa_source	object	0.0%	HDX
esa_processed	object	0.0%	2026-05-05

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Numeric Summary

Column	Min	Max	Mean	Median
year	2021.0	2030.0	2025.5	2025.5

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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. 1 column(s) with >80% missing values were removed: unnamed_1. 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 Global Education Cluster and has not been independently validated by ESA.
• Automated cleaning cannot correct for misreported values, definitional inconsistencies, or sampling bias in the original collection.
• The following columns have >20% missing values and should be treated with caution in modelling: year.
• This dataset spans 30 countries; geographic and methodological inconsistencies across national boundaries may affect cross-country comparability.
• Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

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Citation

@dataset{hdx_asia_education_eie_keyfigures_since2021,
  title     = {Education in Emergencies (EiE) Key Figures},
  author    = {Global Education Cluster},
  year      = {2025},
  url       = {https://data.humdata.org/dataset/eie_keyfigures_since2021},
  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.