electricsheepafrica/africa-world-bank-combined-indicators-for-somalia

Somalia - Economic, Social, Environmental, Health, Education, Development and Energy

Publisher: World Bank Group · Source: HDX · License: cc-by · Updated: 2025-08-28

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Abstract

Contains data from the World Bank's data portal covering the following topics which also exist as individual datasets on HDX: Agriculture and Rural Development, Aid Effectiveness, Economy and Growth, Education, Energy and Mining, Environment, Financial Sector, Health, Infrastructure, Social Protection and Labor, Poverty, Private Sector, Public Sector, Science and Technology, Social Development, Urban Development, Gender, Millenium development goals, Climate Change, External Debt, Trade.

Each row in this dataset represents country-level aggregates. Data was last updated on HDX on 2025-08-28. Geographic scope: SOM.

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

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Dataset Characteristics

Domain	Public health
Unit of observation	Country-level aggregates
Rows (total)	38,580
Columns	8 (2 numeric, 6 categorical, 0 datetime)
Train split	30,864 rows
Test split	7,716 rows
Geographic scope	SOM
Publisher	World Bank Group
HDX last updated	2025-08-28

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Variables

Geographic — country_name (Somalia, #country+name), country_iso3 (SOM, #country+code), year (range 1960.0–2024.0).

Outcome / Measurement — value (range -184947432305300.0–525412601720700.0).

Identifier / Metadata — indicator_name (Domestic credit to private sector (% of GDP), Population ages 65 and above, female, Age population, age 03, male), indicator_code (SP.POP.6569.MA.5Y, SP.POP.3539.FE.5Y, SP.POP.1564.MA.ZS), esa_source (HDX), esa_processed (2026-04-08).

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-world-bank-combined-indicators-for-somalia")
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
country_name	object	0.0%	Somalia, #country+name
country_iso3	object	0.0%	SOM, #country+code
year	float64	0.0%	1960.0 – 2024.0 (mean 1997.4031)
indicator_name	object	0.0%	Domestic credit to private sector (% of GDP), Population ages 65 and above, female, Age population, age 03, male
indicator_code	object	0.0%	SP.POP.6569.MA.5Y, SP.POP.3539.FE.5Y, SP.POP.1564.MA.ZS
value	float64	0.0%	-184947432305300.0 – 525412601720700.0 (mean 1513212414391.4263)
esa_source	object	0.0%	HDX
esa_processed	object	0.0%	2026-04-08

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Numeric Summary

Column	Min	Max	Mean	Median
year	1960.0	2024.0	1997.4031	2000.0
value	-184947432305300.0	525412601720700.0	1513212414391.4263	49.513

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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. 11,010 exact duplicate rows were removed. 2 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.

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Limitations

• Data originates from World Bank Group 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_world_bank_combined_indicators_for_somalia,
  title     = {Somalia - Economic, Social, Environmental, Health, Education, Development and Energy},
  author    = {World Bank Group},
  year      = {2025},
  url       = {https://data.humdata.org/dataset/world-bank-combined-indicators-for-somalia},
  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.