electricsheepafrica/africa-climate-somalia

HDX HAPI Data for Somalia

Publisher: HDX Humanitarian API Data · Source: HDX · License: hdx-other · Updated: 2026-02-18

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Abstract

This dataset contains data obtained from the HDX Humanitarian API (HDX HAPI), which provides standardized humanitarian indicators designed for seamless interoperability from multiple sources. The data facilitates automated workflows and visualizations to support humanitarian decision making. For more information, please see the HDX HAPI landing page and documentation.

Each row in this dataset represents subnational administrative unit observations. Temporal coverage is indicated by the reference_period_start, reference_period_end column(s). Geographic scope: SOM.

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

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Dataset Characteristics

Domain	Food security and nutrition
Unit of observation	Subnational administrative unit observations
Rows (total)	1,052
Columns	18 (3 numeric, 11 categorical, 2 datetime)
Train split	841 rows
Test split	210 rows
Geographic scope	SOM
Publisher	HDX Humanitarian API Data
HDX last updated	2026-02-18

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Variables

Geographic — location_code (SOM), provider_admin1_name (Gedo, Shabelle Hoose, Bari), provider_admin2_name (Doolow, Belet Weyne, Kismaayo), admin1_code (SO26, SO23, SO16), admin1_name (Gedo, Lower Shabelle, Bari) and 5 others.

Temporal — reference_period_start, reference_period_end.

Identifier / Metadata — esa_source (HDX), esa_processed.

Other — has_hrp, in_gho, operation (Somalia displacement, Somalia - Countrywide Monitoring), reporting_round (range 1.0–15.0).

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-climate-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
location_code	object	0.0%	SOM
has_hrp	bool	0.0%
in_gho	bool	0.0%
provider_admin1_name	object	1.8%	Gedo, Shabelle Hoose, Bari
provider_admin2_name	object	25.6%	Doolow, Belet Weyne, Kismaayo
admin1_code	object	1.8%	SO26, SO23, SO16
admin1_name	object	1.8%	Gedo, Lower Shabelle, Bari
admin2_code	object	25.6%	SO2605, SO2001, SO2801
admin2_name	object	25.6%	Doolow, Belet Weyne, Kismaayo
admin_level	int64	0.0%	0.0 – 2.0 (mean 1.7262)
operation	object	0.0%	Somalia displacement, Somalia - Countrywide Monitoring
assessment_type	object	0.0%	BA
population	int64	0.0%	5.0 – 4574256.0 (mean 66205.8279)
reporting_round	int64	0.0%	1.0 – 15.0 (mean 8.1873)
reference_period_start	datetime64[ns]	0.0%
reference_period_end	datetime64[ns]	0.0%
esa_source	object	0.0%	HDX
esa_processed	object	0.0%

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Numeric Summary

Column	Min	Max	Mean	Median
admin_level	0.0	2.0	1.7262	2.0
population	5.0	4574256.0	66205.8279	12199.5
reporting_round	1.0	15.0	8.1873	9.0

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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) 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 HDX Humanitarian API Data 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: provider_admin2_name, admin2_code, admin2_name.
• Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

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Citation

@dataset{hdx_africa_climate_somalia,
  title     = {HDX HAPI Data for Somalia},
  author    = {HDX Humanitarian API Data},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/hdx-hapi-som},
  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.