electricsheepafrica/africa-eswatini-eswatini-risk-assessment-indicators-6ae46fcc

Eswatini - Risk Assessment Indicators | Africa (Eswatini official open data)

55 rows - 1 Africa country - not-applicable - Repackaged by Electric Sheep Africa

TL;DR

This dataset packages one official CSV resource from Eswatini as ML-ready Parquet. The source file is the provenance boundary; all usable indicators or tabular columns from the resource stay together in this repo.

About the source

• Source: Eswatini - Risk Assessment Indicators
• Publisher: HeiGIT (Heidelberg Institute for Geoinformation Technology)
• Resource: SWZ_ADM2_vulnerability.csv
• Format: CSV
• License: CC BY-SA
• Packaging mode: tabular_resource

Geographic coverage

1 Africa country:

Country	Rows	First year	Last year	Name
SWZ	55	n/a	n/a	Eswatini

Indicators or Resource Contents

• This source file is packaged as a normalized tabular resource.

Schema

Column	Type	Description	Example
source_record_id	string	Stable row identifier for tabular resources.	52028c31-c9b4-4098-b367-657fd7a2bc3c:0
country_iso3	category	ISO3 country code.	SWZ
country_name	category	Country name.	Eswatini
adm2_pcode	string	Source column.	SZ101
total_pop	int64	Source column.	13371
female_pop	int64	Source column.	6788
children_u5	int64	Source column.	1396
female_u5	int64	Source column.	690
elderly	int64	Source column.	586
pop_u15	int64	Source column.	4185
female_u15	int64	Source column.	2071
wra_pop	int64	Source column.	3696
dependency_ratio	float64	Source column.	55.48
total_pop_rural	int64	Source column.	13371
female_pop_rural	int64	Source column.	6788
children_u5_rural	int64	Source column.	1396
female_u5_rural	int64	Source column.	690
elderly_rural	int64	Source column.	586
pop_u15_rural	int64	Source column.	4185
female_u15_rural	int64	Source column.	2071
wra_pop_rural	int64	Source column.	3696
dependency_ratio_rural	float64	Source column.	55.48
rural_pop_perc	float64	Source column.	100.0
adm_pcode	string	Source column.	SZ101
source_period_start_year	Int64	First year inferred from source resource metadata.	``
source_period_end_year	Int64	Last year inferred from source resource metadata.	``
source_period_label	string	Human-readable period inferred from source resource metadata.	``
source_provider	category	Publishing organization.	HeiGIT (Heidelberg Institute for Geoinformation Technology)
source_dataset	category	Source package title.	Eswatini - Risk Assessment Indicators
source_resource	category	Source resource title.	SWZ_ADM2_vulnerability.csv
source_package_id	category	CKAN package UUID.	385a956c-b5c6-466a-934f-f7ccda9ed610
source_resource_id	category	CKAN resource UUID.	52028c31-c9b4-4098-b367-657fd7a2bc3c
source_url	category	Original source resource URL.	https://hot.storage.heigit.org/heigit-hdx-public/risk_assessment_inputs/
license_id	category	Source license identifier.	cc-by-sa
retrieved_at	category	UTC retrieval timestamp.	2026-08-30T05:35:43Z

Usage

from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-eswatini-eswatini-risk-assessment-indicators-6ae46fcc")
df = ds["train"].to_pandas()
print(df.head())

Filter to one country

sample_country = df[df["country_iso3"] == "SWZ"]

Work with indicators

if "indicator_id" in df.columns:
    print(df["indicator_id"].value_counts().head())
    sample = df.sort_values([c for c in ["indicator_id", "year"] if c in df.columns])

Citation

@misc{electric_sheep_africa_africa_eswatini_eswatini_risk_assessment_indicators_6ae46fcc_2026,
  title        = {Eswatini - Risk Assessment Indicators | Africa (Eswatini official open data)},
  author       = {HeiGIT (Heidelberg Institute for Geoinformation Technology)},
  year         = {2026},
  url          = {https://data.humdata.org/dataset/eswatini---risk-assessment-indicators},
  publisher    = {HuggingFace Datasets, repackaged by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-eswatini-eswatini-risk-assessment-indicators-6ae46fcc}}
}

License

Released under CC BY-SA.

Original data (c) HeiGIT (Heidelberg Institute for Geoinformation Technology). 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 Hugging Face. 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

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Provenance: ingested 2026-08-30 via the Electric Sheep pipeline. Source URL: https://hot.storage.heigit.org/heigit-hdx-public/risk_assessment_inputs/swz/SWZ_ADM2_vulnerability.csv