Datasetpublic

electricsheepafrica/africa-malawi-eastern-and-southern-africa-risk-analysis-f9fcb526

Eastern and Southern Africa Risk Analysis | Africa (Malawi official open data) 24 rows - 1 Africa country - 2020 - Repackaged by Electric Sheep Africa TL;DR This dataset packages one official XLSX resource from Malawi 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: Eastern and Southern Africa Risk Analysis Publisher:… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/africa-malawi-eastern-and-southern-africa-risk-analysis-f9fcb526.

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Dataset Card

Eastern and Southern Africa Risk Analysis | Africa (Malawi official open data)

24 rows - 1 Africa country - 2020 - Repackaged by Electric Sheep Africa

TL;DR

This dataset packages one official XLSX resource from Malawi 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: Eastern and Southern Africa Risk Analysis
—Publisher: UNICEF Eastern and Southern Africa Regional Office (ESARO) (inactive)
—Resource: UNICEF ESARO RISK ANALYSIS - Oct 2020.xlsx
—Format: XLSX
—License: CC BY 4.0
—Packaging mode: tabular_resource

Geographic coverage

1 Africa country:

Country	Rows	First year	Last year	Name
`MWI`	24	2020	2020	`Malawi`

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.	`1d4d976f-c1e0-4c1f-bbd7-164d6ca11426:risk-analysis:0`
`country_iso3`	`category`	ISO3 country code.	`MWI`
`country_name`	`category`	Country name.	`Malawi`
`source_sheet`	`string`	Workbook sheet name, when the source is a spreadsheet.	`Risk analysis`
`year`	`Int64`	Observation year.	`2020`
`country`	`string`	Source column.	`Somalia`
`iso3`	`string`	Source column.	`SOM`
`inform_risk`	`float64`	Source column.	`6.2`
`risk_class`	`string`	Source column.	`High`
`rank`	`float64`	Source column.	`5.0`
`earthquake`	`float64`	Source column.	`1.6`
`flood`	`float64`	Source column.	`7.5`
`tsunami`	`float64`	Source column.	`8.1`
`tropical_cyclone`	`float64`	Source column.	`1.0`
`drought`	`float64`	Source column.	`10.0`
`epidemic`	`float64`	Source column.	`6.3`
`natural`	`float64`	Source column.	`6.9`
`projected_conflict_risk`	`float64`	Source column.	`10.0`
`current_highly_violent_conflict_intensity`	`float64`	Source column.	`10.0`
`human`	`float64`	Source column.	`10.0`
`hazard_exposure`	`float64`	Source column.	`8.9`
`development_deprivation`	`float64`	Source column.	`0.6`
`inequality`	`string`	Source column.	`2`
`economic_dependency`	`float64`	Source column.	`0.3`
`socio_economic_vulnerability`	`float64`	Source column.	`0.9`
`uprooted_people`	`float64`	Source column.	`7.1`
`health_conditions`	`float64`	Source column.	`0.1`
`children_u5`	`float64`	Source column.	`0.3`
`recent_shocks`	`float64`	Source column.	`0.0`
`food_security`	`float64`	Source column.	`1.2`
`other_vulnerable_groups`	`float64`	Source column.	`0.4`
`vulnerable_groups`	`float64`	Source column.	`4.6`
`vulnerability`	`float64`	Source column.	`3.0`
`drr`	`string`	Source column.	`x`
`governance`	`float64`	Source column.	`9.3`
`institutional`	`float64`	Source column.	`9.3`
`communication`	`float64`	Source column.	`8.0`
`physical_infrastructure`	`float64`	Source column.	`7.8`
`access_to_health_care`	`float64`	Source column.	`9.6`
`infrastructure`	`float64`	Source column.	`8.5`
`lack_of_coping_capacity`	`float64`	Source column.	`8.9`
`lack_of_reliability`	`float64`	Source column.	``
`number_of_missing_indicators`	`float64`	Source column.	`12.0`
`of_missing_indicators`	`float64`	Source column.	`0.2352941176470588`
`countries_in_hvc`	`string`	Source column.	`YES`
`recentness_data_average_years`	`float64`	Source column.	``
`source_period_start_year`	`Int64`	First year inferred from source resource metadata.	`2020`
`source_period_end_year`	`Int64`	Last year inferred from source resource metadata.	`2020`
`source_period_label`	`category`	Human-readable period inferred from source resource metadata.	`2020`
`source_provider`	`category`	Publishing organization.	`UNICEF Eastern and Southern Africa Regional Office (ESARO) (inactive)`
`source_dataset`	`category`	Source package title.	`Eastern and Southern Africa Risk Analysis`
`source_resource`	`category`	Source resource title.	`UNICEF ESARO RISK ANALYSIS - Oct 2020.xlsx`
`source_package_id`	`category`	CKAN package UUID.	`271ef920-e9dd-43c5-9ad6-e6553d29347c`
`source_resource_id`	`category`	CKAN resource UUID.	`1d4d976f-c1e0-4c1f-bbd7-164d6ca11426`
`source_url`	`category`	Original source resource URL.	`https://data.humdata.org/dataset/271ef920-e9dd-43c5-9ad6-e6553d29347c/re`
`license_id`	`category`	Source license identifier.	`cc-by`
`retrieved_at`	`category`	UTC retrieval timestamp.	`2026-08-15T11:40:10Z`

Usage

python

from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-malawi-eastern-and-southern-africa-risk-analysis-f9fcb526")
df = ds["train"].to_pandas()
print(df.head())

Filter to one country

python

sample_country = df[df["country_iso3"] == "MWI"]

Work with indicators

python

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

bibtex

@misc{electric_sheep_africa_africa_malawi_eastern_and_southern_africa_risk_analysis_f9fcb526_2020,
  title        = {Eastern and Southern Africa Risk Analysis | Africa (Malawi official open data)},
  author       = {UNICEF Eastern and Southern Africa Regional Office (ESARO) (inactive)},
  year         = {2020},
  url          = {https://data.humdata.org/dataset/eastern-and-southern-africa-risk-analysis},
  publisher    = {HuggingFace Datasets, repackaged by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-malawi-eastern-and-southern-africa-risk-analysis-f9fcb526}}
}

License

Released under CC BY 4.0.

Original data (c) UNICEF Eastern and Southern Africa Regional Office (ESARO) (inactive). 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

Provenance: ingested 2026-08-15 via the Electric Sheep pipeline. Source URL: https://data.humdata.org/dataset/271ef920-e9dd-43c5-9ad6-e6553d29347c/resource/1d4d976f-c1e0-4c1f-bbd7-164d6ca11426/download/unicef-esaro-risk-analysis-oct-2020.xlsx