electricsheepafrica/africa-kingdom-of-eswatini-acute-food-insecurity-country-data

Kingdom of Eswatini: Acute Food Insecurity Country Data

Publisher: Integrated Food Security Phase Classification (IPC) · Source: HDX · License: other-pd-nr · Updated: 2023-08-19

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Abstract

The IPC Acute Food Insecurity (IPC AFI) classification provides strategically relevant information to decision makers that focuses on short-term objectives to prevent, mitigate or decrease severe food insecurity that threatens lives or livelihoods. This data has been produced by the National IPC Technical Working Groups for IPC population estimates since 2017. All national population figures are based on official country population estimates. IPC estimates are those published in country IPC reports.

There is also a global dataset.

Each row in this dataset represents country-level aggregates. Temporal coverage is indicated by the date_of_analysis, from column(s). Geographic scope: SWZ.

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

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Dataset Characteristics

Domain	Food security and nutrition
Unit of observation	Country-level aggregates
Rows (total)	15
Columns	11 (3 numeric, 5 categorical, 3 datetime)
Train split	12 rows
Test split	3 rows
Geographic scope	SWZ
Publisher	Integrated Food Security Phase Classification (IPC)
HDX last updated	2023-08-19

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Variables

Geographic — date_of_analysis, country (SWZ, #country+code), total_country_population (range 1187956.0–1187956.0), validity_period (current, projected, #period+v_ipc).

Demographic — percentage (range 0.0–1.0).

Outcome / Measurement — phase (all, 3+, 1), number (range 0.0–1188391.0).

Identifier / Metadata — esa_source (HDX), esa_processed (2026-04-04).

Other — from, to.

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-kingdom-of-eswatini-acute-food-insecurity-country-data")
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
date_of_analysis	datetime64[ns]	6.7%
country	object	0.0%	SWZ, #country+code
total_country_population	float64	6.7%	1187956.0 – 1187956.0 (mean 1187956.0)
validity_period	object	0.0%	current, projected, #period+v_ipc
from	datetime64[ns]	6.7%
to	datetime64[ns]	6.7%
phase	object	0.0%	all, 3+, 1
number	float64	6.7%	0.0 – 1188391.0 (mean 376778.0714)
percentage	float64	6.7%	0.0 – 1.0 (mean 0.3164)
esa_source	object	0.0%	HDX
esa_processed	object	0.0%	2026-04-04

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Numeric Summary

Column	Min	Max	Mean	Median
total_country_population	1187956.0	1187956.0	1187956.0	1187956.0
number	0.0	1188391.0	376778.0714	275521.5
percentage	0.0	1.0	0.3164	0.235

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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. 6 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 Integrated Food Security Phase Classification (IPC) 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_kingdom_of_eswatini_acute_food_insecurity_country_data,
  title     = {Kingdom of Eswatini: Acute Food Insecurity Country Data},
  author    = {Integrated Food Security Phase Classification (IPC)},
  year      = {2023},
  url       = {https://data.humdata.org/dataset/kingdom-of-eswatini-acute-food-insecurity-country-data},
  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.