electricsheepafrica/africa-sudan-current-situation-fewsnet-ipc-classification

Sudan Current Situation FEWS NET Acute Food Insecurity Classifications Data

Publisher: FEWS NET · Source: HDX · License: cc-by · Updated: 2026-04-01

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Abstract

Sudan Current Situation FEWS NET Acute Food Insecurity Classifications Data from 2018

Each row in this dataset represents first-level administrative unit observations. Temporal coverage is indicated by the projection_start, projection_end column(s). Geographic scope: SDN.

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

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Dataset Characteristics

Domain	Food security and nutrition
Unit of observation	First-level administrative unit observations
Rows (total)	7,987
Columns	40 (9 numeric, 23 categorical, 7 datetime)
Train split	6,389 rows
Test split	1,597 rows
Geographic scope	SDN
Publisher	FEWS NET
HDX last updated	2026-04-01

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Variables

Geographic — country (Sudan), country_code (SD), fewsnet_region (East Africa), unit_type (fsc_admin_lhz, fsc_admin, idp_camp), specialization_type and 2 others.

Temporal — datacollectionperiod (range 158575.0–378325.0), reporting_date.

Outcome / Measurement — value (range 1.0–5.0).

Identifier / Metadata — source_organization (FEWS NET, Sudan), source_document (Food Security Outlook, Sudan), geographic_unit_full_name (Western Agropastoral Millet and Groundnuts, Bahr Al Arab, East Darfur, Sudan, Eastern Pastoral, Khartoum, Khartoum, Sudan, Western Agropastoral Millet and Groundnuts, Rehaid Albirdi, South Darfur, Sudan), geographic_unit_name (Southeast Rainfed Semi-mechanized Agriculture, Rainfed Sorghum Belt, Western Agropastoral Millet), fnid (SD2021C31503202, SD2021C31601103, SD2021C30100102) and 8 others.

Other — geographic_group (Northern Africa), classification_scale, is_allowing_for_assistance, projection_start, projection_end and 12 others.

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-sudan-current-situation-fewsnet-ipc-classification")
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
source_organization	object	0.0%	FEWS NET, Sudan
source_document	object	0.0%	Food Security Outlook, Sudan
country	object	0.0%	Sudan
country_code	object	0.0%	SD
geographic_group	object	0.0%	Northern Africa
fewsnet_region	object	0.0%	East Africa
geographic_unit_full_name	object	0.0%	Western Agropastoral Millet and Groundnuts, Bahr Al Arab, East Darfur, Sudan, Eastern Pastoral, Khartoum, Khartoum, Sudan, Western Agropastoral Millet and Groundnuts, Rehaid Albirdi, South Darfur, Sudan
geographic_unit_name	object	0.0%	Southeast Rainfed Semi-mechanized Agriculture, Rainfed Sorghum Belt, Western Agropastoral Millet
unit_type	object	0.0%	fsc_admin_lhz, fsc_admin, idp_camp
fnid	object	0.0%	SD2021C31503202, SD2021C31601103, SD2021C30100102
classification_scale	object	0.0%
scenario_name	object	0.0%
preference_rating	int64	0.0%	90.0 – 90.0 (mean 90.0)
is_allowing_for_assistance	bool	0.0%
projection_start	datetime64[ns]	0.0%
projection_end	datetime64[ns]	0.0%
status	object	0.0%
value	float64	0.0%	1.0 – 5.0 (mean 2.3121)
description	object	0.0%
id	int64	0.0%	24365403.0 – 41485444.0 (mean 30182758.6175)
datacollectionperiod	int64	0.0%	158575.0 – 378325.0 (mean 252705.6516)
datacollection	int64	0.0%	168298.0 – 389081.0 (mean 265095.2588)
scenario	object	0.0%
geographic_unit	int64	0.0%	25788.0 – 267786.0 (mean 145775.3715)
datasourceorganization	int64	0.0%	2034.0 – 2034.0 (mean 2034.0)
datasourcedocument	int64	0.0%	6577.0 – 6577.0 (mean 6577.0)
dataseries	int64	0.0%	6480058.0 – 7850082.0 (mean 6789040.6119)
dataseries_name	object	0.0%
specialization_type	object	0.0%
dataseries_specialization_type	object	0.0%
data_usage_policy	object	0.0%
created	datetime64[ns]	0.0%
modified	datetime64[ns]	0.0%
status_changed	datetime64[ns]	0.0%
collection_status	object	0.0%
collection_status_changed	datetime64[ns]	0.0%
collection_schedule	object	0.0%
reporting_date	datetime64[ns]	0.0%
esa_source	object	0.0%
esa_processed	object	0.0%

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Numeric Summary

Column	Min	Max	Mean	Median
preference_rating	90.0	90.0	90.0	90.0
value	1.0	5.0	2.3121	2.0
id	24365403.0	41485444.0	30182758.6175	27078721.0
datacollectionperiod	158575.0	378325.0	252705.6516	252711.0
datacollection	168298.0	389081.0	265095.2588	267905.0
geographic_unit	25788.0	267786.0	145775.3715	201970.0
datasourceorganization	2034.0	2034.0	2034.0	2034.0
datasourcedocument	6577.0	6577.0	6577.0	6577.0
dataseries	6480058.0	7850082.0	6789040.6119	6575486.0

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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. 3 column(s) with >80% missing values were removed: pct_phase3, pct_phase4, pct_phase5. 7 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 FEWS NET 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_sudan_current_situation_fewsnet_ipc_classification,
  title     = {Sudan Current Situation FEWS NET Acute Food Insecurity Classifications Data},
  author    = {FEWS NET},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/sudan_current_situation_fewsnet_ipc_classification},
  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.