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

Kenya Current Situation FEWS NET Acute Food Insecurity Classifications Data

Publisher: FEWS NET · Source: HDX · License: cc-by · Updated: 2026-04-03

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Abstract

Kenya Current Situation FEWS NET Acute Food Insecurity Classifications Data from 2011

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: KEN.

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)	27,694
Columns	40 (9 numeric, 23 categorical, 7 datetime)
Train split	22,155 rows
Test split	5,538 rows
Geographic scope	KEN
Publisher	FEWS NET
HDX last updated	2026-04-03

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Variables

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

Temporal — datacollectionperiod (range 158525.0–378017.0), reporting_date.

Outcome / Measurement — value (range 1.0–4.0).

Identifier / Metadata — source_organization (FEWS NET), source_document (Food Security Outlook, Kenya), geographic_unit_full_name (Northwestern Pastoral Zone, Nyiro, Samburu North, Samburu, Kenya, Northeastern Pastoral Zone, As - Habito, Mandera North, Mandera, Kenya, Lake Turkana Fishing, Katilia, Turkana East, Turkana, Kenya), geographic_unit_name (Western High Potential Zone, Central Highlands, High Potential Zone, Northeastern Pastoral Zone), fnid (KE2016C325020601, KE2016C309040109, KE2016C323030204) and 8 others.

Other — geographic_group (Eastern 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-kenya-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
source_document	object	0.0%	Food Security Outlook, Kenya
country	object	0.0%	Kenya
country_code	object	0.0%	KE
geographic_group	object	0.0%	Eastern Africa
fewsnet_region	object	0.0%	East Africa
geographic_unit_full_name	object	0.0%	Northwestern Pastoral Zone, Nyiro, Samburu North, Samburu, Kenya, Northeastern Pastoral Zone, As - Habito, Mandera North, Mandera, Kenya, Lake Turkana Fishing, Katilia, Turkana East, Turkana, Kenya
geographic_unit_name	object	0.0%	Western High Potential Zone, Central Highlands, High Potential Zone, Northeastern Pastoral Zone
unit_type	object	0.0%	fsc_admin_lhz, fsc_admin, idp_camp
fnid	object	0.0%	KE2016C325020601, KE2016C309040109, KE2016C323030204
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 – 4.0 (mean 1.6475)
description	object	0.0%
id	int64	0.0%	24356764.0 – 41441225.0 (mean 27346475.7801)
datacollectionperiod	int64	0.0%	158525.0 – 378017.0 (mean 206349.7314)
datacollection	int64	0.0%	168256.0 – 388818.0 (mean 216944.8049)
scenario	object	0.0%
geographic_unit	int64	0.0%	22892.0 – 184237.0 (mean 29320.5663)
datasourceorganization	int64	0.0%	1.0 – 1.0 (mean 1.0)
datasourcedocument	int64	0.0%	6549.0 – 6549.0 (mean 6549.0)
dataseries	int64	0.0%	6471043.0 – 6899733.0 (mean 6579163.2559)
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	4.0	1.6475	1.0
id	24356764.0	41441225.0	27346475.7801	24707063.5
datacollectionperiod	158525.0	378017.0	206349.7314	160337.0
datacollection	168256.0	388818.0	216944.8049	168975.0
geographic_unit	22892.0	184237.0	29320.5663	32300.0
datasourceorganization	1.0	1.0	1.0	1.0
datasourcedocument	6549.0	6549.0	6549.0	6549.0
dataseries	6471043.0	6899733.0	6579163.2559	6515532.0

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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_kenya_current_situation_fewsnet_ipc_classification,
  title     = {Kenya Current Situation FEWS NET Acute Food Insecurity Classifications Data},
  author    = {FEWS NET},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/kenya_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.