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

Uganda Current Situation FEWS NET Acute Food Insecurity Classifications Data

Publisher: FEWS NET · Source: HDX · License: cc-by · Updated: 2026-04-05

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Abstract

Uganda 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: UGA.

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)	18,055
Columns	40 (9 numeric, 23 categorical, 7 datetime)
Train split	14,444 rows
Test split	3,611 rows
Geographic scope	UGA
Publisher	FEWS NET
HDX last updated	2026-04-05

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Variables

Geographic — country (Uganda), country_code (UG), fewsnet_region (East Africa), unit_type (fsc_admin_lhz, idp_camp, national_park), specialization_type and 2 others.

Temporal — datacollectionperiod (range 158855.0–343961.0), reporting_date.

Outcome / Measurement — value (range 1.0–3.0).

Identifier / Metadata — source_organization (FEWS NET, Uganda), source_document (Food Security Outlook, Uganda), geographic_unit_full_name (West Nile Tobacco Cassava Sorghum Zone, Yumbe, Northern, Uganda, Southwestern Midland Banana Robusta Coffee Cattle Zone, Ntungamo, Western, Uganda, Lakeshore and Riverbank Fishing Zone, Dokolo, Northern, Uganda), geographic_unit_name (Urban, Lakeshore and Riverbank Fishing Zone, Midwest Central and Lake Victoria Crescent Robusta Coffee Banana Maize and Cattle Zone), fnid (UG2014C3012026, UG2014C3032741, UG2014C3031341) 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-uganda-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, Uganda
source_document	object	0.0%	Food Security Outlook, Uganda
country	object	0.0%	Uganda
country_code	object	0.0%	UG
geographic_group	object	0.0%	Eastern Africa
fewsnet_region	object	0.0%	East Africa
geographic_unit_full_name	object	0.0%	West Nile Tobacco Cassava Sorghum Zone, Yumbe, Northern, Uganda, Southwestern Midland Banana Robusta Coffee Cattle Zone, Ntungamo, Western, Uganda, Lakeshore and Riverbank Fishing Zone, Dokolo, Northern, Uganda
geographic_unit_name	object	0.0%	Urban, Lakeshore and Riverbank Fishing Zone, Midwest Central and Lake Victoria Crescent Robusta Coffee Banana Maize and Cattle Zone
unit_type	object	0.0%	fsc_admin_lhz, idp_camp, national_park
fnid	object	0.0%	UG2014C3012026, UG2014C3032741, UG2014C3031341
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	7.2%	1.0 – 3.0 (mean 1.1138)
description	object	7.2%
id	int64	0.0%	24406365.0 – 37176283.0 (mean 26102483.6817)
datacollectionperiod	int64	0.0%	158855.0 – 343961.0 (mean 188747.5996)
datacollection	int64	0.0%	168457.0 – 354513.0 (mean 198928.3489)
scenario	object	0.0%
geographic_unit	int64	0.0%	28417.0 – 201892.0 (mean 66297.5119)
datasourceorganization	int64	0.0%	2036.0 – 2036.0 (mean 2036.0)
datasourcedocument	int64	0.0%	6593.0 – 6593.0 (mean 6593.0)
dataseries	int64	0.0%	6494432.0 – 6974975.0 (mean 6554110.1)
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	3.0	1.1138	1.0
id	24406365.0	37176283.0	26102483.6817	24602286.0
datacollectionperiod	158855.0	343961.0	188747.5996	160061.0
datacollection	168457.0	354513.0	198928.3489	168883.0
geographic_unit	28417.0	201892.0	66297.5119	29003.0
datasourceorganization	2036.0	2036.0	2036.0	2036.0
datasourcedocument	6593.0	6593.0	6593.0	6593.0
dataseries	6494432.0	6974975.0	6554110.1	6510416.0

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