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

Nigeria 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

Nigeria 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: NGA.

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)	8,307
Columns	40 (9 numeric, 23 categorical, 7 datetime)
Train split	6,645 rows
Test split	1,661 rows
Geographic scope	NGA
Publisher	FEWS NET
HDX last updated	2026-04-03

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Variables

Geographic — country (Nigeria), country_code (NG), fewsnet_region (West Africa), unit_type (fsc_admin_lhz, idp_camp, fsc_admin), specialization_type and 2 others.

Temporal — datacollectionperiod (range 158627.0–378157.0), reporting_date.

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

Identifier / Metadata — source_organization (FEWS NET, Nigeria), source_document (Food Security Outlook, Nigeria), geographic_unit_full_name (Niger and Benue rivers flood plain rice with maize, vegetables, and livestock, Yola North, Adamawa, Nigeria, Niger and Benue rivers flood plain rice with maize, vegetables, and livestock, Delta, Nigeria, Niger and Benue rivers flood plain rice with maize, vegetables, and livestock, Nasarawa, Nigeria), geographic_unit_name (Sokoto millet, cowpeas, groundnuts, and livestock, Sokoto-Rima-Kano riverine flood plain rice and fishing, Kano-Katsina sahelian: millet, sorghum, sesame, and livestock), fnid (NG2014C3020806, NG2014C3100003, NG2014C3090003) and 8 others.

Other — geographic_group (Western 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-nigeria-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, Nigeria
source_document	object	0.0%	Food Security Outlook, Nigeria
country	object	0.0%	Nigeria
country_code	object	0.0%	NG
geographic_group	object	0.0%	Western Africa
fewsnet_region	object	0.0%	West Africa
geographic_unit_full_name	object	0.0%	Niger and Benue rivers flood plain rice with maize, vegetables, and livestock, Yola North, Adamawa, Nigeria, Niger and Benue rivers flood plain rice with maize, vegetables, and livestock, Delta, Nigeria, Niger and Benue rivers flood plain rice with maize, vegetables, and livestock, Nasarawa, Nigeria
geographic_unit_name	object	0.0%	Sokoto millet, cowpeas, groundnuts, and livestock, Sokoto-Rima-Kano riverine flood plain rice and fishing, Kano-Katsina sahelian: millet, sorghum, sesame, and livestock
unit_type	object	0.0%	fsc_admin_lhz, idp_camp, fsc_admin
fnid	object	0.0%	NG2014C3020806, NG2014C3100003, NG2014C3090003
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	0.1%	1.0 – 4.0 (mean 1.8507)
description	object	0.1%
id	int64	0.0%	24370752.0 – 41453119.0 (mean 28815044.581)
datacollectionperiod	int64	0.0%	158627.0 – 378157.0 (mean 235566.5855)
datacollection	int64	0.0%	168346.0 – 388949.0 (mean 247534.1567)
scenario	object	0.0%
geographic_unit	int64	0.0%	25361.0 – 208448.0 (mean 129100.4666)
datasourceorganization	int64	0.0%	2033.0 – 2033.0 (mean 2033.0)
datasourcedocument	int64	0.0%	6573.0 – 6573.0 (mean 6573.0)
dataseries	int64	0.0%	6481912.0 – 7226045.0 (mean 6639518.3507)
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	1.8507	2.0
id	24370752.0	41453119.0	28815044.581	26094482.0
datacollectionperiod	158627.0	378157.0	235566.5855	222028.0
datacollection	168346.0	388949.0	247534.1567	235103.0
geographic_unit	25361.0	208448.0	129100.4666	174655.0
datasourceorganization	2033.0	2033.0	2033.0	2033.0
datasourcedocument	6573.0	6573.0	6573.0	6573.0
dataseries	6481912.0	7226045.0	6639518.3507	6504314.0

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