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

Liberia 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

Liberia Current Situation FEWS NET Acute Food Insecurity Classifications Data from 2015

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: LBR.

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)	187
Columns	40 (9 numeric, 23 categorical, 7 datetime)
Train split	149 rows
Test split	37 rows
Geographic scope	LBR
Publisher	FEWS NET
HDX last updated	2026-04-03

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Variables

Geographic — country (Liberia), country_code (LR), fewsnet_region (West Africa), unit_type (fsc_admin_lhz, fsc_admin), specialization_type and 2 others.

Temporal — datacollectionperiod (range 159415.0–159427.0), reporting_date.

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

Identifier / Metadata — source_organization (FEWS NET, Liberia), source_document (Food Security Outlook, Liberia), geographic_unit_full_name (South-East Rice with Cassava, Sinoe, Liberia, Rubber and Charcoal with Food Crops, Montserrado, Liberia, Rubber and Charcoal with Food Crops, Grand Bassa, Liberia), geographic_unit_name (Coastal Fishing and Cassava, North and Central Rice with Cassava and Market Gardening, Coastal Plain Cassava with Rice and River Fishing), fnid (LR2016C33903, LR2016C33008, LR2016C30908) 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-liberia-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, Liberia
source_document	object	0.0%	Food Security Outlook, Liberia
country	object	0.0%	Liberia
country_code	object	0.0%	LR
geographic_group	object	0.0%	Western Africa
fewsnet_region	object	0.0%	West Africa
geographic_unit_full_name	object	0.0%	South-East Rice with Cassava, Sinoe, Liberia, Rubber and Charcoal with Food Crops, Montserrado, Liberia, Rubber and Charcoal with Food Crops, Grand Bassa, Liberia
geographic_unit_name	object	0.0%	Coastal Fishing and Cassava, North and Central Rice with Cassava and Market Gardening, Coastal Plain Cassava with Rice and River Fishing
unit_type	object	0.0%	fsc_admin_lhz, fsc_admin
fnid	object	0.0%	LR2016C33903, LR2016C33008, LR2016C30908
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 – 1.0 (mean 1.0)
description	object	0.0%
id	int64	0.0%	24488742.0 – 24489300.0 (mean 24489021.0)
datacollectionperiod	int64	0.0%	159415.0 – 159427.0 (mean 159421.8984)
datacollection	int64	0.0%	168666.0 – 168670.0 (mean 168668.2995)
scenario	object	0.0%
geographic_unit	int64	0.0%	24026.0 – 24083.0 (mean 24059.6738)
datasourceorganization	int64	0.0%	2038.0 – 2038.0 (mean 2038.0)
datasourcedocument	int64	0.0%	6613.0 – 6613.0 (mean 6613.0)
dataseries	int64	0.0%	6505723.0 – 6505894.0 (mean 6505824.0214)
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	1.0	1.0	1.0
id	24488742.0	24489300.0	24489021.0	24489021.0
datacollectionperiod	159415.0	159427.0	159421.8984	159421.0
datacollection	168666.0	168670.0	168668.2995	168668.0
geographic_unit	24026.0	24083.0	24059.6738	24060.0
datasourceorganization	2038.0	2038.0	2038.0	2038.0
datasourcedocument	6613.0	6613.0	6613.0	6613.0
dataseries	6505723.0	6505894.0	6505824.0214	6505825.0

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