electricsheepafrica/africa-sierra-leone-current-situation-fewsnet-ipc-classification

Sierra Leone Current Situation FEWS NET Acute Food Insecurity Classifications Data

Publisher: FEWS NET · Source: HDX · License: cc-by · Updated: 2026-04-07

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Abstract

Sierra Leone 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: SLE.

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)	107
Columns	40 (9 numeric, 23 categorical, 7 datetime)
Train split	85 rows
Test split	21 rows
Geographic scope	SLE
Publisher	FEWS NET
HDX last updated	2026-04-07

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Variables

Geographic — country (Sierra Leone), country_code (SL), fewsnet_region (West Africa), unit_type (fsc_admin_lhz, fsc_admin), specialization_type and 2 others.

Temporal — datacollectionperiod (range 158983.0–158992.0), reporting_date.

Outcome / Measurement — value (range 1.0–2.0).

Identifier / Metadata — source_organization (FEWS NET, Sierra Leone), source_document (Food Security Outlook, Sierra Leone), geographic_unit_full_name (Bombali Food Crops, Peppers, Tobacco and Livestock, Bombali, Northern, Sierra Leone, Koinadugu Livestock, Food Crops and Trade, Kono, Eastern, Sierra Leone, Kono-Kenema-Bo Rice, Tree Crops and Timber, Kono, Eastern, Sierra Leone), geographic_unit_name (Western Rice, Root Crops, Cereals and Trade Belt, Rice Bowl Areas, Coastal Food Crops and Fishing), fnid (SL2016C3020102, SL2016C3010307, SL2016C3010306) 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-sierra-leone-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, Sierra Leone
source_document	object	0.0%	Food Security Outlook, Sierra Leone
country	object	0.0%	Sierra Leone
country_code	object	0.0%	SL
geographic_group	object	0.0%	Western Africa
fewsnet_region	object	0.0%	West Africa
geographic_unit_full_name	object	0.0%	Bombali Food Crops, Peppers, Tobacco and Livestock, Bombali, Northern, Sierra Leone, Koinadugu Livestock, Food Crops and Trade, Kono, Eastern, Sierra Leone, Kono-Kenema-Bo Rice, Tree Crops and Timber, Kono, Eastern, Sierra Leone
geographic_unit_name	object	0.0%	Western Rice, Root Crops, Cereals and Trade Belt, Rice Bowl Areas, Coastal Food Crops and Fishing
unit_type	object	0.0%	fsc_admin_lhz, fsc_admin
fnid	object	0.0%	SL2016C3020102, SL2016C3010307, SL2016C3010306
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 – 2.0 (mean 1.6449)
description	object	0.0%
id	int64	0.0%	24448077.0 – 24448395.0 (mean 24448236.0)
datacollectionperiod	int64	0.0%	158983.0 – 158992.0 (mean 158988.215)
datacollection	int64	0.0%	168522.0 – 168525.0 (mean 168523.7383)
scenario	object	0.0%
geographic_unit	int64	0.0%	26202.0 – 26246.0 (mean 26228.0561)
datasourceorganization	int64	0.0%	2039.0 – 2039.0 (mean 2039.0)
datasourcedocument	int64	0.0%	6617.0 – 6617.0 (mean 6617.0)
dataseries	int64	0.0%	6502640.0 – 6502871.0 (mean 6502765.7103)
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	2.0	1.6449	2.0
id	24448077.0	24448395.0	24448236.0	24448236.0
datacollectionperiod	158983.0	158992.0	158988.215	158989.0
datacollection	168522.0	168525.0	168523.7383	168524.0
geographic_unit	26202.0	26246.0	26228.0561	26229.0
datasourceorganization	2039.0	2039.0	2039.0	2039.0
datasourcedocument	6617.0	6617.0	6617.0	6617.0
dataseries	6502640.0	6502871.0	6502765.7103	6502769.0

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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_sierra_leone_current_situation_fewsnet_ipc_classification,
  title     = {Sierra Leone Current Situation FEWS NET Acute Food Insecurity Classifications Data},
  author    = {FEWS NET},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/sierra_leone_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.