electricsheepafrica/africa-burundi-most-likely-fewsnet-fipe

Burundi Most Likely FEWS NET Acutely Food Insecure Population Estimates Data

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

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Abstract

Burundi Most Likely FEWS NET Acutely Food Insecure Population Estimates Data from 2019

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: BDI.

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)	75
Columns	44 (10 numeric, 27 categorical, 7 datetime)
Train split	60 rows
Test split	15 rows
Geographic scope	BDI
Publisher	FEWS NET
HDX last updated	2026-04-07

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Variables

Geographic — country (Burundi), country_code (BI), fewsnet_region (East Africa), admin_0 (Burundi), specialization_type and 3 others.

Temporal — datacollectionperiod (range 310323.0–373073.0), reporting_date.

Outcome / Measurement — phase, low_value (range 100000.0–500000.0), high_value (range 499999.0–999999.0), value (range 100000.0–500000.0), phase_name.

Identifier / Metadata — source_organization (FEWS NET), source_document (Food Assistance Outlook Brief), geographic_unit_full_name (Burundi), geographic_unit_name (Burundi), fnid (BI) and 8 others.

Other — geographic_group (Eastern Africa), indicator_abbreviation, projection_start, projection_end, status and 11 others.

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-burundi-most-likely-fewsnet-fipe")
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 Assistance Outlook Brief
country	object	0.0%	Burundi
country_code	object	0.0%	BI
geographic_group	object	0.0%	Eastern Africa
fewsnet_region	object	0.0%	East Africa
geographic_unit_full_name	object	0.0%	Burundi
geographic_unit_name	object	0.0%	Burundi
fnid	object	0.0%	BI
admin_0	object	0.0%	Burundi
phase	object	0.0%
scenario_name	object	0.0%
indicator_name	object	0.0%
indicator_abbreviation	object	0.0%
projection_start	datetime64[ns]	0.0%
projection_end	datetime64[ns]	0.0%
status	object	0.0%
low_value	float64	0.0%	100000.0 – 500000.0 (mean 382666.6667)
high_value	float64	0.0%	499999.0 – 999999.0 (mean 773332.3333)
value	float64	0.0%	100000.0 – 500000.0 (mean 382666.6667)
id	int64	0.0%	33126742.0 – 40657251.0 (mean 34149602.24)
datacollectionperiod	int64	0.0%	310323.0 – 373073.0 (mean 319280.3333)
datacollection	int64	0.0%	325936.0 – 383437.0 (mean 333815.6)
scenario	object	0.0%
geographic_unit	int64	0.0%	8008.0 – 8008.0 (mean 8008.0)
datasourceorganization	int64	0.0%	1.0 – 1.0 (mean 1.0)
datasourcedocument	int64	0.0%	6986.0 – 6986.0 (mean 6986.0)
dataseries	int64	0.0%	6932787.0 – 6932787.0 (mean 6932787.0)
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%
phase_name	object	0.0%
population_range	object	0.0%
description	object	0.0%
esa_source	object	0.0%
esa_processed	object	0.0%

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Numeric Summary

Column	Min	Max	Mean	Median
low_value	100000.0	500000.0	382666.6667	500000.0
high_value	499999.0	999999.0	773332.3333	749999.0
value	100000.0	500000.0	382666.6667	500000.0
id	33126742.0	40657251.0	34149602.24	33128852.0
datacollectionperiod	310323.0	373073.0	319280.3333	310397.0
datacollection	325936.0	383437.0	333815.6	325973.0
geographic_unit	8008.0	8008.0	8008.0	8008.0
datasourceorganization	1.0	1.0	1.0	1.0
datasourcedocument	6986.0	6986.0	6986.0	6986.0
dataseries	6932787.0	6932787.0	6932787.0	6932787.0

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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. 7 column(s) with >80% missing values were removed: admin_1, admin_2, admin_3, admin_4, pct_phase3, pct_phase4.... 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_burundi_most_likely_fewsnet_fipe,
  title     = {Burundi Most Likely FEWS NET Acutely Food Insecure Population Estimates Data},
  author    = {FEWS NET},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/burundi_most_likely_fewsnet_fipe},
  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.