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

Mauritania Most Likely FEWS NET Acutely Food Insecure Population Estimates Data

Publisher: FEWS NET · Source: HDX · License: cc-by · Updated: 2026-04-01

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Abstract

Mauritania Most Likely FEWS NET Acutely Food Insecure Population Estimates Data from 2020

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: MRT.

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)	59
Columns	44 (10 numeric, 27 categorical, 7 datetime)
Train split	47 rows
Test split	11 rows
Geographic scope	MRT
Publisher	FEWS NET
HDX last updated	2026-04-01

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Variables

Geographic — country (Mauritania), country_code (MR), fewsnet_region (West Africa), admin_0 (Mauritania), specialization_type and 3 others.

Temporal — datacollectionperiod (range 310347.0–344052.0), reporting_date.

Outcome / Measurement — phase, low_value (range 0.0–2000000.0), high_value (range 99999.0–2499999.0), value (range 0.0–2000000.0), phase_name.

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

Other — geographic_group (Western 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-mauritania-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%	Mauritania
country_code	object	0.0%	MR
geographic_group	object	0.0%	Western Africa
fewsnet_region	object	0.0%	West Africa
geographic_unit_full_name	object	0.0%	Mauritania
geographic_unit_name	object	0.0%	Mauritania
fnid	object	0.0%	MR
admin_0	object	0.0%	Mauritania
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%	0.0 – 2000000.0 (mean 196610.1695)
high_value	float64	0.0%	99999.0 – 2499999.0 (mean 530507.4746)
value	float64	0.0%	0.0 – 2000000.0 (mean 196610.1695)
id	int64	0.0%	33127438.0 – 37183563.0 (mean 33954865.5254)
datacollectionperiod	int64	0.0%	310347.0 – 344052.0 (mean 318296.1695)
datacollection	int64	0.0%	325948.0 – 354564.0 (mean 332893.5424)
scenario	object	0.0%
geographic_unit	int64	0.0%	8112.0 – 8112.0 (mean 8112.0)
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%	6932839.0 – 6932839.0 (mean 6932839.0)
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	0.0	2000000.0	196610.1695	100000.0
high_value	99999.0	2499999.0	530507.4746	499999.0
value	0.0	2000000.0	196610.1695	100000.0
id	33127438.0	37183563.0	33954865.5254	33129112.0
datacollectionperiod	310347.0	344052.0	318296.1695	310405.0
datacollection	325948.0	354564.0	332893.5424	325977.0
geographic_unit	8112.0	8112.0	8112.0	8112.0
datasourceorganization	1.0	1.0	1.0	1.0
datasourcedocument	6986.0	6986.0	6986.0	6986.0
dataseries	6932839.0	6932839.0	6932839.0	6932839.0

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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_mauritania_most_likely_fewsnet_fipe,
  title     = {Mauritania Most Likely FEWS NET Acutely Food Insecure Population Estimates Data},
  author    = {FEWS NET},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/mauritania_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.