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metadata 
annotations_creators:
  - no-annotation
language_creators:
  - found
language:
  - en
license: cc-by-4.0
multilinguality:
  - monolingual
size_categories:
  - n<1K
source_datasets:
  - original
task_categories:
  - tabular-classification
  - other
task_ids: []
tags:
  - africa
  - humanitarian
  - hdx
  - electric-sheep-africa
  - food-security
  - caf
pretty_name: >-
  Central African Republic Most Likely FEWS NET Acutely Food Insecure Population
  Estimates Data
dataset_info:
  splits:
    - name: train
      num_examples: 56
    - name: test
      num_examples: 14

Central African Republic Most Likely FEWS NET Acutely Food Insecure Population Estimates Data

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

Abstract

Central African Republic 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: CAF.

Curated into ML-ready Parquet format by Electric Sheep Africa.

Dataset Characteristics

Domain Food security and nutrition
Unit of observation First-level administrative unit observations
Rows (total) 71
Columns 44 (10 numeric, 27 categorical, 7 datetime)
Train split 56 rows
Test split 14 rows
Geographic scope CAF
Publisher FEWS NET
HDX last updated 2026-04-01

Variables

Geographic — country (Central African Republic), country_code (CF), fewsnet_region (West Africa), admin_0 (Central African Republic), specialization_type and 3 others.

Temporal — datacollectionperiod (range 310323.0–344052.0), 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 (Central African Republic), geographic_unit_name (Central African Republic), fnid (CF) and 8 others.

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

Quick Start 

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-central-african-republic-most-likely-fewsnet-fipe")
train = ds["train"].to_pandas()
test  = ds["test"].to_pandas()

print(train.shape)
train.head()

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% Central African Republic
country_code object 0.0% CF
geographic_group object 0.0% Middle Africa
fewsnet_region object 0.0% West Africa
geographic_unit_full_name object 0.0% Central African Republic
geographic_unit_name object 0.0% Central African Republic
fnid object 0.0% CF
admin_0 object 0.0% Central African Republic
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 478169.0141)
high_value float64 0.0% 499999.0 – 999999.0 (mean 904928.5775)
value float64 0.0% 100000.0 – 500000.0 (mean 478169.0141)
id int64 0.0% 33126746.0 – 37183541.0 (mean 33814935.1549)
datacollectionperiod int64 0.0% 310323.0 – 344052.0 (mean 316950.4507)
datacollection int64 0.0% 325936.0 – 354564.0 (mean 331718.5493)
scenario object 0.0%
geographic_unit int64 0.0% 8014.0 – 8014.0 (mean 8014.0)
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% 6932791.0 – 6932791.0 (mean 6932791.0)
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%

Numeric Summary

Column Min Max Mean Median
low_value 100000.0 500000.0 478169.0141 500000.0
high_value 499999.0 999999.0 904928.5775 999999.0
value 100000.0 500000.0 478169.0141 500000.0
id 33126746.0 37183541.0 33814935.1549 33128744.0
datacollectionperiod 310323.0 344052.0 316950.4507 310393.0
datacollection 325936.0 354564.0 331718.5493 325971.0
geographic_unit 8014.0 8014.0 8014.0 8014.0
datasourceorganization 1.0 1.0 1.0 1.0
datasourcedocument 6986.0 6986.0 6986.0 6986.0
dataseries 6932791.0 6932791.0 6932791.0 6932791.0

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.

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.

Citation 

@dataset{hdx_africa_central_african_republic_most_likely_fewsnet_fipe,
  title     = {Central African Republic Most Likely FEWS NET Acutely Food Insecure Population Estimates Data},
  author    = {FEWS NET},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/central_african_republic_most_likely_fewsnet_fipe},
  note      = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}

Electric Sheep Africa — Africa's ML dataset infrastructure. Lagos, Nigeria.