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
- cod
pretty_name: >-
Congo, The Democratic Republic of the Current Situation FEWS NET Acutely Food
Insecure Population Estimates Data
dataset_info:
splits:
- name: train
num_examples: 60
- name: test
num_examples: 15
Congo, The Democratic Republic of the Current Situation FEWS NET Acutely Food Insecure Population Estimates Data
Publisher: FEWS NET · Source: HDX · License: cc-by · Updated: 2026-04-01
Abstract
Congo, The Democratic Republic of the Current Situation 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: COD.
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) | 75 |
| Columns | 44 (10 numeric, 27 categorical, 7 datetime) |
| Train split | 60 rows |
| Test split | 15 rows |
| Geographic scope | COD |
| Publisher | FEWS NET |
| HDX last updated | 2026-04-01 |
Variables
Geographic — country (Democratic Republic of the Congo), country_code (CD), fewsnet_region (Southern Africa), admin_0 (Democratic Republic of Congo), specialization_type and 3 others.
Temporal — datacollectionperiod (range 310322.0–373072.0), reporting_date.
Outcome / Measurement — phase, low_value (range 2500000.0–16000000.0), high_value (range 4999999.0–16999999.0), value (range 2500000.0–16000000.0), phase_name.
Identifier / Metadata — source_organization (FEWS NET), source_document (Food Assistance Outlook Brief), geographic_unit_full_name (Congo, The Democratic Republic of the), geographic_unit_name (Democratic Republic of Congo), fnid (CD) 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-congo-the-democratic-republic-of-the-current-situation-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% | Democratic Republic of the Congo |
country_code |
object | 0.0% | CD |
geographic_group |
object | 0.0% | Middle Africa |
fewsnet_region |
object | 0.0% | Southern Africa |
geographic_unit_full_name |
object | 0.0% | Congo, The Democratic Republic of the |
geographic_unit_name |
object | 0.0% | Democratic Republic of Congo |
fnid |
object | 0.0% | CD |
admin_0 |
object | 0.0% | Democratic Republic of Congo |
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% | 2500000.0 – 16000000.0 (mean 7566666.6667) |
high_value |
float64 | 0.0% | 4999999.0 – 16999999.0 (mean 9586665.6667) |
value |
float64 | 0.0% | 2500000.0 – 16000000.0 (mean 7566666.6667) |
id |
int64 | 0.0% | 33126743.0 – 40657252.0 (mean 34149603.4) |
datacollectionperiod |
int64 | 0.0% | 310322.0 – 373072.0 (mean 319279.3333) |
datacollection |
int64 | 0.0% | 325936.0 – 383437.0 (mean 333815.6) |
scenario |
object | 0.0% | |
geographic_unit |
int64 | 0.0% | 8023.0 – 8023.0 (mean 8023.0) |
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% | 6932788.0 – 6932788.0 (mean 6932788.0) |
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 |
2500000.0 | 16000000.0 | 7566666.6667 | 7500000.0 |
high_value |
4999999.0 | 16999999.0 | 9586665.6667 | 9999999.0 |
value |
2500000.0 | 16000000.0 | 7566666.6667 | 7500000.0 |
id |
33126743.0 | 40657252.0 | 34149603.4 | 33128853.0 |
datacollectionperiod |
310322.0 | 373072.0 | 319279.3333 | 310396.0 |
datacollection |
325936.0 | 383437.0 | 333815.6 | 325973.0 |
geographic_unit |
8023.0 | 8023.0 | 8023.0 | 8023.0 |
datasourceorganization |
1.0 | 1.0 | 1.0 | 1.0 |
datasourcedocument |
6986.0 | 6986.0 | 6986.0 | 6986.0 |
dataseries |
6932788.0 | 6932788.0 | 6932788.0 | 6932788.0 |
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_congo_the_democratic_republic_of_the_current_situation_fewsnet_fipe,
title = {Congo, The Democratic Republic of the Current Situation FEWS NET Acutely Food Insecure Population Estimates Data},
author = {FEWS NET},
year = {2026},
url = {https://data.humdata.org/dataset/congo__the_democratic_republic_of_the_current_situation_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.