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13M
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5.92M
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2026-04-04 00:00:00
Lutte Antimines
PRO-MIN
2,508,214
789,217
HDX
2026-04-04
Violence Basée sur le Genre (VBG)
PRO-GBV
5,225,538
1,000,504
HDX
2026-04-04
Protection (total)
PRO
5,850,814
1,373,466
HDX
2026-04-04
Protection générale
PRO
4,994,650
1,090,900
HDX
2026-04-04
Education
EDU
3,901,894
480,758
HDX
2026-04-04
Eau, hygiène & assainissement
WSH
5,375,240
2,822,998
HDX
2026-04-04
Santé
HEA
7,544,115
2,512,134
HDX
2026-04-04
Nutrition
NUT
4,256,957
1,853,401
HDX
2026-04-04
Protection de l'enfant
PRO-CPN
4,033,808
806,761
HDX
2026-04-04
Logement, Terres et propriété
PRO-HLP
2,705,252
562,687
HDX
2026-04-04
Sécurité alimentaire
FSC
13,003,064
5,922,823
HDX
2026-04-04
Réponse multisectorielle aux réfugiés
MS
512,680
458,749
HDX
2026-04-04

Democratic Republic of the Congo: Humanitarian Needs

Publisher: OCHA Humanitarian Programme Cycle Tools (HPC Tools) · Source: HDX · License: cc-by · Updated: 2026-02-13


Abstract

This dataset was compiled by the United Nations Office for the Coordination of Humanitarian Affairs (UNOCHA) on behalf of the Humanitarian Country Team and partners. It provides the Humanitarian Country Team’s shared understanding of the crisis, including the most pressing humanitarian need and the estimated number of people who need assistance, and represents a consolidated evidence base and helps inform joint strategic response planning.

Each row in this dataset represents tabular records. Data was last updated on HDX on 2026-02-13. Geographic scope: COD.

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


Dataset Characteristics

Domain Humanitarian and development data
Unit of observation Tabular records
Rows (total) 16
Columns 6 (2 numeric, 4 categorical, 0 datetime)
Train split 12 rows
Test split 3 rows
Geographic scope COD
Publisher OCHA Humanitarian Programme Cycle Tools (HPC Tools)
HDX last updated 2026-02-13

Variables

Identifier / Metadataesa_source (HDX), esa_processed (2026-04-04).

Otherdescription (Final Caseload, Coordination et gestion des camps, Education), cluster (PRO, ALL, CCM), in_need (range 512680.0–14940727.0), targeted (range 450860.0–7313763.0).


Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-democratic-republic-of-the-congo-humanitarian-needs")
train = ds["train"].to_pandas()
test  = ds["test"].to_pandas()

print(train.shape)
train.head()

Schema

Column Type Null % Range / Sample Values
description object 0.0% Final Caseload, Coordination et gestion des camps, Education
cluster object 0.0% PRO, ALL, CCM
in_need float64 6.2% 512680.0 – 14940727.0 (mean 5269168.5333)
targeted int64 0.0% 450860.0 – 7313763.0 (mean 1921250.125)
esa_source object 0.0% HDX
esa_processed object 0.0% 2026-04-04

Numeric Summary

Column Min Max Mean Median
in_need 512680.0 14940727.0 5269168.5333 4256957.0
targeted 450860.0 7313763.0 1921250.125 1090880.5

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. 5 column(s) with >80% missing values were removed: category, population, affected, reached, info. 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 OCHA Humanitarian Programme Cycle Tools (HPC Tools) 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_democratic_republic_of_the_congo_humanitarian_needs,
  title     = {Democratic Republic of the Congo: Humanitarian Needs},
  author    = {OCHA Humanitarian Programme Cycle Tools (HPC Tools)},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/democratic-republic-of-the-congo-humanitarian-needs},
  note      = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}

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

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