electricsheepafrica/africa-rdc-ipc-amn

République Démocratique du Congo : Malnutrition Aiguë

Publisher: HDX · Source: HDX · License: cc-by · Updated: 2025-12-31

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Abstract

Ce jeu de données présente l'analyse de la malnutrition aiguë de l'IPC (AMN).

Each row in this dataset represents tabular records. Data was last updated on HDX on 2025-12-31. Geographic scope: COD.

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

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Dataset Characteristics

Domain	Food security and nutrition
Unit of observation	Tabular records
Rows (total)	522
Columns	22 (12 numeric, 10 categorical, 0 datetime)
Train split	417 rows
Test split	104 rows
Geographic scope	COD
Publisher	HDX
HDX last updated	2025-12-31

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Variables

Geographic — nombre_total_de_cas_de_malnutrition_aiguë_ayant_besoin_d_un_traitement_enfant_m5_et_fefa (range 861.0–70685.0).

Identifier / Metadata — unnamed_0 (Ituri, Kinshasa, Nord-Kivu), unnamed_1 (Kinshasa, Djugu, Lubumbashi), unnamed_2 (Lubunga, Bili, Zone de Santé (ZS)), unnamed_3 (High, Mid, Low), unnamed_4 (ZS, T, P) and 16 others.

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-rdc-ipc-amn")
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
unnamed_0	object	0.4%	Ituri, Kinshasa, Nord-Kivu
unnamed_1	object	0.4%	Kinshasa, Djugu, Lubumbashi
unnamed_2	object	0.4%	Lubunga, Bili, Zone de Santé (ZS)
unnamed_3	object	0.4%	High, Mid, Low
unnamed_4	object	0.4%	ZS, T, P
unnamed_5	float64	0.6%	1.0 – 4.0 (mean 2.0751)
unnamed_6	float64	0.6%	1.0 – 4.0 (mean 2.499)
nombre_total_de_cas_de_malnutrition_aiguë_ayant_besoin_d_un_traitement_enfant_m5_et_fefa	float64	0.6%	861.0 – 70685.0 (mean 19850.1869)
unnamed_8	float64	0.6%	834.0 – 71538.0 (mean 19332.2216)
unnamed_9	float64	0.6%	283.0 – 16228.0 (mean 4352.5684)
unnamed_10	float64	0.6%	275.0 – 16098.0 (mean 4234.6898)
unnamed_11	object	0.2%	3,5, 3,1, 9,3
unnamed_12	float64	0.6%	180.0 – 12313.0 (mean 2992.6802)
unnamed_13	float64	0.6%	175.0 – 12462.0 (mean 2915.5645)
unnamed_14	object	0.2%	1,0, 0,5, 3,5
unnamed_15	float64	0.6%	83.0 – 6122.0 (mean 1359.9056)
unnamed_16	float64	0.6%	79.0 – 6073.0 (mean 1319.1329)
unnamed_17	float64	0.6%	758.0 – 63563.0 (mean 17511.6956)
unnamed_18	object	0.4%	11,6, 9,3, 11,9
unnamed_19	float64	0.6%	406.0 – 25571.0 (mean 7156.5202)
esa_source	object	0.0%	HDX
esa_processed	object	0.0%	2026-04-05

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Numeric Summary

Column	Min	Max	Mean	Median
unnamed_5	1.0	4.0	2.0751	2.0
unnamed_6	1.0	4.0	2.499	2.0
nombre_total_de_cas_de_malnutrition_aiguë_ayant_besoin_d_un_traitement_enfant_m5_et_fefa	861.0	70685.0	19850.1869	16906.0
unnamed_8	834.0	71538.0	19332.2216	16292.0
unnamed_9	283.0	16228.0	4352.5684	3543.0
unnamed_10	275.0	16098.0	4234.6898	3427.0
unnamed_12	180.0	12313.0	2992.6802	2302.0
unnamed_13	175.0	12462.0	2915.5645	2246.0
unnamed_15	83.0	6122.0	1359.9056	1108.0
unnamed_16	79.0	6073.0	1319.1329	1071.0
unnamed_17	758.0	63563.0	17511.6956	14841.0
unnamed_19	406.0	25571.0	7156.5202	5770.0

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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. 1 column(s) with >80% missing values were removed: unnamed_20. 5 exact duplicate rows were removed. 12 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 HDX 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_rdc_ipc_amn,
  title     = {République Démocratique du Congo : Malnutrition Aiguë},
  author    = {HDX},
  year      = {2025},
  url       = {https://data.humdata.org/dataset/rdc-ipc-amn},
  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.