Datasets:

electricsheepafrica
/

africa-mali-humanitarian-needs

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 geolocated point observations. Data was last updated on HDX on 2026-02-13. Geographic scope: MLI.

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

Dataset Characteristics


Domain	Demographics and population
Unit of observation	Geolocated point observations
Rows (total)	1
Columns	7 (3 numeric, 4 categorical, 0 datetime)
Train split	0 rows
Test split	0 rows
Geographic scope	MLI
Publisher	OCHA Humanitarian Programme Cycle Tools (HPC Tools)
HDX last updated	2026-02-13

Variables

Geographic — population (range 25200000.0–25200000.0).

Identifier / Metadata — esa_source (HDX), esa_processed (2026-04-04).

Other — description (GHO Estimates), cluster (ALL), in_need (range 5100000.0–5100000.0), targeted (range 3800000.0–3800000.0).

Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-mali-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%	GHO Estimates
`cluster`	object	0.0%	ALL
`population`	int64	0.0%	25200000.0 – 25200000.0 (mean 25200000.0)
`in_need`	int64	0.0%	5100000.0 – 5100000.0 (mean 5100000.0)
`targeted`	int64	0.0%	3800000.0 – 3800000.0 (mean 3800000.0)
`esa_source`	object	0.0%	HDX
`esa_processed`	object	0.0%	2026-04-04

Numeric Summary

Column	Min	Max	Mean	Median
`population`	25200000.0	25200000.0	25200000.0	25200000.0
`in_need`	5100000.0	5100000.0	5100000.0	5100000.0
`targeted`	3800000.0	3800000.0	3800000.0	3800000.0

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. 4 column(s) with >80% missing values were removed: category, 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_mali_humanitarian_needs,
  title     = {Mali: Humanitarian Needs},
  author    = {OCHA Humanitarian Programme Cycle Tools (HPC Tools)},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/mali-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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Collection including electricsheepafrica/africa-mali-humanitarian-needs

Africa · Humanitarian Response

Collection

Humanitarian needs, response plans, and funding data for Africa. Sources: OCHA HRP, IFRC appeals, 5W matrices, and multi-sector needs assessments. • 150 items • Updated 6 days ago