electricsheepafrica/africa-mali-humanitarian-response-plan

Mali Humanitarian Response Plan

Publisher: OCHA Humanitarian Programme Cycle Tools (HPC Tools) · Source: HDX · License: cc-by · Updated: 2025-12-02

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Abstract

This data has been produced by the United Nations Office for the Coordination of Humanitarian Affairs (UNOCHA) on behalf of the Humanitarian Country Team and partners. The data 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. It 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 2025-12-02. Geographic scope: MLI.

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

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Dataset Characteristics

Domain	Humanitarian and development data
Unit of observation	Tabular records
Rows (total)	57
Columns	22 (16 numeric, 6 categorical, 0 datetime)
Train split	45 rows
Test split	11 rows
Geographic scope	MLI
Publisher	OCHA Humanitarian Programme Cycle Tools (HPC Tools)
HDX last updated	2025-12-02

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Variables

Identifier / Metadata — unnamed_2 (MOPTI, KAYES, KOULIKORO), unnamed_3 (ML05, ML01, ML02), unnamed_4 (CERCLES, GOURMA-RHAROUS, MOPTI), unnamed_5 (PCODE_CERCLE, ML0604, ML0501), unnamed_6 (range 0.0–61178.0194) and 17 others.

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-mali-humanitarian-response-plan")
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_2	object	5.3%	MOPTI, KAYES, KOULIKORO
unnamed_3	object	5.3%	ML05, ML01, ML02
unnamed_4	object	5.3%	CERCLES, GOURMA-RHAROUS, MOPTI
unnamed_5	object	5.3%	PCODE_CERCLE, ML0604, ML0501
unnamed_6	float64	5.3%	0.0 – 61178.0194 (mean 2265.8526)
unnamed_7	float64	5.3%	0.0 – 1578.9124 (mean 58.4782)
unnamed_8	float64	5.3%	0.0 – 436.5582 (mean 16.1688)
unnamed_9	float64	5.3%	0.0 – 0.0 (mean 0.0)
unnamed_10	float64	5.3%	0.0 – 250998.0 (mean 9296.2222)
unnamed_11	float64	5.3%	0.0 – 250998.0 (mean 9296.2222)
unnamed_12	float64	5.3%	0.0 – 250998.0 (mean 9296.2222)
unnamed_13	float64	5.3%	0.0 – 255433.0 (mean 9460.4815)
unnamed_15	float64	5.3%	0.0 – 55089.0498 (mean 2040.3352)
unnamed_16	float64	5.3%	0.0 – 4374.0388 (mean 162.0014)
unnamed_17	float64	5.3%	0.0 – 1979.3867 (mean 73.3106)
unnamed_18	float64	5.3%	0.0 – 0.0 (mean 0.0)
unnamed_19	float64	5.3%	0.0 – 243759.5 (mean 9028.1296)
unnamed_20	float64	5.3%	0.0 – 545048.4372 (mean 20186.9792)
unnamed_21	float64	5.3%	0.0 – 0.0 (mean 0.0)
unnamed_22	float64	5.3%	0.0 – 545506.4372 (mean 20203.9421)
esa_source	object	0.0%	HDX
esa_processed	object	0.0%	2026-04-18

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Numeric Summary

Column	Min	Max	Mean	Median
unnamed_6	0.0	61178.0194	2265.8526	347.0286
unnamed_7	0.0	1578.9124	58.4782	0.9116
unnamed_8	0.0	436.5582	16.1688	0.0
unnamed_9	0.0	0.0	0.0	0.0
unnamed_10	0.0	250998.0	9296.2222	751.5
unnamed_11	0.0	250998.0	9296.2222	935.0
unnamed_12	0.0	250998.0	9296.2222	751.5
unnamed_13	0.0	255433.0	9460.4815	935.0
unnamed_15	0.0	55089.0498	2040.3352	1.0
unnamed_16	0.0	4374.0388	162.0014	0.0
unnamed_17	0.0	1979.3867	73.3106	0.0
unnamed_18	0.0	0.0	0.0	0.0
unnamed_19	0.0	243759.5	9028.1296	0.0
unnamed_20	0.0	545048.4372	20186.9792	9.0
unnamed_21	0.0	0.0	0.0	0.0

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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. 3 column(s) with >80% missing values were removed: unnamed_0, unnamed_1, unnamed_14. 15 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 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.

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Citation

@dataset{hdx_africa_mali_humanitarian_response_plan,
  title     = {Mali Humanitarian Response Plan},
  author    = {OCHA Humanitarian Programme Cycle Tools (HPC Tools)},
  year      = {2025},
  url       = {https://data.humdata.org/dataset/mali-humanitarian-response-plan},
  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.