electricsheepafrica/africa-hrp-projects-lso

Lesotho: Response Plan projects

Publisher: OCHA Humanitarian Programme Cycle Tools (HPC Tools) · Source: HDX · License: cc-by-igo · Updated: 2026-03-17

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Abstract

Projects proposed, in progress, or completed as part of the annual Lesotho Humanitarian Response Plans (HRPs) or other Humanitarian Programme Cycle plans. The original data is available on https://hpc.tools

Important: some projects in Lesotho might be missing, and others might not apply specifically to Lesotho. See Caveats under the Additional information tab.

Each row in this dataset represents time-series observations. Temporal coverage is indicated by the startdate, enddate column(s). Geographic scope: LSO.

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

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Dataset Characteristics

Domain	Humanitarian and development data
Unit of observation	Time-series observations
Rows (total)	14
Columns	13 (1 numeric, 10 categorical, 2 datetime)
Train split	11 rows
Test split	2 rows
Geographic scope	LSO
Publisher	OCHA Humanitarian Programme Cycle Tools (HPC Tools)
HDX last updated	2026-03-17

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Variables

Geographic — locations (LSO, #country+code).

Temporal — startdate, enddate.

Identifier / Metadata — name (Food Security, Health, Protection), versioncode (#activity+code+v_hpc, FLSO1920-EDU-165928-1, FLSO1920-FSC-165918-1), response_plan_code (FLSO1920, #response+plan+code), esa_source (HDX), esa_processed (2026-04-07).

Other — currentrequestedfunds (range 30000.0–12000000.0), objective (Food Security, Health, Protection), globalclusters (Food Security, Health, Protection), organizations (United Nations Children's Fund, #org+prog+name, Food and Agriculture Organization of the United Nations), plans (Lesotho Flash Appeal 2019-2020, #response+plan+name).

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-hrp-projects-lso")
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
name	object	0.0%	Food Security, Health, Protection
versioncode	object	0.0%	#activity+code+v_hpc, FLSO1920-EDU-165928-1, FLSO1920-FSC-165918-1
currentrequestedfunds	float64	7.1%	30000.0 – 12000000.0 (mean 2595769.2308)
objective	object	0.0%	Food Security, Health, Protection
startdate	datetime64[ns]	7.1%
enddate	datetime64[ns]	7.1%
globalclusters	object	0.0%	Food Security, Health, Protection
locations	object	0.0%	LSO, #country+code
organizations	object	0.0%	United Nations Children's Fund, #org+prog+name, Food and Agriculture Organization of the United Nations
plans	object	0.0%	Lesotho Flash Appeal 2019-2020, #response+plan+name
response_plan_code	object	0.0%	FLSO1920, #response+plan+code
esa_source	object	0.0%	HDX
esa_processed	object	0.0%	2026-04-07

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Numeric Summary

Column	Min	Max	Mean	Median
currentrequestedfunds	30000.0	12000000.0	2595769.2308	505000.0

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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: partners. 3 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_hrp_projects_lso,
  title     = {Lesotho: Response Plan projects},
  author    = {OCHA Humanitarian Programme Cycle Tools (HPC Tools)},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/hrp-projects-lso},
  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.