electricsheepafrica/africa-hrp-projects-zwe

Zimbabwe: Response Plan projects

Publisher: OCHA Humanitarian Programme Cycle Tools (HPC Tools) · Source: HDX · License: cc-by-igo · Updated: 2026-03-30

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Abstract

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

Important: some projects in Zimbabwe might be missing, and others might not apply specifically to Zimbabwe. 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: ZWE.

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)	56
Columns	13 (1 numeric, 10 categorical, 2 datetime)
Train split	44 rows
Test split	11 rows
Geographic scope	ZWE
Publisher	OCHA Humanitarian Programme Cycle Tools (HPC Tools)
HDX last updated	2026-03-30

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Variables

Geographic — locations (ZWE).

Temporal — startdate, enddate.

Identifier / Metadata — name (Displacement Tracking and Technical Assitance by IOM, Mainstreaming Migration Into Environment Policy and Adaptation Strategies in Zimbabwe, Unaccompanied and separated children (UASC) Separation of children from families is prevented and responded to, and family-based care is promoted in the child’s best interest), versioncode (FZWE25-CSS-220753-1, FZWE25-CSS-220760-1, FZWE25-PRO-220763-1), response_plan_code (FZWE25), esa_source (HDX), esa_processed (2026-04-05).

Other — currentrequestedfunds (range 3567.0–51333333.0), objective (To strengthen the community support systems to protect children and women from climate-induced vulnerabilities by building the capacity of Community Childcare Workers to identify, refer and respond to child protection cases in a timely manner., To improve safe and dignified access to essential WASH services (including access to safe drinking water, safe sanitation facilities, and essential hygiene services and information), for drought affected populations., Improve support for vulnerable children by strengthening parenting programmes that specifically target groups the most at risk from climate change, such as disabled children, children outside the family environment, children left behind, young girls at risk of early marriage, and working children.), globalclusters (Water Sanitation Hygiene, Protection - Child Protection, Multi-sector), organizations (United Nations Children's Fund, United Nations High Commissioner for Refugees, Food and Agriculture Organization of the United Nations), plans (Zimbabwe Drought Flash Appeal 2025).

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-hrp-projects-zwe")
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%	Displacement Tracking and Technical Assitance by IOM, Mainstreaming Migration Into Environment Policy and Adaptation Strategies in Zimbabwe, Unaccompanied and separated children (UASC) Separation of children from families is prevented and responded to, and family-based care is promoted in the child’s best interest
versioncode	object	0.0%	FZWE25-CSS-220753-1, FZWE25-CSS-220760-1, FZWE25-PRO-220763-1
currentrequestedfunds	int64	0.0%	3567.0 – 51333333.0 (mean 2559444.4286)
objective	object	3.6%	To strengthen the community support systems to protect children and women from climate-induced vulnerabilities by building the capacity of Community Childcare Workers to identify, refer and respond to child protection cases in a timely manner., To improve safe and dignified access to essential WASH services (including access to safe drinking water, safe sanitation facilities, and essential hygiene services and information), for drought affected populations., Improve support for vulnerable children by strengthening parenting programmes that specifically target groups the most at risk from climate change, such as disabled children, children outside the family environment, children left behind, young girls at risk of early marriage, and working children.
startdate	datetime64[ns]	0.0%
enddate	datetime64[ns]	0.0%
globalclusters	object	0.0%	Water Sanitation Hygiene, Protection - Child Protection, Multi-sector
locations	object	0.0%	ZWE
organizations	object	0.0%	United Nations Children's Fund, United Nations High Commissioner for Refugees, Food and Agriculture Organization of the United Nations
plans	object	0.0%	Zimbabwe Drought Flash Appeal 2025
response_plan_code	object	0.0%	FZWE25
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
currentrequestedfunds	3567.0	51333333.0	2559444.4286	161530.5

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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. 2 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_zwe,
  title     = {Zimbabwe: Response Plan projects},
  author    = {OCHA Humanitarian Programme Cycle Tools (HPC Tools)},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/hrp-projects-zwe},
  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.