electricsheepasia/asia-afghanistan-who-does-what-where-april-to-june-2017

Afghanistan - Who does What Where (April to June 2017)

Publisher: OCHA Afghanistan · Source: HDX · License: cc-by-igo · Updated: 2025-05-05

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Abstract

The Who does What Where (3W) is a core humanitarian coordination dataset. It is critical to know where humanitarian organizations are working, what they are doing and their capability in order to identify gaps, avoid duplication of efforts, and plan for future humanitarian response (if needed). The data includes a list of humanitarian organizations by district and cluster, as well as a unique count of organizations. An interactive map of the 3W data can be accessed here.

Each row in this dataset represents subnational administrative unit observations. Data was last updated on HDX on 2025-05-05. Geographic scope: AFG.

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

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Dataset Characteristics

Domain	Humanitarian and development data
Unit of observation	Subnational administrative unit observations
Rows (total)	4,358
Columns	13 (2 numeric, 11 categorical, 0 datetime)
Train split	3,486 rows
Test split	871 rows
Geographic scope	AFG
Publisher	OCHA Afghanistan
HDX last updated	2025-05-05

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Variables

Geographic — region (Capital, Northern, Western), province (Kabul, Hirat, Kandahar), district (Qarabagh, Guldara, Estalef), org_acronym (UNICEF, WFP, NPO/RRAA), org_type (International NGO, National NGO, United Nations).

Identifier / Metadata — prov_code (range 1.0–34.0), dist_code (range 101.0–3405.0), org_name (United Nations Children's Fund, World Food Program, Rural Rehabilitation Association for Afghanistan), cluster_sector_code (ESNFI_OC, FSAC_OC, HEALTH_OC), cluster_sector_name (Operational Capacity: Emergency Shelter & Non-Food Items, Operational Capacity: Food Security & Agriculture, Opeartional Capacity: Health) and 2 others.

Other — question (OC).

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/asia-afghanistan-who-does-what-where-april-to-june-2017")
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
region	object	0.0%	Capital, Northern, Western
province	object	0.0%	Kabul, Hirat, Kandahar
prov_code	int64	0.0%	1.0 – 34.0 (mean 16.7354)
district	object	0.0%	Qarabagh, Guldara, Estalef
dist_code	int64	0.0%	101.0 – 3405.0 (mean 1680.816)
org_acronym	object	0.0%	UNICEF, WFP, NPO/RRAA
org_name	object	0.0%	United Nations Children's Fund, World Food Program, Rural Rehabilitation Association for Afghanistan
org_type	object	0.0%	International NGO, National NGO, United Nations
cluster_sector_code	object	0.0%	ESNFI_OC, FSAC_OC, HEALTH_OC
cluster_sector_name	object	0.0%	Operational Capacity: Emergency Shelter & Non-Food Items, Operational Capacity: Food Security & Agriculture, Opeartional Capacity: Health
question	object	0.0%	OC
esa_source	object	0.0%	HDX
esa_processed	object	0.0%

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Numeric Summary

Column	Min	Max	Mean	Median
prov_code	1.0	34.0	16.7354	16.0
dist_code	101.0	3405.0	1680.816	1611.0

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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. 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 Afghanistan 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_asia_afghanistan_who_does_what_where_april_to_june_2017,
  title     = {Afghanistan - Who does What Where (April to June 2017)},
  author    = {OCHA Afghanistan},
  year      = {2025},
  url       = {https://data.humdata.org/dataset/afghanistan-who-does-what-where-april-to-june-2017},
  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.