electricsheepafrica/africa-nigeria-northeast-nigeria-3ws-april-june-2022

Nigeria: Northeast Nigeria 3Ws (April-June 2022)

Publisher: OCHA Nigeria · Source: HDX · License: other-pd-nr · Updated: 2025-04-15

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Abstract

Nigeria: Northeast Nigeria 3Ws (April-June 2022)

Each row in this dataset represents subnational administrative unit observations. Data was last updated on HDX on 2025-04-15. Geographic scope: NGA.

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)	8,905
Columns	18 (1 numeric, 17 categorical, 0 datetime)
Train split	7,124 rows
Test split	1,781 rows
Geographic scope	NGA
Publisher	OCHA Nigeria
HDX last updated	2025-04-15

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Variables

Geographic — org_acronym (International Organization For Migration, United Nations High Commissioner For Refugees, United Nations Children'S Emergency Fund), type_of_organization (INGO, UN Agency, NNGO), operation_type (Reporting, Implementing, #operation+type), states (Borno, Adamawa, Yobe), state_pcode (NGA008, NGA002, NGA036) and 4 others.

Temporal — month.

Identifier / Metadata — esa_source, esa_processed.

Other — organization (IOM, UNHCR, UNICEF), project_sector (Protection, Nutrition, Water, Sanitation & Hygiene), activities (Nutrition, Health, Other (specify in remarks column)), status (Ongoing, Completed, completed), ishrp and 1 others.

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-nigeria-northeast-nigeria-3ws-april-june-2022")
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
organization	object	0.0%	IOM, UNHCR, UNICEF
org_acronym	object	0.0%	International Organization For Migration, United Nations High Commissioner For Refugees, United Nations Children'S Emergency Fund
type_of_organization	object	0.0%	INGO, UN Agency, NNGO
operation_type	object	0.0%	Reporting, Implementing, #operation+type
project_sector	object	0.0%	Protection, Nutrition, Water, Sanitation & Hygiene
activities	object	0.0%	Nutrition, Health, Other (specify in remarks column)
status	object	0.0%	Ongoing, Completed, completed
states	object	0.0%	Borno, Adamawa, Yobe
state_pcode	object	0.0%	NGA008, NGA002, NGA036
lga	object	0.0%	Jere, Maiduguri, Bama
lga_pcode	object	0.0%
ishrp	object	0.0%
response_type	object	0.0%
isrp	object	0.0%
month	object	0.0%
year	float64	0.0%	2022.0 – 2022.0 (mean 2022.0)
esa_source	object	0.0%
esa_processed	object	0.0%

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Numeric Summary

Column	Min	Max	Mean	Median
year	2022.0	2022.0	2022.0	2022.0

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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) 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 Nigeria 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_nigeria_northeast_nigeria_3ws_april_june_2022,
  title     = {Nigeria: Northeast Nigeria 3Ws (April-June 2022)},
  author    = {OCHA Nigeria},
  year      = {2025},
  url       = {https://data.humdata.org/dataset/nigeria-northeast-nigeria-3ws-april-june-2022},
  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.