electricsheepafrica/africa-unicef-esaro-regional-db-31-october-2017

UNICEF ESARO Regional db 31 March 2018

Publisher: UNICEF Eastern and Southern Africa Regional Office (ESARO) (inactive) · Source: HDX · License: cc-by · Updated: 2024-08-30

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Abstract

UNICEF Eastern and Southern Africa database - Target, Response and Funding as of 31 March 2018

Each row in this dataset represents tabular records. Temporal coverage is indicated by the unnamed_1 column(s). Geographic scope: AGO, BDI, ERI, ETH, KEN, MDG, SOM, SSD, and 1 others.

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)	18
Columns	4 (0 numeric, 3 categorical, 1 datetime)
Train split	14 rows
Test split	3 rows
Geographic scope	AGO, BDI, ERI, ETH, KEN, MDG, SOM, SSD, and 1 others
Publisher	UNICEF Eastern and Southern Africa Regional Office (ESARO) (inactive)
HDX last updated	2024-08-30

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Variables

Identifier / Metadata — unnamed_1, esa_source (HDX), esa_processed (2026-04-06).

Other — instructions (1. The sheets to be updated are individual country tabs (Som etc.) and the "funding details" tabs. The "situation" tab updated to reflect any changes in context or situational data., South Sudan, Madagascar).

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-unicef-esaro-regional-db-31-october-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
instructions	object	5.6%	1. The sheets to be updated are individual country tabs (Som etc.) and the "funding details" tabs. The "situation" tab updated to reflect any changes in context or situational data., South Sudan, Madagascar
unnamed_1	datetime64[ns]	55.6%
esa_source	object	0.0%	HDX
esa_processed	object	0.0%	2026-04-06

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Numeric Summary

Column	Min	Max	Mean	Median
No numeric columns.

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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: unnamed_2. 1 exact duplicate rows were removed. 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 UNICEF Eastern and Southern Africa Regional Office (ESARO) (inactive) and has not been independently validated by ESA.
• Automated cleaning cannot correct for misreported values, definitional inconsistencies, or sampling bias in the original collection.
• The following columns have >20% missing values and should be treated with caution in modelling: unnamed_1.
• This dataset spans 9 countries; geographic and methodological inconsistencies across national boundaries may affect cross-country comparability.
• Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

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Citation

@dataset{hdx_africa_unicef_esaro_regional_db_31_october_2017,
  title     = {UNICEF ESARO Regional db 31 March 2018},
  author    = {UNICEF Eastern and Southern Africa Regional Office (ESARO) (inactive)},
  year      = {2024},
  url       = {https://data.humdata.org/dataset/unicef-esaro-regional-db-31-october-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.