electricsheepafrica/africa-swz-requirements-and-funding-data

Eswatini - Requirements and Funding Data

Publisher: OCHA Financial Tracking System (FTS) · Source: HDX · License: cc-by-igo · Updated: 2026-04-06

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Abstract

FTS publishes data on humanitarian funding flows as reported by donors and recipient organizations. It presents all humanitarian funding to a country and funding that is specifically reported or that can be specifically mapped against funding requirements stated in humanitarian response plans. The data comes from OCHA's Financial Tracking Service and is encoded as utf-8.

Each row in this dataset represents country-level aggregates. Data was last updated on HDX on 2026-04-06. Geographic scope: SWZ.

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

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Dataset Characteristics

Domain	Humanitarian and development data
Unit of observation	Country-level aggregates
Rows (total)	27
Columns	6 (2 numeric, 4 categorical, 0 datetime)
Train split	21 rows
Test split	5 rows
Geographic scope	SWZ
Publisher	OCHA Financial Tracking System (FTS)
HDX last updated	2026-04-06

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Variables

Geographic — countrycode (SWZ), year (range 2002.0–2026.0).

Identifier / Metadata — name (Not specified, Swaziland Drought Flash Appeal 2007, Humanitarian Crisis in Southern Africa - SWAZILAND (July 2003 - June 2004)), esa_source (HDX), esa_processed (2026-04-06).

Other — funding (range 20000.0–20772118.0).

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-swz-requirements-and-funding-data")
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
countrycode	object	0.0%	SWZ
name	object	0.0%	Not specified, Swaziland Drought Flash Appeal 2007, Humanitarian Crisis in Southern Africa - SWAZILAND (July 2003 - June 2004)
year	int64	0.0%	2002.0 – 2026.0 (mean 2012.963)
funding	int64	0.0%	20000.0 – 20772118.0 (mean 4614191.0741)
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
year	2002.0	2026.0	2012.963	2013.0
funding	20000.0	20772118.0	4614191.0741	2694140.0

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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. 8 column(s) with >80% missing values were removed: id, code, typeid, typename, startdate, enddate.... 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 Financial Tracking System (FTS) 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_swz_requirements_and_funding_data,
  title     = {Eswatini - Requirements and Funding Data},
  author    = {OCHA Financial Tracking System (FTS)},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/swz-requirements-and-funding-data},
  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.