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

Lesotho - Requirements and Funding Data

Publisher: OCHA Financial Tracking System (FTS) · Source: HDX · License: cc-by-igo · Updated: 2026-04-03

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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 2026-04-03. Geographic scope: LSO.

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)	30
Columns	6 (2 numeric, 4 categorical, 0 datetime)
Train split	24 rows
Test split	6 rows
Geographic scope	LSO
Publisher	OCHA Financial Tracking System (FTS)
HDX last updated	2026-04-03

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Variables

Geographic — countrycode (LSO), year (range 2002.0–2027.0).

Identifier / Metadata — name (Not specified, Lesotho Flash Appeal 2019-2020, Lesotho Food Insecurity (September 2012 - March 2013)), esa_source (HDX), esa_processed (2026-04-05).

Other — funding (range 204965.0–27942430.0).

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-lso-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%	LSO
name	object	0.0%	Not specified, Lesotho Flash Appeal 2019-2020, Lesotho Food Insecurity (September 2012 - March 2013)
year	int64	0.0%	2002.0 – 2027.0 (mean 2013.9)
funding	int64	0.0%	204965.0 – 27942430.0 (mean 6398541.6667)
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
year	2002.0	2027.0	2013.9	2013.5
funding	204965.0	27942430.0	6398541.6667	4386414.5

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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. 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_lso_requirements_and_funding_data,
  title     = {Lesotho - Requirements and Funding Data},
  author    = {OCHA Financial Tracking System (FTS)},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/lso-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.