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

Libya - Requirements and Funding Data

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

---

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. Temporal coverage is indicated by the startdate, enddate column(s). Geographic scope: LBY.

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

---

Dataset Characteristics

Domain	Humanitarian and development data
Unit of observation	Country-level aggregates
Rows (total)	35
Columns	14 (6 numeric, 6 categorical, 2 datetime)
Train split	28 rows
Test split	7 rows
Geographic scope	LBY
Publisher	OCHA Financial Tracking System (FTS)
HDX last updated	2026-04-03

---

Variables

Geographic — countrycode (LBY), typeid (range 4.0–111.0), typename (Humanitarian response plan, Regional response plan, Flash appeal), year (range 2005.0–2027.0).

Temporal — startdate, enddate.

Outcome / Measurement — percentfunded (range 5.0–128.0).

Identifier / Metadata — id (range 365.0–1523.0), name (Not specified, Sudan Emergency: Regional Refugee Response Plan 2026, Sudan Emergency: Regional Refugee Response Plan 2025), code (RREG26a, RRSDN25, FLBY24), esa_source (HDX), esa_processed (2026-04-04).

Other — requirements (range 10676371.0–312740102.0), funding (range 60976.0–150268390.0).

---

Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-lby-requirements-and-funding-data")
train = ds["train"].to_pandas()
test  = ds["test"].to_pandas()

print(train.shape)
train.head()

---

Schema

Column	Type	Null %	Range / Sample Values
countrycode	object	0.0%	LBY
id	float64	57.1%	365.0 – 1523.0 (mean 867.8667)
name	object	0.0%	Not specified, Sudan Emergency: Regional Refugee Response Plan 2026, Sudan Emergency: Regional Refugee Response Plan 2025
code	object	57.1%	RREG26a, RRSDN25, FLBY24
typeid	float64	57.1%	4.0 – 111.0 (mean 32.8)
typename	object	57.1%	Humanitarian response plan, Regional response plan, Flash appeal
startdate	datetime64[ns]	57.1%
enddate	datetime64[ns]	57.1%
year	int64	0.0%	2005.0 – 2027.0 (mean 2018.3429)
requirements	float64	60.0%	10676371.0 – 312740102.0 (mean 114052953.4286)
funding	int64	0.0%	60976.0 – 150268390.0 (mean 44350035.7714)
percentfunded	float64	60.0%	5.0 – 128.0 (mean 62.6429)
esa_source	object	0.0%	HDX
esa_processed	object	0.0%	2026-04-04

---

Numeric Summary

Column	Min	Max	Mean	Median
id	365.0	1523.0	867.8667	931.0
typeid	4.0	111.0	32.8	5.0
year	2005.0	2027.0	2018.3429	2019.0
requirements	10676371.0	312740102.0	114052953.4286	110215636.5
funding	60976.0	150268390.0	44350035.7714	39729359.0
percentfunded	5.0	128.0	62.6429	61.5

---

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. 2 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.

---

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.
• The following columns have >20% missing values and should be treated with caution in modelling: id, code, typeid, typename, startdate, enddate, requirements, percentfunded.
• Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

---

Citation

@dataset{hdx_africa_lby_requirements_and_funding_data,
  title     = {Libya - Requirements and Funding Data},
  author    = {OCHA Financial Tracking System (FTS)},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/lby-requirements-and-funding-data},
  note      = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}

---

Electric Sheep Africa — Africa's ML dataset infrastructure. Lagos, Nigeria.