README.md

---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license: cc-by-4.0
multilinguality:
- monolingual
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- tabular-classification
- tabular-regression
task_ids: []
tags:
- africa
- humanitarian
- hdx
- electric-sheep-africa
- covid-19
- funding
- humanitarian-financial-tracking-service-fts
- lby
pretty_name: "Libya - Requirements and Funding Data"
dataset_info:
  splits:
    - name: train
      num_examples: 28
    - name: test
      num_examples: 7
---

# Libya - Requirements and Funding Data

**Publisher:** OCHA Financial Tracking System (FTS) · **Source:** [HDX](https://data.humdata.org/dataset/lby-requirements-and-funding-data) · **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](https://fts.unocha.org/) 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](https://huggingface.co/electricsheepafrica).*

---

## 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

```python
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](https://data.humdata.org/dataset/lby-requirements-and-funding-data) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@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](https://huggingface.co/electricsheepafrica) — Africa's ML dataset infrastructure. Lagos, Nigeria.*