electricsheepafrica/africa-fts-requirements-and-funding-data-for-cape-verde

---
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
- hxl
- cpv
pretty_name: "Cape Verde - Requirements and Funding Data"
dataset_info:
splits:
- name: train
num_examples: 17
- name: test
num_examples: 4
---

# Cape Verde - Requirements and Funding Data

**Publisher:** OCHA Financial Tracking System (FTS) · **Source:** [HDX](https://data.humdata.org/dataset/fts-requirements-and-funding-data-for-cape-verde) · **License:** `cc-by-igo` · **Updated:** 2025-03-13

---

## 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/), is encoded as utf-8 and the second row of the CSV contains [HXL](http://hxlstandard.org) tags.

Each row in this dataset represents country-level aggregates. Data was last updated on HDX on 2025-03-13. Geographic scope: **CPV**.

*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)** | 22 |
| **Columns** | 6 (2 numeric, 4 categorical, 0 datetime) |
| **Train split** | 17 rows |
| **Test split** | 4 rows |
| **Geographic scope** | CPV |
| **Publisher** | OCHA Financial Tracking System (FTS) |
| **HDX last updated** | 2025-03-13 |

---

## Variables

**Geographic** — `countrycode` (CPV, #country+code), `year` (range 2002.0–2024.0).

**Identifier / Metadata** — `name` (Not specified, #activity+appeal+name, West Africa 2007), `esa_source` (HDX), `esa_processed` (2026-04-07).

**Other** — `funding` (range 5334.0–4097899.0).

---

## Quick Start

```python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-fts-requirements-and-funding-data-for-cape-verde")
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% | CPV, #country+code |
| `name` | object | 0.0% | Not specified, #activity+appeal+name, West Africa 2007 |
| `year` | float64 | 4.5% | 2002.0 – 2024.0 (mean 2012.9048) |
| `funding` | float64 | 9.1% | 5334.0 – 4097899.0 (mean 1102916.15) |
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% | 2026-04-07 |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `year` | 2002.0 | 2024.0 | 2012.9048 | 2012.0 |
| `funding` | 5334.0 | 4097899.0 | 1102916.15 | 657509.0 |

---

## 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`.... 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.
- Refer to the [original HDX dataset page](https://data.humdata.org/dataset/fts-requirements-and-funding-data-for-cape-verde) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_fts_requirements_and_funding_data_for_cape_verde,
title = {Cape Verde - Requirements and Funding Data},
author = {OCHA Financial Tracking System (FTS)},
year = {2025},
url = {https://data.humdata.org/dataset/fts-requirements-and-funding-data-for-cape-verde},
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.*