---
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-regression
task_ids: []
tags:
- africa
- humanitarian
- hdx
- electric-sheep-africa
- economics
- indicators
- ssd
pretty_name: "South Sudan - External Debt"
dataset_info:
  splits:
    - name: train
      num_examples: 111
    - name: test
      num_examples: 27
---

# South Sudan - External Debt

**Publisher:** World Bank Group · **Source:** [HDX](https://data.humdata.org/dataset/world-bank-external-debt-indicators-for-south-sudan) · **License:** `cc-by` · **Updated:** 2026-03-27

---

## Abstract

Contains data from the World Bank's [data portal](http://data.worldbank.org/). There is also a [consolidated country dataset](https://data.humdata.org/dataset/world-bank-combined-indicators-for-south-sudan) on HDX.

Debt statistics provide a detailed picture of debt stocks and flows of developing countries. Data presented as part of the Quarterly External Debt Statistics takes a closer look at the external debt of high-income countries and emerging markets to enable a more complete understanding of global financial flows. The Quarterly Public Sector Debt database provides further data on public sector valuation methods, debt instruments, and clearly defined tiers of debt for central, state and local government, as well as extra-budgetary agencies and funds. Data are gathered from national statistical organizations and central banks as well as by various major multilateral institutions and World Bank staff.

Each row in this dataset represents country-level aggregates. Data was last updated on HDX on 2026-03-27. Geographic scope: **SSD**.

*Curated into ML-ready Parquet format by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica).*

---

## Dataset Characteristics

| | |
|---|---|
| **Domain** | Market and price monitoring |
| **Unit of observation** | Country-level aggregates |
| **Rows (total)** | 139 |
| **Columns** | 8 (2 numeric, 6 categorical, 0 datetime) |
| **Train split** | 111 rows |
| **Test split** | 27 rows |
| **Geographic scope** | SSD |
| **Publisher** | World Bank Group |
| **HDX last updated** | 2026-03-27 |

---

## Variables

**Geographic** — `country_name` (South Sudan), `country_iso3` (SSD), `year` (range 2011.0–2024.0).

**Outcome / Measurement** — `value` (range -1717500000.0–17413371593.2203).

**Identifier / Metadata** — `indicator_name` (Grants, excluding technical cooperation (BoP, current US$), Technical cooperation grants (BoP, current US$), Foreign direct investment, net inflows (BoP, current US$)), `indicator_code` (BX.GRT.EXTA.CD.WD, BX.GRT.TECH.CD.WD, BX.KLT.DINV.CD.WD), `esa_source` (HDX), `esa_processed` (2026-04-10).

---

## Quick Start

```python
from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-world-bank-external-debt-indicators-for-south-sudan")
train = ds["train"].to_pandas()
test  = ds["test"].to_pandas()

print(train.shape)
train.head()
```

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `country_name` | object | 0.0% | South Sudan |
| `country_iso3` | object | 0.0% | SSD |
| `year` | int64 | 0.0% | 2011.0 – 2024.0 (mean 2017.4101) |
| `indicator_name` | object | 0.0% | Grants, excluding technical cooperation (BoP, current US$), Technical cooperation grants (BoP, current US$), Foreign direct investment, net inflows (BoP, current US$) |
| `indicator_code` | object | 0.0% | BX.GRT.EXTA.CD.WD, BX.GRT.TECH.CD.WD, BX.KLT.DINV.CD.WD |
| `value` | float64 | 0.0% | -1717500000.0 – 17413371593.2203 (mean 1357663012.3125) |
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% | 2026-04-10 |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `year` | 2011.0 | 2024.0 | 2017.4101 | 2017.0 |
| `value` | -1717500000.0 | 17413371593.2203 | 1357663012.3125 | 131761234.0 |

---

## 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`. 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 World Bank Group 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/world-bank-external-debt-indicators-for-south-sudan) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_world_bank_external_debt_indicators_for_south_sudan,
  title     = {South Sudan - External Debt},
  author    = {World Bank Group},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/world-bank-external-debt-indicators-for-south-sudan},
  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.*