electricsheepafrica/africa-world-bank-external-debt-indicators-for-sudan

---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license: cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- tabular-regression
task_ids: []
tags:
- africa
- humanitarian
- hdx
- electric-sheep-africa
- economics
- indicators
- sdn
pretty_name: "Sudan - External Debt"
dataset_info:
splits:
- name: train
num_examples: 2037
- name: test
num_examples: 509
---

# Sudan - External Debt

**Publisher:** World Bank Group · **Source:** [HDX](https://data.humdata.org/dataset/world-bank-external-debt-indicators-for-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-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: **SDN**.

*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)** | 2,547 |
| **Columns** | 8 (2 numeric, 6 categorical, 0 datetime) |
| **Train split** | 2,037 rows |
| **Test split** | 509 rows |
| **Geographic scope** | SDN |
| **Publisher** | World Bank Group |
| **HDX last updated** | 2026-03-27 |

---

## Variables

**Geographic** — `country_name` (Sudan), `country_iso3` (SDN), `year` (range 1960.0–2024.0).

**Outcome / Measurement** — `value` (range -6495850087.1589–61819980259.9127).

**Identifier / Metadata** — `indicator_name` (GNI (current US$), Grants, excluding technical cooperation (BoP, current US$), Technical cooperation grants (BoP, current US$)), `indicator_code` (NY.GNP.MKTP.CD, BX.GRT.EXTA.CD.WD, BX.GRT.TECH.CD.WD), `esa_source` (HDX), `esa_processed` (2026-04-09).

---

## Quick Start

```python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-world-bank-external-debt-indicators-for-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% | Sudan |
| `country_iso3` | object | 0.0% | SDN |
| `year` | int64 | 0.0% | 1960.0 – 2024.0 (mean 1995.7664) |
| `indicator_name` | object | 0.0% | GNI (current US$), Grants, excluding technical cooperation (BoP, current US$), Technical cooperation grants (BoP, current US$) |
| `indicator_code` | object | 0.0% | NY.GNP.MKTP.CD, BX.GRT.EXTA.CD.WD, BX.GRT.TECH.CD.WD |
| `value` | float64 | 0.0% | -6495850087.1589 – 61819980259.9127 (mean 1590697941.6692) |
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% | 2026-04-09 |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `year` | 1960.0 | 2024.0 | 1995.7664 | 1996.0 |
| `value` | -6495850087.1589 | 61819980259.9127 | 1590697941.6692 | 14200000.0 |

---

## 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-sudan) for the publisher's own methodology notes and caveats.

---

## Citation

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

annotations_creators: 注释创建者：无注释
language_creators: 语言创建者：获取型
language: 语言：英语
license: 许可协议：CC-BY-4.0
multilinguality: 多语言类型：单语言
size_categories: 数据规模：1000 < n < 10000
source_datasets: 源数据集：原创数据集
task_categories: 任务类别：表格回归（tabular-regression）
task_ids: 任务子项：无
tags: 标签：非洲、人道主义、HDX（Humanitarian Data Exchange）、electric-sheep-africa、经济学、指标、SDN
pretty_name: 官方名称："苏丹 - 外债"
dataset_info:
splits:
- name: train
num_examples: 2037
- name: test
num_examples: 509

# 苏丹外债数据集

**发布方**：世界银行集团 · **数据源**：[HDX（Humanitarian Data Exchange）](https://data.humdata.org/dataset/world-bank-external-debt-indicators-for-sudan) · **许可协议**：`'cc-by'` · **更新时间**：2026-03-27

---

## 摘要

本数据集包含来自世界银行[数据门户](http://data.worldbank.org/)的相关数据。HDX平台上另有一份[苏丹综合国家数据集](https://data.humdata.org/dataset/world-bank-combined-indicators-for-sudan)。
外债统计数据可详细展现发展中国家的债务存量与流量情况。季度外债统计数据集聚焦高收入国家与新兴市场的外债状况，以助力更全面地理解全球资金流动。公共部门季度债务数据库则提供了更多关于公共部门估值方法、债务工具的信息，并明确划分了中央、州及地方政府，以及预算外机构与基金的债务层级。本数据集的数据来源于各国统计机构、中央银行、各大多边机构以及世界银行工作人员。
本数据集的每一行均代表国家级汇总数据。本数据集在HDX平台的最后更新时间为2026-03-27。地理覆盖范围：**SDN**。
*本数据集已由[Electric Sheep Africa](https://huggingface.co/electricsheepafrica)整理为适用于机器学习的Parquet格式。*

---

## 数据集特征

| | |
|---|---|
| **领域** | 市场与价格监测 |
| **观测单元** | 国家级汇总数据 |
| **总数据行数** | 2547 |
| **列数** | 8（2个数值型、6个分类型、0个日期时间型） |
| **训练集拆分** | 2037条数据 |
| **测试集拆分** | 509条数据 |
| **地理覆盖范围** | SDN |
| **发布方** | 世界银行集团 |
| **HDX最后更新时间** | 2026-03-27 |

---

## 变量

**地理相关变量**：`country_name`（国家名称：苏丹）、`country_iso3`（国家ISO3代码：SDN）、`year`（年份：范围1960.0–2024.0）。
**结果/测量变量**：`value`（数值：范围-6495850087.1589–61819980259.9127）。
**标识符/元数据变量**：`indicator_name`（指标名称：国民总收入（现价美元）、剔除技术合作后的赠款（国际收支平衡表，现价美元）、技术合作赠款（国际收支平衡表，现价美元））、`indicator_code`（指标代码：NY.GNP.MKTP.CD、BX.GRT.EXTA.CD.WD、BX.GRT.TECH.CD.WD）、`esa_source`（数据来源：HDX）、`esa_processed`（数据处理时间：2026-04-09）。

---

## 快速入门

python
from datasets import load_dataset

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

print(train.shape)
train.head()

---

## 数据模式

| 列名 | 数据类型 | 空值占比 | 取值范围/示例值 |
|---|---|---|---|
| `country_name` | 字符型（object） | 0.0% | 苏丹 |
| `country_iso3` | 字符型 | 0.0% | SDN |
| `year` | 64位整数（int64） | 0.0% | 1960.0 – 2024.0（均值1995.7664） |
| `indicator_name` | 字符型 | 0.0% | 国民总收入（现价美元）、剔除技术合作后的赠款（国际收支平衡表，现价美元）、技术合作赠款（国际收支平衡表，现价美元） |
| `indicator_code` | 字符型 | 0.0% | NY.GNP.MKTP.CD、BX.GRT.EXTA.CD.WD、BX.GRT.TECH.CD.WD |
| `value` | 64位浮点型（float64） | 0.0% | -6495850087.1589 – 61819980259.9127（均值1590697941.6692） |
| `esa_source` | 字符型 | 0.0% | HDX |
| `esa_processed` | 字符型 | 0.0% | 2026-04-09 |

---

## 数值型变量统计摘要

| 列名 | 最小值 | 最大值 | 均值 | 中位数 |
|---|---|---|---|---|
| `year` | 1960.0 | 2024.0 | 1995.7664 | 1996.0 |
| `value` | -6495850087.1589 | 61819980259.9127 | 1590697941.6692 | 14200000.0 |

---

## 数据整理流程

原始数据通过CKAN API从HDX平台下载，并转换为Parquet格式。列名统一转换为小写并采用蛇形命名法（snake_case）进行标准化。将常见的缺失值标记（`N/A`、`null`、`none`、`-`、`unknown`、`no data`、`#N/A`）统一替换为`NaN`。本数据集以固定随机种子（42）按照80/20的比例划分为训练集与测试集，并以Snappy压缩格式的Parquet文件保存。

---

## 局限性

- 本数据集的数据源自世界银行集团，并未由Electric Sheep Africa进行独立验证。
- 自动化清洗流程无法修正原始数据收集中的错报值、定义不一致问题或抽样偏差。
- 如需查看发布方提供的方法说明与注意事项，请参阅[原始HDX数据集页面](https://data.humdata.org/dataset/world-bank-external-debt-indicators-for-sudan)。

---

## 引用格式

bibtex
@dataset{hdx_africa_world_bank_external_debt_indicators_for_sudan,
title = {苏丹 - 外债},
author = {世界银行集团},
year = {2026},
url = "https://data.humdata.org/dataset/world-bank-external-debt-indicators-for-sudan",
note = {由Electric Sheep Africa（https://huggingface.co/electricsheepafrica）重新打包以适配机器学习场景}
}

---

*[Electric Sheep Africa](https://huggingface.co/electricsheepafrica) — 非洲的机器学习数据集基础设施。尼日利亚拉各斯。*