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

---
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
- tcd
pretty_name: "Chad - External Debt"
dataset_info:
splits:
- name: train
num_examples: 1777
- name: test
num_examples: 444
---

# Chad - External Debt

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

*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)** | 2,222 |
| **Columns** | 8 (2 numeric, 6 categorical, 0 datetime) |
| **Train split** | 1,777 rows |
| **Test split** | 444 rows |
| **Geographic scope** | TCD |
| **Publisher** | World Bank Group |
| **HDX last updated** | 2026-03-27 |

---

## Variables

**Geographic** — `country_name` (Chad), `country_iso3` (TCD), `year` (range 1960.0–2024.0).

**Outcome / Measurement** — `value` (range -675545920.0–19307611603.1826).

**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` (2026-04-15).

---

## Quick Start

```python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-world-bank-external-debt-indicators-for-chad")
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% | Chad |
| `country_iso3` | object | 0.0% | TCD |
| `year` | int64 | 0.0% | 1960.0 – 2024.0 (mean 1995.5666) |
| `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% | -675545920.0 – 19307611603.1826 (mean 303341381.1162) |
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% | 2026-04-15 |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `year` | 1960.0 | 2024.0 | 1995.5666 | 1994.5 |
| `value` | -675545920.0 | 19307611603.1826 | 303341381.1162 | 5650912.4 |

---

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

---

## Citation

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