---
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
- covid-19
- economics
- socioeconomics
- afg
- alb
- ago
- arg
- arm
pretty_name: "Compilation of International Financial Institution and Economic Data"
dataset_info:
  splits:
    - name: train
      num_examples: 216
    - name: test
      num_examples: 54
---

# Compilation of International Financial Institution and Economic Data

**Publisher:** HDX · **Source:** [HDX](https://data.humdata.org/dataset/compilation-of-international-financial-institution-and-economic-data) · **License:** `cc-by` · **Updated:** 2025-08-26

---

## Abstract

Compilation of international financial institution and economic data

Each row in this dataset represents country-level aggregates. Data was last updated on HDX on 2025-08-26. Geographic scope: **AFG, ALB, AGO, ARG, ARM, BGD, BLR, BLZ, and 9 others**.

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

---

## Dataset Characteristics

| | |
|---|---|
| **Domain** | Demographics and population |
| **Unit of observation** | Country-level aggregates |
| **Rows (total)** | 271 |
| **Columns** | 17 (0 numeric, 17 categorical, 0 datetime) |
| **Train split** | 216 rows |
| **Test split** | 54 rows |
| **Geographic scope** | AFG, ALB, AGO, ARG, ARM, BGD, BLR, BLZ, and 9 others |
| **Publisher** | HDX |
| **HDX last updated** | 2025-08-26 |

---

## Variables

**Geographic** — `country_code` ( AFG,  ROU,  PLW), `country` ( Falkland Islands (Malvinas),  Montserrat,  Anguilla), `population`.

**Demographic** — `total_as_percentage_of_gdp`.

**Outcome / Measurement** — `total_usd_mn`, `total_per_capita_usd_mn`, `gdp_per_capita`.

**Identifier / Metadata** — `idb` ( -   ,  990 ,  31 ), `esa_source`, `esa_processed`.

**Other** — `world_bank` ( -   ,  25 ,  3 ), `ifc` ( -   ,  10 ,  15 ), `imf` ( -   ,  29 ,  14 ), `afdb` ( -   ,  14 ,  2 ), `adb` ( -   ,  20 ,  250 ) and 2 others.

---

## Quick Start

```python
from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-compilation-of-international-financial-institution-and-economic-data")
train = ds["train"].to_pandas()
test  = ds["test"].to_pandas()

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

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `country_code` | object | 1.8% |  AFG,  ROU,  PLW |
| `country` | object | 1.1% |  Falkland Islands (Malvinas),  Montserrat,  Anguilla |
| `world_bank` | object | 1.5% |  -   ,  25 ,  3  |
| `ifc` | object | 1.5% |  -   ,  10 ,  15  |
| `imf` | object | 1.5% |  -   ,  29 ,  14  |
| `afdb` | object | 1.5% |  -   ,  14 ,  2  |
| `adb` | object | 1.5% |  -   ,  20 ,  250  |
| `idb` | object | 1.5% |  -   ,  990 ,  31  |
| `isdb` | object | 1.5% |  -   ,  20 ,  9  |
| `ebrd` | object | 1.5% |  -   ,  63 ,  49  |
| `total_usd_mn` | object | 1.5% |  |
| `total_per_capita_usd_mn` | object | 2.2% |  |
| `total_as_percentage_of_gdp` | object | 29.5% |  |
| `population` | object | 2.2% |  |
| `gdp_per_capita` | object | 8.1% |  |
| `esa_source` | object | 0.0% |  |
| `esa_processed` | object | 0.0% |  |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
_No numeric columns._

---

## 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`. 134 exact duplicate rows were removed. 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 HDX 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: `total_as_percentage_of_gdp`.
- This dataset spans 17 countries; geographic and methodological inconsistencies across national boundaries may affect cross-country comparability.
- Refer to the [original HDX dataset page](https://data.humdata.org/dataset/compilation-of-international-financial-institution-and-economic-data) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_compilation_of_international_financial_institution_and_economic_data,
  title     = {Compilation of International Financial Institution and Economic Data},
  author    = {HDX},
  year      = {2025},
  url       = {https://data.humdata.org/dataset/compilation-of-international-financial-institution-and-economic-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.*