Datasets:

electricsheepafrica
/

africa-compilation-of-international-financial-institution-and-economic-data

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 · 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.

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

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 for the publisher's own methodology notes and caveats.

Citation

@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 — Africa's ML dataset infrastructure. Lagos, Nigeria.