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-classification
task_ids: []
tags:
- africa
- humanitarian
- hdx
- electric-sheep-africa
- aid-effectiveness
- indicators
- dji
pretty_name: Djibouti - Aid Effectiveness
dataset_info:
splits:
- name: train
num_examples: 1423
- name: test
num_examples: 355
Djibouti - Aid Effectiveness
Publisher: World Bank Group · Source: HDX · License: cc-by · Updated: 2026-03-27
Abstract
Contains data from the World Bank's data portal. There is also a consolidated country dataset on HDX.
Aid effectiveness is the impact that aid has in reducing poverty and inequality, increasing growth, building capacity, and accelerating achievement of the Millennium Development Goals set by the international community. Indicators here cover aid received as well as progress in reducing poverty and improving education, health, and other measures of human welfare.
Each row in this dataset represents country-level aggregates. Data was last updated on HDX on 2026-03-27. Geographic scope: DJI.
Curated into ML-ready Parquet format by Electric Sheep Africa.
Dataset Characteristics
| Domain | Public health |
| Unit of observation | Country-level aggregates |
| Rows (total) | 1,779 |
| Columns | 8 (2 numeric, 6 categorical, 0 datetime) |
| Train split | 1,423 rows |
| Test split | 355 rows |
| Geographic scope | DJI |
| Publisher | World Bank Group |
| HDX last updated | 2026-03-27 |
Variables
Geographic — country_name (Djibouti), country_iso3 (DJI), year (range 1960.0–2025.0).
Outcome / Measurement — value (range -1206163.0487–323470458.9844).
Identifier / Metadata — indicator_name (Net migration, Net bilateral aid flows from DAC donors, France (current US$), Net ODA received per capita (current US$)), indicator_code (SM.POP.NETM, DC.DAC.FRAL.CD, DT.ODA.ODAT.PC.ZS), esa_source (HDX), esa_processed (2026-04-14).
Quick Start
from datasets import load_dataset
ds = load_dataset("electricsheepafrica/africa-world-bank-aid-effectiveness-indicators-for-djibouti")
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% | Djibouti |
country_iso3 |
object | 0.0% | DJI |
year |
int64 | 0.0% | 1960.0 – 2025.0 (mean 2000.0253) |
indicator_name |
object | 0.0% | Net migration, Net bilateral aid flows from DAC donors, France (current US$), Net ODA received per capita (current US$) |
indicator_code |
object | 0.0% | SM.POP.NETM, DC.DAC.FRAL.CD, DT.ODA.ODAT.PC.ZS |
value |
float64 | 0.0% | -1206163.0487 – 323470458.9844 (mean 23114722.9224) |
esa_source |
object | 0.0% | HDX |
esa_processed |
object | 0.0% | 2026-04-14 |
Numeric Summary
| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
year |
1960.0 | 2025.0 | 2000.0253 | 2002.0 |
value |
-1206163.0487 | 323470458.9844 | 23114722.9224 | 692461.0138 |
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 for the publisher's own methodology notes and caveats.
Citation
@dataset{hdx_africa_world_bank_aid_effectiveness_indicators_for_djibouti,
title = {Djibouti - Aid Effectiveness},
author = {World Bank Group},
year = {2026},
url = {https://data.humdata.org/dataset/world-bank-aid-effectiveness-indicators-for-djibouti},
note = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}
Electric Sheep Africa — Africa's ML dataset infrastructure. Lagos, Nigeria.