Datasets:

electricsheepafrica
/

africa-world-bank-infrastructure-indicators-for-djibouti

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
  - facilities-infrastructure
  - indicators
  - dji
pretty_name: Djibouti - Infrastructure
dataset_info:
  splits:
    - name: train
      num_examples: 945
    - name: test
      num_examples: 236

Djibouti - Infrastructure

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.

Infrastructure helps determine the success of manufacturing and agricultural activities. Investments in water, sanitation, energy, housing, and transport also improve lives and help reduce poverty. And new information and communication technologies promote growth, improve delivery of health and other services, expand the reach of education, and support social and cultural advances. Data here are compiled from such sources as the International Road Federation, Containerisation International, the International Civil Aviation Organization, the International Energy Association, and the International Telecommunications Union.

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)	1,182
Columns	8 (2 numeric, 6 categorical, 0 datetime)
Train split	945 rows
Test split	236 rows
Geographic scope	DJI
Publisher	World Bank Group
HDX last updated	2026-03-27

Variables

Geographic — country_name (Djibouti), country_iso3 (DJI), year (range 1960.0–2024.0).

Outcome / Measurement — value (range 0.0–10705670766.0).

Identifier / Metadata — indicator_name (Renewable internal freshwater resources, total (billion cubic meters), Renewable internal freshwater resources per capita (cubic meters), Fixed telephone subscriptions), indicator_code (ER.H2O.INTR.K3, ER.H2O.INTR.PC, IT.MLT.MAIN), esa_source (HDX), esa_processed (2026-04-14).

Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-world-bank-infrastructure-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 – 2024.0 (mean 2000.7107)
`indicator_name`	object	0.0%	Renewable internal freshwater resources, total (billion cubic meters), Renewable internal freshwater resources per capita (cubic meters), Fixed telephone subscriptions
`indicator_code`	object	0.0%	ER.H2O.INTR.K3, ER.H2O.INTR.PC, IT.MLT.MAIN
`value`	float64	0.0%	0.0 – 10705670766.0 (mean 122038310.1786)
`esa_source`	object	0.0%	HDX
`esa_processed`	object	0.0%	2026-04-14

Numeric Summary

Column	Min	Max	Mean	Median
`year`	1960.0	2024.0	2000.7107	2002.0
`value`	0.0	10705670766.0	122038310.1786	12.5704

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_infrastructure_indicators_for_djibouti,
  title     = {Djibouti - Infrastructure},
  author    = {World Bank Group},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/world-bank-infrastructure-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.