README.md

---
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](https://data.humdata.org/dataset/world-bank-infrastructure-indicators-for-djibouti) · **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-djibouti) 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](https://huggingface.co/electricsheepafrica).*

---

## 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

```python
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](https://data.humdata.org/dataset/world-bank-infrastructure-indicators-for-djibouti) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@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](https://huggingface.co/electricsheepafrica) — Africa's ML dataset infrastructure. Lagos, Nigeria.*