Datasets:

electricsheepafrica
/

africa-djibouti-djibouti-high-resolution-population-density-maps-demograph-ee7bfe69

license: cc-by-4.0
language:
  - en
task_categories:
  - tabular-classification
  - tabular-regression
multilinguality: monolingual
size_categories:
  - 10K<n<100K
tags:
  - tabular
  - zip
  - africa
  - djibouti
  - official-statistics
  - open-data
  - health
  - population
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-00000-of-00001.parquet
pretty_name: >-
  Djibouti: High Resolution Population Density Maps + Demographic Estimates |
  Africa (Djibouti official open data)

Djibouti: High Resolution Population Density Maps + Demographic Estimates | Africa (Djibouti official open data)

54,553 rows - 1 Africa country - 2020 - Repackaged by Electric Sheep Africa

TL;DR

This dataset packages one official ZIP resource from Djibouti as ML-ready Parquet. The source file is the provenance boundary; all usable indicators or tabular columns from the resource stay together in this repo.

About the source

Source: Djibouti: High Resolution Population Density Maps + Demographic Estimates
Publisher: AI for Good at Meta
Resource: dji_children_under_five_2020_csv.zip
Format: ZIP
License: CC BY 4.0
Packaging mode: tabular_resource

Geographic coverage

1 Africa country:

Country	Rows	First year	Last year	Name
`DJI`	54,553	2020	2020	`Djibouti`

Indicators or Resource Contents

This source file is packaged as a normalized tabular resource.

Schema

Column	Type	Description	Example
`source_record_id`	`string`	Stable row identifier for tabular resources.	`f3fafddd-94b4-4dec-af8c-bb40638ef0e7:0`
`country_iso3`	`category`	ISO3 country code.	`DJI`
`country_name`	`category`	Country name.	`Djibouti`
`year`	`Int64`	Observation year.	`2020`
`longitude`	`float64`	Source column.	`42.739305555734695`
`latitude`	`float64`	Source column.	`13.000138888841969`
`dji_children_under_five_2020`	`float64`	Source column.	`2.479968`
`source_period_start_year`	`Int64`	First year inferred from source resource metadata.	`2020`
`source_period_end_year`	`Int64`	Last year inferred from source resource metadata.	`2020`
`source_period_label`	`category`	Human-readable period inferred from source resource metadata.	`2020`
`source_provider`	`category`	Publishing organization.	`AI for Good at Meta`
`source_dataset`	`category`	Source package title.	`Djibouti: High Resolution Population Density Maps + Demographic Estimate`
`source_resource`	`category`	Source resource title.	`dji_children_under_five_2020_csv.zip`
`source_package_id`	`category`	CKAN package UUID.	`f419e2e0-8f49-4c91-94d3-0b7a901aaef3`
`source_resource_id`	`category`	CKAN resource UUID.	`f3fafddd-94b4-4dec-af8c-bb40638ef0e7`
`source_url`	`category`	Original source resource URL.	`https://data.humdata.org/dataset/f419e2e0-8f49-4c91-94d3-0b7a901aaef3/re`
`license_id`	`category`	Source license identifier.	`cc-by`
`retrieved_at`	`category`	UTC retrieval timestamp.	`2026-08-16T10:32:57Z`

Usage

from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-djibouti-djibouti-high-resolution-population-density-maps-demograph-ee7bfe69")
df = ds["train"].to_pandas()
print(df.head())

Filter to one country

sample_country = df[df["country_iso3"] == "DJI"]

Work with indicators

if "indicator_id" in df.columns:
    print(df["indicator_id"].value_counts().head())
    sample = df.sort_values([c for c in ["indicator_id", "year"] if c in df.columns])

Citation

@misc{electric_sheep_africa_africa_djibouti_djibouti_high_resolution_population_density_maps_demograph_ee7bf_2020,
  title        = {Djibouti: High Resolution Population Density Maps + Demographic Estimates | Africa (Djibouti official open data)},
  author       = {AI for Good at Meta},
  year         = {2020},
  url          = {https://data.humdata.org/dataset/highresolutionpopulationdensitymaps-dji},
  publisher    = {HuggingFace Datasets, repackaged by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-djibouti-djibouti-high-resolution-population-density-maps-demograph-ee7bfe69}}
}

License

Released under CC BY 4.0.

Original data (c) AI for Good at Meta. When using this dataset, please cite both the original source above and the Electric Sheep Africa repackaging.

About Electric Sheep

Electric Sheep Africa is part of the Electric Sheep mission: a unified, ML-ready data layer for Africa on Hugging Face. We pull data from authoritative open sources, normalize the schemas, package as Parquet, and publish with consistent dataset cards so researchers and developers can use load_dataset() to start working in seconds.

Browse the full collection: huggingface.co/electricsheepafrica

Provenance: ingested 2026-08-16 via the Electric Sheep pipeline. Source URL: https://data.humdata.org/dataset/f419e2e0-8f49-4c91-94d3-0b7a901aaef3/resource/f3fafddd-94b4-4dec-af8c-bb40638ef0e7/download/dji_children_under_five_2020_csv.zip