Datasets:

electricsheepafrica
/

africa-unhcr-population-data-for-mus

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-classification
  - tabular-regression
task_ids: []
tags:
  - africa
  - humanitarian
  - hdx
  - electric-sheep-africa
  - asylum-seekers
  - internally-displaced-persons-idp
  - population
  - refugees
  - stateless-persons
  - mus
pretty_name: Mauritius - Data on forcibly displaced populations and stateless persons
dataset_info:
  splits:
    - name: train
      num_examples: 149
    - name: test
      num_examples: 37

Mauritius - Data on forcibly displaced populations and stateless persons

Publisher: UNHCR - The UN Refugee Agency · Source: HDX · License: cc-by-igo · Updated: 2026-02-25

Abstract

Data collated by UNHCR, containing information about forcibly displaced populations and stateless persons, spanning across more than 70 years of statistical activities. The data includes the countries / territories of asylum and origin. Specific resources are available for end-year population totals, demographics, asylum applications, decisions, and solutions availed by refugees and IDPs (resettlement, naturalisation or returns).

Each row in this dataset represents first-level administrative unit observations. Data was last updated on HDX on 2026-02-25. Geographic scope: MUS.

Curated into ML-ready Parquet format by Electric Sheep Africa.

Dataset Characteristics


Domain	Demographics and population
Unit of observation	First-level administrative unit observations
Rows (total)	187
Columns	14 (8 numeric, 6 categorical, 0 datetime)
Train split	149 rows
Test split	37 rows
Geographic scope	MUS
Publisher	UNHCR - The UN Refugee Agency
HDX last updated	2026-02-25

Variables

Geographic — year (range 1995.0–2025.0), country_of_origin_code (MUS), country_of_asylum_code (USA, CAN, FRA), country_of_origin_name (Mauritius), country_of_asylum_name (United States of America, Canada, France) and 4 others.

Identifier / Metadata — refugees (range 0.0–99.0), esa_source (HDX), esa_processed (2026-04-04).

Other — other_people_in_need_of_international_protection (range 0.0–0.0), others_of_concern_to_unhcr (range 0.0–0.0).

Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-unhcr-population-data-for-mus")
train = ds["train"].to_pandas()
test  = ds["test"].to_pandas()

print(train.shape)
train.head()

Schema

Column	Type	Null %	Range / Sample Values
`year`	int64	0.0%	1995.0 – 2025.0 (mean 2014.0)
`country_of_origin_code`	object	0.0%	MUS
`country_of_asylum_code`	object	0.0%	USA, CAN, FRA
`country_of_origin_name`	object	0.0%	Mauritius
`country_of_asylum_name`	object	0.0%	United States of America, Canada, France
`refugees`	int64	0.0%	0.0 – 99.0 (mean 14.5829)
`asylum_seekers`	int64	0.0%	0.0 – 235.0 (mean 25.7487)
`other_people_in_need_of_international_protection`	int64	0.0%	0.0 – 0.0 (mean 0.0)
`internally_displaced_persons`	int64	0.0%	0.0 – 0.0 (mean 0.0)
`stateless_persons`	int64	0.0%	0.0 – 0.0 (mean 0.0)
`others_of_concern_to_unhcr`	int64	0.0%	0.0 – 0.0 (mean 0.0)
`host_community`	int64	0.0%	0.0 – 0.0 (mean 0.0)
`esa_source`	object	0.0%	HDX
`esa_processed`	object	0.0%	2026-04-04

Numeric Summary

Column	Min	Max	Mean	Median
`year`	1995.0	2025.0	2014.0	2015.0
`refugees`	0.0	99.0	14.5829	7.0
`asylum_seekers`	0.0	235.0	25.7487	6.0
`other_people_in_need_of_international_protection`	0.0	0.0	0.0	0.0
`internally_displaced_persons`	0.0	0.0	0.0	0.0
`stateless_persons`	0.0	0.0	0.0	0.0
`others_of_concern_to_unhcr`	0.0	0.0	0.0	0.0
`host_community`	0.0	0.0	0.0	0.0

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 UNHCR - The UN Refugee Agency 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_unhcr_population_data_for_mus,
  title     = {Mauritius - Data on forcibly displaced populations and stateless persons},
  author    = {UNHCR - The UN Refugee Agency},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/unhcr-population-data-for-mus},
  note      = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}

Electric Sheep Africa — Africa's ML dataset infrastructure. Lagos, Nigeria.