---
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](https://data.humdata.org/dataset/unhcr-population-data-for-mus) · **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](https://huggingface.co/electricsheepafrica).*

---

## 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

```python
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](https://data.humdata.org/dataset/unhcr-population-data-for-mus) for the publisher's own methodology notes and caveats.

---

## Citation

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