| --- |
| 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.* |
