electricsheepafrica/africa-mauritius-cyclone-1001052

---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license: cc-by-sa-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
- affected-population
- cyclones-hurricanes-typhoons
- geodata
- mus
pretty_name: "Mauritius: Cyclone - Tropical storm - Feb 2024"
dataset_info:
splits:
- name: train
num_examples: 9
- name: test
num_examples: 2
---

# Mauritius: Cyclone - Tropical storm - Feb 2024

**Publisher:** WFP Advanced Disaster Analysis & Mapping · **Source:** [HDX](https://data.humdata.org/dataset/mauritius-cyclone-1001052) · **License:** `cc-by-sa` · **Updated:** 2025-11-24

---

## Abstract

**ADAM ID: 1001052\_19** Cyclone (tropical storm) during the period Feb 19 2024-Feb 23 2024 in Miscellaneous (French) Indian Ocean Islands, Mauritius. It impacted 0 people.

Each row in this dataset represents tabular records. Data was last updated on HDX on 2025-11-24. 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** | Tabular records |
| **Rows (total)** | 12 |
| **Columns** | 8 (3 numeric, 5 categorical, 0 datetime) |
| **Train split** | 9 rows |
| **Test split** | 2 rows |
| **Geographic scope** | MUS |
| **Publisher** | WFP Advanced Disaster Analysis & Mapping |
| **HDX last updated** | 2025-11-24 |

---

## Variables

**Demographic** — `pop_60_kmh` (range 0.0–550358.0), `pop_90_kmh` (range 0.0–357295.0).

**Identifier / Metadata** — `unnamed_0` (range 0.0–12.0), `adm0_name` (Mauritius, Réunion), `adm1_name` (Black River, Flacq, Grand Port), `adm2_name` (---), `esa_source` (HDX) and 1 others.

---

## Quick Start

```python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-mauritius-cyclone-1001052")
train = ds["train"].to_pandas()
test = ds["test"].to_pandas()

print(train.shape)
train.head()
```

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `unnamed_0` | int64 | 0.0% | 0.0 – 12.0 (mean 5.6667) |
| `adm0_name` | object | 0.0% | Mauritius, Réunion |
| `adm1_name` | object | 0.0% | Black River, Flacq, Grand Port |
| `adm2_name` | object | 0.0% | --- |
| `pop_60_kmh` | int64 | 0.0% | 0.0 – 550358.0 (mean 72645.9167) |
| `pop_90_kmh` | int64 | 0.0% | 0.0 – 357295.0 (mean 99521.75) |
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% | 2026-04-06 |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `unnamed_0` | 0.0 | 12.0 | 5.6667 | 5.5 |
| `pop_60_kmh` | 0.0 | 550358.0 | 72645.9167 | 0.0 |
| `pop_90_kmh` | 0.0 | 357295.0 | 99521.75 | 96865.0 |

---

## 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 WFP Advanced Disaster Analysis & Mapping 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/mauritius-cyclone-1001052) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_mauritius_cyclone_1001052,
title = {Mauritius: Cyclone - Tropical storm - Feb 2024},
author = {WFP Advanced Disaster Analysis & Mapping},
year = {2025},
url = {https://data.humdata.org/dataset/mauritius-cyclone-1001052},
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.*