license: cc-by-4.0
language:
- en
task_categories:
- tabular-regression
- time-series-forecasting
multilinguality: monolingual
size_categories:
- n<1K
tags:
- tabular
- csv
- africa
- mauritius
- official-statistics
- open-data
- tourism
configs:
- config_name: default
data_files:
- split: train
path: data/train-00000-of-00001.parquet
pretty_name: >-
Air seats and tourist arrivals, 2019 and 2022 – 2023 | Africa (Mauritius
official open data)
Air seats and tourist arrivals, 2019 and 2022 – 2023 | Africa (Mauritius official open data)
8 rows - 1 Africa country - 2019-2024 - Repackaged by Electric Sheep Africa
TL;DR
This dataset packages one official CSV resource from Mauritius 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: Air seats and tourist arrivals, 2019 and 2022 – 2023
- Publisher: MDPA
- Resource: CSV File
- Format:
CSV - License: CC BY 4.0
- Packaging mode:
indicator_long
Geographic coverage
1 Africa country:
| Country | Rows | First year | Last year | Name |
|---|---|---|---|---|
MU |
8 | 2019 | 2024 | Mauritius |
Indicators or Resource Contents
air-seats-and-tourist-arrivals-2019-and-2022-2023-1732c085- Air seats and tourist arrivals, 2019 and 2022 – 2023
Schema
| Column | Type | Description | Example |
|---|---|---|---|
indicator_id |
string |
Stable indicator identifier. | air-seats-and-tourist-arrivals-2019-and-2022-2023-1732c085 |
indicator_name |
string |
Human-readable indicator name. | Air seats and tourist arrivals, 2019 and 2022 – 2023 |
country_iso3 |
string |
ISO3 country code. | MU |
country_name |
string |
Country name. | Mauritius |
year |
Int64 |
Observation year. | 2019 |
value |
float64 |
Numeric observation value. | 2397287.0 |
unit |
string |
Measurement unit, when available. | source_units_unspecified |
dimension_year |
string |
Source dimension. | Total air seats |
source_period_start_year |
Int64 |
First year inferred from source resource metadata. | 2019 |
source_period_end_year |
Int64 |
Last year inferred from source resource metadata. | 2023 |
source_period_label |
category |
Human-readable period inferred from source resource metadata. | 2019-2023 |
source_provider |
category |
Publishing organization. | MDPA |
source_dataset |
category |
Source package title. | Air seats and tourist arrivals, 2019 and 2022 – 2023 |
source_resource |
category |
Source resource title. | CSV File |
source_package_id |
category |
CKAN package UUID. | d82e6028-8927-42d1-8e4e-87a77bb89279 |
source_resource_id |
category |
CKAN resource UUID. | fd78ecf3-6674-4925-a1b3-ffd77b4b61b1 |
source_url |
category |
Original source resource URL. | https://data.govmu.org/dataset/d82e6028-8927-42d1-8e4e-87a77bb89279/reso |
license_id |
category |
Source license identifier. | cc-by |
retrieved_at |
category |
UTC retrieval timestamp. | 2026-08-08T16:26:20Z |
Usage
from datasets import load_dataset
ds = load_dataset("electricsheepafrica/africa-mauritius-air-seats-and-tourist-arrivals-2019-and-2022-2023-1732c085")
df = ds["train"].to_pandas()
print(df.head())
Filter to one country
sample_country = df[df["country_iso3"] == "MU"]
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_mauritius_air_seats_and_tourist_arrivals_2019_and_2022_2023_1732c085_2024,
title = {Air seats and tourist arrivals, 2019 and 2022 – 2023 | Africa (Mauritius official open data)},
author = {MDPA},
year = {2024},
url = {https://data.govmu.org/dataset/air-seats-and-tourist-arrivals-2019-and-2022-2023},
publisher = {HuggingFace Datasets, repackaged by Electric Sheep Africa},
howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-mauritius-air-seats-and-tourist-arrivals-2019-and-2022-2023-1732c085}}
}
License
Released under CC BY 4.0.
Original data (c) MDPA. 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 2026-08-08 via the Electric Sheep pipeline. Source URL: https://data.govmu.org/dataset/d82e6028-8927-42d1-8e4e-87a77bb89279/resource/fd78ecf3-6674-4925-a1b3-ffd77b4b61b1/download/table1.csv