Datasets:

electricsheepafrica
/

africa-mauritius-air-seats-and-tourist-arrivals-2019-and-2022-2023-1732c085

license: cc-by-4.0
language:
  - en
task_categories:
  - tabular-regression
  - time-series-forecasting
multilinguality: multilingual
size_categories:
  - n<1K
tags:
  - tabular
  - africa
  - open-data
  - official-statistics
  - mauritius
  - mdpa
  - transport
  - travel-and-tourism
  - tourism
  - tourist
  - accommodation
  - air-seats
  - arrivals
  - facilities
  - hotel
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 (MDPA)

Air Seats and Tourist Arrivals 2019 and 2022 2023 | Africa (MDPA)

8 rows - 1 Africa country/area - 2019-2024 - 1 indicator - Engineered by Electric Sheep Africa

TL;DR

This dataset contains 8 rows from MDPA, covering Air Seats and Tourist Arrivals 2019 and 2022 2023. It is published as ML-ready Parquet with consistent Hugging Face metadata, source provenance, and analysis-friendly loading examples.

What This Dataset Measures

Transport datasets help analysts examine mobility, infrastructure, passenger movement, logistics, and access to services.

Source-provided context: Dataset shows Air seats and tourist arrivals, 2019 and 2022 – 2023

How To Read This Dataset

One row means: one indicator observation for one geography, time period, and optional source dimensions.
Primary geography column: country_iso3.
Best time column: year.
Time coverage basis: year.
Recommended join keys: country_iso3, year, indicator_id.

Coverage

Dimension	Value
Rows	8
Countries/areas	1
First period	2019
Last period	2024
Indicators	1
Columns	19
Source format	CSV

Geographic Coverage

Top areas shown below, sorted by row count when available:

Area	Rows	First year	Last year	Name
`MU`	8	2019	2024	`Mauritius`

Indicators, Variables, Or Resource Contents

air-seats-and-tourist-arrivals-2019-and-2022-2023-1732c085 - Air seats and tourist arrivals, 2019 and 2022 – 2023(source_units_unspecified)

Schema

Column	Type	Description	Example
`indicator_id`	`string`	Stable source or Electric Sheep Africa 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 or area code.	`MU`
`country_name`	`string`	Country or area name.	`Mauritius`
`year`	`int64`	Observation year.	`2019`
`value`	`double`	Numeric observation value.	`2397287.0`
`unit`	`string`	Measurement unit, when supplied by the source.	`source_units_unspecified`
`dimension_year`	`string`	Source dimension retained during long-form normalization.	`Total air seats`
`source_period_start_year`	`int64`	Start year inferred from source metadata.	`2019`
`source_period_end_year`	`int64`	End year inferred from source metadata.	`2023`
`source_period_label`	`dictionary<values=string, indices=int8, ordered=0>`	Source column from the original resource.	`2019-2023`
`source_provider`	`dictionary<values=string, indices=int8, ordered=0>`	Publishing organization.	`MDPA`
`source_dataset`	`dictionary<values=string, indices=int8, ordered=0>`	Source dataset or package title.	`Air seats and tourist arrivals, 2019 and 2022 – 2023`
`source_resource`	`dictionary<values=string, indices=int8, ordered=0>`	Source resource title, table name, or file name.	`CSV File`
`source_package_id`	`dictionary<values=string, indices=int8, ordered=0>`	Source package identifier.	`d82e6028-8927-42d1-8e4e-87a77bb89279`
`source_resource_id`	`dictionary<values=string, indices=int8, ordered=0>`	Source resource identifier.	`fd78ecf3-6674-4925-a1b3-ffd77b4b61b1`
`source_url`	`dictionary<values=string, indices=int8, ordered=0>`	Original source URL or download URL.	`https://data.govmu.org/dataset/d82e6028-8927-42d1-8e4e-87a77bb89279/r...`
`license_id`	`dictionary<values=string, indices=int8, ordered=0>`	Source license identifier.	`cc-by`
`retrieved_at`	`dictionary<values=string, indices=int8, ordered=0>`	UTC source retrieval timestamp from the Electric Sheep Africa pipeline.	`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())

Inspect Columns

print(df.info())
print(df.head())

Filter By Geography

if "country_iso3" in df.columns:
    sample = df[df["country_iso3"] == "MU"]

Time-Series Pattern

if "value" in df.columns and "year" in df.columns:
    trend = df.sort_values("year")

Pivot For Analysis

if {"indicator_id", "year", "value"}.issubset(df.columns):
    matrix = df.pivot_table(index="year", columns="indicator_id", values="value")
    print(matrix.tail())

Data Quality Notes

Canonical time field: year.
Missing values are preserved rather than silently imputed.
Column names are standardized for machine use; source meanings are preserved where known.
Always confirm source methodology, units, and collection definitions before policy, production, or redistribution-sensitive use.

Source And Provenance

Source: MDPA
Publisher: MDPA
Portal: https://data.govmu.org
Resource: CSV File
License: CC BY 4.0
Retrieved/generated: 2026-08-08T16:41:28Z
Hugging Face repo: electricsheepafrica/africa-mauritius-air-seats-and-tourist-arrivals-2019-and-2022-2023-1732c085

Transformations Applied

Converted the source table to Parquet for efficient analytics and ML workflows.
Added or preserved source provenance columns where available.
Standardized README metadata, dataset loading configuration, schema documentation, and citation format.
Preserved source-reported values without analytical imputation.

Suggested Analyses

Track mobility over time
Compare routes or geographies
Join with economic and population data
Build time-series views and period-over-period comparisons
Pivot to geography x period or indicator x period matrices
Check missingness before modeling
Use country_iso3 as the safest geography join key when present

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 (MDPA)},
  author       = {MDPA},
  year         = {2024},
  url          = {https://data.govmu.org/dataset/air-seats-and-tourist-arrivals-2019-and-2022-2023},
  publisher    = {Hugging Face Datasets, engineered 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 is published by MDPA. Electric Sheep Africa engineering standardizes the data for discovery, loading, and analysis on Hugging Face. Cite both the original source and this ML-ready dataset when used.

About Electric Sheep Africa

Electric Sheep Africa publishes ML-ready African public datasets on Hugging Face.

Provenance: README standardized 2026-08-11 by the Electric Sheep Africa README system. Source URL: https://data.govmu.org/dataset/air-seats-and-tourist-arrivals-2019-and-2022-2023