electricsheepafrica/africa-mauritius-international-arrivals-by-sea-and-by-type-2019-2022-64ece408

International Arrivals by Sea and by Type 2019 2022 | Africa (MDPA)

18 rows - 1 Africa country/area - 2019-2023 - 1 indicator - Engineered by Electric Sheep Africa

TL;DR

This dataset contains 18 rows from MDPA, covering International Arrivals by Sea and by Type 2019 2022. It is published as ML-ready Parquet with consistent Hugging Face metadata, source provenance, and analysis-friendly loading examples.

What This Dataset Measures

Labour and workforce datasets help analysts study employment, participation, skills, sectoral structure, and the movement of people through work and livelihoods.

Source-provided context: Dataset shows International arrivals by sea and by type, 2019 - 2022

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	18
Countries/areas	1
First period	2019
Last period	2023
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	18	2019	2023	Mauritius

Indicators, Variables, Or Resource Contents

• international-arrivals-by-sea-and-by-type-2019-2022-64ece408 - International arrivals by sea and by type, 2019 - 2022(source_units_unspecified)

Schema

Column	Type	Description	Example
indicator_id	string	Stable source or Electric Sheep Africa indicator identifier.	international-arrivals-by-sea-and-by-type-2019-2022-64ece408
indicator_name	string	Human-readable indicator name.	International arrivals by sea and by type, 2019 - 2022
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.	79225.0
unit	string	Measurement unit, when supplied by the source.	source_units_unspecified
dimension_year	string	Source dimension retained during long-form normalization.	Cruise travellers
source_period_start_year	int64	Start year inferred from source metadata.	2019
source_period_end_year	int64	End year inferred from source metadata.	2022
source_period_label	dictionary<values=string, indices=int8, ordered=0>	Source column from the original resource.	2019-2022
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.	International arrivals by sea and by type, 2019 - 2022
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.	42c9388e-37ae-4970-b1b2-13cff5f0bdfd
source_resource_id	dictionary<values=string, indices=int8, ordered=0>	Source resource identifier.	7936d94e-d23f-4e13-99d4-620244eac3d1
source_url	dictionary<values=string, indices=int8, ordered=0>	Original source URL or download URL.	https://data.govmu.org/dataset/42c9388e-37ae-4970-b1b2-13cff5f0bdfd/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-international-arrivals-by-sea-and-by-type-2019-2022-64ece408")
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: 2026-08-08T16:52:42Z
• Hugging Face repo: electricsheepafrica/africa-mauritius-international-arrivals-by-sea-and-by-type-2019-2022-64ece408

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 workforce composition over time
• Compare employment patterns across groups
• Join with education, population, and sector 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_international_arrivals_by_sea_and_by_type_2019_2022_64ece408_2023,
  title        = {International Arrivals by Sea and by Type 2019 2022 | Africa (MDPA)},
  author       = {MDPA},
  year         = {2023},
  url          = {https://data.govmu.org/dataset/international-arrivals-by-sea-and-by-type-2019-2022},
  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-mauritius-international-arrivals-by-sea-and-by-type-2019-2022-64ece408}}
}

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.

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Provenance: README standardized 2026-08-11 by the Electric Sheep Africa README system. Source URL: https://data.govmu.org/dataset/international-arrivals-by-sea-and-by-type-2019-2022