electricsheepafrica/africa-mauritius-monthly-tourist-arrivals-for-top-ten-source-markets-for-th-53b9f599

Monthly Tourist Arrivals for Top Ten Source Markets for Th | Africa (MDPA)

10 rows - 1 Africa country/area - 2023 - source table - Engineered by Electric Sheep Africa

TL;DR

This dataset contains 10 rows from MDPA, covering Monthly Tourist Arrivals for Top Ten Source Markets for Th. 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 Monthly tourist arrivals for top ten source markets for the year 2023

How To Read This Dataset

• One row means: one source record from the original tabular resource, with Electric Sheep Africa provenance columns added where available.
• Primary geography column: country_iso3.
• Best time column: year.
• Time coverage basis: year.
• Recommended join keys: country_iso3 where available plus source-specific keys.

Coverage

Dimension	Value
Rows	10
Countries/areas	1
First period	2023
Last period	2023
Indicators	0
Columns	28
Source format	CSV

Geographic Coverage

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

Area	Rows	First year	Last year	Name
MU	10	2023	2023	Mauritius

Indicators, Variables, Or Resource Contents

• This repo preserves one source tabular resource with its usable columns kept together.

Schema

Column	Type	Description	Example
source_record_id	string	Stable row identifier assigned during Electric Sheep Africa engineering.	a900b98d-8618-46d8-b49b-5bbd670c0f3c:0
country_iso3	dictionary<values=string, indices=int8, ordered=0>	ISO3 country or area code.	MU
country_name	dictionary<values=string, indices=int8, ordered=0>	Country or area name.	Mauritius
year	int64	Observation year.	2023
month	string	Source column from the original resource.	France
january	int64	Source column from the original resource.	28022
february	int64	Source column from the original resource.	30211
march	int64	Source column from the original resource.	23589
april	int64	Source column from the original resource.	29716
may	int64	Source column from the original resource.	22664
june	int64	Source column from the original resource.	13123
july	int64	Source column from the original resource.	23603
august	int64	Source column from the original resource.	22132
september	int64	Source column from the original resource.	18009
october	int64	Source column from the original resource.	35196
november	int64	Source column from the original resource.	36337
december	int64	Source column from the original resource.	36920
source_period_start_year	int64	Start year inferred from source metadata.	2023
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.	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.	Monthly tourist arrivals for top ten source markets for the year 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.	0f51af17-9dd8-4151-8192-12a928da502f
source_resource_id	dictionary<values=string, indices=int8, ordered=0>	Source resource identifier.	a900b98d-8618-46d8-b49b-5bbd670c0f3c
source_url	dictionary<values=string, indices=int8, ordered=0>	Original source URL or download URL.	https://data.govmu.org/dataset/0f51af17-9dd8-4151-8192-12a928da502f/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-monthly-tourist-arrivals-for-top-ten-source-markets-for-th-53b9f599")
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: 2026-08-08T16:51:49Z
• Hugging Face repo: electricsheepafrica/africa-mauritius-monthly-tourist-arrivals-for-top-ten-source-markets-for-th-53b9f599

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
• Check missingness before modeling
• Use country_iso3 as the safest geography join key when present

Citation

@misc{electric_sheep_africa_africa_mauritius_monthly_tourist_arrivals_for_top_ten_source_markets_for_th_53b9_2023,
  title        = {Monthly Tourist Arrivals for Top Ten Source Markets for Th | Africa (MDPA)},
  author       = {MDPA},
  year         = {2023},
  url          = {https://data.govmu.org/dataset/monthly-tourist-arrivals-for-top-ten-source-markets-for-the-year-2023},
  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-mauritius-monthly-tourist-arrivals-for-top-ten-source-markets-for-th-53b9f599}}
}

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/monthly-tourist-arrivals-for-top-ten-source-markets-for-the-year-2023