electricsheepafrica/africa-mauritius-monthly-tourist-arrival-by-country-of-residence-for-the-ye-933c7f44

Monthly Tourist Arrival by Country of Residence for the Ye | Africa (MDPA)

236 rows - 1 Africa country/area - 2017 - source table - Engineered by Electric Sheep Africa

TL;DR

This dataset contains 236 rows from MDPA, covering Monthly Tourist Arrival by Country of Residence for the Ye. It is published as ML-ready Parquet with consistent Hugging Face metadata, source provenance, and analysis-friendly loading examples.

What This Dataset Measures

Official statistics datasets help analysts inspect public data as published by governments, national statistical systems, and regional data portals.

Source-provided context: Source_file 2017

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	236
Countries/areas	1
First period	2017
Last period	2017
Indicators	0
Columns	88
Source format	XLS

Geographic Coverage

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

Area	Rows	First year	Last year	Name
MU	236	2017	2017	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.	d8c6b535-b957-4cda-bd6a-a29f1f628996:table-of-contents: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
source_sheet	string	Source column from the original resource.	Table of Contents
year	int64	Observation year.	2017
monthly_tourism_statistics_december_2017	string	Source column from the original resource.	Contents
source_period_start_year	int64	Start year inferred from source metadata.	2017
source_period_end_year	int64	End year inferred from source metadata.	2017
source_period_label	dictionary<values=string, indices=int8, ordered=0>	Source column from the original resource.	2017
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 arrival by country of residence for the year 2017-2020
source_resource	dictionary<values=string, indices=int8, ordered=0>	Source resource title, table name, or file name.	Tourist_M_Dec17.xls
source_package_id	dictionary<values=string, indices=int8, ordered=0>	Source package identifier.	3e903628-688f-402a-a248-f0779d9d272f
source_resource_id	dictionary<values=string, indices=int8, ordered=0>	Source resource identifier.	d8c6b535-b957-4cda-bd6a-a29f1f628996
source_url	dictionary<values=string, indices=int8, ordered=0>	Original source URL or download URL.	https://data.govmu.org/dataset/3e903628-688f-402a-a248-f0779d9d272f/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
tourist_arrivals_number	string	Source column from the original resource.	``
d_118426	double	Source column from the original resource.	``
d_100706	double	Source column from the original resource.	``
d_108704	double	Source column from the original resource.	``
d_91992	double	Source column from the original resource.	``
d_94830	double	Source column from the original resource.	``
d_71806	double	Source column from the original resource.	``
d_108122	double	Source column from the original resource.	``
d_94920	double	Source column from the original resource.	``
d_91384	double	Source column from the original resource.	``
d_130421	double	Source column from the original resource.	``
d_115782	double	Source column from the original resource.	``
d_148134	double	Source column from the original resource.	``
d_1275227	double	Source column from the original resource.	``
d_124362	double	Source column from the original resource.	``
d_105049	double	Source column from the original resource.	``
d_110271	double	Source column from the original resource.	``
d_111432	double	Source column from the original resource.	``
d_96557	double	Source column from the original resource.	``
d_78188	double	Source column from the original resource.	``
d_112347	double	Source column from the original resource.	``
d_100191	double	Source column from the original resource.	``
d_96282	double	Source column from the original resource.	``
d_130070	double	Source column from the original resource.	``
d_121496	string	Source column from the original resource.	``
d_155615	string	Source column from the original resource.	``
d_1341860	string	Source column from the original resource.	``
d_5_050157290021197	string	Source column from the original resource.	``
d_5_225187358799649	string	Source column from the original resource.	``
europe	string	Source column from the original resource.	``
d_67016	double	Source column from the original resource.	``
d_67461	double	Source column from the original resource.	``
d_65048	double	Source column from the original resource.	``
d_56892	double	Source column from the original resource.	``
d_46159	double	Source column from the original resource.	``
d_33162	double	Source column from the original resource.	``
d_54548	double	Source column from the original resource.	``
d_52310	double	Source column from the original resource.	``
d_51565	double	Source column from the original resource.	``
d_79415	double	Source column from the original resource.	``
d_79305	double	Source column from the original resource.	``
d_81625	double	Source column from the original resource.	``
d_734506	double	Source column from the original resource.	``
d_70434	double	Source column from the original resource.	``
d_72102	double	Source column from the original resource.	``
d_66340	double	Source column from the original resource.	``
d_67536	double	Source column from the original resource.	``
d_44763	double	Source column from the original resource.	``
d_34523	double	Source column from the original resource.	``
d_57026	double	Source column from the original resource.	``
d_54982	double	Source column from the original resource.	``
d_54373	double	Source column from the original resource.	``
d_81263	double	Source column from the original resource.	``
d_87319	double	Source column from the original resource.	``
d_89548	double	Source column from the original resource.	``
d_780209	double	Source column from the original resource.	``
d_9_706584992343032	double	Source column from the original resource.	``
d_6_22227728568589	double	Source column from the original resource.	``
sweden	string	Source column from the original resource.	``
d_2623	double	Source column from the original resource.	``
d_1_7706941012866728	double	Source column from the original resource.	``
d_11	double	Source column from the original resource.	``
d_2843	double	Source column from the original resource.	``
d_1_8269447032741062	double	Source column from the original resource.	``
d_10	double	Source column from the original resource.	``
d_14551	double	Source column from the original resource.	``
d_1_1410517500021564	double	Source column from the original resource.	``
d_14	double	Source column from the original resource.	``
d_15516	double	Source column from the original resource.	``
d_1_1563054267956419	double	Source column from the original resource.	``
d_13	double	Source column from the original resource.	``

Usage

from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-mauritius-monthly-tourist-arrival-by-country-of-residence-for-the-ye-933c7f44")
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: Tourist_M_Dec17.xls
• License: CC BY 4.0
• Retrieved/generated: 2026-08-08T16:56:09Z
• Hugging Face repo: electricsheepafrica/africa-mauritius-monthly-tourist-arrival-by-country-of-residence-for-the-ye-933c7f44

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

• Profile the distribution of values
• Compare categories or geographies
• Join with complementary public datasets
• 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_arrival_by_country_of_residence_for_the_ye_933c_2017,
  title        = {Monthly Tourist Arrival by Country of Residence for the Ye | Africa (MDPA)},
  author       = {MDPA},
  year         = {2017},
  url          = {https://data.govmu.org/dataset/monthly-tourist-arrival-by-country-of-residence-for-the-year-2017-2020},
  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-mauritius-monthly-tourist-arrival-by-country-of-residence-for-the-ye-933c7f44}}
}

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/monthly-tourist-arrival-by-country-of-residence-for-the-year-2017-2020