--- 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" - "arrivals" - "continent" - "passengers" configs: - config_name: default data_files: - split: train path: data/train-00000-of-00001.parquet pretty_name: "Tourist Arrivals Market Share by Top 10 Markets 2019 and 2 | Africa (MDPA)" --- # Tourist Arrivals Market Share by Top 10 Markets 2019 and 2 | Africa (MDPA) **30 rows** - **1 Africa country/area** - **2019-2023** - **1 indicator** - *Engineered by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)* ![rows](https://img.shields.io/badge/rows-30-blue) ![countries](https://img.shields.io/badge/countries-1-green) ![period](https://img.shields.io/badge/period-2019--2023-orange) ![indicators](https://img.shields.io/badge/indicators-1-purple) ![license](https://img.shields.io/badge/license-cc--by--4.0-lightgrey) ## TL;DR This dataset contains **30 rows** from **MDPA**, covering **Tourist Arrivals Market Share by Top 10 Markets 2019 and 2**. 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 Tourist arrivals - market share by top 10 markets, 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 | 30 | | Countries/areas | 1 | | First period | 2019 | | Last period | 2023 | | Indicators | 1 | | Columns | 20 | | Source format | XLSX | ## Geographic Coverage Top areas shown below, sorted by row count when available: | Area | Rows | First year | Last year | Name | |------|-----:|-----------:|----------:|------| | `MU` | 30 | 2019 | 2023 | `Mauritius` | ## Indicators, Variables, Or Resource Contents - `tourist-arrivals-market-share-by-top-10-markets-2019-and-2022-2023-0fb0b518` - Tourist arrivals - market share by top 10 markets, 2019 and 2022 – 2023(source_units_unspecified) ## Schema | Column | Type | Description | Example | |--------|------|-------------|---------| | `indicator_id` | `string` | Stable source or Electric Sheep Africa indicator identifier. | `tourist-arrivals-market-share-by-top-10-markets-2019-and-2022-2023-0f...` | | `indicator_name` | `string` | Human-readable indicator name. | `Tourist arrivals - market share by top 10 markets, 2019 and 2022 – 2023` | | `country_iso3` | `string` | ISO3 country or area code. | `MU` | | `source_sheet` | `string` | Source column from the original resource. | `Sheet1` | | `country_name` | `string` | Country or area name. | `Mauritius` | | `year` | `int64` | Observation year. | `2019` | | `value` | `double` | Numeric observation value. | `0.218` | | `unit` | `string` | Measurement unit, when supplied by the source. | `source_units_unspecified` | | `dimension_country` | `string` | Source dimension retained during long-form normalization. | `France` | | `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` | Source column from the original resource. | `2019-2023` | | `source_provider` | `dictionary` | Publishing organization. | `MDPA` | | `source_dataset` | `dictionary` | Source dataset or package title. | `Tourist arrivals - market share by top 10 markets, 2019 and 2022 – 2023` | | `source_resource` | `dictionary` | Source resource title, table name, or file name. | `Source File` | | `source_package_id` | `dictionary` | Source package identifier. | `a8762063-dae1-41e3-b038-1d37e3f024ba` | | `source_resource_id` | `dictionary` | Source resource identifier. | `886c781c-e9d2-4894-80ee-969feeea05df` | | `source_url` | `dictionary` | Original source URL or download URL. | `https://data.govmu.org/dataset/a8762063-dae1-41e3-b038-1d37e3f024ba/r...` | | `license_id` | `dictionary` | Source license identifier. | `cc-by` | | `retrieved_at` | `dictionary` | UTC source retrieval timestamp from the Electric Sheep Africa pipeline. | `2026-08-08T16:26:20Z` | ## Usage ```python from datasets import load_dataset ds = load_dataset("electricsheepafrica/africa-mauritius-tourist-arrivals-market-share-by-top-10-markets-2019-and-2-0fb0b518") df = ds["train"].to_pandas() print(df.head()) ``` ### Inspect Columns ```python print(df.info()) print(df.head()) ``` ### Filter By Geography ```python if "country_iso3" in df.columns: sample = df[df["country_iso3"] == "MU"] ``` ### Time-Series Pattern ```python if "value" in df.columns and "year" in df.columns: trend = df.sort_values("year") ``` ### Pivot For Analysis ```python 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](https://data.govmu.org/dataset/tourist-arrivals-market-share-by-top-10-markets-2019-and-2022-2023) - **Publisher:** MDPA - **Portal:** [https://data.govmu.org](https://data.govmu.org) - **Resource:** [Source File](https://data.govmu.org/dataset/a8762063-dae1-41e3-b038-1d37e3f024ba/resource/886c781c-e9d2-4894-80ee-969feeea05df/download/book1.xlsx) - **License:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) - **Retrieved/generated:** `2026-08-08T16:52:07Z` - **Hugging Face repo:** [electricsheepafrica/africa-mauritius-tourist-arrivals-market-share-by-top-10-markets-2019-and-2-0fb0b518](https://huggingface.co/datasets/electricsheepafrica/africa-mauritius-tourist-arrivals-market-share-by-top-10-markets-2019-and-2-0fb0b518) ## 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 ```bibtex @misc{electric_sheep_africa_africa_mauritius_tourist_arrivals_market_share_by_top_10_markets_2019_and_2_0fb0_2023, title = {Tourist Arrivals Market Share by Top 10 Markets 2019 and 2 | Africa (MDPA)}, author = {MDPA}, year = {2023}, url = {https://data.govmu.org/dataset/tourist-arrivals-market-share-by-top-10-markets-2019-and-2022-2023}, publisher = {Hugging Face Datasets, engineered by Electric Sheep Africa}, howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-mauritius-tourist-arrivals-market-share-by-top-10-markets-2019-and-2-0fb0b518}} } ``` ## License Released under [CC BY 4.0](https://creativecommons.org/licenses/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/tourist-arrivals-market-share-by-top-10-markets-2019-and-2022-2023