--- license: cc-by-sa-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" - "demographics" - "travel-and-tourism" - "arrival" - "departure" configs: - config_name: default data_files: - split: train path: data/train-00000-of-00001.parquet pretty_name: "Arrival and Departure of Tourist by Year Age Group and Gen | Africa (MDPA)" --- # Arrival and Departure of Tourist by Year Age Group and Gen | Africa (MDPA) **136 rows** - **1 Africa country/area** - **2019-2020** - **17 indicators** - *Engineered by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)* ![rows](https://img.shields.io/badge/rows-136-blue) ![countries](https://img.shields.io/badge/countries-1-green) ![period](https://img.shields.io/badge/period-2019--2020-orange) ![indicators](https://img.shields.io/badge/indicators-17-purple) ![license](https://img.shields.io/badge/license-cc--by--sa--4.0-lightgrey) ## TL;DR This dataset contains **136 rows** from **MDPA**, covering **Arrival and Departure of Tourist by Year Age Group and Gen**. It is published as ML-ready Parquet with consistent Hugging Face metadata, source provenance, and analysis-friendly loading examples. ## What This Dataset Measures Demographic datasets help analysts understand population structure, household conditions, migration, gender, age, and settlement patterns. Source-provided context: The data shows number of arrival and departure of tourist by year, by travel status, by age group and by gender ## 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 | 136 | | Countries/areas | 1 | | First period | 2019 | | Last period | 2020 | | Indicators | 17 | | Columns | 20 | | Source format | CSV | ## Geographic Coverage Top areas shown below, sorted by row count when available: | Area | Rows | First year | Last year | Name | |------|-----:|-----------:|----------:|------| | `MU` | 136 | 2019 | 2020 | `Mauritius` | ## Indicators, Variables, Or Resource Contents - `arrival-and-departure-of-tourist-by-year-age-group-and-gender-under-1-56b9f4ae` - Arrival and Departure of Tourist by year, age group and gender - under 1(source_units_unspecified) - `arrival-and-departure-of-tourist-by-year-age-group-and-gender-d-1-4-a76f519e` - Arrival and Departure of Tourist by year, age group and gender - d 1 4(source_units_unspecified) - `arrival-and-departure-of-tourist-by-year-age-group-and-gender-d-5-9-027c859a` - Arrival and Departure of Tourist by year, age group and gender - d 5 9(source_units_unspecified) - `arrival-and-departure-of-tourist-by-year-age-group-and-gender-d-10-14-51e75f59` - Arrival and Departure of Tourist by year, age group and gender - d 10 14(source_units_unspecified) - `arrival-and-departure-of-tourist-by-year-age-group-and-gender-d-15-19-58d9ff8a` - Arrival and Departure of Tourist by year, age group and gender - d 15 19(source_units_unspecified) - `arrival-and-departure-of-tourist-by-year-age-group-and-gender-d-20-24-c9e1f7c0` - Arrival and Departure of Tourist by year, age group and gender - d 20 24(source_units_unspecified) - `arrival-and-departure-of-tourist-by-year-age-group-and-gender-d-25-29-c769dd67` - Arrival and Departure of Tourist by year, age group and gender - d 25 29(source_units_unspecified) - `arrival-and-departure-of-tourist-by-year-age-group-and-gender-d-30-34-9a285493` - Arrival and Departure of Tourist by year, age group and gender - d 30 34(source_units_unspecified) - `arrival-and-departure-of-tourist-by-year-age-group-and-gender-d-35-39-569157ac` - Arrival and Departure of Tourist by year, age group and gender - d 35 39(source_units_unspecified) - `arrival-and-departure-of-tourist-by-year-age-group-and-gender-d-40-44-44ca86a4` - Arrival and Departure of Tourist by year, age group and gender - d 40 44(source_units_unspecified) - `arrival-and-departure-of-tourist-by-year-age-group-and-gender-d-45-49-b278edc1` - Arrival and Departure of Tourist by year, age group and gender - d 45 49(source_units_unspecified) - `arrival-and-departure-of-tourist-by-year-age-group-and-gender-d-50-54-6065293c` - Arrival and Departure of Tourist by year, age group and gender - d 50 54(source_units_unspecified) - `arrival-and-departure-of-tourist-by-year-age-group-and-gender-d-55-59-0f0932ef` - Arrival and Departure of Tourist by year, age group and gender - d 55 59(source_units_unspecified) - `arrival-and-departure-of-tourist-by-year-age-group-and-gender-d-60-64-3964f3d5` - Arrival and Departure of Tourist by year, age group and gender - d 60 64(source_units_unspecified) - `arrival-and-departure-of-tourist-by-year-age-group-and-gender-d-65-69-ed4ebc88` - Arrival and Departure of Tourist by year, age group and gender - d 65 69(source_units_unspecified) - `arrival-and-departure-of-tourist-by-year-age-group-and-gender-d-70-and-o-bd9c9022` - Arrival and Departure of Tourist by year, age group and gender - d 70 and over(source_units_unspecified) - `arrival-and-departure-of-tourist-by-year-age-group-and-gender-not-stated-2391a5c4` - Arrival and Departure of Tourist by year, age group and gender - not stated(source_units_unspecified) ## Schema | Column | Type | Description | Example | |--------|------|-------------|---------| | `indicator_id` | `string` | Stable source or Electric Sheep Africa indicator identifier. | `arrival-and-departure-of-tourist-by-year-age-group-and-gender-under-1...` | | `indicator_name` | `string` | Human-readable indicator name. | `Arrival and Departure of Tourist by year, age group and gender - under 1` | | `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. | `4822.0` | | `unit` | `string` | Measurement unit, when supplied by the source. | `source_units_unspecified` | | `dimension_status` | `string` | Source dimension retained during long-form normalization. | `Arrival` | | `dimension_gender` | `string` | Source dimension retained during long-form normalization. | `Male` | | `source_period_start_year` | `int64` | Start year inferred from source metadata. | `` | | `source_period_end_year` | `int64` | End year inferred from source metadata. | `` | | `source_period_label` | `string` | Source column from the original resource. | `` | | `source_provider` | `dictionary` | Publishing organization. | `MDPA` | | `source_dataset` | `dictionary` | Source dataset or package title. | `Arrival and Departure of Tourist by year, age group and gender` | | `source_resource` | `dictionary` | Source resource title, table name, or file name. | `Arrival-and-Departure-of-Tourist-by-year-age-group-and-gender_0.csv` | | `source_package_id` | `dictionary` | Source package identifier. | `452a5676-4fec-4451-808f-6edfeb3ea6de` | | `source_resource_id` | `dictionary` | Source resource identifier. | `53d5103d-9498-4765-be6f-fa94eaf90293` | | `source_url` | `dictionary` | Original source URL or download URL. | `https://data.govmu.org/dataset/452a5676-4fec-4451-808f-6edfeb3ea6de/r...` | | `license_id` | `dictionary` | Source license identifier. | `CC-BY-SA-4.0` | | `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-arrival-and-departure-of-tourist-by-year-age-group-and-gen-647d1ea9") 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/arrival-and-departure-tourist-year-age-group-and-gender) - **Publisher:** MDPA - **Portal:** [https://data.govmu.org](https://data.govmu.org) - **Resource:** [Arrival-and-Departure-of-Tourist-by-year-age-group-and-gender_0.csv](https://data.govmu.org/dataset/452a5676-4fec-4451-808f-6edfeb3ea6de/resource/53d5103d-9498-4765-be6f-fa94eaf90293/download/arrival-and-departure-of-tourist-by-year-age-group-and-gender_0.csv) - **License:** [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/) - **Retrieved/generated:** `2026-08-08T16:42:03Z` - **Hugging Face repo:** [electricsheepafrica/africa-mauritius-arrival-and-departure-of-tourist-by-year-age-group-and-gen-647d1ea9](https://huggingface.co/datasets/electricsheepafrica/africa-mauritius-arrival-and-departure-of-tourist-by-year-age-group-and-gen-647d1ea9) ## 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 - Build demographic profiles - Normalize indicators per capita - Join with service-delivery datasets - 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_arrival_and_departure_of_tourist_by_year_age_group_and_gen_647d_2020, title = {Arrival and Departure of Tourist by Year Age Group and Gen | Africa (MDPA)}, author = {MDPA}, year = {2020}, url = {https://data.govmu.org/dataset/arrival-and-departure-tourist-year-age-group-and-gender}, publisher = {Hugging Face Datasets, engineered by Electric Sheep Africa}, howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-mauritius-arrival-and-departure-of-tourist-by-year-age-group-and-gen-647d1ea9}} } ``` ## License Released under [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/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/arrival-and-departure-tourist-year-age-group-and-gender