Standardize Electric Sheep Africa dataset card
Browse files
README.md
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task_categories:
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- tabular-classification
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- tabular-regression
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multilinguality:
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size_categories:
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- n<1K
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tags:
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- tabular
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- tourism
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configs:
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- config_name: default
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data_files:
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- split: train
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path: data/train-00000-of-00001.parquet
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pretty_name: "Earnings and Value Added of the Tourism Sector | Africa (
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---
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# Earnings and Value Added of the Tourism Sector | Africa (
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796 rows - 1 Africa country - 1980-2022 -
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## TL;DR
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This dataset
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ML-ready Parquet. The source file is the provenance boundary; all usable
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indicators or tabular columns from the resource stay together in this repo.
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##
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## Geographic
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|------
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| `MU` | 796 | 1980 | 2022 | `Mauritius` |
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## Indicators
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- This source
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## Schema
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| Column | Type | Description | Example |
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|--------|------|-------------|---------|
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| `source_record_id` | `string` | Stable row identifier
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| `country_iso3` | `
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| `country_name` | `
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| `source_sheet` | `string` |
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| `year` | `
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| `d_5a` | `string` | Source column. | `5b` |
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| `tourist_arrivals_by_country_of_residence_1983_2012` | `string` | Source column. | `Tourist arrivals by country of residence (covering a wider range of
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| `source_period_start_year` | `
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| `source_period_end_year` | `
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| `source_period_label` | `
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| `source_provider` | `
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| `source_dataset` | `
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| `source_resource` | `
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| `source_package_id` | `
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| `source_resource_id` | `
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| `source_url` | `
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| `license_id` | `
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| `retrieved_at` | `
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| `d_1` | `
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| `tourist` | `string` | Source column. | `` |
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| `a_tourist_is_defined_as_a_non_resident_staying_overnight` | `string` | Source column. | `` |
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| `1980` | `
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| `d_163230` | `
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| `d_167269` | `
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| `d_6042` | `string` | Source column. | `` |
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| `d_5722` | `
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| `country_of_disembarkation` | `string` | Source column. | `` |
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| `1983` | `
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| `1984` | `
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| `1985` | `
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| `1986` | `
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| `1987` | `
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| `1988` | `
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| `1989` | `
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| `1990` | `
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| `1991` | `
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| `1992` | `
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| `1993` | `
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| `1994` | `
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| `1995` | `
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| `1996` | `
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| `1997` | `
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| `1998` | `
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| `1999` | `
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| `2000` | `
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| `2001` | `
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| `2002` | `
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| `2003` | `
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| `2004` | `
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| `2005` | `
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| `2006` | `
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| `2007` | `
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| `2008` | `
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| `2009` | `
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| `2010` | `
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| `2011` | `
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| `2012` | `
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| `2013` | `
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| `2014` | `
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| `2015` | `
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| `2016` | `
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| `2017` | `
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| `2018` | `
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| `2019` | `
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| `2020` | `
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| `2021` | `
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| `2022` | `
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| `month_of_arrival` | `string` | Source column. | `` |
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| `1974` | `
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| `1975` | `
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| `1976` | `
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| `1977` | `
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| `1978` | `
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| `1979` | `
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| `1981` | `
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| `1982` | `
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| `country_of_residence` | `string` | Source column. | `` |
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| `europe` | `string` | Source column. | `` |
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| `d_547061` | `
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| `d_570684` | `
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| `d_631627` | `
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| `d_734506` | `
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| `d_780209` | `
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| `d_824334` | `
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| `d_835946` | `
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| `d_207641` | `
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| `d_145812` | `
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| `d_674511` | `
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| `d_55_1` | `
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| `d_55` | `
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| `d_54_9` | `
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| `d_57_6` | `
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| `d_58_1` | `
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| `d_58_9` | `
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| `d_60_4` | `
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| `d_67_2` | `
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| `d_81_1` | `
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| `d_67_6` | `
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| `tourism_earnings_1_rs_million` | `
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| `value_added_2_rs_million` | `string` | Source column. | `` |
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| `contribution_to_gdp_2` | `string` | Source column. | `` |
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| `food_service` | `
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| `hotels` | `
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| `travel_other_services_2` | `
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| `total_tourism_industry` | `
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| `d_470` | `
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| `d_114610` | `
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| `d_115080` | `
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| `d_1301730` | `
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| `d_43` | `
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| `d_4000` | `
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| `september` | `string` | Source column. | `` |
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| `d_66_8` | `
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| `d_56_8` | `string` | Source column. | `` |
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| `d_82_9` | `
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| `d_74_4` | `
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| `d_84_9` | `
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| `d_75_9` | `
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| `d_81` | `
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| `d_73_4` | `
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| `d_74_5` | `
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| `d_67_4` | `
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| `d_63_9` | `
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| `d_59_1` | `
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| `d_67_9` | `
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| `d_72_3` | `
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| `d_66_5` | `
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| `d_67` | `
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| `d_85` | `
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| `d_75` | `
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| `d_82` | `
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| `d_72` | `
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| `d_63_2` | `
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| `accommodation` | `
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| `meals_beverages` | `
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| `transport` | `
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| `sightseeing` | `
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| `entertainment` | `
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| `shopping` | `
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| `other` | `
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| `total_1` | `
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| `country` | `string` | Source column. | `` |
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| `island_of_mauritius_1` | `
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## Usage
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print(df.head())
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```
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###
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```python
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```
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###
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```python
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if "
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sample = df.sort_values([c for c in ["indicator_id", "year"] if c in df.columns])
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```
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## Citation
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```bibtex
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@misc{electric_sheep_africa_africa_mauritius_earnings_and_value_added_of_the_tourism_sector_5c358d8e_2022,
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title = {Earnings and Value Added of the Tourism Sector | Africa (
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author = {MDPA},
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year = {2022},
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url = {https://data.govmu.org/dataset/earnings-and-value-added-tourism-sector},
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publisher = {
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howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-mauritius-earnings-and-value-added-of-the-tourism-sector-5c358d8e}}
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}
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```
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Released under [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/).
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Original data
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## About Electric Sheep
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ML-ready data layer for Africa on Hugging Face. We pull data from authoritative
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open sources, normalize the schemas, package as Parquet, and publish with
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consistent dataset cards so researchers and developers can use `load_dataset()`
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to start working in seconds.
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---
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Provenance:
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https://data.govmu.org/dataset/bf849e4c-49ca-4c54-8d9f-a05710082cab/resource/3420c2ad-6b29-4021-9c78-df2cbfc7d3a7/download/hs_tourism_yr22_250523_0.xls
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task_categories:
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- tabular-classification
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- tabular-regression
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multilinguality: multilingual
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size_categories:
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- n<1K
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tags:
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- "tabular"
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- "africa"
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- "open-data"
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- "official-statistics"
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- "mauritius"
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- "mdpa"
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- "travel-and-tourism"
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- "earnings"
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- "tourism"
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- "tourist"
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- "value-added"
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configs:
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- config_name: default
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data_files:
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- split: train
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path: data/train-00000-of-00001.parquet
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pretty_name: "Earnings and Value Added of the Tourism Sector | Africa (MDPA)"
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---
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# Earnings and Value Added of the Tourism Sector | Africa (MDPA)
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**796 rows** - **1 Africa country/area** - **1980-2022** - **source table** - *Engineered by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*
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## TL;DR
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This dataset contains **796 rows** from **MDPA**, covering **Earnings and Value Added of the Tourism Sector**. It is published as ML-ready Parquet with consistent Hugging Face metadata, source provenance, and analysis-friendly loading examples.
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## What This Dataset Measures
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Official statistics datasets help analysts inspect public data as published by governments, national statistical systems, and regional data portals.
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Source-provided context: Dataset shows Tourism Earnings, % contribution to GDP and Value Added (at current basic prices) of the Tourism Sector as from 1983
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## How To Read This Dataset
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- **One row means:** one source record from the original tabular resource, with Electric Sheep Africa provenance columns added where available.
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- **Primary geography column:** `country_iso3`.
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- **Best time column:** `year`.
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- **Time coverage basis:** year.
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- **Recommended join keys:** `country_iso3` where available plus source-specific keys.
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## Coverage
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| Dimension | Value |
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|---|---:|
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| Rows | 796 |
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| Countries/areas | 1 |
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| First period | 1980 |
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| Last period | 2022 |
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| Indicators | 0 |
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| Columns | 194 |
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| Source format | XLS |
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| 71 |
+
## Geographic Coverage
|
| 72 |
|
| 73 |
+
Top areas shown below, sorted by row count when available:
|
| 74 |
|
| 75 |
+
| Area | Rows | First year | Last year | Name |
|
| 76 |
+
|------|-----:|-----------:|----------:|------|
|
| 77 |
| `MU` | 796 | 1980 | 2022 | `Mauritius` |
|
| 78 |
|
| 79 |
+
## Indicators, Variables, Or Resource Contents
|
| 80 |
|
| 81 |
+
- This repo preserves one source tabular resource with its usable columns kept together.
|
| 82 |
|
| 83 |
## Schema
|
| 84 |
|
| 85 |
| Column | Type | Description | Example |
|
| 86 |
|--------|------|-------------|---------|
|
| 87 |
+
| `source_record_id` | `string` | Stable row identifier assigned during Electric Sheep Africa engineering. | `3420c2ad-6b29-4021-9c78-df2cbfc7d3a7:table-of-contents:0` |
|
| 88 |
+
| `country_iso3` | `dictionary<values=string, indices=int8, ordered=0>` | ISO3 country or area code. | `MU` |
|
| 89 |
+
| `country_name` | `dictionary<values=string, indices=int8, ordered=0>` | Country or area name. | `Mauritius` |
|
| 90 |
+
| `source_sheet` | `string` | Source column from the original resource. | `Table of contents` |
|
| 91 |
+
| `year` | `double` | Observation year. | `1983.0` |
|
| 92 |
+
| `d_5a` | `string` | Source column from the original resource. | `5b` |
|
| 93 |
+
| `tourist_arrivals_by_country_of_residence_1983_2012` | `string` | Source column from the original resource. | `Tourist arrivals by country of residence (covering a wider range of c...` |
|
| 94 |
+
| `source_period_start_year` | `int64` | Start year inferred from source metadata. | `1983` |
|
| 95 |
+
| `source_period_end_year` | `int64` | End year inferred from source metadata. | `1983` |
|
| 96 |
+
| `source_period_label` | `dictionary<values=string, indices=int8, ordered=0>` | Source column from the original resource. | `1983` |
|
| 97 |
+
| `source_provider` | `dictionary<values=string, indices=int8, ordered=0>` | Publishing organization. | `MDPA` |
|
| 98 |
+
| `source_dataset` | `dictionary<values=string, indices=int8, ordered=0>` | Source dataset or package title. | `Earnings and Value Added of the Tourism Sector` |
|
| 99 |
+
| `source_resource` | `dictionary<values=string, indices=int8, ordered=0>` | Source resource title, table name, or file name. | `HS_Tourism_Yr22_250523_0.xls` |
|
| 100 |
+
| `source_package_id` | `dictionary<values=string, indices=int8, ordered=0>` | Source package identifier. | `bf849e4c-49ca-4c54-8d9f-a05710082cab` |
|
| 101 |
+
| `source_resource_id` | `dictionary<values=string, indices=int8, ordered=0>` | Source resource identifier. | `3420c2ad-6b29-4021-9c78-df2cbfc7d3a7` |
|
| 102 |
+
| `source_url` | `dictionary<values=string, indices=int8, ordered=0>` | Original source URL or download URL. | `https://data.govmu.org/dataset/bf849e4c-49ca-4c54-8d9f-a05710082cab/r...` |
|
| 103 |
+
| `license_id` | `dictionary<values=string, indices=int8, ordered=0>` | Source license identifier. | `CC-BY-SA-4.0` |
|
| 104 |
+
| `retrieved_at` | `dictionary<values=string, indices=int8, ordered=0>` | UTC source retrieval timestamp from the Electric Sheep Africa pipeline. | `2026-08-08T16:26:20Z` |
|
| 105 |
+
| `d_1` | `double` | Source column from the original resource. | `` |
|
| 106 |
+
| `tourist` | `string` | Source column from the original resource. | `` |
|
| 107 |
+
| `a_tourist_is_defined_as_a_non_resident_staying_overnight` | `string` | Source column from the original resource. | `` |
|
| 108 |
+
| `1980` | `double` | Source column from the original resource. | `` |
|
| 109 |
+
| `d_163230` | `double` | Source column from the original resource. | `` |
|
| 110 |
+
| `d_167269` | `double` | Source column from the original resource. | `` |
|
| 111 |
+
| `d_6042` | `string` | Source column from the original resource. | `` |
|
| 112 |
+
| `d_5722` | `double` | Source column from the original resource. | `` |
|
| 113 |
+
| `country_of_disembarkation` | `string` | Source column from the original resource. | `` |
|
| 114 |
+
| `1983` | `double` | Source column from the original resource. | `` |
|
| 115 |
+
| `1984` | `double` | Source column from the original resource. | `` |
|
| 116 |
+
| `1985` | `double` | Source column from the original resource. | `` |
|
| 117 |
+
| `1986` | `double` | Source column from the original resource. | `` |
|
| 118 |
+
| `1987` | `double` | Source column from the original resource. | `` |
|
| 119 |
+
| `1988` | `double` | Source column from the original resource. | `` |
|
| 120 |
+
| `1989` | `double` | Source column from the original resource. | `` |
|
| 121 |
+
| `1990` | `double` | Source column from the original resource. | `` |
|
| 122 |
+
| `1991` | `double` | Source column from the original resource. | `` |
|
| 123 |
+
| `1992` | `double` | Source column from the original resource. | `` |
|
| 124 |
+
| `1993` | `double` | Source column from the original resource. | `` |
|
| 125 |
+
| `1994` | `double` | Source column from the original resource. | `` |
|
| 126 |
+
| `1995` | `double` | Source column from the original resource. | `` |
|
| 127 |
+
| `1996` | `double` | Source column from the original resource. | `` |
|
| 128 |
+
| `1997` | `double` | Source column from the original resource. | `` |
|
| 129 |
+
| `1998` | `double` | Source column from the original resource. | `` |
|
| 130 |
+
| `1999` | `double` | Source column from the original resource. | `` |
|
| 131 |
+
| `2000` | `double` | Source column from the original resource. | `` |
|
| 132 |
+
| `2001` | `double` | Source column from the original resource. | `` |
|
| 133 |
+
| `2002` | `double` | Source column from the original resource. | `` |
|
| 134 |
+
| `2003` | `double` | Source column from the original resource. | `` |
|
| 135 |
+
| `2004` | `double` | Source column from the original resource. | `` |
|
| 136 |
+
| `2005` | `double` | Source column from the original resource. | `` |
|
| 137 |
+
| `2006` | `double` | Source column from the original resource. | `` |
|
| 138 |
+
| `2007` | `double` | Source column from the original resource. | `` |
|
| 139 |
+
| `2008` | `double` | Source column from the original resource. | `` |
|
| 140 |
+
| `2009` | `double` | Source column from the original resource. | `` |
|
| 141 |
+
| `2010` | `double` | Source column from the original resource. | `` |
|
| 142 |
+
| `2011` | `double` | Source column from the original resource. | `` |
|
| 143 |
+
| `2012` | `double` | Source column from the original resource. | `` |
|
| 144 |
+
| `2013` | `double` | Source column from the original resource. | `` |
|
| 145 |
+
| `2014` | `double` | Source column from the original resource. | `` |
|
| 146 |
+
| `2015` | `double` | Source column from the original resource. | `` |
|
| 147 |
+
| `2016` | `double` | Source column from the original resource. | `` |
|
| 148 |
+
| `2017` | `double` | Source column from the original resource. | `` |
|
| 149 |
+
| `2018` | `double` | Source column from the original resource. | `` |
|
| 150 |
+
| `2019` | `double` | Source column from the original resource. | `` |
|
| 151 |
+
| `2020` | `double` | Source column from the original resource. | `` |
|
| 152 |
+
| `2021` | `double` | Source column from the original resource. | `` |
|
| 153 |
+
| `2022` | `double` | Source column from the original resource. | `` |
|
| 154 |
+
| `month_of_arrival` | `string` | Source column from the original resource. | `` |
|
| 155 |
+
| `1974` | `double` | Source column from the original resource. | `` |
|
| 156 |
+
| `1975` | `double` | Source column from the original resource. | `` |
|
| 157 |
+
| `1976` | `double` | Source column from the original resource. | `` |
|
| 158 |
+
| `1977` | `double` | Source column from the original resource. | `` |
|
| 159 |
+
| `1978` | `double` | Source column from the original resource. | `` |
|
| 160 |
+
| `1979` | `double` | Source column from the original resource. | `` |
|
| 161 |
+
| `1981` | `double` | Source column from the original resource. | `` |
|
| 162 |
+
| `1982` | `double` | Source column from the original resource. | `` |
|
| 163 |
+
| `country_of_residence` | `string` | Source column from the original resource. | `` |
|
| 164 |
+
| `europe` | `string` | Source column from the original resource. | `` |
|
| 165 |
+
| `d_547061` | `double` | Source column from the original resource. | `` |
|
| 166 |
+
| `d_570684` | `double` | Source column from the original resource. | `` |
|
| 167 |
+
| `d_631627` | `double` | Source column from the original resource. | `` |
|
| 168 |
+
| `d_734506` | `double` | Source column from the original resource. | `` |
|
| 169 |
+
| `d_780209` | `double` | Source column from the original resource. | `` |
|
| 170 |
+
| `d_824334` | `double` | Source column from the original resource. | `` |
|
| 171 |
+
| `d_835946` | `double` | Source column from the original resource. | `` |
|
| 172 |
+
| `d_207641` | `double` | Source column from the original resource. | `` |
|
| 173 |
+
| `d_145812` | `double` | Source column from the original resource. | `` |
|
| 174 |
+
| `d_674511` | `double` | Source column from the original resource. | `` |
|
| 175 |
+
| `d_55_1` | `double` | Source column from the original resource. | `` |
|
| 176 |
+
| `d_55` | `double` | Source column from the original resource. | `` |
|
| 177 |
+
| `d_54_9` | `double` | Source column from the original resource. | `` |
|
| 178 |
+
| `d_57_6` | `double` | Source column from the original resource. | `` |
|
| 179 |
+
| `d_58_1` | `double` | Source column from the original resource. | `` |
|
| 180 |
+
| `d_58_9` | `double` | Source column from the original resource. | `` |
|
| 181 |
+
| `d_60_4` | `double` | Source column from the original resource. | `` |
|
| 182 |
+
| `d_67_2` | `double` | Source column from the original resource. | `` |
|
| 183 |
+
| `d_81_1` | `double` | Source column from the original resource. | `` |
|
| 184 |
+
| `d_67_6` | `double` | Source column from the original resource. | `` |
|
| 185 |
+
| `tourism_earnings_1_rs_million` | `double` | Source column from the original resource. | `` |
|
| 186 |
+
| `value_added_2_rs_million` | `string` | Source column from the original resource. | `` |
|
| 187 |
+
| `contribution_to_gdp_2` | `string` | Source column from the original resource. | `` |
|
| 188 |
+
| `food_service` | `double` | Source column from the original resource. | `` |
|
| 189 |
+
| `hotels` | `double` | Source column from the original resource. | `` |
|
| 190 |
+
| `travel_other_services_2` | `double` | Source column from the original resource. | `` |
|
| 191 |
+
| `total_tourism_industry` | `double` | Source column from the original resource. | `` |
|
| 192 |
+
| `d_470` | `double` | Source column from the original resource. | `` |
|
| 193 |
+
| `d_114610` | `double` | Source column from the original resource. | `` |
|
| 194 |
+
| `d_115080` | `double` | Source column from the original resource. | `` |
|
| 195 |
+
| `d_1301730` | `double` | Source column from the original resource. | `` |
|
| 196 |
+
| `d_43` | `double` | Source column from the original resource. | `` |
|
| 197 |
+
| `d_4000` | `double` | Source column from the original resource. | `` |
|
| 198 |
+
| `september` | `string` | Source column from the original resource. | `` |
|
| 199 |
+
| `d_66_8` | `double` | Source column from the original resource. | `` |
|
| 200 |
+
| `d_56_8` | `string` | Source column from the original resource. | `` |
|
| 201 |
+
| `d_82_9` | `double` | Source column from the original resource. | `` |
|
| 202 |
+
| `d_74_4` | `double` | Source column from the original resource. | `` |
|
| 203 |
+
| `d_84_9` | `double` | Source column from the original resource. | `` |
|
| 204 |
+
| `d_75_9` | `double` | Source column from the original resource. | `` |
|
| 205 |
+
| `d_81` | `double` | Source column from the original resource. | `` |
|
| 206 |
+
| `d_73_4` | `double` | Source column from the original resource. | `` |
|
| 207 |
+
| `d_74_5` | `double` | Source column from the original resource. | `` |
|
| 208 |
+
| `d_67_4` | `double` | Source column from the original resource. | `` |
|
| 209 |
+
| `d_63_9` | `double` | Source column from the original resource. | `` |
|
| 210 |
+
| `d_59_1` | `double` | Source column from the original resource. | `` |
|
| 211 |
+
| `d_67_9` | `double` | Source column from the original resource. | `` |
|
| 212 |
+
| `d_63_5` | `double` | Source column from the original resource. | `` |
|
| 213 |
+
| `d_72_3` | `double` | Source column from the original resource. | `` |
|
| 214 |
+
| `d_66_5` | `double` | Source column from the original resource. | `` |
|
| 215 |
+
| `d_76` | `double` | Source column from the original resource. | `` |
|
| 216 |
+
| `d_71` | `double` | Source column from the original resource. | `` |
|
| 217 |
+
| `d_73` | `double` | Source column from the original resource. | `` |
|
| 218 |
+
| `d_67` | `double` | Source column from the original resource. | `` |
|
| 219 |
+
| `d_77` | `double` | Source column from the original resource. | `` |
|
| 220 |
+
| `d_68` | `double` | Source column from the original resource. | `` |
|
| 221 |
+
| `d_85` | `double` | Source column from the original resource. | `` |
|
| 222 |
+
| `d_75` | `double` | Source column from the original resource. | `` |
|
| 223 |
+
| `d_82` | `double` | Source column from the original resource. | `` |
|
| 224 |
+
| `d_72` | `double` | Source column from the original resource. | `` |
|
| 225 |
+
| `d_77_2` | `double` | Source column from the original resource. | `` |
|
| 226 |
+
| `d_68_2` | `double` | Source column from the original resource. | `` |
|
| 227 |
+
| `d_76_2` | `double` | Source column from the original resource. | `` |
|
| 228 |
+
| `d_74` | `double` | Source column from the original resource. | `` |
|
| 229 |
+
| `d_66` | `double` | Source column from the original resource. | `` |
|
| 230 |
+
| `d_76_3` | `double` | Source column from the original resource. | `` |
|
| 231 |
+
| `d_68_3` | `double` | Source column from the original resource. | `` |
|
| 232 |
+
| `d_67_3` | `double` | Source column from the original resource. | `` |
|
| 233 |
+
| `d_59` | `double` | Source column from the original resource. | `` |
|
| 234 |
+
| `d_69` | `double` | Source column from the original resource. | `` |
|
| 235 |
+
| `d_62` | `double` | Source column from the original resource. | `` |
|
| 236 |
+
| `d_67_5` | `double` | Source column from the original resource. | `` |
|
| 237 |
+
| `d_60` | `double` | Source column from the original resource. | `` |
|
| 238 |
+
| `d_71_2` | `double` | Source column from the original resource. | `` |
|
| 239 |
+
| `d_63` | `double` | Source column from the original resource. | `` |
|
| 240 |
+
| `d_78` | `double` | Source column from the original resource. | `` |
|
| 241 |
+
| `d_70` | `double` | Source column from the original resource. | `` |
|
| 242 |
+
| `d_68_4` | `double` | Source column from the original resource. | `` |
|
| 243 |
+
| `d_61` | `double` | Source column from the original resource. | `` |
|
| 244 |
+
| `d_64` | `double` | Source column from the original resource. | `` |
|
| 245 |
+
| `d_56` | `double` | Source column from the original resource. | `` |
|
| 246 |
+
| `d_58` | `double` | Source column from the original resource. | `` |
|
| 247 |
+
| `d_66_2` | `double` | Source column from the original resource. | `` |
|
| 248 |
+
| `d_58_2` | `double` | Source column from the original resource. | `` |
|
| 249 |
+
| `d_62_2` | `double` | Source column from the original resource. | `` |
|
| 250 |
+
| `d_54` | `double` | Source column from the original resource. | `` |
|
| 251 |
+
| `d_67_7` | `double` | Source column from the original resource. | `` |
|
| 252 |
+
| `d_60_2` | `double` | Source column from the original resource. | `` |
|
| 253 |
+
| `d_68_5` | `double` | Source column from the original resource. | `` |
|
| 254 |
+
| `d_61_2` | `double` | Source column from the original resource. | `` |
|
| 255 |
+
| `d_74_2` | `double` | Source column from the original resource. | `` |
|
| 256 |
+
| `d_68_6` | `double` | Source column from the original resource. | `` |
|
| 257 |
+
| `d_78_2` | `double` | Source column from the original resource. | `` |
|
| 258 |
+
| `d_68_7` | `double` | Source column from the original resource. | `` |
|
| 259 |
+
| `d_79` | `double` | Source column from the original resource. | `` |
|
| 260 |
+
| `d_69_2` | `double` | Source column from the original resource. | `` |
|
| 261 |
+
| `d_82_2` | `double` | Source column from the original resource. | `` |
|
| 262 |
+
| `d_72_2` | `double` | Source column from the original resource. | `` |
|
| 263 |
+
| `d_77_3` | `double` | Source column from the original resource. | `` |
|
| 264 |
+
| `d_65` | `double` | Source column from the original resource. | `` |
|
| 265 |
+
| `d_8` | `double` | Source column from the original resource. | `` |
|
| 266 |
+
| `d_8_2` | `double` | Source column from the original resource. | `` |
|
| 267 |
+
| `d_11` | `double` | Source column from the original resource. | `` |
|
| 268 |
+
| `d_9` | `double` | Source column from the original resource. | `` |
|
| 269 |
+
| `d_75_2` | `double` | Source column from the original resource. | `` |
|
| 270 |
+
| `d_63_2` | `double` | Source column from the original resource. | `` |
|
| 271 |
+
| `accommodation` | `double` | Source column from the original resource. | `` |
|
| 272 |
+
| `meals_beverages` | `double` | Source column from the original resource. | `` |
|
| 273 |
+
| `transport` | `double` | Source column from the original resource. | `` |
|
| 274 |
+
| `sightseeing` | `double` | Source column from the original resource. | `` |
|
| 275 |
+
| `entertainment` | `double` | Source column from the original resource. | `` |
|
| 276 |
+
| `shopping` | `double` | Source column from the original resource. | `` |
|
| 277 |
+
| `other` | `double` | Source column from the original resource. | `` |
|
| 278 |
+
| `total_1` | `double` | Source column from the original resource. | `` |
|
| 279 |
+
| `country` | `string` | Source column from the original resource. | `` |
|
| 280 |
+
| `island_of_mauritius_1` | `double` | Source column from the original resource. | `` |
|
| 281 |
|
| 282 |
## Usage
|
| 283 |
|
|
|
|
| 289 |
print(df.head())
|
| 290 |
```
|
| 291 |
|
| 292 |
+
### Inspect Columns
|
| 293 |
|
| 294 |
```python
|
| 295 |
+
print(df.info())
|
| 296 |
+
print(df.head())
|
| 297 |
```
|
| 298 |
|
| 299 |
+
### Filter By Geography
|
| 300 |
|
| 301 |
```python
|
| 302 |
+
if "country_iso3" in df.columns:
|
| 303 |
+
sample = df[df["country_iso3"] == "MU"]
|
|
|
|
| 304 |
```
|
| 305 |
|
| 306 |
+
### Time-Series Pattern
|
| 307 |
+
|
| 308 |
+
```python
|
| 309 |
+
if "value" in df.columns and "year" in df.columns:
|
| 310 |
+
trend = df.sort_values("year")
|
| 311 |
+
```
|
| 312 |
+
|
| 313 |
+
### Pivot For Analysis
|
| 314 |
+
|
| 315 |
+
```python
|
| 316 |
+
if {"indicator_id", "year", "value"}.issubset(df.columns):
|
| 317 |
+
matrix = df.pivot_table(index="year", columns="indicator_id", values="value")
|
| 318 |
+
print(matrix.tail())
|
| 319 |
+
```
|
| 320 |
+
|
| 321 |
+
## Data Quality Notes
|
| 322 |
+
|
| 323 |
+
- Canonical time field: `year`.
|
| 324 |
+
- Missing values are preserved rather than silently imputed.
|
| 325 |
+
- Column names are standardized for machine use; source meanings are preserved where known.
|
| 326 |
+
- Always confirm source methodology, units, and collection definitions before policy, production, or redistribution-sensitive use.
|
| 327 |
+
|
| 328 |
+
## Source And Provenance
|
| 329 |
+
|
| 330 |
+
- **Source:** [MDPA](https://data.govmu.org/dataset/earnings-and-value-added-tourism-sector)
|
| 331 |
+
- **Publisher:** MDPA
|
| 332 |
+
- **Portal:** [https://data.govmu.org](https://data.govmu.org)
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+
- **Resource:** [HS_Tourism_Yr22_250523_0.xls](https://data.govmu.org/dataset/bf849e4c-49ca-4c54-8d9f-a05710082cab/resource/3420c2ad-6b29-4021-9c78-df2cbfc7d3a7/download/hs_tourism_yr22_250523_0.xls)
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- **License:** [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/)
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- **Retrieved/generated:** `2026-08-08T16:45:04Z`
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- **Hugging Face repo:** [electricsheepafrica/africa-mauritius-earnings-and-value-added-of-the-tourism-sector-5c358d8e](https://huggingface.co/datasets/electricsheepafrica/africa-mauritius-earnings-and-value-added-of-the-tourism-sector-5c358d8e)
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+
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## Transformations Applied
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- Converted the source table to Parquet for efficient analytics and ML workflows.
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- Added or preserved source provenance columns where available.
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- Standardized README metadata, dataset loading configuration, schema documentation, and citation format.
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- Preserved source-reported values without analytical imputation.
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+
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## Suggested Analyses
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- Profile the distribution of values
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- Compare categories or geographies
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- Join with complementary public datasets
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- Build time-series views and period-over-period comparisons
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- Check missingness before modeling
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- Use `country_iso3` as the safest geography join key when present
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+
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## Citation
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```bibtex
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| 357 |
@misc{electric_sheep_africa_africa_mauritius_earnings_and_value_added_of_the_tourism_sector_5c358d8e_2022,
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| 358 |
+
title = {Earnings and Value Added of the Tourism Sector | Africa (MDPA)},
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| 359 |
author = {MDPA},
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| 360 |
year = {2022},
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| 361 |
url = {https://data.govmu.org/dataset/earnings-and-value-added-tourism-sector},
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+
publisher = {Hugging Face Datasets, engineered by Electric Sheep Africa},
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| 363 |
howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-mauritius-earnings-and-value-added-of-the-tourism-sector-5c358d8e}}
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| 364 |
}
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| 365 |
```
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| 369 |
Released under [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/).
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| 371 |
+
Original data is published by MDPA. Electric Sheep Africa
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+
engineering standardizes the data for discovery, loading, and analysis on
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| 373 |
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Hugging Face. Cite both the original source and this ML-ready dataset when used.
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## About Electric Sheep Africa
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| 377 |
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Electric Sheep Africa publishes ML-ready African public datasets on Hugging Face.
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---
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Provenance: README standardized 2026-08-11 by the Electric Sheep Africa README system. Source URL: https://data.govmu.org/dataset/earnings-and-value-added-tourism-sector
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