--- license: cc-by-sa-4.0 language: - en task_categories: - tabular-classification - tabular-regression multilinguality: multilingual size_categories: - n<1K tags: - "tabular" - "africa" - "open-data" - "official-statistics" - "mauritius" - "mdpa" - "climate" - "environment-and-natural-resources" - "commercial" - "domestic" - "industrial" - "religious" - "water" configs: - config_name: default data_files: - split: train path: data/train-00000-of-00001.parquet pretty_name: "Water Sales by Tariff of Subscriber | Africa (MDPA)" --- # Water Sales by Tariff of Subscriber | Africa (MDPA) **28 rows** - **1 Africa country/area** - **2016-2022** - **source table** - *Engineered by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)* ![rows](https://img.shields.io/badge/rows-28-blue) ![countries](https://img.shields.io/badge/countries-1-green) ![period](https://img.shields.io/badge/period-2016--2022-orange) ![indicators](https://img.shields.io/badge/indicators-0-purple) ![license](https://img.shields.io/badge/license-cc--by--sa--4.0-lightgrey) ## TL;DR This dataset contains **28 rows** from **MDPA**, covering **Water Sales by Tariff of Subscriber**. It is published as ML-ready Parquet with consistent Hugging Face metadata, source provenance, and analysis-friendly loading examples. ## What This Dataset Measures Climate and environment datasets help analysts study exposure, resource conditions, environmental pressure, and climate-related trends. Source-provided context: The data shows the water sales by tariff of subscribers for island of Mauritius for the year 2016 to 2022 ## 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:** `not detected`. - **Time coverage basis:** source metadata. - **Recommended join keys:** `country_iso3` where available plus source-specific keys. ## Coverage | Dimension | Value | |---|---:| | Rows | 28 | | Countries/areas | 1 | | First period | 2016 | | Last period | 2022 | | Indicators | 0 | | Columns | 55 | | Source format | XLSX | ## Geographic Coverage Top areas shown below, sorted by row count when available: | Area | Rows | First year | Last year | Name | |------|-----:|-----------:|----------:|------| | `MU` | 28 | 2016 | 2022 | `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. | `ff1be866-1f8f-4c16-af99-f14cd51ddfb5:tab16:0` | | `country_iso3` | `dictionary` | ISO3 country or area code. | `MU` | | `country_name` | `dictionary` | Country or area name. | `Mauritius` | | `source_sheet` | `string` | Source column from the original resource. | `TAB16` | | `column_1` | `string` | Source column from the original resource. | `` | | `domestic` | `string` | Source column from the original resource. | `Public Sector Agency` | | `d_365971` | `double` | Source column from the original resource. | `2587.0` | | `d_92_92495588253963` | `double` | Source column from the original resource. | `0.6568740716289817` | | `d_85053_399` | `double` | Source column from the original resource. | `4096.654` | | `d_69_77075636118678` | `double` | Source column from the original resource. | `3.360555268697507` | | `d_832555_79997` | `double` | Source column from the original resource. | `98681.707` | | `d_55_00829401135688` | `double` | Source column from the original resource. | `6.52005829806744` | | `d_232_40475064964164` | `double` | Source column from the original resource. | `1583.5539234634712` | | `d_9_788624672953988` | `double` | Source column from the original resource. | `24.088367482340463` | | `d_372734` | `double` | Source column from the original resource. | `2603.0` | | `d_92_94069244099789` | `double` | Source column from the original resource. | `0.649054345522323` | | `d_87497_492` | `double` | Source column from the original resource. | `4338.04` | | `d_68_68942650464635` | `double` | Source column from the original resource. | `3.40555452439958` | | `d_870695_6203399999` | `double` | Source column from the original resource. | `104306.187` | | `d_53_14584534898735` | `double` | Source column from the original resource. | `6.366680104672956` | | `d_234_74513191713126` | `double` | Source column from the original resource. | `1666.5539761813293` | | `d_9_951092316337478` | `double` | Source column from the original resource. | `24.04454246618289` | | `source_period_start_year` | `int64` | Start year inferred from source metadata. | `2016` | | `source_period_end_year` | `int64` | End year inferred from source metadata. | `2022` | | `source_period_label` | `dictionary` | Source column from the original resource. | `2016-2022` | | `source_provider` | `dictionary` | Publishing organization. | `MDPA` | | `source_dataset` | `dictionary` | Source dataset or package title. | `Water sales by tariff of subscriber` | | `source_resource` | `dictionary` | Source resource title, table name, or file name. | `Energy_Water_Yr22_060623_Source_File.xlsx` | | `source_package_id` | `dictionary` | Source package identifier. | `9584b934-20df-4dd5-98e7-e3ff15f7aa8e` | | `source_resource_id` | `dictionary` | Source resource identifier. | `ff1be866-1f8f-4c16-af99-f14cd51ddfb5` | | `source_url` | `dictionary` | Original source URL or download URL. | `https://data.govmu.org/dataset/9584b934-20df-4dd5-98e7-e3ff15f7aa8e/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` | | `type_of_tariff` | `string` | Source column from the original resource. | `` | | `column_2` | `string` | Source column from the original resource. | `` | | `no_of_consumers` | `double` | Source column from the original resource. | `` | | `volume_sold_thousand_m3` | `double` | Source column from the original resource. | `` | | `amount_collectible_rs_000` | `double` | Source column from the original resource. | `` | | `average_sales_price_per_m3` | `double` | Source column from the original resource. | `` | | `no_of_consumers_2` | `double` | Source column from the original resource. | `` | | `volume_sold_thousand_m3_2` | `double` | Source column from the original resource. | `` | | `amount_collectible_rs_000_2` | `double` | Source column from the original resource. | `` | | `average_sales_price_per_m3_2` | `double` | Source column from the original resource. | `` | | `no_of_consumers_3` | `double` | Source column from the original resource. | `` | | `volume_sold_thousand_m3_3` | `double` | Source column from the original resource. | `` | | `amount_collectible_rs_000_3` | `double` | Source column from the original resource. | `` | | `average_sales_price_per_m3_3` | `double` | Source column from the original resource. | `` | | `no_of_consumers_4` | `double` | Source column from the original resource. | `` | | `volume_sold_thousand_m3_4` | `double` | Source column from the original resource. | `` | | `amount_collectible_rs_000_4` | `double` | Source column from the original resource. | `` | | `average_sales_price_per_m3_4` | `double` | Source column from the original resource. | `` | | `no_of_consumers_5` | `double` | Source column from the original resource. | `` | | `volume_sold_thousand_m3_5` | `double` | Source column from the original resource. | `` | | `amount_collectible_rs_000_5` | `double` | Source column from the original resource. | `` | | `average_sales_price_per_m3_5` | `double` | Source column from the original resource. | `` | ## Usage ```python from datasets import load_dataset ds = load_dataset("electricsheepafrica/africa-mauritius-water-sales-by-tariff-of-subscriber-685f5471") 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 - No canonical year/date column was detected in the packaged table; use source metadata and domain context for temporal interpretation. - 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/water-sales-tariff-subscriber) - **Publisher:** MDPA - **Portal:** [https://data.govmu.org](https://data.govmu.org) - **Resource:** [Energy_Water_Yr22_060623_Source_File.xlsx](https://data.govmu.org/dataset/9584b934-20df-4dd5-98e7-e3ff15f7aa8e/resource/ff1be866-1f8f-4c16-af99-f14cd51ddfb5/download/energy_water_yr22_060623_source_file.xlsx) - **License:** [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/) - **Retrieved/generated:** `2026-08-08T16:30:12Z` - **Hugging Face repo:** [electricsheepafrica/africa-mauritius-water-sales-by-tariff-of-subscriber-685f5471](https://huggingface.co/datasets/electricsheepafrica/africa-mauritius-water-sales-by-tariff-of-subscriber-685f5471) ## 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 - Analyze seasonal or annual patterns - Join with agriculture or health data - Map geographic exposure - Check missingness before modeling - Use `country_iso3` as the safest geography join key when present ## Citation ```bibtex @misc{electric_sheep_africa_africa_mauritius_water_sales_by_tariff_of_subscriber_685f5471_2022, title = {Water Sales by Tariff of Subscriber | Africa (MDPA)}, author = {MDPA}, year = {2022}, url = {https://data.govmu.org/dataset/water-sales-tariff-subscriber}, publisher = {Hugging Face Datasets, engineered by Electric Sheep Africa}, howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-mauritius-water-sales-by-tariff-of-subscriber-685f5471}} } ``` ## 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/water-sales-tariff-subscriber