--- 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" - "energy" - "environment-and-natural-resources" - "commercial" - "domestic" - "ceb" - "gwh" - "industrial" - "tarrif" configs: - config_name: default data_files: - split: train path: data/train-00000-of-00001.parquet pretty_name: "Sales of Electricity by Type of Tariff | Africa (MDPA)" --- # Sales of Electricity by Type of Tariff | Africa (MDPA) **163 rows** - **1 Africa country/area** - **2017-2021** - **source table** - *Engineered by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)* ![rows](https://img.shields.io/badge/rows-163-blue) ![countries](https://img.shields.io/badge/countries-1-green) ![period](https://img.shields.io/badge/period-2017--2021-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 **163 rows** from **MDPA**, covering **Sales of Electricity by Type of Tariff**. It is published as ML-ready Parquet with consistent Hugging Face metadata, source provenance, and analysis-friendly loading examples. ## What This Dataset Measures Energy datasets help analysts study supply, demand, prices, generation, access, and the infrastructure behind economic activity. Source-provided context: Data shows electricity sold by type of tariff for the year 2017 to 2021 ## 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 | 163 | | Countries/areas | 1 | | First period | 2017 | | Last period | 2021 | | Indicators | 0 | | Columns | 34 | | Source format | XLSX | ## Geographic Coverage Top areas shown below, sorted by row count when available: | Area | Rows | First year | Last year | Name | |------|-----:|-----------:|----------:|------| | `MU` | 163 | 2017 | 2021 | `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. | `e754caf8-029f-4b07-bd14-9265052c8860:symbols-abbreviation-acronym: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. | `Symbols, Abbreviation & Acronym` | | `column` | `string` | Source column from the original resource. | `N.A : Not available` | | `not_applicable_or_nil` | `string` | Source column from the original resource. | `` | | `source_period_start_year` | `int64` | Start year inferred from source metadata. | `2017` | | `source_period_end_year` | `int64` | End year inferred from source metadata. | `2021` | | `source_period_label` | `dictionary` | Source column from the original resource. | `2017-2021` | | `source_provider` | `dictionary` | Publishing organization. | `MDPA` | | `source_dataset` | `dictionary` | Source dataset or package title. | `Sales of electricity by type of tariff` | | `source_resource` | `dictionary` | Source resource title, table name, or file name. | `Digest_Industrial_Stats_Yr21_281022_sourceFile.xlsx` | | `source_package_id` | `dictionary` | Source package identifier. | `89d7ecdc-fff6-4dce-a4a0-7edcc597b5c7` | | `source_resource_id` | `dictionary` | Source resource identifier. | `e754caf8-029f-4b07-bd14-9265052c8860` | | `source_url` | `dictionary` | Original source URL or download URL. | `https://data.govmu.org/dataset/89d7ecdc-fff6-4dce-a4a0-7edcc597b5c7/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` | | `d_1_mining_and_quarrying` | `string` | Source column from the original resource. | `` | | `the_activity_of_mining_and_quarrying_comprises_activitie` | `string` | Source column from the original resource. | `` | | `productivity_and_unit_labour_cost_indices` | `string` | Source column from the original resource. | `` | | `introduction` | `string` | Source column from the original resource. | `` | | `d_1_coverage` | `string` | Source column from the original resource. | `` | | `the_industrial_sector_according_to_the_international_rec` | `string` | Source column from the original resource. | `` | | `2017` | `string` | Source column from the original resource. | `` | | `d_872_698676` | `double` | Source column from the original resource. | `` | | `d_420876` | `double` | Source column from the original resource. | `` | | `d_951_9582607637849` | `double` | Source column from the original resource. | `` | | `d_42761` | `double` | Source column from the original resource. | `` | | `d_755_253732` | `double` | Source column from the original resource. | `` | | `d_6353` | `double` | Source column from the original resource. | `` | | `d_38_212101636206356` | `double` | Source column from the original resource. | `` | | `d_676` | `double` | Source column from the original resource. | `` | | `d_2618_122770399991` | `double` | Source column from the original resource. | `` | | `d_470666` | `double` | Source column from the original resource. | `` | ## Usage ```python from datasets import load_dataset ds = load_dataset("electricsheepafrica/africa-mauritius-sales-of-electricity-by-type-of-tariff-fc65bded") 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/sales-electricity-type-tariff) - **Publisher:** MDPA - **Portal:** [https://data.govmu.org](https://data.govmu.org) - **Resource:** [Digest_Industrial_Stats_Yr21_281022_sourceFile.xlsx](https://data.govmu.org/dataset/89d7ecdc-fff6-4dce-a4a0-7edcc597b5c7/resource/e754caf8-029f-4b07-bd14-9265052c8860/download/digest_industrial_stats_yr21_281022_sourcefile.xlsx) - **License:** [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/) - **Retrieved/generated:** `2026-08-08T16:33:00Z` - **Hugging Face repo:** [electricsheepafrica/africa-mauritius-sales-of-electricity-by-type-of-tariff-fc65bded](https://huggingface.co/datasets/electricsheepafrica/africa-mauritius-sales-of-electricity-by-type-of-tariff-fc65bded) ## 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 supply or price trends - Compare energy sources - Join with population, industry, or emissions data - Check missingness before modeling - Use `country_iso3` as the safest geography join key when present ## Citation ```bibtex @misc{electric_sheep_africa_africa_mauritius_sales_of_electricity_by_type_of_tariff_fc65bded_2021, title = {Sales of Electricity by Type of Tariff | Africa (MDPA)}, author = {MDPA}, year = {2021}, url = {https://data.govmu.org/dataset/sales-electricity-type-tariff}, publisher = {Hugging Face Datasets, engineered by Electric Sheep Africa}, howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-mauritius-sales-of-electricity-by-type-of-tariff-fc65bded}} } ``` ## 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/sales-electricity-type-tariff