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africa-mauritius-main-energy-indicators-republic-of-mauritius-2018-2024-percentag-c0e01021

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Tabular Regression
Time Series Forecasting
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tabular
africa
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metadata 
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
  - energy
  - environment-and-natural-resources
  - consumption
  - electricity
  - generated
  - renewable-energy
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-00000-of-00001.parquet
pretty_name: >-
  Main Energy indicators, Republic of Mauritius, 2018-2024 - Percentage of
  renewables from total final energy consumption | Africa (MDPA)

Main Energy indicators, Republic of Mauritius, 2018-2024 - Percentage of renewables from total final energy consumption | Africa (MDPA)

7 rows - 1 Africa country/area - 2018-2024 - 1 indicator - Engineered by Electric Sheep Africa

rows countries period indicators license

TL;DR

This dataset contains 7 rows from MDPA, covering Main Energy indicators, Republic of Mauritius, 2018-2024 - Percentage of renewables from total final energy consumption. 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: Dataset shows the main Energy indicators, Republic of Mauritius, 2018 to 2024

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 7
Countries/areas 1
First period 2018
Last period 2024
Indicators 1
Columns 15
Source format CSV

Geographic Coverage

Top areas shown below, sorted by row count when available:

Area Rows First year Last year Name
MUS 7 2018 2024 Mauritius

Indicators, Variables, Or Resource Contents

  • main-energy-indicators-republic-of-mauritius-2018-2024-percentage-of-ren-c0e01021 - Main Energy indicators, Republic of Mauritius, 2018-2024 - Percentage of renewables from total final energy consumption(source_units_unspecified)

Schema

Column Type Description Example
indicator_id string Stable source or Electric Sheep Africa indicator identifier. main-energy-indicators-republic-of-mauritius-2018-2024-percentage-of-...
indicator_name string Human-readable indicator name. Main Energy indicators, Republic of Mauritius, 2018-2024 - Percentage...
country_iso3 string ISO3 country or area code. MUS
country_name string Country or area name. Mauritius
year int64 Observation year. 2018
value double Numeric observation value. 2.5
unit string Measurement unit, when supplied by the source. source_units_unspecified
source_provider string Publishing organization. MDPA
source_dataset string Source dataset or package title. Main Energy indicators, Republic of Mauritius, 2018-2024
source_resource string Source resource title, table name, or file name. main_energy_indicators-republic-of-mauritius-2018-2024v.csv
source_package_id string Source package identifier. e398d0b3-75e4-424c-89cb-cb2caa281e94
source_resource_id string Source resource identifier. 0f445a42-d734-4293-a8ff-724efff02c29
source_url string Original source URL or download URL. https://data.govmu.org/dataset/e398d0b3-75e4-424c-89cb-cb2caa281e94/r...
license_id string Source license identifier. cc-by
retrieved_at string UTC source retrieval timestamp from the Electric Sheep Africa pipeline. 2026-07-16T18:12:36Z

Usage 

from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-mauritius-main-energy-indicators-republic-of-mauritius-2018-2024-percentag-c0e01021")
df = ds["train"].to_pandas()
print(df.head())

Inspect Columns 

print(df.info())
print(df.head())

Filter By Geography 

if "country_iso3" in df.columns:
    sample = df[df["country_iso3"] == "MUS"]

Time-Series Pattern 

if "value" in df.columns and "year" in df.columns:
    trend = df.sort_values("year")

Pivot For Analysis 

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

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
  • 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 

@misc{electric_sheep_africa_africa_mauritius_main_energy_indicators_republic_of_mauritius_2018_2024_percenta_2024,
  title        = {Main Energy indicators, Republic of Mauritius, 2018-2024 - Percentage of renewables from total final energy consumption | Africa (MDPA)},
  author       = {MDPA},
  year         = {2024},
  url          = {https://data.govmu.org/dataset/main-energy-indicators-republic-of-mauritius-2018-2024},
  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-mauritius-main-energy-indicators-republic-of-mauritius-2018-2024-percentag-c0e01021}}
}

License

Released under CC 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/main-energy-indicators-republic-of-mauritius-2018-2024