Hugging Face's logo

Datasets:
electricsheepafrica
/
africa-mauritius-sales-of-electricity-by-type-of-tariff-group-in-mauritius-cd104b94

Tasks:
Tabular Regression
Time Series Forecasting
Modalities:
Tabular
Text
Formats:
parquet
Languages:
English
Size:
< 1K
Tags:
tabular
africa
open-data
official-statistics
mauritius
mdpa
Libraries:
Datasets
pandas
Polars
License:
Dataset card Data Studio Files
xet
 Community
africa-mauritius-sales-of-electricity-by-type-of-tariff-group-in-mauritius-cd104b94 / README.md
Olaroti's picture
Olaroti
Standardize Electric Sheep Africa dataset card
f24064b verified
preview code
|
Raw
History Blame Contribute Delete
9.03 kB
metadata 
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
  - energy
  - environment-and-natural-resources
  - commercial
  - domestic
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-00000-of-00001.parquet
pretty_name: Sales of Electricity by Type of Tariff Group in Mauritius | Africa (MDPA)

Sales of Electricity by Type of Tariff Group in Mauritius | Africa (MDPA)

750 rows - 1 Africa country/area - 1990-2019 - 1 indicator - Engineered by Electric Sheep Africa

rows countries period indicators license

TL;DR

This dataset contains 750 rows from MDPA, covering Sales of Electricity by Type of Tariff Group in Mauritius. 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: Sales of Electricity by Type of Tariff Group for the year 1990 to 2019 in Mauritius

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 750
Countries/areas 1
First period 1990
Last period 2019
Indicators 1
Columns 20
Source format XLSX

Geographic Coverage

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

Area Rows First year Last year Name
MU 750 1990 2019 Mauritius

Indicators, Variables, Or Resource Contents

  • sales-of-electricity-by-type-of-tariff-group-in-mauritius-cd104b94 - Sales of Electricity by Type of Tariff Group in Mauritius(source_units_unspecified)

Schema

Column Type Description Example
indicator_id string Stable source or Electric Sheep Africa indicator identifier. sales-of-electricity-by-type-of-tariff-group-in-mauritius-cd104b94
indicator_name string Human-readable indicator name. Sales of Electricity by Type of Tariff Group in Mauritius
country_iso3 string ISO3 country or area code. MU
source_sheet string Source column from the original resource. T10
country_name string Country or area name. Mauritius
year int64 Observation year. 1990
value double Numeric observation value. 201878.0
unit string Measurement unit, when supplied by the source. source_units_unspecified
dimension_tariff_group string Source dimension retained during long-form normalization. Domestic
source_period_start_year int64 Start year inferred from source metadata. 1990
source_period_end_year int64 End year inferred from source metadata. 2019
source_period_label dictionary<values=string, indices=int8, ordered=0> Source column from the original resource. 1990-2019
source_provider dictionary<values=string, indices=int8, ordered=0> Publishing organization. MDPA
source_dataset dictionary<values=string, indices=int8, ordered=0> Source dataset or package title. Sales of Electricity by Type of Tariff Group in Mauritius
source_resource dictionary<values=string, indices=int8, ordered=0> Source resource title, table name, or file name. Source-Sales-of-Electricity-by-Type-of-Tariff-Group-for-the-year-1990...
source_package_id dictionary<values=string, indices=int8, ordered=0> Source package identifier. 81555bf5-8418-4c3e-9e6e-588f86f3ddcd
source_resource_id dictionary<values=string, indices=int8, ordered=0> Source resource identifier. ab03aff6-8af3-40d3-a721-f6944633641b
source_url dictionary<values=string, indices=int8, ordered=0> Original source URL or download URL. https://data.govmu.org/dataset/81555bf5-8418-4c3e-9e6e-588f86f3ddcd/r...
license_id dictionary<values=string, indices=int8, ordered=0> Source license identifier. CC-BY-SA-4.0
retrieved_at dictionary<values=string, indices=int8, ordered=0> UTC source retrieval timestamp from the Electric Sheep Africa pipeline. 2026-08-08T16:26:20Z

Usage 

from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-mauritius-sales-of-electricity-by-type-of-tariff-group-in-mauritius-cd104b94")
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"] == "MU"]

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_sales_of_electricity_by_type_of_tariff_group_in_mauritius_cd104_2019,
  title        = {Sales of Electricity by Type of Tariff Group in Mauritius | Africa (MDPA)},
  author       = {MDPA},
  year         = {2019},
  url          = {https://data.govmu.org/dataset/sales-electricity-type-tariff-group-mauritius},
  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-group-in-mauritius-cd104b94}}
}

License

Released under CC 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-group-mauritius