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electricsheepafrica
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africa-mauritius-sales-of-electricity-by-type-of-tariff-fc65bded

Tasks:
Tabular Classification
Tabular Regression
Modalities:
Tabular
Text
Formats:
parquet
Languages:
English
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< 1K
Tags:
tabular
africa
open-data
official-statistics
mauritius
mdpa
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africa-mauritius-sales-of-electricity-by-type-of-tariff-fc65bded / README.md
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metadata 
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

rows countries period indicators license

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<values=string, indices=int8, ordered=0> ISO3 country or area code. MU
country_name dictionary<values=string, indices=int8, ordered=0> 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<values=string, indices=int8, ordered=0> Source column from the original resource. 2017-2021
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
source_resource dictionary<values=string, indices=int8, ordered=0> Source resource title, table name, or file name. Digest_Industrial_Stats_Yr21_281022_sourceFile.xlsx
source_package_id dictionary<values=string, indices=int8, ordered=0> Source package identifier. 89d7ecdc-fff6-4dce-a4a0-7edcc597b5c7
source_resource_id dictionary<values=string, indices=int8, ordered=0> Source resource identifier. e754caf8-029f-4b07-bd14-9265052c8860
source_url dictionary<values=string, indices=int8, ordered=0> Original source URL or download URL. https://data.govmu.org/dataset/89d7ecdc-fff6-4dce-a4a0-7edcc597b5c7/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
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 

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 

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

  • 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

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 

@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.

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