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africa-mauritius-earnings-of-sugar-producers-ex-syndicate-before-charging-s-dee032ba

Tasks:
Tabular Regression
Time Series Forecasting
Modalities:
Tabular
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Formats:
parquet
Languages:
English
Size:
< 1K
Tags:
tabular
africa
open-data
official-statistics
mauritius
mdpa
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africa-mauritius-earnings-of-sugar-producers-ex-syndicate-before-charging-s-dee032ba / README.md
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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
  - agriculture
  - earnings
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-00000-of-00001.parquet
pretty_name: Earnings of Sugar Producers Ex Syndicate Before Charging S | Africa (MDPA)

Earnings of Sugar Producers Ex Syndicate Before Charging S | Africa (MDPA)

44 rows - 1 Africa country/area - 1979-2022 - 1 indicator - Engineered by Electric Sheep Africa

rows countries period indicators license

TL;DR

This dataset contains 44 rows from MDPA, covering Earnings of Sugar Producers Ex Syndicate Before Charging S. It is published as ML-ready Parquet with consistent Hugging Face metadata, source provenance, and analysis-friendly loading examples.

What This Dataset Measures

Agriculture datasets help analysts examine production, prices, inputs, land use, food systems, and rural economic activity.

Source-provided context: Dataset shows the Earnings of sugar producers - ex-Syndicate, before charging sugar insurance premium, 1979-2022

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 44
Countries/areas 1
First period 1979
Last period 2022
Indicators 1
Columns 18
Source format CSV

Geographic Coverage

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

Area Rows First year Last year Name
MU 44 1979 2022 Mauritius

Indicators, Variables, Or Resource Contents

  • earnings-of-sugar-producers-ex-syndicate-before-charging-sugar-insurance-57f22ba3 - Earnings of sugar producers - ex-Syndicate, before charging sugar insurance premium(source_units_unspecified)

Schema

Column Type Description Example
indicator_id string Stable source or Electric Sheep Africa indicator identifier. earnings-of-sugar-producers-ex-syndicate-before-charging-sugar-insura...
indicator_name string Human-readable indicator name. Earnings of sugar producers - ex-Syndicate, before charging sugar ins...
country_iso3 string ISO3 country or area code. MU
country_name string Country or area name. Mauritius
year int64 Observation year. 1979
value double Numeric observation value. 2296.0
unit string Measurement unit, when supplied by the source. source_units_unspecified
source_period_start_year int64 Start year inferred from source metadata. 1979
source_period_end_year int64 End year inferred from source metadata. 2022
source_period_label dictionary<values=string, indices=int8, ordered=0> Source column from the original resource. 1979-2022
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. Earnings of sugar producers - ex-Syndicate, before charging sugar ins...
source_resource dictionary<values=string, indices=int8, ordered=0> Source resource title, table name, or file name. Earnings of sugar producers - ex-Syndicate, before charging sugar ins...
source_package_id dictionary<values=string, indices=int8, ordered=0> Source package identifier. af467254-e1d2-41f5-af9d-c06a595ecc72
source_resource_id dictionary<values=string, indices=int8, ordered=0> Source resource identifier. 30ae52a5-72ce-4af7-90a5-4e566eb7354b
source_url dictionary<values=string, indices=int8, ordered=0> Original source URL or download URL. https://data.govmu.org/dataset/af467254-e1d2-41f5-af9d-c06a595ecc72/r...
license_id dictionary<values=string, indices=int8, ordered=0> Source license identifier. cc-by
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-earnings-of-sugar-producers-ex-syndicate-before-charging-s-dee032ba")
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 production or price movements
  • Compare regions or commodities
  • Join with climate and trade 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_earnings_of_sugar_producers_ex_syndicate_before_charging_s_dee0_2022,
  title        = {Earnings of Sugar Producers Ex Syndicate Before Charging S | Africa (MDPA)},
  author       = {MDPA},
  year         = {2022},
  url          = {https://data.govmu.org/dataset/earnings-of-sugar-producers-ex-syndicate-before-charging-sugar-insurance-premium},
  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-mauritius-earnings-of-sugar-producers-ex-syndicate-before-charging-s-dee032ba}}
}

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/earnings-of-sugar-producers-ex-syndicate-before-charging-sugar-insurance-premium