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electricsheepafrica/africa-mauritius-number-of-enterprises-by-product-group-eoe-sector-2020-to-22e7711c

Number of Enterprises by Product Group Eoe Sector 2020 to | Africa (MDPA) 208 rows - 1 Africa country/area - 2020-2023 - 4 indicators - Engineered by Electric Sheep Africa TL;DR This dataset contains 208 rows from MDPA, covering Number of Enterprises by Product Group Eoe Sector 2020 to. It is published as ML-ready Parquet with consistent Hugging Face metadata, source provenance, and analysis-friendly loading examples. What This Dataset Measures… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/africa-mauritius-number-of-enterprises-by-product-group-eoe-sector-2020-to-22e7711c.

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Number of Enterprises by Product Group Eoe Sector 2020 to | Africa (MDPA)

208 rows - 1 Africa country/area - 2020-2023 - 4 indicators - Engineered by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)

rows countries period indicators license

TL;DR

This dataset contains 208 rows from MDPA, covering Number of Enterprises by Product Group Eoe Sector 2020 to. 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 number of enterprises by product group in the Export Oriented Enterprise Sector quarterly for year 2020 to 2023

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

DimensionValue
Rows208
Countries/areas1
First period2020
Last period2023
Indicators4
Columns20
Source formatCSV

Geographic Coverage

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

AreaRowsFirst yearLast yearName
MU20820202023Mauritius

Indicators, Variables, Or Resource Contents

  • —number-of-enterprises-by-product-group-eoe-sector-2020-to-2023-march-c83c08a7 - Number of Enterprises by product group, EOE sector 2020 to 2023 - march(sourceunitsunspecified)
  • —number-of-enterprises-by-product-group-eoe-sector-2020-to-2023-june-77177d9a - Number of Enterprises by product group, EOE sector 2020 to 2023 - june(sourceunitsunspecified)
  • —number-of-enterprises-by-product-group-eoe-sector-2020-to-2023-sept-56da4e54 - Number of Enterprises by product group, EOE sector 2020 to 2023 - sept(sourceunitsunspecified)
  • —number-of-enterprises-by-product-group-eoe-sector-2020-to-2023-dec-bc611fb8 - Number of Enterprises by product group, EOE sector 2020 to 2023 - dec(sourceunitsunspecified)

Schema

ColumnTypeDescriptionExample
indicator_idstringStable source or Electric Sheep Africa indicator identifier.number-of-enterprises-by-product-group-eoe-sector-2020-to-2023-march-...
indicator_namestringHuman-readable indicator name.Number of Enterprises by product group, EOE sector 2020 to 2023 - march
country_iso3stringISO3 country or area code.MU
country_namestringCountry or area name.Mauritius
yearint64Observation year.2020
valuedoubleNumeric observation value.18.0
unitstringMeasurement unit, when supplied by the source.source_units_unspecified
dimension_product_groupstringSource dimension retained during long-form normalization.Food
dimension_sub_categorystringSource dimension retained during long-form normalization.``
source_period_start_yearint64Start year inferred from source metadata.2020
source_period_end_yearint64End year inferred from source metadata.2023
source_period_labeldictionary<values=string, indices=int8, ordered=0>Source column from the original resource.2020-2023
source_providerdictionary<values=string, indices=int8, ordered=0>Publishing organization.MDPA
source_datasetdictionary<values=string, indices=int8, ordered=0>Source dataset or package title.Number of Enterprises by product group, EOE sector 2020 to 2023
source_resourcedictionary<values=string, indices=int8, ordered=0>Source resource title, table name, or file name.Enterprises by product group, EOE Sector, March 2020 - December 2023.csv
source_package_iddictionary<values=string, indices=int8, ordered=0>Source package identifier.d69a1a13-fff3-4e7f-9d8d-3461f9026363
source_resource_iddictionary<values=string, indices=int8, ordered=0>Source resource identifier.b4df7124-b9af-487a-a9ec-e1665824f568
source_urldictionary<values=string, indices=int8, ordered=0>Original source URL or download URL.https://data.govmu.org/dataset/d69a1a13-fff3-4e7f-9d8d-3461f9026363/r...
license_iddictionary<values=string, indices=int8, ordered=0>Source license identifier.cc-by
retrieved_atdictionary<values=string, indices=int8, ordered=0>UTC source retrieval timestamp from the Electric Sheep Africa pipeline.2026-08-08T16:26:20Z

Usage

python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-mauritius-number-of-enterprises-by-product-group-eoe-sector-2020-to-22e7711c")
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

  • —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

bibtex
@misc{electric_sheep_africa_africa_mauritius_number_of_enterprises_by_product_group_eoe_sector_2020_to_22e77_2023,
  title        = {Number of Enterprises by Product Group Eoe Sector 2020 to | Africa (MDPA)},
  author       = {MDPA},
  year         = {2023},
  url          = {https://data.govmu.org/dataset/number-of-enterprises-by-product-group-eoe-sector-2020-to-2023},
  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-mauritius-number-of-enterprises-by-product-group-eoe-sector-2020-to-22e7711c}}
}

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/number-of-enterprises-by-product-group-eoe-sector-2020-to-2023