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electricsheepafrica/africa-mauritius-number-of-enterprises-by-product-group-eoe-sector-2020-to-db63e648

Number of Enterprises by Product Group Eoe Sector 2020 to | Africa (MDPA) 12 rows - 1 Africa country/area - 2020-2023 - source table - Engineered by Electric Sheep Africa TL;DR This dataset contains 12 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-db63e648.

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

12 rows - 1 Africa country/area - 2020-2023 - source table - Engineered by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)

rows countries period indicators license

TL;DR

This dataset contains 12 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 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

DimensionValue
Rows12
Countries/areas1
First period2020
Last period2023
Indicators0
Columns34
Source formatXLSX

Geographic Coverage

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

AreaRowsFirst yearLast yearName
MU1220202023Mauritius

Indicators, Variables, Or Resource Contents

  • —This repo preserves one source tabular resource with its usable columns kept together.

Schema

ColumnTypeDescriptionExample
source_record_idstringStable row identifier assigned during Electric Sheep Africa engineering.99a367c6-f5b0-4fcd-bd43-a10bc86a325f:sheet1:0
country_iso3dictionary<values=string, indices=int8, ordered=0>ISO3 country or area code.MU
country_namedictionary<values=string, indices=int8, ordered=0>Country or area name.Mauritius
source_sheetstringSource column from the original resource.Sheet1
d_3doubleSource column from the original resource.4.0
textile_yarn_and_fabricsstringSource column from the original resource.Wearing apparel :
d_23int64Source column from the original resource.84
d_23_2int64Source column from the original resource.84
d_23_3int64Source column from the original resource.84
d_22int64Source column from the original resource.83
d_22_2int64Source column from the original resource.83
d_22_3int64Source column from the original resource.83
d_22_4int64Source column from the original resource.83
d_21int64Source column from the original resource.83
d_21_2int64Source column from the original resource.83
d_20int64Source column from the original resource.83
d_20_2int64Source column from the original resource.83
d_20_3int64Source column from the original resource.83
d_20_4int64Source column from the original resource.83
d_20_5int64Source column from the original resource.83
d_20_6int64Source column from the original resource.83
d_20_7int64Source column from the original resource.83
d_20_8int64Source column from the original resource.83
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.No_0f_enterprises by product 2020 to 2023.xlsx
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.99a367c6-f5b0-4fcd-bd43-a10bc86a325f
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-db63e648")
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

  • —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 production or price movements
  • —Compare regions or commodities
  • —Join with climate and trade data
  • —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_db63e_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-db63e648}}
}

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