electricsheepafrica/africa-mauritius-eoe-enterprises-imports-breakdown-2020-to-2023-27f5de6b

Eoe Enterprises Imports Breakdown 2020 to 2023 | Africa (MDPA)

6 rows - 1 Africa country/area - 2020-2023 - source table - Engineered by Electric Sheep Africa

TL;DR

This dataset contains 6 rows from MDPA, covering Eoe Enterprises Imports Breakdown 2020 to 2023. It is published as ML-ready Parquet with consistent Hugging Face metadata, source provenance, and analysis-friendly loading examples.

What This Dataset Measures

Economic datasets help analysts examine production, prices, public finance, trade flows, market conditions, and macroeconomic change.

Source-provided context: Dataset shows the Imports breakdown by raw materials and machinery & spare parts in the Export Oriented Enterprise Sector 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

Dimension	Value
Rows	6
Countries/areas	1
First period	2020
Last period	2023
Indicators	0
Columns	21
Source format	XLSX

Geographic Coverage

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

Area	Rows	First year	Last year	Name
MU	6	2020	2023	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.	6e7eb839-17d3-4cc1-a88b-cf17f19dbe56:sheet1:0
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.	Sheet1
d_4_imports_c_i_f_r_million	string	Source column from the original resource.	``
column_2	string	Source column from the original resource.	- Raw materials
d_19629	double	Source column from the original resource.	18786.0
d_25673	double	Source column from the original resource.	24684.0
d_33466	double	Source column from the original resource.	32147.0
d_26781	double	Source column from the original resource.	25421.0
source_period_start_year	int64	Start year inferred from source metadata.	2020
source_period_end_year	int64	End year inferred from source metadata.	2023
source_period_label	dictionary<values=string, indices=int8, ordered=0>	Source column from the original resource.	2020-2023
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.	EOE enterprises Imports breakdown 2020 to 2023
source_resource	dictionary<values=string, indices=int8, ordered=0>	Source resource title, table name, or file name.	Source file: Imports Breakdown 2020 to 2023.xlsx
source_package_id	dictionary<values=string, indices=int8, ordered=0>	Source package identifier.	6eac3018-2e0c-4e54-8cd4-f88e4f726039
source_resource_id	dictionary<values=string, indices=int8, ordered=0>	Source resource identifier.	6e7eb839-17d3-4cc1-a88b-cf17f19dbe56
source_url	dictionary<values=string, indices=int8, ordered=0>	Original source URL or download URL.	https://data.govmu.org/dataset/6eac3018-2e0c-4e54-8cd4-f88e4f726039/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-eoe-enterprises-imports-breakdown-2020-to-2023-27f5de6b")
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

• Source: MDPA
• Publisher: MDPA
• Portal: https://data.govmu.org
• Resource: Source file: Imports Breakdown 2020 to 2023.xlsx
• License: CC BY 4.0
• Retrieved/generated: 2026-08-08T16:49:15Z
• Hugging Face repo: electricsheepafrica/africa-mauritius-eoe-enterprises-imports-breakdown-2020-to-2023-27f5de6b

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

• Build time-series dashboards
• Compare economic indicators
• Join with population or sector data
• Check missingness before modeling
• Use country_iso3 as the safest geography join key when present

Citation

@misc{electric_sheep_africa_africa_mauritius_eoe_enterprises_imports_breakdown_2020_to_2023_27f5de6b_2023,
  title        = {Eoe Enterprises Imports Breakdown 2020 to 2023 | Africa (MDPA)},
  author       = {MDPA},
  year         = {2023},
  url          = {https://data.govmu.org/dataset/eoe-enterprises-imports-breakdown-2020-to-2023},
  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-mauritius-eoe-enterprises-imports-breakdown-2020-to-2023-27f5de6b}}
}

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.

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Provenance: README standardized 2026-08-11 by the Electric Sheep Africa README system. Source URL: https://data.govmu.org/dataset/eoe-enterprises-imports-breakdown-2020-to-2023