electricsheepafrica/africa-mauritius-number-of-smes-registered-with-exsmeda-sme-registration-un-08386afd

Number of Smes Registered With Exsmeda Sme Registration Un | Africa (MDPA)

263 rows - 1 Africa country/area - 2002-2018 - source table - Engineered by Electric Sheep Africa

TL;DR

This dataset contains 263 rows from MDPA, covering Number of Smes Registered With Exsmeda Sme Registration Un. 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: Number of SMEs registered with exSMEDA SME Registration Unit by industry group and year, Jan 2014 - Dec 2018

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: year.
• Time coverage basis: year.
• Recommended join keys: country_iso3 where available plus source-specific keys.

Coverage

Dimension	Value
Rows	263
Countries/areas	1
First period	2002
Last period	2018
Indicators	0
Columns	55
Source format	XLSX

Geographic Coverage

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

Area	Rows	First year	Last year	Name
MU	263	2002	2018	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.	a8ff0968-da27-45b4-8eec-e15c8ffa806b: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
industry_group	string	Source column from the original resource.	Agriculture, forestry and fishing
2014	double	Source column from the original resource.	117.0
2015	double	Source column from the original resource.	171.0
2016	double	Source column from the original resource.	258.0
2017	double	Source column from the original resource.	287.0
2018	double	Source column from the original resource.	211.0
source_period_start_year	int64	Start year inferred from source metadata.	2014
source_period_end_year	int64	End year inferred from source metadata.	2018
source_period_label	dictionary<values=string, indices=int8, ordered=0>	Source column from the original resource.	2014-2018
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.	Number of SMEs registered with exSMEDA SME Registration Unit by indus...
source_resource	dictionary<values=string, indices=int8, ordered=0>	Source resource title, table name, or file name.	Source_Number-of-SMEs-registered-with-exSMEDA-SME-Registration-Unit-b...
source_package_id	dictionary<values=string, indices=int8, ordered=0>	Source package identifier.	ede506b1-2fa6-4375-b476-29ae901fcce3
source_resource_id	dictionary<values=string, indices=int8, ordered=0>	Source resource identifier.	a8ff0968-da27-45b4-8eec-e15c8ffa806b
source_url	dictionary<values=string, indices=int8, ordered=0>	Original source URL or download URL.	https://data.govmu.org/dataset/ede506b1-2fa6-4375-b476-29ae901fcce3/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
year	double	Observation year.	``
number_of_smes_registered	double	Source column from the original resource.	``
contribution_of_smes_to_the_economy	string	Source column from the original resource.	``
2007	double	Source column from the original resource.	``
2013	double	Source column from the original resource.	``
employment_in_smes	double	Source column from the original resource.	``
total_employees	double	Source column from the original resource.	``
share_of_smes_in_total_employment	double	Source column from the original resource.	``
type	string	Source column from the original resource.	``
number_of_smes_registered_with_mobec	double	Source column from the original resource.	``
export_of_smes_rs_million	string	Source column from the original resource.	``
column	string	Source column from the original resource.	``
d_4200	double	Source column from the original resource.	``
d_6600	double	Source column from the original resource.	``
export_of_smes_millions	double	Source column from the original resource.	``
share_sme_total_export_percentage	double	Source column from the original resource.	``
number	string	Source column from the original resource.	``
number_of_visitors	double	Source column from the original resource.	``
2002	double	Source column from the original resource.	``
contribution_of_smes_to_gdp	double	Source column from the original resource.	``
total	string	Source column from the original resource.	``
d_2924	string	Source column from the original resource.	``
d_3528128220	string	Source column from the original resource.	``
d_140	string	Source column from the original resource.	``
d_183747731	string	Source column from the original resource.	``
d_2925025837	string	Source column from the original resource.	``
d_789950896	string	Source column from the original resource.	``
category_of_procurement	string	Source column from the original resource.	``
2012	double	Source column from the original resource.	``
2016_2	double	Source column from the original resource.	``
value_of_contract_awarded_to_sme	double	Source column from the original resource.	``
smes_only_rsm	double	Source column from the original resource.	``
all_contracts_rsm	double	Source column from the original resource.	``
percentage_contracts_to_smes	double	Source column from the original resource.	``

Usage

from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-mauritius-number-of-smes-registered-with-exsmeda-sme-registration-un-08386afd")
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

• Source: MDPA
• Publisher: MDPA
• Portal: https://data.govmu.org
• Resource: Source_Number-of-SMEs-registered-with-exSMEDA-SME-Registration-Unit-by-industry-group-and-year-Jan-2014-Dec-2018.xlsx
• License: CC BY-SA 4.0
• Retrieved/generated: 2026-08-08T17:17:04Z
• Hugging Face repo: electricsheepafrica/africa-mauritius-number-of-smes-registered-with-exsmeda-sme-registration-un-08386afd

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
• Build time-series views and period-over-period comparisons
• Check missingness before modeling
• Use country_iso3 as the safest geography join key when present

Citation

@misc{electric_sheep_africa_africa_mauritius_number_of_smes_registered_with_exsmeda_sme_registration_un_0838_2018,
  title        = {Number of Smes Registered With Exsmeda Sme Registration Un | Africa (MDPA)},
  author       = {MDPA},
  year         = {2018},
  url          = {https://data.govmu.org/dataset/number-smes-registered-exsmeda-sme-registration-unit-industry-group-and-year-jan-2014-dec},
  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-mauritius-number-of-smes-registered-with-exsmeda-sme-registration-un-08386afd}}
}

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.

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Provenance: README standardized 2026-08-11 by the Electric Sheep Africa README system. Source URL: https://data.govmu.org/dataset/number-smes-registered-exsmeda-sme-registration-unit-industry-group-and-year-jan-2014-dec