CoolFace
Datasetpublic

electricsheepafrica/africa-mauritius-homebased-staff-directory-as-at-19-08-2020-d21f18fd

Homebased Staff Directory As At 19 08 2020 | Africa (MDPA) 64 rows - 1 Africa country/area - 2021 - source table - Engineered by Electric Sheep Africa TL;DR This dataset contains 64 rows from MDPA, covering Homebased Staff Directory As At 19 08 2020. 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… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/africa-mauritius-homebased-staff-directory-as-at-19-08-2020-d21f18fd.

sourceHugging Facecc-by-sa-4.0updated 2mo agoView on Hugging Face
0likes8downloads
Dataset Card

Homebased Staff Directory As At 19 08 2020 | Africa (MDPA)

64 rows - 1 Africa country/area - 2021 - source table - Engineered by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)

rows countries period indicators license

TL;DR

This dataset contains 64 rows from MDPA, covering Homebased Staff Directory As At 19 08 2020. 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: List of Staff

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

DimensionValue
Rows64
Countries/areas1
First period2021
Last period2021
Indicators0
Columns26
Source formatCSV

Geographic Coverage

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

AreaRowsFirst yearLast yearName
MU6420212021Mauritius

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.01c259c4-4b30-4b75-bb77-b4c976a4809f: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
yearint64Observation year.2021
countrystringSource column from the original resource.Australia
citystringSource column from the original resource.Canberra
namestringSource column from the original resource.Mrs. Marie Helene CHAVRIMOOTOO
designationstringSource column from the original resource.Minister Counsellor
address1stringSource column from the original resource.223/2 Grose Street
address2stringSource column from the original resource.Deakin
address3stringSource column from the original resource.ACT 2600
address4stringSource column from the original resource.Cranberra
email1stringSource column from the original resource.hchavrimootoo@govmu.org
email2stringSource column from the original resource.``
website_urlstringSource column from the original resource.https://mauritius-canberra.govmu.org
source_period_start_yearint64Start year inferred from source metadata.2021
source_period_end_yearint64End year inferred from source metadata.2021
source_period_labeldictionary<values=string, indices=int8, ordered=0>Source column from the original resource.2021
source_providerdictionary<values=string, indices=int8, ordered=0>Publishing organization.MDPA
source_datasetdictionary<values=string, indices=int8, ordered=0>Source dataset or package title.Homebased staff - Directory as at 19.08.2020
source_resourcedictionary<values=string, indices=int8, ordered=0>Source resource title, table name, or file name.EmbassiesAbroad2021.csv
source_package_iddictionary<values=string, indices=int8, ordered=0>Source package identifier.70b0e6cb-90af-4464-a9f5-0cece36c4f6b
source_resource_iddictionary<values=string, indices=int8, ordered=0>Source resource identifier.01c259c4-4b30-4b75-bb77-b4c976a4809f
source_urldictionary<values=string, indices=int8, ordered=0>Original source URL or download URL.https://data.govmu.org/dataset/70b0e6cb-90af-4464-a9f5-0cece36c4f6b/r...
license_iddictionary<values=string, indices=int8, ordered=0>Source license identifier.CC-BY-SA-4.0
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-homebased-staff-directory-as-at-19-08-2020-d21f18fd")
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

  • —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

bibtex
@misc{electric_sheep_africa_africa_mauritius_homebased_staff_directory_as_at_19_08_2020_d21f18fd_2021,
  title        = {Homebased Staff Directory As At 19 08 2020 | Africa (MDPA)},
  author       = {MDPA},
  year         = {2021},
  url          = {https://data.govmu.org/dataset/homebased-staff-directory-19082020},
  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-mauritius-homebased-staff-directory-as-at-19-08-2020-d21f18fd}}
}

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.


Provenance: README standardized 2026-08-11 by the Electric Sheep Africa README system. Source URL: https://data.govmu.org/dataset/homebased-staff-directory-19082020