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electricsheepafrica/africa-mauritius-mean-wind-speed-and-highest-gusts-at-plaisance-aeronautica-18863eee

Mean Wind Speed and Highest Gusts At Plaisance Aeronautica | Africa (MDPA) 50 rows - 1 Africa country/area - 2005-2020 - source table - Engineered by Electric Sheep Africa TL;DR This dataset contains 50 rows from MDPA, covering Mean Wind Speed and Highest Gusts At Plaisance Aeronautica. 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-mean-wind-speed-and-highest-gusts-at-plaisance-aeronautica-18863eee.

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

Mean Wind Speed and Highest Gusts At Plaisance Aeronautica | Africa (MDPA)

50 rows - 1 Africa country/area - 2005-2020 - source table - Engineered by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)

rows countries period indicators license

TL;DR

This dataset contains 50 rows from MDPA, covering Mean Wind Speed and Highest Gusts At Plaisance Aeronautica. It is published as ML-ready Parquet with consistent Hugging Face metadata, source provenance, and analysis-friendly loading examples.

What This Dataset Measures

Climate and environment datasets help analysts study exposure, resource conditions, environmental pressure, and climate-related trends.

Source-provided context: Dataset shows the Mean Wind Speed and Highest Gusts at Plaisance Aeronautical Station by Month in Mauritius as from January 2005 to December 2020.

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
Rows50
Countries/areas1
First period2005
Last period2020
Indicators0
Columns34
Source formatXLS

Geographic Coverage

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

AreaRowsFirst yearLast yearName
MU5020052020Mauritius

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.0beef983-c291-4cce-ad75-b13208951a04:2005-2015:0
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.2005-2015
januarystringSource column from the original resource.``
mean_wind_speedstringSource column from the original resource.Highest gust
d_13_866935483870968doubleSource column from the original resource.55.1
d_17_099999999999998doubleSource column from the original resource.59.2
d_17_099999999999998_2doubleSource column from the original resource.59.5217391304348
d_0doubleSource column from the original resource.62.4
d_9_5doubleSource column from the original resource.54.52173913043479
d_11_399999999999999doubleSource column from the original resource.59.5217391304348
d_15_2doubleSource column from the original resource.48.0
d_13_3doubleSource column from the original resource.52.37760000000001
d_19doubleSource column from the original resource.83.2
d_17_099999999999998_3doubleSource column from the original resource.72.0
d_16doubleSource column from the original resource.67.0
source_period_start_yearint64Start year inferred from source metadata.2005
source_period_end_yearint64End year inferred from source metadata.2020
source_period_labeldictionary<values=string, indices=int8, ordered=0>Source column from the original resource.2005-2020
source_providerdictionary<values=string, indices=int8, ordered=0>Publishing organization.MDPA
source_datasetdictionary<values=string, indices=int8, ordered=0>Source dataset or package title.Mean Wind Speed and Highest Gusts at Plaisance Aeronautical Station i...
source_resourcedictionary<values=string, indices=int8, ordered=0>Source resource title, table name, or file name.Source-File_0.xls
source_package_iddictionary<values=string, indices=int8, ordered=0>Source package identifier.ffb85910-23ef-40fd-a4d3-dafe7351048f
source_resource_iddictionary<values=string, indices=int8, ordered=0>Source resource identifier.0beef983-c291-4cce-ad75-b13208951a04
source_urldictionary<values=string, indices=int8, ordered=0>Original source URL or download URL.https://data.govmu.org/dataset/ffb85910-23ef-40fd-a4d3-dafe7351048f/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
d_13_299999999999999doubleSource column from the original resource.``
d_13_5doubleSource column from the original resource.``
d_18_6doubleSource column from the original resource.``
d_19_4doubleSource column from the original resource.``
d_15_874193548387098doubleSource column from the original resource.``
d_12_5doubleSource column from the original resource.``

Usage

python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-mauritius-mean-wind-speed-and-highest-gusts-at-plaisance-aeronautica-18863eee")
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

  • —Analyze seasonal or annual patterns
  • —Join with agriculture or health data
  • —Map geographic exposure
  • —Check missingness before modeling
  • —Use country_iso3 as the safest geography join key when present

Citation

bibtex
@misc{electric_sheep_africa_africa_mauritius_mean_wind_speed_and_highest_gusts_at_plaisance_aeronautica_1886_2020,
  title        = {Mean Wind Speed and Highest Gusts At Plaisance Aeronautica | Africa (MDPA)},
  author       = {MDPA},
  year         = {2020},
  url          = {https://data.govmu.org/dataset/mean-wind-speed-and-highest-gusts-plaisance-aeronautical-station-mauritius},
  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-mauritius-mean-wind-speed-and-highest-gusts-at-plaisance-aeronautica-18863eee}}
}

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/mean-wind-speed-and-highest-gusts-plaisance-aeronautical-station-mauritius