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electricsheepafrica/africa-mauritius-mean-sea-level-atmospheric-pressure-at-plaisance-aeronauti-6a64f91c

Mean Sea Level Atmospheric Pressure At Plaisance Aeronauti | Africa (MDPA) 30 rows - 1 Africa country/area - 2011-2020 - source table - Engineered by Electric Sheep Africa TL;DR This dataset contains 30 rows from MDPA, covering Mean Sea Level Atmospheric Pressure At Plaisance Aeronauti. 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-sea-level-atmospheric-pressure-at-plaisance-aeronauti-6a64f91c.

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

Mean Sea Level Atmospheric Pressure At Plaisance Aeronauti | Africa (MDPA)

30 rows - 1 Africa country/area - 2011-2020 - source table - Engineered by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)

rows countries period indicators license

TL;DR

This dataset contains 30 rows from MDPA, covering Mean Sea Level Atmospheric Pressure At Plaisance Aeronauti. 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 (Monthly) and Extreme Values of Mean Sea Level Atmospheric Pressure at Plaisance Aeronautical Station in Mauritius as from January 2011 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
Rows30
Countries/areas1
First period2011
Last period2020
Indicators0
Columns27
Source formatXLSX

Geographic Coverage

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

AreaRowsFirst yearLast yearName
MU3020112020Mauritius

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.73976477-e558-4060-bdc2-03caa63c0f2a:table-8:0
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.Table 8
marchstringSource column from the original resource.``
meanstringSource column from the original resource.Highest
d_1012_8doubleSource column from the original resource.1017.5
d_1013_5doubleSource column from the original resource.1020.0
d_1014doubleSource column from the original resource.1018.6
d_1013_4doubleSource column from the original resource.1018.6
d_1013_8doubleSource column from the original resource.1019.2
d_1013_9doubleSource column from the original resource.1019.8
d_1013doubleSource column from the original resource.1019.1
d_1011_5doubleSource column from the original resource.1016.4
d_1011_9doubleSource column from the original resource.1016.4
d_1011_0689516129032doubleSource column from the original resource.1019.1
source_period_start_yearint64Start year inferred from source metadata.2011
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.2011-2020
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 Sea Level Atmospheric Pressure at Plaisance Aeronautical Station...
source_resourcedictionary<values=string, indices=int8, ordered=0>Source resource title, table name, or file name.Source-Mean-Sea-Level-Atmospheric-Pressure-at-Plaisance-Aeronautical-...
source_package_iddictionary<values=string, indices=int8, ordered=0>Source package identifier.5c32a406-0376-41b5-8220-756c475a93a5
source_resource_iddictionary<values=string, indices=int8, ordered=0>Source resource identifier.73976477-e558-4060-bdc2-03caa63c0f2a
source_urldictionary<values=string, indices=int8, ordered=0>Original source URL or download URL.https://data.govmu.org/dataset/5c32a406-0376-41b5-8220-756c475a93a5/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-mean-sea-level-atmospheric-pressure-at-plaisance-aeronauti-6a64f91c")
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_sea_level_atmospheric_pressure_at_plaisance_aeronauti_6a64_2020,
  title        = {Mean Sea Level Atmospheric Pressure At Plaisance Aeronauti | Africa (MDPA)},
  author       = {MDPA},
  year         = {2020},
  url          = {https://data.govmu.org/dataset/mean-sea-level-atmospheric-pressure-plaisance-aeronautical-station-mauritius},
  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-mauritius-mean-sea-level-atmospheric-pressure-at-plaisance-aeronauti-6a64f91c}}
}

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-sea-level-atmospheric-pressure-plaisance-aeronautical-station-mauritius