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electricsheepafrica
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africa-mauritius-location-of-fish-aggregating-devices-322d2936

Tasks:
Tabular Classification
Tabular Regression
Modalities:
Tabular
Text
Formats:
parquet
Languages:
English
Size:
< 1K
Tags:
tabular
africa
open-data
official-statistics
mauritius
mdpa
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Datasets
pandas
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africa-mauritius-location-of-fish-aggregating-devices-322d2936 / README.md
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metadata 
license: cc-by-sa-4.0
language:
  - en
task_categories:
  - tabular-classification
  - tabular-regression
multilinguality: multilingual
size_categories:
  - n<1K
tags:
  - tabular
  - africa
  - open-data
  - official-statistics
  - mauritius
  - mdpa
  - agriculture
  - environment-and-natural-resources
  - fads
  - device
  - fish
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-00000-of-00001.parquet
pretty_name: Location of Fish Aggregating Devices | Africa (MDPA)

Location of Fish Aggregating Devices | Africa (MDPA)

28 rows - 1 Africa country/area - detected - source table - Engineered by Electric Sheep Africa

rows countries period indicators license

TL;DR

This dataset contains 28 rows from MDPA, covering Location of Fish Aggregating Devices. It is published as ML-ready Parquet with consistent Hugging Face metadata, source provenance, and analysis-friendly loading examples.

What This Dataset Measures

Agriculture datasets help analysts examine production, prices, inputs, land use, food systems, and rural economic activity.

Source-provided context: The dataset shows the number of FADs set around the island of Mauritius. Fish aggregating devices (FADs) are floating objects that are specifically designed and located to attract tunas, and therefore allow fishers to find them more easily.

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

Coverage

Dimension Value
Rows 28
Countries/areas 1
First period detected
Last period detected
Indicators 0
Columns 20
Source format CSV

Geographic Coverage

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

Area Rows First year Last year Name
MU 28 detected detected 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. f8f801e4-8c58-4cdd-8630-41678c3dda22:0
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
sn int64 Source column from the original resource. 1
name string Source column from the original resource. Albion I
depth int64 Source column from the original resource. 1100
coast double Source column from the original resource. 2.3
latitude double Source column from the original resource. -20.164833
longitude double Source column from the original resource. 57.389295
source_period_start_year int64 Start year inferred from source metadata. ``
source_period_end_year int64 End year inferred from source metadata. ``
source_period_label string Source column from the original resource. ``
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. Location of Fish Aggregating Devices
source_resource dictionary<values=string, indices=int8, ordered=0> Source resource title, table name, or file name. FishAggregatingDevice.csv
source_package_id dictionary<values=string, indices=int8, ordered=0> Source package identifier. b6f9317d-9a0a-49e1-85f1-6154f603fec0
source_resource_id dictionary<values=string, indices=int8, ordered=0> Source resource identifier. f8f801e4-8c58-4cdd-8630-41678c3dda22
source_url dictionary<values=string, indices=int8, ordered=0> Original source URL or download URL. https://data.govmu.org/dataset/b6f9317d-9a0a-49e1-85f1-6154f603fec0/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

Usage 

from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-mauritius-location-of-fish-aggregating-devices-322d2936")
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 "source_period_start_year" in df.columns:
    trend = df.sort_values("source_period_start_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: source_period_start_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

  • Track production or price movements
  • Compare regions or commodities
  • Join with climate and trade 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_location_of_fish_aggregating_devices_322d2936_2026,
  title        = {Location of Fish Aggregating Devices | Africa (MDPA)},
  author       = {MDPA},
  year         = {2026},
  url          = {https://data.govmu.org/dataset/location-fish-aggregating-devices},
  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-mauritius-location-of-fish-aggregating-devices-322d2936}}
}

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/location-fish-aggregating-devices