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electricsheepafrica/africa-mauritius-area-harvested-of-food-crops-by-district-for-mauritius-f9c0bcaa

Area Harvested of Food Crops by District for Mauritius | Africa (MDPA) 42 rows - 1 Africa country/area - 2019 - source table - Engineered by Electric Sheep Africa TL;DR This dataset contains 42 rows from MDPA, covering Area Harvested of Food Crops by District for Mauritius. It is published as ML-ready Parquet with consistent Hugging Face metadata, source provenance, and analysis-friendly loading examples. What This Dataset Measures Agriculture… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/africa-mauritius-area-harvested-of-food-crops-by-district-for-mauritius-f9c0bcaa.

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

Area Harvested of Food Crops by District for Mauritius | Africa (MDPA)

42 rows - 1 Africa country/area - 2019 - source table - Engineered by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)

rows countries period indicators license

TL;DR

This dataset contains 42 rows from MDPA, covering Area Harvested of Food Crops by District for Mauritius. 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: Area Harvested of Fruits and Vegetables by District for the year 2019 in Mauritius

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
Rows42
Countries/areas1
First period2019
Last period2019
Indicators0
Columns25
Source formatCSV

Geographic Coverage

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

AreaRowsFirst yearLast yearName
MU4220192019Mauritius

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.769a5e23-3840-42d4-bd0e-3452e0060ff6:0
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.2019
category_food_cropsstringSource column from the original resource.Banana
sub_category_food_cropsstringSource column from the original resource.``
black_riverdoubleSource column from the original resource.24.24
flacqdoubleSource column from the original resource.167.43
grand_portdoubleSource column from the original resource.35.46
mokadoubleSource column from the original resource.32.78
pamplemoussesdoubleSource column from the original resource.26.51
plaine_wilhemsdoubleSource column from the original resource.7.6
riviere_du_rempartdoubleSource column from the original resource.6.71
savannedoubleSource column from the original resource.173.88
source_period_start_yearint64Start year inferred from source metadata.2019
source_period_end_yearint64End year inferred from source metadata.2019
source_period_labeldictionary<values=string, indices=int8, ordered=0>Source column from the original resource.2019
source_providerdictionary<values=string, indices=int8, ordered=0>Publishing organization.MDPA
source_datasetdictionary<values=string, indices=int8, ordered=0>Source dataset or package title.Area Harvested of Food Crops by District for Mauritius
source_resourcedictionary<values=string, indices=int8, ordered=0>Source resource title, table name, or file name.Area-Harvested-of-Food-Crops-by-District-Island-of-Mauritius-2019.csv
source_package_iddictionary<values=string, indices=int8, ordered=0>Source package identifier.b0c8c53e-a49d-4275-b93c-2d815efc8e09
source_resource_iddictionary<values=string, indices=int8, ordered=0>Source resource identifier.769a5e23-3840-42d4-bd0e-3452e0060ff6
source_urldictionary<values=string, indices=int8, ordered=0>Original source URL or download URL.https://data.govmu.org/dataset/b0c8c53e-a49d-4275-b93c-2d815efc8e09/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-area-harvested-of-food-crops-by-district-for-mauritius-f9c0bcaa")
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

  • —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

bibtex
@misc{electric_sheep_africa_africa_mauritius_area_harvested_of_food_crops_by_district_for_mauritius_f9c0bcaa_2019,
  title        = {Area Harvested of Food Crops by District for Mauritius | Africa (MDPA)},
  author       = {MDPA},
  year         = {2019},
  url          = {https://data.govmu.org/dataset/area-harvested-food-crops-district-mauritius},
  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-mauritius-area-harvested-of-food-crops-by-district-for-mauritius-f9c0bcaa}}
}

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/area-harvested-food-crops-district-mauritius