electricsheepafrica/africa-mauritius-food-crops-monthly-production-for-mauritius-6325b72b

Food Crops Monthly Production for Mauritius | Africa (MDPA)

189 rows - 1 Africa country/area - 2017-2019 - source table - Engineered by Electric Sheep Africa

TL;DR

This dataset contains 189 rows from MDPA, covering Food Crops Monthly Production 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: Production of Vegetables by Month for the year 2017 to 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: not detected.
• Time coverage basis: source metadata.
• Recommended join keys: country_iso3 where available plus source-specific keys.

Coverage

Dimension	Value
Rows	189
Countries/areas	1
First period	2017
Last period	2019
Indicators	0
Columns	87
Source format	XLSX

Geographic Coverage

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

Area	Rows	First year	Last year	Name
MU	189	2017	2019	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.	c02255e7-cbb5-4c0e-87a8-aa51c3d8993b:introduction: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
source_sheet	string	Source column from the original resource.	Introduction
introduction	string	Source column from the original resource.	This Digest of Agricultural Statistics which is an annual report was ...
source_period_start_year	int64	Start year inferred from source metadata.	2017
source_period_end_year	int64	End year inferred from source metadata.	2019
source_period_label	dictionary<values=string, indices=int8, ordered=0>	Source column from the original resource.	2017-2019
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.	Food Crops Monthly Production for Mauritius
source_resource	dictionary<values=string, indices=int8, ordered=0>	Source resource title, table name, or file name.	Source-File-Food-Crops-Monthly-Production-for-Mauritius.xlsx
source_package_id	dictionary<values=string, indices=int8, ordered=0>	Source package identifier.	644cea13-d27f-4880-bf71-84e9d5e0dc1b
source_resource_id	dictionary<values=string, indices=int8, ordered=0>	Source resource identifier.	c02255e7-cbb5-4c0e-87a8-aa51c3d8993b
source_url	dictionary<values=string, indices=int8, ordered=0>	Original source URL or download URL.	https://data.govmu.org/dataset/644cea13-d27f-4880-bf71-84e9d5e0dc1b/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
d_1_1_agriculture_forestry_and_fishing	string	Source column from the original resource.	``
methodology	string	Source column from the original resource.	``
coverage_concepts_and_definitions	string	Source column from the original resource.	``
symbols_and_abbreviations	string	Source column from the original resource.	``
food_crops	string	Source column from the original resource.	``
january	double	Source column from the original resource.	``
february	double	Source column from the original resource.	``
march	double	Source column from the original resource.	``
april	double	Source column from the original resource.	``
may	double	Source column from the original resource.	``
june	double	Source column from the original resource.	``
july	double	Source column from the original resource.	``
august	double	Source column from the original resource.	``
september	double	Source column from the original resource.	``
october	double	Source column from the original resource.	``
november	double	Source column from the original resource.	``
december	double	Source column from the original resource.	``
total	double	Source column from the original resource.	``
cauliflower	string	Source column from the original resource.	``
d_2_5	double	Source column from the original resource.	``
d_39_50586666666667	double	Source column from the original resource.	``
d_0_8300000000000001	double	Source column from the original resource.	``
d_11_224900000000002	double	Source column from the original resource.	``
d_1_8900000000000001	double	Source column from the original resource.	``
d_26_7808	double	Source column from the original resource.	``
d_0_8049999999999999	double	Source column from the original resource.	``
d_11_8279	double	Source column from the original resource.	``
d_1_83	double	Source column from the original resource.	``
d_26_901	double	Source column from the original resource.	``
d_11_722	double	Source column from the original resource.	``
d_172_6311	double	Source column from the original resource.	``
d_11_2	double	Source column from the original resource.	``
d_185_33550000000002	double	Source column from the original resource.	``
d_17_04	double	Source column from the original resource.	``
d_313_34479999999996	double	Source column from the original resource.	``
d_15_260000000000005	double	Source column from the original resource.	``
d_246_8	double	Source column from the original resource.	``
d_16	double	Source column from the original resource.	``
d_222_38259999999997	double	Source column from the original resource.	``
d_8_08	double	Source column from the original resource.	``
d_92_66990000000001	double	Source column from the original resource.	``
d_2_8400000000000003	double	Source column from the original resource.	``
d_32_6629	double	Source column from the original resource.	``
d_89_997	double	Source column from the original resource.	``
d_1382_0672666666667	double	Source column from the original resource.	``
d_2_2	double	Source column from the original resource.	``
d_18	double	Source column from the original resource.	``
d_0_4	double	Source column from the original resource.	``
d_3	double	Source column from the original resource.	``
d_0_2	double	Source column from the original resource.	``
d_1	double	Source column from the original resource.	``
d_0_9	double	Source column from the original resource.	``
d_12	double	Source column from the original resource.	``
d_2	double	Source column from the original resource.	``
d_15	double	Source column from the original resource.	``
d_6_1	double	Source column from the original resource.	``
d_78	double	Source column from the original resource.	``
d_11_8	double	Source column from the original resource.	``
d_180	double	Source column from the original resource.	``
d_10_8	double	Source column from the original resource.	``
d_134	double	Source column from the original resource.	``
d_9_8	double	Source column from the original resource.	``
d_101	double	Source column from the original resource.	``
d_9_2	double	Source column from the original resource.	``
d_107	double	Source column from the original resource.	``
d_4_1	double	Source column from the original resource.	``
d_40	double	Source column from the original resource.	``
d_3_2	double	Source column from the original resource.	``
d_36	double	Source column from the original resource.	``
d_60_7	double	Source column from the original resource.	``
d_725	double	Source column from the original resource.	``

Usage

from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-mauritius-food-crops-monthly-production-for-mauritius-6325b72b")
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 "year" in df.columns:
    trend = df.sort_values("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

• 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

• Source: MDPA
• Publisher: MDPA
• Portal: https://data.govmu.org
• Resource: Source-File-Food-Crops-Monthly-Production-for-Mauritius.xlsx
• License: CC BY-SA 4.0
• Retrieved/generated: 2026-08-08T17:13:29Z
• Hugging Face repo: electricsheepafrica/africa-mauritius-food-crops-monthly-production-for-mauritius-6325b72b

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
• Check missingness before modeling
• Use country_iso3 as the safest geography join key when present

Citation

@misc{electric_sheep_africa_africa_mauritius_food_crops_monthly_production_for_mauritius_6325b72b_2019,
  title        = {Food Crops Monthly Production for Mauritius | Africa (MDPA)},
  author       = {MDPA},
  year         = {2019},
  url          = {https://data.govmu.org/dataset/food-crops-monthly-production-mauritius},
  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-mauritius-food-crops-monthly-production-for-mauritius-6325b72b}}
}

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.

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Provenance: README standardized 2026-08-11 by the Electric Sheep Africa README system. Source URL: https://data.govmu.org/dataset/food-crops-monthly-production-mauritius