electricsheepafrica/africa-mauritius-area-harvested-and-production-of-foodcrops-2018-2024-b1eb336d

Area Harvested and Production of Foodcrops 2018 2024 | Africa (MDPA)

27 rows - 1 Africa country/area - 1980-2024 - source table - Engineered by Electric Sheep Africa

TL;DR

This dataset contains 27 rows from MDPA, covering Area Harvested and Production of Foodcrops 2018 2024. 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 Area harvested and production of foodcrops, 2018 to 2024

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	27
Countries/areas	1
First period	1980
Last period	2024
Indicators	0
Columns	106
Source format	XLSX

Geographic Coverage

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

Area	Rows	First year	Last year	Name
MU	27	1980	2024	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.	6ae1f1c7-2c79-44d3-9c56-cb921551b990:sheet1:0
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.	Sheet1
greens	string	Source column from the original resource.	Green peas
na	double	Source column from the original resource.	27.0
na_2	double	Source column from the original resource.	39.0
na_3	double	Source column from the original resource.	29.0
na_4	double	Source column from the original resource.	45.0
na_5	double	Source column from the original resource.	22.79231139362322
na_6	double	Source column from the original resource.	35.0
na_7	double	Source column from the original resource.	27.857269481095045
na_8	double	Source column from the original resource.	45.0
na_9	double	Source column from the original resource.	27.0
na_10	double	Source column from the original resource.	70.0
na_11	double	Source column from the original resource.	29.0
na_12	double	Source column from the original resource.	100.0
na_13	double	Source column from the original resource.	18.0
na_14	double	Source column from the original resource.	40.0
na_15	double	Source column from the original resource.	13.0
na_16	double	Source column from the original resource.	30.0
na_17	double	Source column from the original resource.	18.0
na_18	double	Source column from the original resource.	35.0
na_19	double	Source column from the original resource.	18.0
na_20	double	Source column from the original resource.	35.0
na_21	double	Source column from the original resource.	13.0
na_22	double	Source column from the original resource.	45.0
na_23	double	Source column from the original resource.	10.0
na_24	double	Source column from the original resource.	30.0
na_25	double	Source column from the original resource.	13.0
na_26	double	Source column from the original resource.	40.0
na_27	double	Source column from the original resource.	11.0
na_28	double	Source column from the original resource.	42.0
na_29	double	Source column from the original resource.	7.0
na_30	double	Source column from the original resource.	30.0
na_31	double	Source column from the original resource.	4.0
na_32	double	Source column from the original resource.	13.0
na_33	double	Source column from the original resource.	5.0
na_34	double	Source column from the original resource.	15.0
na_35	double	Source column from the original resource.	7.0
na_36	double	Source column from the original resource.	13.0
na_37	double	Source column from the original resource.	6.0
na_38	double	Source column from the original resource.	11.0
na_39	double	Source column from the original resource.	3.0
na_40	double	Source column from the original resource.	7.0
na_41	double	Source column from the original resource.	3.0
na_42	double	Source column from the original resource.	7.0
na_43	double	Source column from the original resource.	1.0
na_44	double	Source column from the original resource.	3.0
na_45	double	Source column from the original resource.	1.0
na_46	double	Source column from the original resource.	2.0
na_47	double	Source column from the original resource.	5.0
na_48	double	Source column from the original resource.	12.0
na_49	double	Source column from the original resource.	5.0
na_50	double	Source column from the original resource.	7.0
na_51	double	Source column from the original resource.	3.0
na_52	double	Source column from the original resource.	8.0
na_53	double	Source column from the original resource.	6.0
na_54	double	Source column from the original resource.	15.0
na_55	double	Source column from the original resource.	6.0
na_56	double	Source column from the original resource.	20.0
na_57	double	Source column from the original resource.	4.0
na_58	double	Source column from the original resource.	14.0
na_59	double	Source column from the original resource.	3.0
na_60	double	Source column from the original resource.	10.0
na_61	double	Source column from the original resource.	3.0
na_62	double	Source column from the original resource.	13.0
na_63	double	Source column from the original resource.	2.0
na_64	double	Source column from the original resource.	6.0
na_65	double	Source column from the original resource.	1.0
na_66	double	Source column from the original resource.	4.500800000000001
na_67	double	Source column from the original resource.	1.0
na_68	double	Source column from the original resource.	2.0
na_69	double	Source column from the original resource.	1.3299999999999998
na_70	double	Source column from the original resource.	5.7700000000000005
na_71	double	Source column from the original resource.	5.41
na_72	double	Source column from the original resource.	14.87
d_259	double	Source column from the original resource.	``
d_2706	double	Source column from the original resource.	1.0
d_246_84277777777763	double	Source column from the original resource.	0.5049999999999999
d_2406_167388888889	double	Source column from the original resource.	``
d_244_3	double	Source column from the original resource.	``
d_2085	double	Source column from the original resource.	``
d_255	double	Source column from the original resource.	``
d_2257	double	Source column from the original resource.	``
d_261_1	double	Source column from the original resource.	``
d_2126	double	Source column from the original resource.	``
d_297_1	double	Source column from the original resource.	``
d_2417	double	Source column from the original resource.	``
d_265_7	double	Source column from the original resource.	``
d_2028	double	Source column from the original resource.	``
d_313	double	Source column from the original resource.	``
d_3026	double	Source column from the original resource.	``
d_413_5	double	Source column from the original resource.	``
d_4971	double	Source column from the original resource.	``
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	dictionary<values=string, indices=int8, ordered=0>	Source column from the original resource.	``
source_provider	dictionary<values=string, indices=int8, ordered=0>	Publishing organization.	``
source_dataset	dictionary<values=string, indices=int8, ordered=0>	Source dataset or package title.	``
source_resource	dictionary<values=string, indices=int8, ordered=0>	Source resource title, table name, or file name.	``
source_package_id	dictionary<values=string, indices=int8, ordered=0>	Source package identifier.	``
source_resource_id	dictionary<values=string, indices=int8, ordered=0>	Source resource identifier.	``
source_url	dictionary<values=string, indices=int8, ordered=0>	Original source URL or download URL.	``
license_id	dictionary<values=string, indices=int8, ordered=0>	Source license identifier.	``
retrieved_at	dictionary<values=string, indices=int8, ordered=0>	UTC source retrieval timestamp from the Electric Sheep Africa pipeline.	``

Usage

from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-mauritius-area-harvested-and-production-of-foodcrops-2018-2024-b1eb336d")
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: Area harvested and production of foodcrops, 1980 - 2024.xlsx
• License: CC BY 4.0
• Retrieved/generated: 2026-08-08T16:24:55Z
• Hugging Face repo: electricsheepafrica/africa-mauritius-area-harvested-and-production-of-foodcrops-2018-2024-b1eb336d

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_area_harvested_and_production_of_foodcrops_2018_2024_b1eb336d_2024,
  title        = {Area Harvested and Production of Foodcrops 2018 2024 | Africa (MDPA)},
  author       = {MDPA},
  year         = {2024},
  url          = {https://data.govmu.org/dataset/area-harvested-and-production-of-foodcrops-2018-2024},
  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-mauritius-area-harvested-and-production-of-foodcrops-2018-2024-b1eb336d}}
}

License

Released under CC BY 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/area-harvested-and-production-of-foodcrops-2018-2024