--- 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" - "production" - "vegetables" configs: - config_name: default data_files: - split: train path: data/train-00000-of-00001.parquet pretty_name: "Food Crops Monthly Production for Mauritius | Africa (MDPA)" --- # Food Crops Monthly Production for Mauritius | Africa (MDPA) **189 rows** - **1 Africa country/area** - **2017-2019** - **source table** - *Engineered by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)* ![rows](https://img.shields.io/badge/rows-189-blue) ![countries](https://img.shields.io/badge/countries-1-green) ![period](https://img.shields.io/badge/period-2017--2019-orange) ![indicators](https://img.shields.io/badge/indicators-0-purple) ![license](https://img.shields.io/badge/license-cc--by--sa--4.0-lightgrey) ## 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` | ISO3 country or area code. | `MU` | | `country_name` | `dictionary` | 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` | Source column from the original resource. | `2017-2019` | | `source_provider` | `dictionary` | Publishing organization. | `MDPA` | | `source_dataset` | `dictionary` | Source dataset or package title. | `Food Crops Monthly Production for Mauritius` | | `source_resource` | `dictionary` | Source resource title, table name, or file name. | `Source-File-Food-Crops-Monthly-Production-for-Mauritius.xlsx` | | `source_package_id` | `dictionary` | Source package identifier. | `644cea13-d27f-4880-bf71-84e9d5e0dc1b` | | `source_resource_id` | `dictionary` | Source resource identifier. | `c02255e7-cbb5-4c0e-87a8-aa51c3d8993b` | | `source_url` | `dictionary` | Original source URL or download URL. | `https://data.govmu.org/dataset/644cea13-d27f-4880-bf71-84e9d5e0dc1b/r...` | | `license_id` | `dictionary` | Source license identifier. | `CC-BY-SA-4.0` | | `retrieved_at` | `dictionary` | 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 ```python 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 ```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 - **Source:** [MDPA](https://data.govmu.org/dataset/food-crops-monthly-production-mauritius) - **Publisher:** MDPA - **Portal:** [https://data.govmu.org](https://data.govmu.org) - **Resource:** [Source-File-Food-Crops-Monthly-Production-for-Mauritius.xlsx](https://data.govmu.org/dataset/644cea13-d27f-4880-bf71-84e9d5e0dc1b/resource/c02255e7-cbb5-4c0e-87a8-aa51c3d8993b/download/source-file-food-crops-monthly-production-for-mauritius.xlsx) - **License:** [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/) - **Retrieved/generated:** `2026-08-08T17:13:29Z` - **Hugging Face repo:** [electricsheepafrica/africa-mauritius-food-crops-monthly-production-for-mauritius-6325b72b](https://huggingface.co/datasets/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 ```bibtex @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](https://creativecommons.org/licenses/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/food-crops-monthly-production-mauritius