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electricsheepafrica/africa-mauritius-livestock-herd-and-poultry-status-by-geographical-district-28f4d75f

Livestock Herd and Poultry Status by Geographical District | Africa (MDPA) 10 rows - 1 Africa country/area - 2019 - source table - Engineered by Electric Sheep Africa TL;DR This dataset contains 10 rows from MDPA, covering Livestock Herd and Poultry Status by Geographical District. It is published as ML-ready Parquet with consistent Hugging Face metadata, source provenance, and analysis-friendly loading examples. What This Dataset Measures… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/africa-mauritius-livestock-herd-and-poultry-status-by-geographical-district-28f4d75f.

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

Livestock Herd and Poultry Status by Geographical District | Africa (MDPA)

10 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 10 rows from MDPA, covering Livestock Herd and Poultry Status by Geographical District. 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: Data shows number of livestock herd and poultry status by geographical district for the year 2017 to 2021.

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
Rows10
Countries/areas1
First period2019
Last period2019
Indicators0
Columns44
Source formatXLSX

Geographic Coverage

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

AreaRowsFirst yearLast yearName
MU1020192019Mauritius

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.beed428a-6d2d-4dc2-bee2-194ee0d6e36d:table-50:0
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
source_sheetstringSource column from the original resource.Table 50
yearint64Observation year.2019
pamplemoussesstringSource column from the original resource.Riviere du Rempart
d_57doubleSource column from the original resource.147.0
d_150doubleSource column from the original resource.298.0
d_11doubleSource column from the original resource.58.0
d_113doubleSource column from the original resource.243.0
d_108doubleSource column from the original resource.248.0
d_382doubleSource column from the original resource.847.0
d_37doubleSource column from the original resource.23.0
d_35doubleSource column from the original resource.25.0
d_210doubleSource column from the original resource.67.0
d_360doubleSource column from the original resource.129.0
d_427doubleSource column from the original resource.414.0
d_64doubleSource column from the original resource.19.0
d_1096doubleSource column from the original resource.654.0
d_49doubleSource column from the original resource.49.0
d_152doubleSource column from the original resource.191.0
d_55doubleSource column from the original resource.62.0
d_203doubleSource column from the original resource.401.0
d_410doubleSource column from the original resource.654.0
d_376doubleSource column from the original resource.448.0
d_419doubleSource column from the original resource.508.0
d_1363doubleSource column from the original resource.1549.0
1921doubleSource column from the original resource.3373.0
d_3703doubleSource column from the original resource.5430.0
d_21doubleSource column from the original resource.47.0
d_23230doubleSource column from the original resource.83502.0
d_20doubleSource column from the original resource.13.0
d_27687doubleSource column from the original resource.14880.0
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.Livestock herd and poultry status by geographical district
source_resourcedictionary<values=string, indices=int8, ordered=0>Source resource title, table name, or file name.Source_File_2019.xlsx
source_package_iddictionary<values=string, indices=int8, ordered=0>Source package identifier.bf008954-e0af-450b-96e6-a28f719a82bb
source_resource_iddictionary<values=string, indices=int8, ordered=0>Source resource identifier.beed428a-6d2d-4dc2-bee2-194ee0d6e36d
source_urldictionary<values=string, indices=int8, ordered=0>Original source URL or download URL.https://data.govmu.org/dataset/bf008954-e0af-450b-96e6-a28f719a82bb/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-livestock-herd-and-poultry-status-by-geographical-district-28f4d75f")
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_livestock_herd_and_poultry_status_by_geographical_district_28f4_2019,
  title        = {Livestock Herd and Poultry Status by Geographical District | Africa (MDPA)},
  author       = {MDPA},
  year         = {2019},
  url          = {https://data.govmu.org/dataset/livestock-herd-and-poultry-status-geographical-district},
  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-mauritius-livestock-herd-and-poultry-status-by-geographical-district-28f4d75f}}
}

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/livestock-herd-and-poultry-status-geographical-district