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electricsheepafrica/africa-senegal-production-de-viande-et-d-abats-par-an-en-tonnes-893385fc

Production De Viande Et D Abats Par an En Tonnes | Africa (CEP/MEPA) 1 rows - 1 Africa country/area - detected - source table - Engineered by Electric Sheep Africa TL;DR This dataset contains 1 rows from CEP/MEPA, covering Production De Viande Et D Abats Par an En Tonnes. It is published as ML-ready Parquet with consistent Hugging Face metadata, source provenance, and analysis-friendly loading examples. What This Dataset Measures Demographic… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/africa-senegal-production-de-viande-et-d-abats-par-an-en-tonnes-893385fc.

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Production De Viande Et D Abats Par an En Tonnes | Africa (CEP/MEPA)

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

rows countries period indicators license

TL;DR

This dataset contains 1 rows from CEP/MEPA, covering Production De Viande Et D Abats Par an En Tonnes. It is published as ML-ready Parquet with consistent Hugging Face metadata, source provenance, and analysis-friendly loading examples.

What This Dataset Measures

Demographic datasets help analysts understand population structure, household conditions, migration, gender, age, and settlement patterns.

Source-provided context: META DATA de l'indicateur de production de viande et d'abats par année (en tonnes)

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: source_period_start_year.
  • —Time coverage basis: sourceperiodstart_year.
  • —Recommended join keys: country_iso3 where available plus source-specific keys.

Coverage

DimensionValue
Rows1
Countries/areas1
First perioddetected
Last perioddetected
Indicators0
Columns23
Source formatXLSX

Geographic Coverage

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

AreaRowsFirst yearLast yearName
SEN1detecteddetectedSenegal

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.22cfa17b-8057-4ec2-8fbc-1207551beb3b:metadata:0
country_iso3dictionary<values=string, indices=int8, ordered=0>ISO3 country or area code.SEN
country_namedictionary<values=string, indices=int8, ordered=0>Country or area name.Senegal
source_sheetstringSource column from the original resource.METADATA
methode_de_collectestringSource column from the original resource.Enquête Données administratives
mode_de_calculstringSource column from the original resource.Somme Produit
frequence_de_productionstringSource column from the original resource.Annuelle
indicateur_diffusestringSource column from the original resource.Production de viande et d'abats
niveau_de_desagregationstringSource column from the original resource.espèce animale département
statut_de_l_indicateurstringSource column from the original resource.définitif
unite_echellestringSource column from the original resource.En tonnes
sourcestringSource column from the original resource.DPES/MASAE
source_period_start_yearint64Start year inferred from source metadata.``
source_period_end_yearint64End year inferred from source metadata.``
source_period_labelstringSource column from the original resource.``
source_providerdictionary<values=string, indices=int8, ordered=0>Publishing organization.CEP/MEPA
source_datasetdictionary<values=string, indices=int8, ordered=0>Source dataset or package title.Production de viande et d'abats par an (en tonnes)
source_resourcedictionary<values=string, indices=int8, ordered=0>Source resource title, table name, or file name.META DONNEES
source_package_iddictionary<values=string, indices=int8, ordered=0>Source package identifier.fc180714-8387-42c5-bcd6-9d930c0eba8a
source_resource_iddictionary<values=string, indices=int8, ordered=0>Source resource identifier.22cfa17b-8057-4ec2-8fbc-1207551beb3b
source_urldictionary<values=string, indices=int8, ordered=0>Original source URL or download URL.https://agridata.ansd.sn/dataset/fc180714-8387-42c5-bcd6-9d930c0eba8a...
license_iddictionary<values=string, indices=int8, ordered=0>Source license identifier.odc-odbl
retrieved_atdictionary<values=string, indices=int8, ordered=0>UTC source retrieval timestamp from the Electric Sheep Africa pipeline.2026-07-27T11:04:43Z

Usage

python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-senegal-production-de-viande-et-d-abats-par-an-en-tonnes-893385fc")
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"] == "SEN"]

Time-Series Pattern

python
if "value" in df.columns and "source_period_start_year" in df.columns:
    trend = df.sort_values("source_period_start_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: source_period_start_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

  • —Build demographic profiles
  • —Normalize indicators per capita
  • —Join with service-delivery datasets
  • —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_senegal_production_de_viande_et_d_abats_par_an_en_tonnes_893385fc_2026,
  title        = {Production De Viande Et D Abats Par an En Tonnes | Africa (CEP/MEPA)},
  author       = {CEP/MEPA},
  year         = {2026},
  url          = {https://agridata.ansd.sn/dataset/productiondeviandeetabats},
  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-senegal-production-de-viande-et-d-abats-par-an-en-tonnes-893385fc}}
}

License

Released under Open Data Commons Open Database License.

Original data is published by CEP/MEPA. 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 2026-08-12 by the Electric Sheep Africa README system. Source URL: https://agridata.ansd.sn/dataset/productiondeviandeetabats