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electricsheepafrica/africa-senegal-nombre-de-stations-et-d-ecloseries-renforcees-e886db5b

Nombre De Stations Et D Ecloseries Renforcees | Africa (Ministère des pêches, des Infrastructures maritimes et portuaires) 1 rows - 1 Africa country/area - 2016 - source table - Engineered by Electric Sheep Africa TL;DR This dataset contains 1 rows from Ministère des pêches, des Infrastructures maritimes et portuaires, covering Nombre De Stations Et D Ecloseries Renforcees. It is published as ML-ready Parquet with consistent Hugging Face metadata, source… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/africa-senegal-nombre-de-stations-et-d-ecloseries-renforcees-e886db5b.

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Dataset Card

Nombre De Stations Et D Ecloseries Renforcees | Africa (Ministère des pêches, des Infrastructures maritimes et portuaires)

1 rows - 1 Africa country/area - 2016 - source table - Engineered by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)

rows countries period indicators license

TL;DR

This dataset contains 1 rows from Ministère des pêches, des Infrastructures maritimes et portuaires, covering Nombre De Stations Et D Ecloseries Renforcees. It is published as ML-ready Parquet with consistent Hugging Face metadata, source provenance, and analysis-friendly loading examples.

What This Dataset Measures

Official statistics datasets help analysts inspect public data as published by governments, national statistical systems, and regional data portals.

Source-provided context: Nombre de stations et d'écloseries renforcées

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

Coverage

DimensionValue
Rows1
Countries/areas1
First period2016
Last period2016
Indicators0
Columns24
Source formatXLSX

Geographic Coverage

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

AreaRowsFirst yearLast yearName
SEN120162016Senegal

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.b49ea192-f7f5-4932-a404-0866eb747a4d:nombre-de-stations-e: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.Nombre de stations e
valeurdelasituationderefereint64Source column from the original resource.3
datesdrint64Source column from the original resource.2016
methodedecollecteenqueterastringSource column from the original resource.Rapports
modedecalcultauxproportiostringSource column from the original resource.Somme
frequencedeproductionirregulstringSource column from the original resource.Annuelle
niveaudedesagregationagesestringSource column from the original resource.Région
statutdelindicateurdefinitistringSource column from the original resource.Provisoire
uniteechellemilliermistringSource column from the original resource.Unité
sourcestringSource column from the original resource.ANA
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.Ministère des pêches, des Infrastructures maritimes et portuaires
source_datasetdictionary<values=string, indices=int8, ordered=0>Source dataset or package title.Nombre de stations et d'écloseries renforcées
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.7d1c2664-8841-428b-870f-e8dc342e29f3
source_resource_iddictionary<values=string, indices=int8, ordered=0>Source resource identifier.b49ea192-f7f5-4932-a404-0866eb747a4d
source_urldictionary<values=string, indices=int8, ordered=0>Original source URL or download URL.https://agridata.ansd.sn/dataset/7d1c2664-8841-428b-870f-e8dc342e29f3...
license_iddictionary<values=string, indices=int8, ordered=0>Source license identifier.cc-by
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-nombre-de-stations-et-d-ecloseries-renforcees-e886db5b")
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 "datesdr" in df.columns:
    trend = df.sort_values("datesdr")

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: datesdr.
  • —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

  • —Profile the distribution of values
  • —Compare categories or geographies
  • —Join with complementary public 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_nombre_de_stations_et_d_ecloseries_renforcees_e886db5b_2016,
  title        = {Nombre De Stations Et D Ecloseries Renforcees | Africa (Ministère des pêches, des Infrastructures maritimes et portuaires)},
  author       = {Ministère des pêches, des Infrastructures maritimes et portuaires},
  year         = {2016},
  url          = {https://agridata.ansd.sn/dataset/nombredestationsetdecloseriesrenforcees},
  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-senegal-nombre-de-stations-et-d-ecloseries-renforcees-e886db5b}}
}

License

Released under CC BY 4.0.

Original data is published by Ministère des pêches, des Infrastructures maritimes et portuaires. 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/nombredestationsetdecloseriesrenforcees