Datasets:

electricsheepafrica
/

africa-senegal-synthese-des-debarquements-94974290

license: cc-by-4.0
language:
  - en
task_categories:
  - tabular-regression
  - time-series-forecasting
multilinguality: monolingual
size_categories:
  - n<1K
tags:
  - tabular
  - xlsx
  - africa
  - senegal
  - official-statistics
  - open-data
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-00000-of-00001.parquet
pretty_name: Synthése des débarquements | Africa (Senegal official open data)

Synthése des débarquements | Africa (Senegal official open data)

34 rows - 1 Africa country - 2022-2024 - Repackaged by Electric Sheep Africa

TL;DR

This dataset packages one official XLSX resource from Senegal as ML-ready Parquet. The source file is the provenance boundary; all usable indicators or tabular columns from the resource stay together in this repo.

About the source

Source: Synthése des débarquements
Publisher: CEPSE/MPIMP
Resource: synthése des débarquements
Format: XLSX
License: CC BY 4.0
Packaging mode: indicator_long

Geographic coverage

1 Africa country:

Country	Rows	First year	Last year	Name
`SEN`	34	2022	2024	`Senegal`

Indicators or Resource Contents

synthese-des-debarquements-quantite-t-6f3b3205 - Synthése des débarquements - quantite t
synthese-des-debarquements-vce-x-1000-c758b458 - Synthése des débarquements - vce x 1000

Schema

Column	Type	Description	Example
`indicator_id`	`string`	Stable indicator identifier.	`synthese-des-debarquements-quantite-t-6f3b3205`
`indicator_name`	`string`	Human-readable indicator name.	`Synthése des débarquements - quantite t`
`country_iso3`	`string`	ISO3 country code.	`SEN`
`source_sheet`	`string`	Workbook sheet name, when the source is a spreadsheet.	`Feuil1`
`country_name`	`string`	Country name.	`Senegal`
`year`	`Int64`	Observation year.	`2022`
`value`	`float64`	Numeric observation value.	`343405.9796524265`
`unit`	`string`	Measurement unit, when available.	`source_units_unspecified`
`dimension_pecherie`	`string`	Source dimension.	`Pêche artisanale`
`dimension_indicateurs`	`string`	Source dimension.	`Production de la pêche maritime`
`source_period_start_year`	`Int64`	First year inferred from source resource metadata.	`2022`
`source_period_end_year`	`Int64`	Last year inferred from source resource metadata.	`2024`
`source_period_label`	`category`	Human-readable period inferred from source resource metadata.	`2022-2024`
`source_provider`	`category`	Publishing organization.	`CEPSE/MPIMP`
`source_dataset`	`category`	Source package title.	`Synthése des débarquements`
`source_resource`	`category`	Source resource title.	`synthése des débarquements`
`source_package_id`	`category`	CKAN package UUID.	`4ca5afe0-a761-4336-9a41-5f4b2ee6422f`
`source_resource_id`	`category`	CKAN resource UUID.	`87772e3d-6c75-4b9a-9d12-06589787cc20`
`source_url`	`category`	Original source resource URL.	`https://agridata.ansd.sn/dataset/4ca5afe0-a761-4336-9a41-5f4b2ee6422f/re`
`license_id`	`category`	Source license identifier.	`cc-by`
`retrieved_at`	`category`	UTC retrieval timestamp.	`2026-07-27T11:04:43Z`

Usage

from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-senegal-synthese-des-debarquements-94974290")
df = ds["train"].to_pandas()
print(df.head())

Filter to one country

sample_country = df[df["country_iso3"] == "SEN"]

Work with indicators

if "indicator_id" in df.columns:
    print(df["indicator_id"].value_counts().head())
    sample = df.sort_values([c for c in ["indicator_id", "year"] if c in df.columns])

Citation

@misc{electric_sheep_africa_africa_senegal_synthese_des_debarquements_94974290_2024,
  title        = {Synthése des débarquements | Africa (Senegal official open data)},
  author       = {CEPSE/MPIMP},
  year         = {2024},
  url          = {https://agridata.ansd.sn/dataset/donneesdelapechemaritimeetexportations},
  publisher    = {HuggingFace Datasets, repackaged by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-senegal-synthese-des-debarquements-94974290}}
}

License

Released under CC BY 4.0.

Original data (c) CEPSE/MPIMP. When using this dataset, please cite both the original source above and the Electric Sheep Africa repackaging.

About Electric Sheep

Electric Sheep Africa is part of the Electric Sheep mission: a unified, ML-ready data layer for Africa on Hugging Face. We pull data from authoritative open sources, normalize the schemas, package as Parquet, and publish with consistent dataset cards so researchers and developers can use load_dataset() to start working in seconds.

Browse the full collection: huggingface.co/electricsheepafrica

Provenance: ingested 2026-07-27 via the Electric Sheep pipeline. Source URL: https://agridata.ansd.sn/dataset/4ca5afe0-a761-4336-9a41-5f4b2ee6422f/resource/87772e3d-6c75-4b9a-9d12-06589787cc20/download/synthese-des-debarquements1.xlsx