electricsheepafrica/africa-senegal-2-1-1-prevalence-de-la-sous-alimentation-9b44883b

2.1.1 Prévalence de la sous-alimentation | Africa (Senegal official open data)

1 rows - 1 Africa country - not-applicable - 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: 2.1.1 Prévalence de la sous-alimentation
• Publisher: ANSD
• Resource: Prévalence de la sous-alimentation
• Format: XLSX
• License: Open Data Commons Open Database License
• Packaging mode: tabular_resource

Geographic coverage

1 Africa country:

Country	Rows	First year	Last year	Name
SEN	1	n/a	n/a	Senegal

Indicators or Resource Contents

• This source file is packaged as a normalized tabular resource.

Schema

Column	Type	Description	Example
source_record_id	string	Stable row identifier for tabular resources.	0e4ba675-bc2d-4c9e-aa4a-8bb742e52142:superficie-pasteque:0
country_iso3	category	ISO3 country code.	SEN
country_name	category	Country name.	Senegal
source_sheet	string	Workbook sheet name, when the source is a spreadsheet.	Superficie Pasteque
vi1999	int64	Source column.	20226
vi2005	int64	Source column.	22139
vi2006	int64	Source column.	16274
vi2007	float64	Source column.	9893.28215225
vi2008	int64	Source column.	22665
vi2009	float64	Source column.	17533.386964627018
vi2010	int64	Source column.	23411
vi2011	float64	Source column.	13939.525813460548
vi2012	float64	Source column.	16582.342101157043
vi2013	float64	Source column.	12265.76110309106
vi2014	float64	Source column.	20167.01589249999
vi2015	float64	Source column.	19728.56966261902
vi2016	float64	Source column.	19315.5205526912
vi2017	float64	Source column.	44024.80934098655
vi2018	float64	Source column.	72268.72783118869
vi2019	float64	Source column.	82027.7
vi2020	float64	Source column.	86564.08814586984
vi2021	float64	Source column.	85710.41163362715
vi2022	float64	Source column.	89554.64153239987
vi2023	float64	Source column.	92014.1529121642
methodedecollecteenquetera	string	Source column.	Enquete
modedecalcultauxproportio	string	Source column.	Somme
frequencedeproductionirregul	string	Source column.	annuelle
delaidediffusion	string	Source column.	Novembre
lindicateurestildiffusepre	string	Source column.	dapsa.gouv.sn
niveaudedesagregationagese	string	Source column.	departement
statutdelindicateurdefiniti	string	Source column.	définitif
uniteechellemilliermi	string	Source column.	Ha
source	string	Source column.	DAPSA
source_period_start_year	Int64	First year inferred from source resource metadata.	``
source_period_end_year	Int64	Last year inferred from source resource metadata.	``
source_period_label	string	Human-readable period inferred from source resource metadata.	``
source_provider	category	Publishing organization.	ANSD
source_dataset	category	Source package title.	2.1.1 Prévalence de la sous-alimentation
source_resource	category	Source resource title.	Prévalence de la sous-alimentation
source_package_id	category	CKAN package UUID.	b726d4e6-f047-4d9b-9b47-ba4dc06f1f5e
source_resource_id	category	CKAN resource UUID.	0e4ba675-bc2d-4c9e-aa4a-8bb742e52142
source_url	category	Original source resource URL.	https://agridata.ansd.sn/dataset/b726d4e6-f047-4d9b-9b47-ba4dc06f1f5e/re
license_id	category	Source license identifier.	odc-odbl
retrieved_at	category	UTC retrieval timestamp.	2026-07-27T11:04:43Z

Usage

from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-senegal-2-1-1-prevalence-de-la-sous-alimentation-9b44883b")
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_2_1_1_prevalence_de_la_sous_alimentation_9b44883b_2026,
  title        = {2.1.1 Prévalence de la sous-alimentation | Africa (Senegal official open data)},
  author       = {ANSD},
  year         = {2026},
  url          = {https://agridata.ansd.sn/dataset/prevalencedelasousalimentation},
  publisher    = {HuggingFace Datasets, repackaged by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-senegal-2-1-1-prevalence-de-la-sous-alimentation-9b44883b}}
}

License

Released under Open Data Commons Open Database License.

Original data (c) ANSD. 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

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Provenance: ingested 2026-07-27 via the Electric Sheep pipeline. Source URL: https://agridata.ansd.sn/dataset/b726d4e6-f047-4d9b-9b47-ba4dc06f1f5e/resource/0e4ba675-bc2d-4c9e-aa4a-8bb742e52142/download/dapsa_ind28.xlsx