electricsheepafrica/africa-senegal-anomalies-4b4b4357

anomalies | Africa (Senegal official open data)

22,561 rows - 1 Africa country - 1991-2023 - 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: anomalies
• Publisher: ANACIM
• Resource: anomalies des températures minimales mensuelles 1991-2020
• Format: XLSX
• License: CC BY 4.0
• Packaging mode: indicator_long

Geographic coverage

1 Africa country:

Country	Rows	First year	Last year	Name
SEN	22,561	1991	2023	Senegal

Indicators or Resource Contents

• anomalies-moy-janv-a622fac1 - anomalies - moy janv
• anomalies-normale-janv-993b5bf6 - anomalies - normale janv
• anomalies-anomalies-janv-0f436547 - anomalies - anomalies janv
• anomalies-moy-fev-50ae7c8a - anomalies - moy fev
• anomalies-normale-fev-aaf0ab60 - anomalies - normale fev
• anomalies-anomalies-fev-5d759361 - anomalies - anomalies fev
• anomalies-moy-mar-9fc78442 - anomalies - moy mar
• anomalies-normale-mar-901005f2 - anomalies - normale mar
• anomalies-anomalies-mar-6586ed0d - anomalies - anomalies mar
• anomalies-moy-avr-7f914c24 - anomalies - moy avr
• anomalies-normale-avr-54c8fdf7 - anomalies - normale avr
• anomalies-anomalies-avr-a2ba839c - anomalies - anomalies avr
• anomalies-moy-mai-d61e11cb - anomalies - moy mai
• anomalies-normale-mai-717fac00 - anomalies - normale mai
• anomalies-anomalies-mai-42d80728 - anomalies - anomalies mai
• anomalies-moy-juin-5ec5f54d - anomalies - moy juin
• anomalies-normale-juin-ef569c60 - anomalies - normale juin
• anomalies-anomalies-juin-7d6e8331 - anomalies - anomalies juin
• anomalies-moy-juil-e98b0c02 - anomalies - moy juil
• anomalies-normale-juil-f9f43878 - anomalies - normale juil

Schema

Column	Type	Description	Example
indicator_id	string	Stable indicator identifier.	anomalies-moy-janv-a622fac1
indicator_name	string	Human-readable indicator name.	anomalies - moy janv
country_iso3	string	ISO3 country code.	SEN
source_sheet	string	Workbook sheet name, when the source is a spreadsheet.	Bambey Tmin
country_name	string	Country name.	Senegal
year	Int64	Observation year.	1991
value	float64	Numeric observation value.	18.0451612919569
unit	string	Measurement unit, when available.	source_units_unspecified
source_period_start_year	Int64	First year inferred from source resource metadata.	1991
source_period_end_year	Int64	Last year inferred from source resource metadata.	2023
source_period_label	category	Human-readable period inferred from source resource metadata.	1991-2023
source_provider	category	Publishing organization.	ANACIM
source_dataset	category	Source package title.	anomalies
source_resource	category	Source resource title.	anomalies des températures minimales mensuelles 1991-2020
source_package_id	category	CKAN package UUID.	016497bc-0ff7-4b65-84cb-297cfe3cf10f
source_resource_id	category	CKAN resource UUID.	128367c0-86dc-4d67-b706-1e93068d0135
source_url	category	Original source resource URL.	https://agridata.ansd.sn/dataset/016497bc-0ff7-4b65-84cb-297cfe3cf10f/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-anomalies-4b4b4357")
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_anomalies_4b4b4357_2023,
  title        = {anomalies | Africa (Senegal official open data)},
  author       = {ANACIM},
  year         = {2023},
  url          = {https://agridata.ansd.sn/dataset/anomaliestemperatesmini},
  publisher    = {HuggingFace Datasets, repackaged by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-senegal-anomalies-4b4b4357}}
}

License

Released under CC BY 4.0.

Original data (c) ANACIM. 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/016497bc-0ff7-4b65-84cb-297cfe3cf10f/resource/128367c0-86dc-4d67-b706-1e93068d0135/download/anomalies-tmin-91_2023.xlsx