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electricsheepafrica/africa-seychelles-seychelles-gender-ceeafd99

Seychelles - Gender | Africa (Seychelles official open data) 3,309 rows - 1 Africa country - 1960-2025 - Repackaged by Electric Sheep Africa TL;DR This dataset packages one official CSV resource from Seychelles 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: Seychelles - Gender Publisher: World Bank Group Resource:… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/africa-seychelles-seychelles-gender-ceeafd99.

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Dataset Card

Seychelles - Gender | Africa (Seychelles official open data)

3,309 rows - 1 Africa country - 1960-2025 - Repackaged by Electric Sheep Africa

TL;DR

This dataset packages one official CSV resource from Seychelles 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: Seychelles - Gender
—Publisher: World Bank Group
—Resource: Gender Indicators for Seychelles
—Format: CSV
—License: CC BY 4.0
—Packaging mode: indicator_long

Geographic coverage

1 Africa country:

Country	Rows	First year	Last year	Name
`SYC`	3,309	1960	2025	`Seychelles`

Indicators or Resource Contents

—seychelles-gender-c7193bf8 - Seychelles - Gender

Schema

Column	Type	Description	Example
`indicator_id`	`string`	Stable indicator identifier.	`seychelles-gender-c7193bf8`
`indicator_name`	`string`	Human-readable indicator name.	`Seychelles - Gender`
`country_iso3`	`string`	ISO3 country code.	`SYC`
`country_name`	`string`	Country name.	`Seychelles`
`year`	`Int64`	Observation year.	`2023`
`value`	`float64`	Numeric observation value.	`26.31266403`
`unit`	`string`	Measurement unit, when available.	`source_units_unspecified`
`dimension_country_name`	`string`	Source dimension.	`Seychelles`
`dimension_country_iso3`	`string`	Source dimension.	`SYC`
`dimension_indicator_name`	`string`	Source dimension.	`Firms with female top manager (% of firms)`
`dimension_indicator_code`	`string`	Source dimension.	`IC.FRM.FEMM.ZS`
`source_period_start_year`	`Int64`	First year inferred from source resource metadata.	`2015`
`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.	`2015-2023`
`source_provider`	`category`	Publishing organization.	`World Bank Group`
`source_dataset`	`category`	Source package title.	`Seychelles - Gender`
`source_resource`	`category`	Source resource title.	`Gender Indicators for Seychelles`
`source_package_id`	`category`	CKAN package UUID.	`72529113-162b-4369-8a83-0d91bec9cdeb`
`source_resource_id`	`category`	CKAN resource UUID.	`0134b445-f6ce-4a4c-b091-d35c511fa47d`
`source_url`	`category`	Original source resource URL.	`https://data.humdata.org/dataset/72529113-162b-4369-8a83-0d91bec9cdeb/re`
`license_id`	`category`	Source license identifier.	`cc-by`
`retrieved_at`	`category`	UTC retrieval timestamp.	`2026-08-24T21:35:26Z`

Usage

python

from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-seychelles-seychelles-gender-ceeafd99")
df = ds["train"].to_pandas()
print(df.head())

Filter to one country

python

sample_country = df[df["country_iso3"] == "SYC"]

Work with indicators

python

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

bibtex

@misc{electric_sheep_africa_africa_seychelles_seychelles_gender_ceeafd99_2025,
  title        = {Seychelles - Gender | Africa (Seychelles official open data)},
  author       = {World Bank Group},
  year         = {2025},
  url          = {https://data.humdata.org/dataset/world-bank-gender-indicators-for-seychelles},
  publisher    = {HuggingFace Datasets, repackaged by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-seychelles-seychelles-gender-ceeafd99}}
}

License

Released under CC BY 4.0.

Original data (c) World Bank Group. 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 2026-08-24 via the Electric Sheep pipeline. Source URL: https://data.humdata.org/dataset/72529113-162b-4369-8a83-0d91bec9cdeb/resource/0134b445-f6ce-4a4c-b091-d35c511fa47d/download/gender_syc.csv