electricsheepafrica/africa-worldbank-decision-maker-about-a-woman-s-visits-to-her-family-or-relatives-mainly-husband

Decision maker about a woman's visits to her family or relatives: mainly husband (% of women age 15-49) | Africa (World Bank — Gender Statistics)

🌍 106 observations · 40 Africa countries · 1999–2022 · Repackaged by Electric Sheep Africa

TL;DR

This dataset contains 106 observations of Gender Statistics data across 40 Africa countries, spanning 1999–2022, covering 1 distinct indicators.

About the source

• Source: World Bank — Gender Statistics
• Publisher: World Bank
• License: cc-by-4.0
• Topic: Gender Statistics

Geographic coverage

40 Africa countries · top rows shown below, sorted by row count:

Country	Rows	First year	Last year
SEN	9	2005	2019
RWA	5	2000	2020
BEN	4	2001	2018
MLI	4	2001	2018
KEN	4	2003	2022
EGY	4	2000	2014
MWI	4	2000	2016
NGA	4	2003	2018
UGA	4	2001	2016
ZMB	4	2002	2018
ZWE	4	1999	2015
MDG	3	2004	2021
LBR	3	2007	2020
LSO	3	2004	2014
CMR	3	2004	2018
...	25 more countries

Indicators (sample)

• SG.DMK.VISI.HB.ZS — Decision maker about a woman's visits to her family or relatives: mainly husband (% of women age 15-49)

Schema

Column	Type	Description	Example
indicator_id	string	—	SG.DMK.VISI.HB.ZS
indicator_name	string	—	Decision maker about a woman's visits…
country_iso3	string	—	AGO
country_name	string	—	Angola
year	int64	—	2016
value	float64	—	12.0

Usage

from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-worldbank-decision-maker-about-a-woman-s-visits-to-her-family-or-relatives-mainly-husband")
df = ds["train"].to_pandas()
print(df.head())

Filter to one country

kenya = df[df["country_iso3"] == "KEN"]

Time-series for a single indicator

sample = (df[df["indicator_id"] == "SG.DMK.VISI.HB.ZS"]
          .sort_values("year"))
sample.plot(x="year", y="value", title="SG.DMK.VISI.HB.ZS")

Pivot to country × year matrix

matrix = (df[df["indicator_id"] == "SG.DMK.VISI.HB.ZS"]
          .pivot_table(index="year", columns="country_iso3", values="value"))
print(matrix.tail())

Citation

@misc{africa_worldbank_decision_maker_about_a_woman_s_visits_to_her_family_or_relatives_mainly_husband_2022,
  title        = {Decision maker about a woman's visits to her family or relatives: mainly husband (% of women age 15-49) | Africa (World Bank — Gender Statistics)},
  author       = {World Bank},
  year         = {2022},
  url          = {https://databank.worldbank.org/},
  publisher    = {HuggingFace Datasets, repackaged by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-worldbank-decision-maker-about-a-woman-s-visits-to-her-family-or-relatives-mainly-husband}}
}

License

Released under cc-by-4.0.

Original data © World Bank. 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 HuggingFace. 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 2026-06-17 via the Electric Sheep pipeline. Source URL: https://databank.worldbank.org/