electricsheepafrica/africa-world-bank-gender-indicators-for-mali

Mali - Gender

Publisher: World Bank Group · Source: HDX · License: cc-by · Updated: 2026-03-27

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Abstract

Contains data from the World Bank's data portal. There is also a consolidated country dataset on HDX.

Gender equality is a core development objective in its own right. It is also smart development policy and sound business practice. It is integral to economic growth, business growth and good development outcomes. Gender equality can boost productivity, enhance prospects for the next generation, build resilience, and make institutions more representative and effective. In December 2015, the World Bank Group Board discussed our new Gender Equality Strategy 2016-2023, which aims to address persistent gaps and proposed a sharpened focus on more and better gender data. The Bank Group is continually scaling up commitments and expanding partnerships to fill significant gaps in gender data. The database hosts the latest sex-disaggregated data and gender statistics covering demography, education, health, access to economic opportunities, public life and decision-making, and agency.

Each row in this dataset represents country-level aggregates. Data was last updated on HDX on 2026-03-27. Geographic scope: MLI.

Curated into ML-ready Parquet format by Electric Sheep Africa.

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Dataset Characteristics

Domain	Public health
Unit of observation	Country-level aggregates
Rows (total)	4,955
Columns	8 (2 numeric, 6 categorical, 0 datetime)
Train split	3,964 rows
Test split	991 rows
Geographic scope	MLI
Publisher	World Bank Group
HDX last updated	2026-03-27

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Variables

Geographic — country_name (Mali), country_iso3 (MLI), year (range 1960.0–2025.0).

Outcome / Measurement — value (range 0.0–3582401.0).

Identifier / Metadata — indicator_name (Age population, age 04, female, Age population, age 03, male, Age population, age 03, female), indicator_code (SP.POP.AG04.FE.IN, SP.POP.AG03.MA.IN, SP.POP.AG03.FE.IN), esa_source (HDX), esa_processed (2026-04-13).

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-world-bank-gender-indicators-for-mali")
train = ds["train"].to_pandas()
test  = ds["test"].to_pandas()

print(train.shape)
train.head()

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Schema

Column	Type	Null %	Range / Sample Values
country_name	object	0.0%	Mali
country_iso3	object	0.0%	MLI
year	int64	0.0%	1960.0 – 2025.0 (mean 2000.9526)
indicator_name	object	0.0%	Age population, age 04, female, Age population, age 03, male, Age population, age 03, female
indicator_code	object	0.0%	SP.POP.AG04.FE.IN, SP.POP.AG03.MA.IN, SP.POP.AG03.FE.IN
value	float64	0.0%	0.0 – 3582401.0 (mean 53708.4325)
esa_source	object	0.0%	HDX
esa_processed	object	0.0%	2026-04-13

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Numeric Summary

Column	Min	Max	Mean	Median
year	1960.0	2025.0	2000.9526	2004.0
value	0.0	3582401.0	53708.4325	35.6718

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Curation

Raw data was downloaded from HDX via the CKAN API and converted to Parquet. Column names were lowercased and standardised to snake_case. Common missing-value markers (N/A, null, none, -, unknown, no data, #N/A) were unified to NaN. The dataset was split 80/20 into train and test partitions using a fixed random seed (42) and saved as Snappy-compressed Parquet.

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Limitations

• Data originates from World Bank Group and has not been independently validated by ESA.
• Automated cleaning cannot correct for misreported values, definitional inconsistencies, or sampling bias in the original collection.
• Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

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Citation

@dataset{hdx_africa_world_bank_gender_indicators_for_mali,
  title     = {Mali - Gender},
  author    = {World Bank Group},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/world-bank-gender-indicators-for-mali},
  note      = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}

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Electric Sheep Africa — Africa's ML dataset infrastructure. Lagos, Nigeria.