electricsheepafrica/africa-world-bank-public-sector-indicators-for-tunisia

Tunisia - Public Sector

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.

Effective governments improve people's standard of living by ensuring access to essential services – health, education, water and sanitation, electricity, transport – and the opportunity to live and work in peace and security. Data here includes World Bank staff assessments of country performance in economic management, structural policies, policies for social inclusion and equity, and public sector management and institutions for the poorest countries. Also included are indicators on revenues and expenses from the International Monetary Fund's Government Finance Statistics, and on tax policies from various sources.

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

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)	3,130
Columns	8 (2 numeric, 6 categorical, 0 datetime)
Train split	2,504 rows
Test split	626 rows
Geographic scope	TUN
Publisher	World Bank Group
HDX last updated	2026-03-27

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Variables

Geographic — country_name (Tunisia), country_iso3 (TUN), year (range 1960.0–2024.0).

Outcome / Measurement — value (range -3560600000.0–31420100000.0).

Identifier / Metadata — indicator_name (Military expenditure (% of GDP), Military expenditure (current LCU), Military expenditure (current USD)), indicator_code (MS.MIL.XPND.GD.ZS, MS.MIL.XPND.CN, MS.MIL.XPND.CD), 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-public-sector-indicators-for-tunisia")
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%	Tunisia
country_iso3	object	0.0%	TUN
year	int64	0.0%	1960.0 – 2024.0 (mean 1999.6796)
indicator_name	object	0.0%	Military expenditure (% of GDP), Military expenditure (current LCU), Military expenditure (current USD)
indicator_code	object	0.0%	MS.MIL.XPND.GD.ZS, MS.MIL.XPND.CN, MS.MIL.XPND.CD
value	float64	0.0%	-3560600000.0 – 31420100000.0 (mean 531952453.6368)
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	2024.0	1999.6796	2002.0
value	-3560600000.0	31420100000.0	531952453.6368	30.8689

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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_public_sector_indicators_for_tunisia,
  title     = {Tunisia - Public Sector},
  author    = {World Bank Group},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/world-bank-public-sector-indicators-for-tunisia},
  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.