electricsheepafrica/africa-world-bank-infrastructure-indicators-for-zimbabwe

Zimbabwe - Infrastructure

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.

Infrastructure helps determine the success of manufacturing and agricultural activities. Investments in water, sanitation, energy, housing, and transport also improve lives and help reduce poverty. And new information and communication technologies promote growth, improve delivery of health and other services, expand the reach of education, and support social and cultural advances. Data here are compiled from such sources as the International Road Federation, Containerisation International, the International Civil Aviation Organization, the International Energy Association, and the International Telecommunications Union.

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

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)	1,422
Columns	8 (2 numeric, 6 categorical, 0 datetime)
Train split	1,137 rows
Test split	284 rows
Geographic scope	ZWE
Publisher	World Bank Group
HDX last updated	2026-03-27

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Variables

Geographic — country_name (Zimbabwe), country_iso3 (ZWE), year (range 1960.0–2024.0).

Outcome / Measurement — value (range 0.0–6502387000000.0).

Identifier / Metadata — indicator_name (Fixed telephone subscriptions, Fixed telephone subscriptions (per 100 people), Renewable internal freshwater resources, total (billion cubic meters)), indicator_code (IT.MLT.MAIN, IT.MLT.MAIN.P2, ER.H2O.INTR.K3), esa_source (HDX), esa_processed (2026-04-10).

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-world-bank-infrastructure-indicators-for-zimbabwe")
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%	Zimbabwe
country_iso3	object	0.0%	ZWE
year	int64	0.0%	1960.0 – 2024.0 (mean 2000.6399)
indicator_name	object	0.0%	Fixed telephone subscriptions, Fixed telephone subscriptions (per 100 people), Renewable internal freshwater resources, total (billion cubic meters)
indicator_code	object	0.0%	IT.MLT.MAIN, IT.MLT.MAIN.P2, ER.H2O.INTR.K3
value	float64	0.0%	0.0 – 6502387000000.0 (mean 6965091138.5017)
esa_source	object	0.0%	HDX
esa_processed	object	0.0%	2026-04-10

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Numeric Summary

Column	Min	Max	Mean	Median
year	1960.0	2024.0	2000.6399	2002.0
value	0.0	6502387000000.0	6965091138.5017	23.0282

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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_infrastructure_indicators_for_zimbabwe,
  title     = {Zimbabwe - Infrastructure},
  author    = {World Bank Group},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/world-bank-infrastructure-indicators-for-zimbabwe},
  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.