electricsheepafrica/africa-world-bank-energy-and-mining-indicators-for-zambia

Zambia - Energy and Mining

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.

The world economy needs ever-increasing amounts of energy to sustain economic growth, raise living standards, and reduce poverty. But today's trends in energy use are not sustainable. As the world's population grows and economies become more industrialized, nonrenewable energy sources will become scarcer and more costly. Data here on energy production, use, dependency, and efficiency are compiled by the World Bank from the International Energy Agency and the Carbon Dioxide Information Analysis Center.

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

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

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Dataset Characteristics

Domain	Demographics and population
Unit of observation	Country-level aggregates
Rows (total)	1,417
Columns	8 (2 numeric, 6 categorical, 0 datetime)
Train split	1,133 rows
Test split	283 rows
Geographic scope	ZMB
Publisher	World Bank Group
HDX last updated	2026-03-27

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Variables

Geographic — country_name (Zambia), country_iso3 (ZMB), year (range 1966.0–2024.0).

Outcome / Measurement — value (range -360670000.0–4251462464.1203).

Identifier / Metadata — indicator_name (Mineral rents (% of GDP), Adjusted savings: mineral depletion (current US$), Adjusted savings: mineral depletion (% of GNI)), indicator_code (NY.GDP.MINR.RT.ZS, NY.ADJ.DMIN.CD, NY.ADJ.DMIN.GN.ZS), esa_source (HDX), esa_processed (2026-04-09).

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-world-bank-energy-and-mining-indicators-for-zambia")
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%	Zambia
country_iso3	object	0.0%	ZMB
year	int64	0.0%	1966.0 – 2024.0 (mean 2001.9795)
indicator_name	object	0.0%	Mineral rents (% of GDP), Adjusted savings: mineral depletion (current US$), Adjusted savings: mineral depletion (% of GNI)
indicator_code	object	0.0%	NY.GDP.MINR.RT.ZS, NY.ADJ.DMIN.CD, NY.ADJ.DMIN.GN.ZS
value	float64	0.0%	-360670000.0 – 4251462464.1203 (mean 22076683.699)
esa_source	object	0.0%	HDX
esa_processed	object	0.0%	2026-04-09

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Numeric Summary

Column	Min	Max	Mean	Median
year	1966.0	2024.0	2001.9795	2003.0
value	-360670000.0	4251462464.1203	22076683.699	8.9485

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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_energy_and_mining_indicators_for_zambia,
  title     = {Zambia - Energy and Mining},
  author    = {World Bank Group},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/world-bank-energy-and-mining-indicators-for-zambia},
  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.