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

Zimbabwe - Environment

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.

Natural and man-made environmental resources – fresh water, clean air, forests, grasslands, marine resources, and agro-ecosystems – provide sustenance and a foundation for social and economic development. The need to safeguard these resources crosses all borders. Today, the World Bank is one of the key promoters and financiers of environmental upgrading in the developing world. Data here cover forests, biodiversity, emissions, and pollution. Other indicators relevant to the environment are found under data pages for Agriculture & Rural Development, Energy & Mining, Infrastructure, and Urban Development.

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	Water, sanitation and hygiene (wash)
Unit of observation	Country-level aggregates
Rows (total)	4,999
Columns	8 (2 numeric, 6 categorical, 0 datetime)
Train split	3,999 rows
Test split	999 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 -4783172733.5931–4035257154.6539).

Identifier / Metadata — indicator_name (Total fisheries production (metric tons), Capture fisheries production (metric tons), Aquaculture production (metric tons)), indicator_code (ER.FSH.PROD.MT, ER.FSH.CAPT.MT, ER.FSH.AQUA.MT), 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-environment-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 2000.2793)
indicator_name	object	0.0%	Total fisheries production (metric tons), Capture fisheries production (metric tons), Aquaculture production (metric tons)
indicator_code	object	0.0%	ER.FSH.PROD.MT, ER.FSH.CAPT.MT, ER.FSH.AQUA.MT
value	float64	0.0%	-4783172733.5931 – 4035257154.6539 (mean 7374777.7049)
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	2000.2793	2003.0
value	-4783172733.5931	4035257154.6539	7374777.7049	4.2407

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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_environment_indicators_for_zimbabwe,
  title     = {Zimbabwe - Environment},
  author    = {World Bank Group},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/world-bank-environment-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.