electricsheepafrica/africa-world-bank-economy-and-growth-indicators-for-zimbabwe

Zimbabwe - Economy and Growth

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.

Economic growth is central to economic development. When national income grows, real people benefit. While there is no known formula for stimulating economic growth, data can help policy-makers better understand their countries' economic situations and guide any work toward improvement. Data here covers measures of economic growth, such as gross domestic product (GDP) and gross national income (GNI). It also includes indicators representing factors known to be relevant to economic growth, such as capital stock, employment, investment, savings, consumption, government spending, imports, and exports.

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	Humanitarian and development data
Unit of observation	Country-level aggregates
Rows (total)	10,231
Columns	8 (2 numeric, 6 categorical, 0 datetime)
Train split	8,184 rows
Test split	2,046 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 -44346890200.0–867285179600.0).

Identifier / Metadata — indicator_name (Households and NPISHs Final consumption expenditure (current US$), Final consumption expenditure (current LCU), Gross value added at basic prices (GVA) (current US$)), indicator_code (NE.CON.PRVT.CD, NE.CON.TOTL.CN, NY.GDP.FCST.CD), 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-economy-and-growth-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 1998.0428)
indicator_name	object	0.0%	Households and NPISHs Final consumption expenditure (current US$), Final consumption expenditure (current LCU), Gross value added at basic prices (GVA) (current US$)
indicator_code	object	0.0%	NE.CON.PRVT.CD, NE.CON.TOTL.CN, NY.GDP.FCST.CD
value	float64	0.0%	-44346890200.0 – 867285179600.0 (mean 4375498376.6298)
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	1998.0428	1999.0
value	-44346890200.0	867285179600.0	4375498376.6298	410700.0

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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_economy_and_growth_indicators_for_zimbabwe,
  title     = {Zimbabwe - Economy and Growth},
  author    = {World Bank Group},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/world-bank-economy-and-growth-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.