Datasets:

electricsheepafrica
/

africa-world-bank-agriculture-and-rural-development-indicators-for-zimbabwe

annotations_creators:
  - no-annotation
language_creators:
  - found
language:
  - en
license: cc-by-4.0
multilinguality:
  - monolingual
size_categories:
  - 1K<n<10K
source_datasets:
  - original
task_categories:
  - tabular-regression
task_ids: []
tags:
  - africa
  - humanitarian
  - hdx
  - electric-sheep-africa
  - agriculture-livestock
  - development
  - indicators
  - zwe
pretty_name: Zimbabwe - Agriculture and Rural Development
dataset_info:
  splits:
    - name: train
      num_examples: 1392
    - name: test
      num_examples: 348

Zimbabwe - Agriculture and Rural Development

Publisher: World Bank Group · Source: HDX · License: cc-by · Updated: 2026-03-27

Abstract

Contains data from the World Bank's data portal. There is also a consolidated country dataset on HDX.

For the 70 percent of the world's poor who live in rural areas, agriculture is the main source of income and employment. But depletion and degradation of land and water pose serious challenges to producing enough food and other agricultural products to sustain livelihoods here and meet the needs of urban populations. Data presented here include measures of agricultural inputs, outputs, and productivity compiled by the UN's Food and Agriculture Organization.

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.

Dataset Characteristics


Domain	Food security and nutrition
Unit of observation	Country-level aggregates
Rows (total)	1,740
Columns	8 (2 numeric, 6 categorical, 0 datetime)
Train split	1,392 rows
Test split	348 rows
Geographic scope	ZWE
Publisher	World Bank Group
HDX last updated	2026-03-27

Variables

Geographic — country_name (Zimbabwe), country_iso3 (ZWE), year (range 1960.0–2025.0).

Outcome / Measurement — value (range -0.2971–8870926307.453).

Identifier / Metadata — indicator_name (Rural population, Rural population (% of total population), Rural population growth (annual %)), indicator_code (SP.RUR.TOTL, SP.RUR.TOTL.ZS, SP.RUR.TOTL.ZG), esa_source (HDX), esa_processed (2026-04-10).

Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-world-bank-agriculture-and-rural-development-indicators-for-zimbabwe")
train = ds["train"].to_pandas()
test  = ds["test"].to_pandas()

print(train.shape)
train.head()

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 – 2025.0 (mean 1994.8925)
`indicator_name`	object	0.0%	Rural population, Rural population (% of total population), Rural population growth (annual %)
`indicator_code`	object	0.0%	SP.RUR.TOTL, SP.RUR.TOTL.ZS, SP.RUR.TOTL.ZG
`value`	float64	0.0%	-0.2971 – 8870926307.453 (mean 54270533.2954)
`esa_source`	object	0.0%	HDX
`esa_processed`	object	0.0%	2026-04-10

Numeric Summary

Column	Min	Max	Mean	Median
`year`	1960.0	2025.0	1994.8925	1996.0
`value`	-0.2971	8870926307.453	54270533.2954	86.42

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.

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.

Citation

@dataset{hdx_africa_world_bank_agriculture_and_rural_development_indicators_for_zimbabwe,
  title     = {Zimbabwe - Agriculture and Rural Development},
  author    = {World Bank Group},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/world-bank-agriculture-and-rural-development-indicators-for-zimbabwe},
  note      = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}

Electric Sheep Africa — Africa's ML dataset infrastructure. Lagos, Nigeria.