| --- |
| 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](https://data.humdata.org/dataset/world-bank-agriculture-and-rural-development-indicators-for-zimbabwe) · **License:** `cc-by` · **Updated:** 2026-03-27 |
|
|
| --- |
|
|
| ## Abstract |
|
|
| Contains data from the World Bank's [data portal](http://data.worldbank.org/). There is also a [consolidated country dataset](https://data.humdata.org/dataset/world-bank-combined-indicators-for-zimbabwe) 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](https://huggingface.co/electricsheepafrica).* |
|
|
| --- |
|
|
| ## 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 |
|
|
| ```python |
| 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](https://data.humdata.org/dataset/world-bank-agriculture-and-rural-development-indicators-for-zimbabwe) for the publisher's own methodology notes and caveats. |
| |
| --- |
| |
| ## Citation |
| |
| ```bibtex |
| @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](https://huggingface.co/electricsheepafrica) — Africa's ML dataset infrastructure. Lagos, Nigeria.* |
