README.md

---
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-classification
- tabular-regression
task_ids: []
tags:
- africa
- humanitarian
- hdx
- electric-sheep-africa
- environment
- indicators
- zwe
pretty_name: "Zimbabwe - Environment"
dataset_info:
  splits:
    - name: train
      num_examples: 3999
    - name: test
      num_examples: 999
---

# Zimbabwe - Environment

**Publisher:** World Bank Group · **Source:** [HDX](https://data.humdata.org/dataset/world-bank-environment-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.

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](https://huggingface.co/electricsheepafrica).*

---

## 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 |

---

## 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).

---

## Quick Start

```python
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()
```

---

## 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 |

---

## 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 |

---

## 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-environment-indicators-for-zimbabwe) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@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)}
}
```

---

*[Electric Sheep Africa](https://huggingface.co/electricsheepafrica) — Africa's ML dataset infrastructure. Lagos, Nigeria.*