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

---
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
- zaf
pretty_name: "South Africa - Environment"
dataset_info:
splits:
- name: train
num_examples: 4064
- name: test
num_examples: 1016
---

# South Africa - Environment

**Publisher:** World Bank Group · **Source:** [HDX](https://data.humdata.org/dataset/world-bank-environment-indicators-for-south-africa) · **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-south-africa) 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: **ZAF**.

*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)** | 5,081 |
| **Columns** | 8 (2 numeric, 6 categorical, 0 datetime) |
| **Train split** | 4,064 rows |
| **Test split** | 1,016 rows |
| **Geographic scope** | ZAF |
| **Publisher** | World Bank Group |
| **HDX last updated** | 2026-03-27 |

---

## Variables

**Geographic** — `country_name` (South Africa), `country_iso3` (ZAF), `year` (range 1960.0–2024.0).

**Outcome / Measurement** — `value` (range -10073565462.4808–59085564114.3656).

**Identifier / Metadata** — `indicator_name` (Total fisheries production (metric tons), Aquaculture production (metric tons), Capture fisheries production (metric tons)), `indicator_code` (ER.FSH.PROD.MT, ER.FSH.AQUA.MT, ER.FSH.CAPT.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-south-africa")
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% | South Africa |
| `country_iso3` | object | 0.0% | ZAF |
| `year` | int64 | 0.0% | 1960.0 – 2024.0 (mean 2000.1866) |
| `indicator_name` | object | 0.0% | Total fisheries production (metric tons), Aquaculture production (metric tons), Capture fisheries production (metric tons) |
| `indicator_code` | object | 0.0% | ER.FSH.PROD.MT, ER.FSH.AQUA.MT, ER.FSH.CAPT.MT |
| `value` | float64 | 0.0% | -10073565462.4808 – 59085564114.3656 (mean 583921980.7946) |
| `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 | 2000.1866 | 2002.0 |
| `value` | -10073565462.4808 | 59085564114.3656 | 583921980.7946 | 14.7258 |

---

## 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-south-africa) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_world_bank_environment_indicators_for_south_africa,
title = {South Africa - Environment},
author = {World Bank Group},
year = {2026},
url = {https://data.humdata.org/dataset/world-bank-environment-indicators-for-south-africa},
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.*

annotations_creators:
- 无注释
language_creators:
- 语言数据源自现有公开资源
language:
- 英语
license:
- CC BY 4.0
multilinguality:
- 单语言
size_categories:
- 1000<n<10000条样本
source_datasets:
- 原创数据集
task_categories:
- 表格分类
- 表格回归
task_ids:
- 无
tags:
- 非洲
- 人道主义
- HDX（人道主义数据交换平台）
- Electric Sheep Africa
- 环境
- 指标
- ZAF
pretty_name: "南非——环境"
dataset_info:
splits:
- name: train
num_examples: 4064
- name: test
num_examples: 1016

---

# 南非——环境

**发布方：世界银行集团 · 来源：[HDX（人道主义数据交换平台）](https://data.humdata.org/dataset/world-bank-environment-indicators-for-south-africa) · 许可证：`cc-by` · 最后更新：2026-03-27**

---

## 摘要

本数据集包含来自世界银行[数据门户](http://data.worldbank.org/)的相关数据。HDX平台上另有一份[整合型国家数据集](https://data.humdata.org/dataset/world-bank-combined-indicators-for-south-africa)可供获取。

自然与人工环境资源——淡水、洁净空气、森林、草原、海洋资源以及农业生态系统——为社会与经济发展提供支撑与基础。保护此类资源的需求跨越所有国界。如今，世界银行是发展中国家环境升级改造的核心推动者与融资方之一。本数据集收录的数据涵盖森林、生物多样性、碳排放与污染相关内容。其他与环境相关的指标可在农业与农村发展、能源与矿业、基础设施以及城市发展等数据页面中查询。

本数据集的每一行均代表国家层面的汇总数据。本数据集在HDX平台的最后更新时间为2026年3月27日。地理覆盖范围：**ZAF（南非）**。

*本数据集已由[Electric Sheep Africa](https://huggingface.co/electricsheepafrica)整理为可供机器学习直接使用的Parquet格式。*

---

## 数据集特征

| | |
|---|---|
| **领域** | 水、环境卫生与个人卫生（WASH） |
| **观测单元** | 国家层面汇总数据 |
| **总行数** | 5081 |
| **列数** | 8列（2个数值型、6个分类型、0个日期时间型） |
| **训练集划分** | 4064条数据 |
| **测试集划分** | 1016条数据 |
| **地理覆盖范围** | ZAF |
| **发布方** | 世界银行集团 |
| **HDX最后更新时间** | 2026-03-27 |

---

## 字段说明

**地理类字段** — `country_name`（国家名称：南非）、`country_iso3`（国家ISO3代码：ZAF）、`year`（年份范围：1960.0–2024.0）。
**结果/测量类字段** — `value`（数值范围：-10073565462.4808–59085564114.3656）。
**标识符/元数据字段** — `indicator_name`（指标名称：渔业总产量（公吨）、水产养殖产量（公吨）、捕捞渔业产量（公吨））、`indicator_code`（指标代码：ER.FSH.PROD.MT、ER.FSH.AQUA.MT、ER.FSH.CAPT.MT）、`esa_source`（数据来源：HDX）、`esa_processed`（数据处理时间：2026-04-10）。

---

## 快速入门

python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-world-bank-environment-indicators-for-south-africa")
train = ds["train"].to_pandas()
test = ds["test"].to_pandas()

print(train.shape)
train.head()

---

## 数据模式

| 列名 | 数据类型 | 空值占比 | 取值范围/示例值 |
|---|---|---|---|
| `country_name` | 字符串型（object） | 0.0% | 南非 |
| `country_iso3` | 字符串型 | 0.0% | ZAF |
| `year` | 64位整型（int64） | 0.0% | 1960.0 – 2024.0（均值：2000.1866） |
| `indicator_name` | 字符串型 | 0.0% | 渔业总产量（公吨）、水产养殖产量（公吨）、捕捞渔业产量（公吨） |
| `indicator_code` | 字符串型 | 0.0% | ER.FSH.PROD.MT、ER.FSH.AQUA.MT、ER.FSH.CAPT.MT |
| `value` | 64位浮点型（float64） | 0.0% | -10073565462.4808 – 59085564114.3656（均值：583921980.7946） |
| `esa_source` | 字符串型 | 0.0% | HDX |
| `esa_processed` | 字符串型 | 0.0% | 2026-04-10 |

---

## 数值型字段统计

| 字段 | 最小值 | 最大值 | 均值 | 中位数 |
|---|---|---|---|---|
| `year` | 1960.0 | 2024.0 | 2000.1866 | 2002.0 |
| `value` | -10073565462.4808 | 59085564114.3656 | 583921980.7946 | 14.7258 |

---

## 数据整理流程

原始数据通过CKAN API从HDX平台下载，并转换为Parquet格式。列名统一转换为小写并采用蛇形命名法（snake_case）进行标准化。常见缺失值标记（`N/A`、`null`、`none`、`-`、`unknown`、`no data`、`#N/A`）均被统一替换为`NaN`。本数据集以固定随机种子（42）按照80/20的比例划分为训练集与测试集，并以Snappy压缩格式保存为Parquet文件。

---

## 局限性说明

- 本数据集的数据源自世界银行集团，未由Electric Sheep Africa进行独立验证。
- 自动化清洗流程无法修正原始数据收集中的错报值、定义不一致或抽样偏差问题。
- 如需查看发布方提供的方法论说明与免责声明，请参阅[原始HDX数据集页面](https://data.humdata.org/dataset/world-bank-environment-indicators-for-south-africa)。

---

## 引用格式

bibtex
@dataset{hdx_africa_world_bank_environment_indicators_for_south_africa,
title = {南非——环境},
author = {世界银行集团},
year = {2026},
url = {https://data.humdata.org/dataset/world-bank-environment-indicators-for-south-africa},
note = {由Electric Sheep Africa重新打包以适配机器学习使用（https://huggingface.co/electricsheepafrica)}
}

---

*[Electric Sheep Africa](https://huggingface.co/electricsheepafrica) — 非洲的机器学习数据集基础设施。尼日利亚拉各斯。*