electricsheepafrica/africa-world-bank-energy-and-mining-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 - development - energy - indicators - zwe pretty_name: "Zimbabwe - Energy and Mining" dataset_info: splits: - name: train num_examples: 1112 - name: test num_examples: 278 --- # Zimbabwe - Energy and Mining **Publisher:** World Bank Group · **Source:** [HDX](https://data.humdata.org/dataset/world-bank-energy-and-mining-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. The world economy needs ever-increasing amounts of energy to sustain economic growth, raise living standards, and reduce poverty. But today's trends in energy use are not sustainable. As the world's population grows and economies become more industrialized, nonrenewable energy sources will become scarcer and more costly. Data here on energy production, use, dependency, and efficiency are compiled by the World Bank from the International Energy Agency and the Carbon Dioxide Information Analysis Center. 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** | Demographics and population | | **Unit of observation** | Country-level aggregates | | **Rows (total)** | 1,391 | | **Columns** | 8 (2 numeric, 6 categorical, 0 datetime) | | **Train split** | 1,112 rows | | **Test split** | 278 rows | | **Geographic scope** | ZWE | | **Publisher** | World Bank Group | | **HDX last updated** | 2026-03-27 | --- ## Variables **Geographic** — `country_name` (Zimbabwe), `country_iso3` (ZWE), `year` (range 1970.0–2024.0). **Outcome / Measurement** — `value` (range 0.0–1608360000.0). **Identifier / Metadata** — `indicator_name` (Adjusted savings: mineral depletion (current US$), Adjusted savings: mineral depletion (% of GNI), Total natural resources rents (% of GDP)), `indicator_code` (NY.ADJ.DMIN.CD, NY.ADJ.DMIN.GN.ZS, NY.GDP.TOTL.RT.ZS), `esa_source` (HDX), `esa_processed` (2026-04-10). --- ## Quick Start ```python from datasets import load_dataset ds = load_dataset("electricsheepafrica/africa-world-bank-energy-and-mining-indicators-for-zimbabwe") train = ds["train"].to_pandas() test = ds["test"].to_pandas() print(train.shape) train.head() ``` --- ## Schema | Column | Type | Null % | Range / Sample Values | |---|---|---|---| | `country_name` | object | 0.0% | Zimbabwe | | `country_iso3` | object | 0.0% | ZWE | | `year` | int64 | 0.0% | 1970.0 – 2024.0 (mean 2002.6794) | | `indicator_name` | object | 0.0% | Adjusted savings: mineral depletion (current US$), Adjusted savings: mineral depletion (% of GNI), Total natural resources rents (% of GDP) | | `indicator_code` | object | 0.0% | NY.ADJ.DMIN.CD, NY.ADJ.DMIN.GN.ZS, NY.GDP.TOTL.RT.ZS | | `value` | float64 | 0.0% | 0.0 – 1608360000.0 (mean 9671475.4676) | | `esa_source` | object | 0.0% | HDX | | `esa_processed` | object | 0.0% | 2026-04-10 | --- ## Numeric Summary | Column | Min | Max | Mean | Median | |---|---|---|---|---| | `year` | 1970.0 | 2024.0 | 2002.6794 | 2004.0 | | `value` | 0.0 | 1608360000.0 | 9671475.4676 | 10.9788 | --- ## 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-energy-and-mining-indicators-for-zimbabwe) for the publisher's own methodology notes and caveats. --- ## Citation ```bibtex @dataset{hdx_africa_world_bank_energy_and_mining_indicators_for_zimbabwe, title = {Zimbabwe - Energy and Mining}, author = {World Bank Group}, year = {2026}, url = {https://data.humdata.org/dataset/world-bank-energy-and-mining-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.*annotations_creators: - 无标注 language_creators: - 现有数据采集 language: - 英语 license: CC BY 4.0 (cc-by-4.0) multilinguality: - 单语言 size_categories: - 1000<n<10000 source_datasets: - 原创数据集 task_categories: - 表格回归（tabular-regression） task_ids: [] tags: - 非洲 - 人道主义 - HDX - Electric Sheep Africa - 发展 - 能源 - 指标 - ZWE pretty_name: "津巴布韦——能源与矿业" dataset_info: splits: - name: train num_examples: 1112 - name: test num_examples: 278 --- # 津巴布韦——能源与矿业 **发布方：世界银行集团（World Bank Group）** · **来源：[人道主义数据交换平台（HDX）](https://data.humdata.org/dataset/world-bank-energy-and-mining-indicators-for-zimbabwe)** · **许可证：`CC BY 4.0`** · **更新时间：2026-03-27** --- ## 摘要 本数据集包含来自世界银行[数据门户（World Bank Data Portal）](http://data.worldbank.org/)的相关数据，同时人道主义数据交换平台（HDX）上还提供了一份整合后的国家级数据集[《世界银行津巴布韦综合指标数据集》](https://data.humdata.org/dataset/world-bank-combined-indicators-for-zimbabwe)。 世界经济需要持续增长的能源供给以维持经济增长、提升生活水平并减少贫困，但当前的能源使用趋势并不具备可持续性。随着全球人口增长与经济工业化程度加深，不可再生能源将愈发稀缺且成本高企。本数据集收录的能源生产、使用、依赖度与效率相关数据，由世界银行整合自国际能源署（International Energy Agency, IEA）与二氧化碳信息分析中心（Carbon Dioxide Information Analysis Center, CDIAC）。 本数据集的每一行均代表国家层面的汇总数据。数据最近一次在HDX平台更新的时间为2026-03-27，地理覆盖范围：**ZWE（津巴布韦）**。 *本数据集已由[Electric Sheep Africa团队](https://huggingface.co/electricsheepafrica)整理为适配机器学习的Parquet格式。* --- ## 数据集特征 | | | |---|---| | **领域** | 人口与人口统计 | | **观测单元** | 国家层面汇总数据 | | **总行数** | 1391 | | **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** | 1112行 | | **测试集划分** | 278行 | | **地理覆盖范围** | ZWE（津巴布韦） | | **发布方** | 世界银行集团 | | **HDX平台最后更新时间** | 2026-03-27 | --- ## 变量说明 **地理特征字段**：`country_name`（国家名称：津巴布韦）、`country_iso3`（国家ISO3代码：ZWE）、`year`（年份：范围1970.0–2024.0）。 **结果/测量字段**：`value`（数值：范围0.0–1608360000.0）。 **标识符/元数据字段**：`indicator_name`（指标名称：调整后储蓄：矿产损耗（当前美元价）、调整后储蓄：矿产损耗（占国民总收入百分比）、自然资源总租金（占GDP百分比））、`indicator_code`（指标代码：NY.ADJ.DMIN.CD、NY.ADJ.DMIN.GN.ZS、NY.GDP.TOTL.RT.ZS）、`esa_source`（数据来源：HDX）、`esa_processed`（数据处理时间：2026-04-10）。 --- ## 快速上手 python from datasets import load_dataset ds = load_dataset("electricsheepafrica/africa-world-bank-energy-and-mining-indicators-for-zimbabwe") train = ds["train"].to_pandas() test = ds["test"].to_pandas() print(train.shape) train.head() --- ## 数据结构 | 列名 | 数据类型 | 空值占比 | 取值范围/示例值 | |---|---|---|---| | `country_name` | 字符型 | 0.0% | 津巴布韦 | | `country_iso3` | 字符型 | 0.0% | ZWE | | `year` | 整型 | 0.0% | 1970.0 – 2024.0（均值2002.6794） | | `indicator_name` | 字符型 | 0.0% | 调整后储蓄：矿产损耗（当前美元价）、调整后储蓄：矿产损耗（占国民总收入百分比）、自然资源总租金（占GDP百分比） | | `indicator_code` | 字符型 | 0.0% | NY.ADJ.DMIN.CD, NY.ADJ.DMIN.GN.ZS, NY.GDP.TOTL.RT.ZS | | `value` | 浮点型 | 0.0% | 0.0 – 1608360000.0（均值9671475.4676） | | `esa_source` | 字符型 | 0.0% | HDX | | `esa_processed` | 字符型 | 0.0% | 2026-04-10 | --- ## 数值型统计摘要 | 列名 | 最小值 | 最大值 | 均值 | 中位数 | |---|---|---|---|---| | `year` | 1970.0 | 2024.0 | 2002.6794 | 2004.0 | | `value` | 0.0 | 1608360000.0 | 9671475.4676 | 10.9788 | --- ## 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。列名均转换为小写并统一为蛇形命名法（snake_case）。常见的缺失值标记（`N/A`、`null`、`none`、`-`、`unknown`、`no data`、`#N/A`）均被统一替换为`NaN`。本数据集以80/20的比例划分为训练集与测试集，划分时使用固定随机种子（42），并以Snappy压缩的Parquet格式存储。 --- ## 局限性说明 - 本数据集源自世界银行集团，尚未由Electric Sheep Africa（ESA）进行独立验证。 - 自动化清洗流程无法修正原始数据集中的错报值、定义不一致或采样偏差问题。 - 请查阅[原始HDX数据集页面](https://data.humdata.org/dataset/world-bank-energy-and-mining-indicators-for-zimbabwe)以获取发布方提供的方法说明与免责条款。 --- ## 引用格式 bibtex @dataset{hdx_africa_world_bank_energy_and_mining_indicators_for_zimbabwe, title = {Zimbabwe - Energy and Mining}, author = {World Bank Group}, year = {2026}, url = {https://data.humdata.org/dataset/world-bank-energy-and-mining-indicators-for-zimbabwe}, note = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)} } --- *[Electric Sheep Africa团队](https://huggingface.co/electricsheepafrica)——非洲机器学习数据集基础设施提供商，尼日利亚拉各斯。*