electricsheepafrica/africa-world-bank-financial-sector-indicators-for-cabo-verde

--- 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 - other task_ids: [] tags: - africa - humanitarian - hdx - electric-sheep-africa - economics - indicators - cpv pretty_name: "Cabo Verde - Financial Sector" dataset_info: splits: - name: train num_examples: 2355 - name: test num_examples: 588 --- # Cabo Verde - Financial Sector **Publisher:** World Bank Group · **Source:** [HDX](https://data.humdata.org/dataset/world-bank-financial-sector-indicators-for-cabo-verde) · **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-cabo-verde) on HDX. An economy's financial markets are critical to its overall development. Banking systems and stock markets enhance growth, the main factor in poverty reduction. Strong financial systems provide reliable and accessible information that lowers transaction costs, which in turn bolsters resource allocation and economic growth. Indicators here include the size and liquidity of stock markets; the accessibility, stability, and efficiency of financial systems; and international migration and workers\ remittances, which affect growth and social welfare in both sending and receiving countries. Each row in this dataset represents country-level aggregates. Data was last updated on HDX on 2026-03-27. Geographic scope: **CPV**. *Curated into ML-ready Parquet format by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica).* --- ## Dataset Characteristics | | | |---|---| | **Domain** | Poverty and economic vulnerability | | **Unit of observation** | Country-level aggregates | | **Rows (total)** | 2,944 | | **Columns** | 8 (2 numeric, 6 categorical, 0 datetime) | | **Train split** | 2,355 rows | | **Test split** | 588 rows | | **Geographic scope** | CPV | | **Publisher** | World Bank Group | | **HDX last updated** | 2026-03-27 | --- ## Variables **Geographic** — `country_name` (Cabo Verde), `country_iso3` (CPV), `year` (range 1960.0–2025.0). **Outcome / Measurement** — `value` (range -213735822.3808–239417086081.53). **Identifier / Metadata** — `indicator_name` (Domestic credit to private sector (% of GDP), Net migration, Official exchange rate (LCU per US$, period average)), `indicator_code` (SM.POP.NETM, PA.NUS.FCRF, PA.NUS.ATLS), `esa_source` (HDX), `esa_processed` (2026-04-17). --- ## Quick Start ```python from datasets import load_dataset ds = load_dataset("electricsheepafrica/africa-world-bank-financial-sector-indicators-for-cabo-verde") train = ds["train"].to_pandas() test = ds["test"].to_pandas() print(train.shape) train.head() ``` --- ## Schema | Column | Type | Null % | Range / Sample Values | |---|---|---|---| | `country_name` | object | 0.0% | Cabo Verde | | `country_iso3` | object | 0.0% | CPV | | `year` | int64 | 0.0% | 1960.0 – 2025.0 (mean 2003.0774) | | `indicator_name` | object | 0.0% | Domestic credit to private sector (% of GDP), Net migration, Official exchange rate (LCU per US$, period average) | | `indicator_code` | object | 0.0% | SM.POP.NETM, PA.NUS.FCRF, PA.NUS.ATLS | | `value` | float64 | 0.0% | -213735822.3808 – 239417086081.53 (mean 2625013576.0182) | | `esa_source` | object | 0.0% | HDX | | `esa_processed` | object | 0.0% | 2026-04-17 | --- ## Numeric Summary | Column | Min | Max | Mean | Median | |---|---|---|---|---| | `year` | 1960.0 | 2025.0 | 2003.0774 | 2006.0 | | `value` | -213735822.3808 | 239417086081.53 | 2625013576.0182 | 28.75 | --- ## 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-financial-sector-indicators-for-cabo-verde) for the publisher's own methodology notes and caveats. --- ## Citation ```bibtex @dataset{hdx_africa_world_bank_financial_sector_indicators_for_cabo_verde, title = {Cabo Verde - Financial Sector}, author = {World Bank Group}, year = {2026}, url = {https://data.humdata.org/dataset/world-bank-financial-sector-indicators-for-cabo-verde}, 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: - 采集所得 language: - 英语 license: 知识共享署名4.0协议（CC BY 4.0） multilinguality: - 单语种（monolingual） size_categories: - 1000条＜数据量＜10000条 source_datasets: - 原创数据集 task_categories: - 表格回归（tabular-regression） - 其他 task_ids: [] tags: - 非洲 - 人道主义 - 人道主义数据交换（Humanitarian Data Exchange，HDX） - Electric Sheep Africa - 经济学 - 指标 - CPV pretty_name: "佛得角——金融领域" dataset_info: splits: - name: train num_examples: 2355 - name: test num_examples: 588 # 佛得角——金融领域 **发布方：** 世界银行集团 · **来源：** [人道主义数据交换（Humanitarian Data Exchange，HDX）](https://data.humdata.org/dataset/world-bank-financial-sector-indicators-for-cabo-verde) · **许可证：** `cc-by` · **最后更新：** 2026-03-27 --- ## 数据集摘要 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。人道主义数据交换（HDX）平台上还提供了一份[整合后的国家数据集](https://data.humdata.org/dataset/world-bank-combined-indicators-for-cabo-verde)。 一个经济体的金融市场对其整体发展至关重要。银行体系与股票市场能够促进增长——这是减贫的核心驱动因素。健全的金融体系可提供可靠且可获取的信息，降低交易成本，进而优化资源配置并推动经济增长。本数据集收录的指标包括：股票市场的规模与流动性、金融体系的可及性、稳定性与效率，以及影响移出国与移入国经济增长与社会福利的国际移民及劳工汇款数据。 本数据集的每一行均代表国家层面的汇总统计数据。该数据集在HDX平台的最后更新时间为2026-03-27。地理覆盖范围：**CPV**。 *本数据集已由[Electric Sheep Africa](https://huggingface.co/electricsheepafrica)整理为适配机器学习的Parquet格式。* --- ## 数据集特征 | | | |---|---| | **研究领域** | 贫困与经济脆弱性 | | **观测单元** | 国家层面汇总数据 | | **总数据行数** | 2944 | | **总列数** | 8（其中数值型列2个、分类型列6个、日期时间型列0个） | | **训练集划分** | 2355行 | | **测试集划分** | 588行 | | **地理覆盖范围** | CPV | | **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 **地理类变量** — `country_name`（佛得角（Cabo Verde））、`country_iso3`（CPV）、`year`（取值范围：1960.0–2025.0）。 **结果/测量类变量** — `value`（取值范围：-213735822.3808–239417086081.53）。 **标识符/元数据类变量** — `indicator_name`（私人部门国内信贷占GDP百分比、净移民数、官方汇率（本地货币兑美元，当期平均））、`indicator_code`（SM.POP.NETM、PA.NUS.FCRF、PA.NUS.ATLS）、`esa_source`（HDX）、`esa_processed`（2026-04-17）。 --- ## 快速使用指南 python from datasets import load_dataset ds = load_dataset("electricsheepafrica/africa-world-bank-financial-sector-indicators-for-cabo-verde") train = ds["train"].to_pandas() test = ds["test"].to_pandas() print(train.shape) train.head() --- ## 数据架构 | 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% | 佛得角 | | `country_iso3` | 字符串型（object） | 0.0% | CPV | | `year` | 64位整型（int64） | 0.0% | 1960.0 – 2025.0（均值2003.0774） | | `indicator_name` | 字符串型（object） | 0.0% | 私人部门国内信贷占GDP百分比、净移民数、官方汇率（本地货币兑美元，当期平均） | | `indicator_code` | 字符串型（object） | 0.0% | SM.POP.NETM、PA.NUS.FCRF、PA.NUS.ATLS | | `value` | 64位浮点型（float64） | 0.0% | -213735822.3808 – 239417086081.53（均值2625013576.0182） | | `esa_source` | 字符串型（object） | 0.0% | HDX | | `esa_processed` | 字符串型（object） | 0.0% | 2026-04-17 | --- ## 数值型变量统计摘要 | 列名 | 最小值 | 最大值 | 均值 | 中位数 | |---|---|---|---|---| | `year` | 1960.0 | 2025.0 | 2003.0774 | 2006.0 | | `value` | -213735822.3808 | 239417086081.53 | 2625013576.0182 | 28.75 | --- ## 数据整理流程 原始数据通过CKAN应用程序编程接口（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。将常见的缺失值标记（`N/A`、`null`、`none`、`-`、`unknown`、`no data`、`#N/A`）统一替换为`NaN`。本数据集采用固定随机种子（42）按照80/20的比例划分为训练集与测试集，并以Snappy压缩格式的Parquet文件进行存储。 --- ## 数据集局限性 - 本数据集的原始数据来自世界银行集团，Electric Sheep Africa未对其进行独立验证。 - 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-financial-sector-indicators-for-cabo-verde)。 --- ## 引用格式 bibtex @dataset{hdx_africa_world_bank_financial_sector_indicators_for_cabo_verde, title = {佛得角——金融领域}, author = {世界银行集团}, year = {2026}, url = {https://data.humdata.org/dataset/world-bank-financial-sector-indicators-for-cabo-verde}, note = {由Electric Sheep Africa（https://huggingface.co/electricsheepafrica）重新打包以适配机器学习场景} } --- *[Electric Sheep Africa](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。*