electricsheepafrica/africa-worldbank-financial-sector

该数据集是一个关于非洲国家金融部门发展的时序数据集，包含101,614个观测值，涵盖54个非洲国家，时间跨度为1960年至2025年，覆盖72个不同的金融部门指标。数据来源于世界银行的世界发展指标（WDI），这是全球发展数据最常引用的参考，编译了来自国民账户、政府机构和合作伙伴组织（如IMF、WHO、FAO、UNESCO、ILO、联合国机构）的官方认可国际统计数据。数据通过世界银行的公共API（api.worldbank.org/v2）获取，删除了没有观测值的记录，年份规范化为整数，值强制转换为float64。数据集包含以下列：indicator_id（世界银行WDI指标代码）、indicator_name（人类可读的指标名称）、country_iso2（ISO 3166-1 alpha-2国家代码）、country_iso3（ISO 3166-1 alpha-3国家代码，建议的连接键）、country_name（世界银行发布的英文国家名称）、year（观测年份，公元纪年）、value（观测到的指标值）、unit（测量单位，通常为空）、obs_status（世界银行观测状态标志，如初步、估计等）。数据集中每个（指标×国家×年份）组合不一定都存在，指标覆盖范围因国家和年份而异。该数据集由Electric Sheep Africa重新打包，旨在为非洲提供一个统一、机器学习就绪的数据层。This is a time-series dataset focused on financial sector development across African countries. It contains 101,614 observations, covering 54 African countries over the period from 1960 to 2025, and includes 72 distinct financial sector indicators. The dataset is sourced from the World Bank's World Development Indicators (WDI), the most frequently cited reference for global development data, which compiles officially recognized international statistical data from national accounts, government agencies, and partner organizations including the IMF, WHO, FAO, UNESCO, ILO, and United Nations bodies. Data was retrieved via the World Bank's public API (api.worldbank.org/v2), with records with no valid observations removed, years standardized to integer values, and all measurement values cast to float64 data type. The dataset includes the following columns: indicator_id (World Bank WDI indicator code), indicator_name (human-readable indicator name), country_iso2 (ISO 3166-1 alpha-2 country code), country_iso3 (ISO 3166-1 alpha-3 country code, recommended join key), country_name (English country name as published by the World Bank), year (observation year in Common Era), value (observed indicator value), unit (measurement unit, typically empty), and obs_status (World Bank observation status flag, e.g., preliminary, estimated, etc.). Not every (indicator × country × year) combination exists in the dataset, and the coverage of indicators varies across countries and years. This dataset was repackaged by Electric Sheep Africa, with the aim of providing a unified, machine learning-ready data layer for African research and development applications.