electricsheepafrica/africa-world-bank-financial-sector-indicators-for-kenya

--- 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 - ken pretty_name: "Kenya - Financial Sector" dataset_info: splits: - name: train num_examples: 3540 - name: test num_examples: 885 --- # Kenya - Financial Sector **Publisher:** World Bank Group · **Source:** [HDX](https://data.humdata.org/dataset/world-bank-financial-sector-indicators-for-kenya) · **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-kenya) 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: **KEN**. *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)** | 4,426 | | **Columns** | 8 (2 numeric, 6 categorical, 0 datetime) | | **Train split** | 3,540 rows | | **Test split** | 885 rows | | **Geographic scope** | KEN | | **Publisher** | World Bank Group | | **HDX last updated** | 2026-03-27 | --- ## Variables **Geographic** — `country_name` (Kenya), `country_iso3` (KEN), `year` (range 1960.0–2025.0). **Outcome / Measurement** — `value` (range -8034274946.6296–7149144037202.66). **Identifier / Metadata** — `indicator_name` (Domestic credit to private sector (% of GDP), Net migration, Official exchange rate (LCU per US$, period average)), `indicator_code` (SM.POP.NETM, PA.NUS.FCRF, PA.NUS.ATLS), `esa_source` (HDX), `esa_processed` (2026-04-09). --- ## Quick Start ```python from datasets import load_dataset ds = load_dataset("electricsheepafrica/africa-world-bank-financial-sector-indicators-for-kenya") train = ds["train"].to_pandas() test = ds["test"].to_pandas() print(train.shape) train.head() ``` --- ## Schema | Column | Type | Null % | Range / Sample Values | |---|---|---|---| | `country_name` | object | 0.0% | Kenya | | `country_iso3` | object | 0.0% | KEN | | `year` | int64 | 0.0% | 1960.0 – 2025.0 (mean 1999.5861) | | `indicator_name` | object | 0.0% | Domestic credit to private sector (% of GDP), Net migration, Official exchange rate (LCU per US$, period average) | | `indicator_code` | object | 0.0% | SM.POP.NETM, PA.NUS.FCRF, PA.NUS.ATLS | | `value` | float64 | 0.0% | -8034274946.6296 – 7149144037202.66 (mean 28692905922.2631) | | `esa_source` | object | 0.0% | HDX | | `esa_processed` | object | 0.0% | 2026-04-09 | --- ## Numeric Summary | Column | Min | Max | Mean | Median | |---|---|---|---|---| | `year` | 1960.0 | 2025.0 | 1999.5861 | 2003.0 | | `value` | -8034274946.6296 | 7149144037202.66 | 28692905922.2631 | 17.7959 | --- ## 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-kenya) for the publisher's own methodology notes and caveats. --- ## Citation ```bibtex @dataset{hdx_africa_world_bank_financial_sector_indicators_for_kenya, title = {Kenya - Financial Sector}, author = {World Bank Group}, year = {2026}, url = {https://data.humdata.org/dataset/world-bank-financial-sector-indicators-for-kenya}, 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（no-annotation） language_creators: - 抓取生成（found） language: - 英语（en） license: CC BY 4.0 multilinguality: - 单语言（monolingual） size_categories: - 1000 < 样本数 < 10000 source_datasets: - 原生数据集（original） task_categories: - 表格回归（tabular-regression） - 其他（other） task_ids: [] tags: - 非洲 - 人道主义 - HDX（Humanitarian Data Exchange） - Electric Sheep Africa - 经济学 - 指标 - 肯尼亚（KEN） pretty_name: "肯尼亚——金融领域" dataset_info: splits: - name: 训练集（train） num_examples: 3540 - name: 测试集（test） num_examples: 885 # 肯尼亚——金融领域数据集 **发布方：世界银行集团（World Bank Group） · 数据源：[HDX（Humanitarian Data Exchange）](https://data.humdata.org/dataset/world-bank-financial-sector-indicators-for-kenya) · 授权协议：`cc-by` · 最后更新：2026-03-27** --- ## 摘要 本数据集包含世界银行[数据门户](http://data.worldbank.org/)公开的相关数据。HDX平台上另有一份整合后的肯尼亚国家数据集[合并国家数据集](https://data.humdata.org/dataset/world-bank-combined-indicators-for-kenya)。 一国金融市场对其整体发展至关重要。银行体系与股票市场可推动经济增长，而经济增长是减贫的核心驱动因素。健全的金融体系能够提供可靠且可及的信息，降低交易成本，进而优化资源配置并促进经济增长。本数据集收录的指标涵盖股票市场规模与流动性、金融体系的可及性、稳定性与效率，以及影响输出国与接收国经济增长及社会福利的国际移民与劳工侨汇。 本数据集的每一行均代表国家级汇总数据。数据最后更新于HDX平台的时间为2026年3月27日，地理覆盖范围为肯尼亚（KEN）。 *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（Parquet）。* --- ## 数据集特征 | | | |---|---| | **领域** | 贫困与经济脆弱性 | | **观测单元** | 国家级汇总数据 | | **总样本行数** | 4426 | | **字段数** | 8个（2个数值型、6个分类型、0个日期时间型） | | **训练集划分** | 3540行 | | **测试集划分** | 885行 | | **地理覆盖范围** | 肯尼亚（KEN） | | **发布方** | 世界银行集团 | | **HDX平台最后更新时间** | 2026年3月27日 | --- ## 变量说明 **地理类变量**：`country_name`（国家名称：肯尼亚）、`country_iso3`（国家ISO3代码：KEN）、`year`（年份范围：1960.0至2025.0）。 **结果/测度类变量**：`value`（指标数值，取值范围：-8034274946.6296 至 7149144037202.66）。 **标识/元数据类变量**：`indicator_name`（指标名称：私人部门国内信贷占GDP比重、净移民数、官方汇率（当期平均本地货币兑美元汇率））、`indicator_code`（指标代码：SM.POP.NETM、PA.NUS.FCRF、PA.NUS.ATLS）、`esa_source`（数据来源：HDX）、`esa_processed`（数据处理时间：2026-04-09）。 --- ## 快速上手 python from datasets import load_dataset ds = load_dataset("electricsheepafrica/africa-world-bank-financial-sector-indicators-for-kenya") train = ds["train"].to_pandas() test = ds["test"].to_pandas() print(train.shape) train.head() --- ## 数据Schema | 字段名 | 数据类型 | 空值占比 | 取值范围/示例值 | |---|---|---|---| | `country_name` | 字符型（object） | 0.0% | 肯尼亚（Kenya） | | `country_iso3` | 字符型（object） | 0.0% | KEN | | `year` | int64 | 0.0% | 1960.0 – 2025.0（均值：1999.5861） | | `indicator_name` | 字符型（object） | 0.0% | 私人部门国内信贷占GDP比重、净移民数、官方汇率（当期平均本地货币兑美元汇率） | | `indicator_code` | 字符型（object） | 0.0% | SM.POP.NETM, PA.NUS.FCRF, PA.NUS.ATLS | | `value` | 浮点型（float64） | 0.0% | -8034274946.6296 – 7149144037202.66（均值：28692905922.2631） | | `esa_source` | 字符型（object） | 0.0% | HDX | | `esa_processed` | 字符型（object） | 0.0% | 2026-04-09 | --- ## 数值型变量统计摘要 | 字段名 | 最小值 | 最大值 | 均值 | 中位数 | |---|---|---|---|---| | `year` | 1960.0 | 2025.0 | 1999.5861 | 2003.0 | | `value` | -8034274946.6296 | 7149144037202.66 | 28692905922.2631 | 17.7959 | --- ## 数据整理流程 原始数据通过CKAN应用程序编程接口（CKAN API）从HDX平台下载，并转换为Parquet格式（Parquet）。字段名称统一转换为小写并采用蛇形命名法（snake_case）进行标准化。常见的缺失值标记（`N/A`、`null`、`none`、`-`、`unknown`、`no data`、`#N/A`）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，划分过程使用固定随机种子（42），最终以Snappy压缩的Parquet格式存储。 --- ## 数据集局限性 - 本数据集源自世界银行集团，未由Electric Sheep Africa（ESA）进行独立验证。 - 自动化数据清洗无法修正原始数据集中的错报值、定义不一致问题或采样偏差。 - 有关发布方的方法论说明与免责条款，请参阅[原始HDX数据集页面](https://data.humdata.org/dataset/world-bank-financial-sector-indicators-for-kenya)。 --- ## 引用格式 bibtex @dataset{hdx_africa_world_bank_financial_sector_indicators_for_kenya, title = {Kenya - Financial Sector}, author = {World Bank Group}, year = {2026}, url = {https://data.humdata.org/dataset/world-bank-financial-sector-indicators-for-kenya}, note = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)} } --- *[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。*