electricsheepafrica/africa-world-bank-financial-sector-indicators-for-somalia-fed-rep

--- 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 - som pretty_name: "Somalia, Fed. Rep. - Financial Sector" dataset_info: splits: - name: train num_examples: 888 - name: test num_examples: 222 --- # Somalia, Fed. Rep. - Financial Sector **Publisher:** World Bank Group · **Source:** [HDX](https://data.humdata.org/dataset/world-bank-financial-sector-indicators-for-somalia-fed-rep) · **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-somalia-fed-rep) 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: **SOM**. *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)** | 1,111 | | **Columns** | 8 (2 numeric, 6 categorical, 0 datetime) | | **Train split** | 888 rows | | **Test split** | 222 rows | | **Geographic scope** | SOM | | **Publisher** | World Bank Group | | **HDX last updated** | 2026-03-27 | --- ## Variables **Geographic** — `country_name` (Somalia, Fed. Rep.), `country_iso3` (SOM), `year` (range 1960.0–2025.0). **Outcome / Measurement** — `value` (range -231046067203.182–157986000000.0). **Identifier / Metadata** — `indicator_name` (Domestic credit to private sector (% of GDP), Net migration, DEC alternative conversion factor (LCU per US$)), `indicator_code` (SM.POP.NETM, PA.NUS.ATLS, BX.KLT.DINV.WD.GD.ZS), `esa_source` (HDX), `esa_processed` (2026-04-09). --- ## Quick Start ```python from datasets import load_dataset ds = load_dataset("electricsheepafrica/africa-world-bank-financial-sector-indicators-for-somalia-fed-rep") train = ds["train"].to_pandas() test = ds["test"].to_pandas() print(train.shape) train.head() ``` --- ## Schema | Column | Type | Null % | Range / Sample Values | |---|---|---|---| | `country_name` | object | 0.0% | Somalia, Fed. Rep. | | `country_iso3` | object | 0.0% | SOM | | `year` | int64 | 0.0% | 1960.0 – 2025.0 (mean 1987.4104) | | `indicator_name` | object | 0.0% | Domestic credit to private sector (% of GDP), Net migration, DEC alternative conversion factor (LCU per US$) | | `indicator_code` | object | 0.0% | SM.POP.NETM, PA.NUS.ATLS, BX.KLT.DINV.WD.GD.ZS | | `value` | float64 | 0.0% | -231046067203.182 – 157986000000.0 (mean 236094395.3325) | | `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 | 1987.4104 | 1983.0 | | `value` | -231046067203.182 | 157986000000.0 | 236094395.3325 | 10.907 | --- ## 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-somalia-fed-rep) for the publisher's own methodology notes and caveats. --- ## Citation ```bibtex @dataset{hdx_africa_world_bank_financial_sector_indicators_for_somalia_fed_rep, title = {Somalia, Fed. Rep. - Financial Sector}, author = {World Bank Group}, year = {2026}, url = {https://data.humdata.org/dataset/world-bank-financial-sector-indicators-for-somalia-fed-rep}, 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: - 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 - som pretty_name: "索马里联邦共和国 - 金融部门" dataset_info: splits: - name: train num_examples: 888 - name: test num_examples: 222 # 索马里联邦共和国 - 金融部门 **发布方**：世界银行集团 · **来源**：[HDX](https://data.humdata.org/dataset/world-bank-financial-sector-indicators-for-somalia-fed-rep) · **许可证**：`cc-by` · **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](http://data.worldbank.org/)的相关数据。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-somalia-fed-rep)。 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易获取的信息，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，以及国际移民与侨汇——二者均可对输出国与接收国的经济增长及社会福利产生影响。 本数据集的每一行均代表国家层面的汇总数据。HDX平台上的最新更新时间为2026-03-27。地理覆盖范围：**SOM**。 *本数据集已由[Electric Sheep Africa](https://huggingface.co/electricsheepafrica)整理为适用于机器学习的Parquet格式。* --- ## 数据集特征 | | | |---|---| | **领域** | 贫困与经济脆弱性 | | **观测单元** | 国家层面汇总数据 | | **总行数** | 1,111 | | **列数** | 8（2个数值型，6个分类型，0个日期时间型） | | **训练集划分** | 888行 | | **测试集划分** | 222行 | | **地理覆盖范围** | SOM | | **发布方** | 世界银行集团 | | **HDX最后更新时间** | 2026-03-27 | --- ## 变量 **地理类** — `country_name`（索马里联邦共和国）、`country_iso3`（SOM）、`year`（取值范围1960.0–2025.0）。 **结果/测量类** — `value`（取值范围-231046067203.182–157986000000.0）。 **标识符/元数据类** — `indicator_name`（私人部门国内信贷占GDP百分比、净移民量、DEC替代转换因子（当地货币单位/美元））、`indicator_code`（SM.POP.NETM、PA.NUS.ATLS、BX.KLT.DINV.WD.GD.ZS）、`esa_source`（HDX）、`esa_processed`（2026-04-09）。 --- ## 快速入门 python from datasets import load_dataset ds = load_dataset("electricsheepafrica/africa-world-bank-financial-sector-indicators-for-somalia-fed-rep") 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` | object | 0.0% | 索马里联邦共和国 | | `country_iso3` | object | 0.0% | SOM | | `year` | int64 | 0.0% | 1960.0 – 2025.0（均值1987.4104） | | `indicator_name` | object | 0.0% | 私人部门国内信贷占GDP百分比、净移民量、DEC替代转换因子（当地货币单位/美元） | | `indicator_code` | object | 0.0% | SM.POP.NETM、PA.NUS.ATLS、BX.KLT.DINV.WD.GD.ZS | | `value` | float64 | 0.0% | -231046067203.182 – 157986000000.0（均值236094395.3325） | | `esa_source` | object | 0.0% | HDX | | `esa_processed` | object | 0.0% | 2026-04-09 | --- ## 数值型统计量 | 列名 | 最小值 | 最大值 | 均值 | 中位数 | |---|---|---|---|---| | `year` | 1960.0 | 2025.0 | 1987.4104 | 1983.0 | | `value` | -231046067203.182 | 157986000000.0 | 236094395.3325 | 10.907 | --- ## 数据整理 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标准化为蛇形命名法。将常见的缺失值标记（`N/A`、`null`、`none`、`-`、`unknown`、`no data`、`#N/A`）统一替换为`NaN`。本数据集以固定随机种子（42）按80/20的比例划分为训练集与测试集，并保存为采用Snappy压缩的Parquet文件。 --- ## 局限性 - 本数据集源自世界银行集团，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过程中存在的错报值、定义不一致或抽样偏差问题。 - 如需查看发布方提供的官方方法论说明与免责条款，请参阅[HDX原始数据集页面](https://data.humdata.org/dataset/world-bank-financial-sector-indicators-for-somalia-fed-rep)。 --- ## 引用 bibtex @dataset{hdx_africa_world_bank_financial_sector_indicators_for_somalia_fed_rep, title = {索马里联邦共和国 - 金融部门}, author = {世界银行集团}, year = {2026}, url = {https://data.humdata.org/dataset/world-bank-financial-sector-indicators-for-somalia-fed-rep}, note = {由Electric Sheep Africa（https://huggingface.co/electricsheepafrica）重新打包以适配机器学习应用} } --- *[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。*