electricsheepafrica/africa-world-bank-public-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-classification
task_ids: []
tags:
- africa
- humanitarian
- hdx
- electric-sheep-africa
- economics
- indicators
- som
pretty_name: "Somalia, Fed. Rep. - Public Sector"
dataset_info:
splits:
- name: train
num_examples: 1365
- name: test
num_examples: 341
---

# Somalia, Fed. Rep. - Public Sector

**Publisher:** World Bank Group · **Source:** [HDX](https://data.humdata.org/dataset/world-bank-public-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.

Effective governments improve people's standard of living by ensuring access to essential services – health, education, water and sanitation, electricity, transport – and the opportunity to live and work in peace and security. Data here includes World Bank staff assessments of country performance in economic management, structural policies, policies for social inclusion and equity, and public sector management and institutions for the poorest countries. Also included are indicators on revenues and expenses from the International Monetary Fund's Government Finance Statistics, and on tax policies from various sources.

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** | Public health |
| **Unit of observation** | Country-level aggregates |
| **Rows (total)** | 1,707 |
| **Columns** | 8 (2 numeric, 6 categorical, 0 datetime) |
| **Train split** | 1,365 rows |
| **Test split** | 341 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 1960.0–2024.0).

**Outcome / Measurement** — `value` (range -1699483737.0–4486120000.0).

**Identifier / Metadata** — `indicator_name` (Military expenditure (current LCU), Military expenditure (current USD), Military expenditure (% of GDP)), `indicator_code` (MS.MIL.XPND.CN, MS.MIL.XPND.CD, MS.MIL.XPND.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-public-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 – 2024.0 (mean 2011.3538) |
| `indicator_name` | object | 0.0% | Military expenditure (current LCU), Military expenditure (current USD), Military expenditure (% of GDP) |
| `indicator_code` | object | 0.0% | MS.MIL.XPND.CN, MS.MIL.XPND.CD, MS.MIL.XPND.GD.ZS |
| `value` | float64 | 0.0% | -1699483737.0 – 4486120000.0 (mean 19102045.2528) |
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% | 2026-04-09 |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `year` | 1960.0 | 2024.0 | 2011.3538 | 2016.0 |
| `value` | -1699483737.0 | 4486120000.0 | 19102045.2528 | 2.0 |

---

## 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-public-sector-indicators-for-somalia-fed-rep) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_world_bank_public_sector_indicators_for_somalia_fed_rep,
title = {Somalia, Fed. Rep. - Public Sector},
author = {World Bank Group},
year = {2026},
url = {https://data.humdata.org/dataset/world-bank-public-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: 注释生成方式：无注释
language_creators: 语言获取方式：现有资源采集
language: 语言：英语（en）
license: 许可证：CC BY 4.0
multilinguality: 多语言属性：单语言
size_categories: 样本规模区间：1000 < 样本数 < 10000
source_datasets: 源数据集：原创数据集
task_categories: 任务类别：表格分类
task_ids: 任务子项：无
tags: 标签：非洲、人道主义、人道主义数据交换平台（HDX）、Electric Sheep Africa、经济学、指标、索马里（SOM）
pretty_name: "索马里联邦共和国——公共部门"
dataset_info:
splits:
- name: train
num_examples: 1365
- name: test
num_examples: 341

# 索马里联邦共和国——公共部门

**发布方**：世界银行集团 · **数据源**：[HDX](https://data.humdata.org/dataset/world-bank-public-sector-indicators-for-somalia-fed-rep) · **许可证**：`cc-by` · **更新时间**：2026-03-27

---

## 摘要
本数据集包含源自世界银行[数据门户](http://data.worldbank.org/)的相关数据。HDX平台上还提供了[整合版国家数据集](https://data.humdata.org/dataset/world-bank-combined-indicators-for-somalia-fed-rep)。

高效政府通过保障民众获取医疗、教育、水卫生、电力、交通等基础服务，以及提供和平安全的生活与工作环境，来提升人民生活水平。本数据集收录的内容包括世界银行工作人员对最不发达国家在经济管理、结构性政策、社会包容与公平政策、公共部门管理与机构建设方面的绩效评估。同时纳入了国际货币基金组织政府财政统计中的收支指标，以及多渠道来源的税收政策相关数据。

本数据集每一行代表国家级汇总数据。本数据集最近一次在HDX平台的更新时间为2026-03-27。地理覆盖范围：**SOM（索马里）**。

*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** | 公共卫生 |
| **观测单元** | 国家级汇总数据 |
| **总行数** | 1707 |
| **列数** | 8（2个数值型、6个分类型、0个日期时间型） |
| **训练集拆分** | 1365行 |
| **测试集拆分** | 341行 |
| **地理覆盖范围** | SOM |
| **发布方** | 世界银行集团 |
| **HDX平台最后更新时间** | 2026-03-27 |

---

## 变量
**地理类变量** — `country_name`（索马里联邦共和国）、`country_iso3`（SOM）、`year`（取值范围1960.0–2024.0）。

**结果/测量类变量** — `value`（取值范围-1699483737.0–4486120000.0）。

**标识符/元数据类变量** — `indicator_name`（如：以当前当地货币计价的军事支出、以当前美元计价的军事支出、军事支出占GDP百分比）、`indicator_code`（MS.MIL.XPND.CN、MS.MIL.XPND.CD、MS.MIL.XPND.GD.ZS）、`esa_source`（HDX）、`esa_processed`（2026-04-09）。

---

## 快速上手
python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-world-bank-public-sector-indicators-for-somalia-fed-rep")
train = ds["train"].to_pandas()
test = ds["test"].to_pandas()

print(train.shape)
train.head()

---

## 数据模式
| 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% | 索马里联邦共和国 |
| `country_iso3` | 字符串型 | 0.0% | SOM |
| `year` | 64位整型（int64） | 0.0% | 1960.0 – 2024.0（均值2011.3538） |
| `indicator_name` | 字符串型 | 0.0% | 以当前当地货币计价的军事支出、以当前美元计价的军事支出、军事支出占GDP百分比 |
| `indicator_code` | 字符串型 | 0.0% | MS.MIL.XPND.CN、MS.MIL.XPND.CD、MS.MIL.XPND.GD.ZS |
| `value` | 64位浮点型（float64） | 0.0% | -1699483737.0 – 4486120000.0（均值19102045.2528） |
| `esa_source` | 字符串型 | 0.0% | HDX |
| `esa_processed` | 字符串型 | 0.0% | 2026-04-09 |

---

## 数值统计摘要
| 列名 | 最小值 | 最大值 | 均值 | 中位数 |
|---|---|---|---|---|
| `year` | 1960.0 | 2024.0 | 2011.3538 | 2016.0 |
| `value` | -1699483737.0 | 4486120000.0 | 19102045.2528 | 2.0 |

---

## 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文件保存。

---

## 局限性说明
- 本数据集源自世界银行集团，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阶段的错报值、定义不一致或采样偏差问题。
- 如需查看发布方提供的官方方法论说明与免责声明，请参阅[原始HDX数据集页面](https://data.humdata.org/dataset/world-bank-public-sector-indicators-for-somalia-fed-rep)。

---

## 引用格式
bibtex
@dataset{hdx_africa_world_bank_public_sector_indicators_for_somalia_fed_rep,
title = {Somalia, Fed. Rep. - Public Sector},
author = {World Bank Group},
year = {2026},
url = {https://data.humdata.org/dataset/world-bank-public-sector-indicators-for-somalia-fed-rep},
note = {由Electric Sheep Africa重新打包以适配机器学习应用（https://huggingface.co/electricsheepafrica)}
}

---

*[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。*