electricsheepafrica/africa-unesco-data-for-guinea-bissau

---
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
- tabular-regression
task_ids: []
tags:
- africa
- humanitarian
- hdx
- electric-sheep-africa
- demographics
- education
- indicators
- socioeconomics
- sustainable-development
- sustainable-development-goals-sdg
- gnb
pretty_name: "Guinea-Bissau - Education Indicators"
dataset_info:
splits:
- name: train
num_examples: 2224
- name: test
num_examples: 556
---

# Guinea-Bissau - Education Indicators

**Publisher:** UNESCO · **Source:** [HDX](https://data.humdata.org/dataset/unesco-data-for-guinea-bissau) · **License:** `cc-by-igo` · **Updated:** 2026-03-02

---

## Abstract

Education indicators for Guinea-Bissau.

Contains data from the UNESCO Institute for Statistics [bulk data service](http://data.uis.unesco.org) covering the following categories: SDG 4 Global and Thematic (made 2026 February), Other Policy Relevant Indicators (made 2026 February), Demographic and Socio-economic (made 2026 February)

Each row in this dataset represents country-level aggregates. Data was last updated on HDX on 2026-03-02. Geographic scope: **GNB**.

*Curated into ML-ready Parquet format by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica).*

---

## Dataset Characteristics

| | |
|---|---|
| **Domain** | Education |
| **Unit of observation** | Country-level aggregates |
| **Rows (total)** | 2,780 |
| **Columns** | 6 (2 numeric, 4 categorical, 0 datetime) |
| **Train split** | 2,224 rows |
| **Test split** | 556 rows |
| **Geographic scope** | GNB |
| **Publisher** | UNESCO |
| **HDX last updated** | 2026-03-02 |

---

## Variables

**Geographic** — `country_id` (GNB), `year` (range 1976.0–2025.0).

**Outcome / Measurement** — `value` (range 0.0–3520028.0).

**Identifier / Metadata** — `indicator_id` (CR.MOD.1.F, CR.MOD.1, CR.MOD.1.GPIA), `esa_source` (HDX), `esa_processed` (2026-04-04).

---

## Quick Start

```python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-unesco-data-for-guinea-bissau")
train = ds["train"].to_pandas()
test = ds["test"].to_pandas()

print(train.shape)
train.head()
```

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `indicator_id` | object | 0.0% | CR.MOD.1.F, CR.MOD.1, CR.MOD.1.GPIA |
| `country_id` | object | 0.0% | GNB |
| `year` | int64 | 0.0% | 1976.0 – 2025.0 (mean 2010.477) |
| `value` | float64 | 0.0% | 0.0 – 3520028.0 (mean 9243.7181) |
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% | 2026-04-04 |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `year` | 1976.0 | 2025.0 | 2010.477 | 2014.0 |
| `value` | 0.0 | 3520028.0 | 9243.7181 | 8.2583 |

---

## 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`. 2 column(s) with >80% missing values were removed: `magnitude`, `qualifier`. 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 UNESCO 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/unesco-data-for-guinea-bissau) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_unesco_data_for_guinea_bissau,
title = {Guinea-Bissau - Education Indicators},
author = {UNESCO},
year = {2026},
url = {https://data.humdata.org/dataset/unesco-data-for-guinea-bissau},
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: 注释生成者：无注释
language_creators: 语言来源：现有资源采集
language: 语言：英语（en）
license: 许可协议：CC-BY-4.0
multilinguality: 多语言属性：单语言
size_categories: 数据规模：1000<n<10000条样本
source_datasets: 源数据集：原生数据集
task_categories: 任务类别：表格分类、表格回归
task_ids: 任务子类别：无
tags: 标签：非洲、人道主义、HDX（Humanitarian Data Exchange，人道主义数据交换）、电羊非洲（Electric Sheep Africa）、人口统计学、教育、指标、社会经济学、可持续发展、可持续发展目标（Sustainable Development Goals, SDG）、GNB（几内亚比绍国家代码）
pretty_name: "几内亚比绍——教育指标数据集"
dataset_info: 数据集信息
splits:
- name: 训练集（train）
num_examples: 2224
- name: 测试集（test）
num_examples: 556

# 几内亚比绍——教育指标数据集

**发布方**：联合国教科文组织（UNESCO） · **来源**：[HDX（Humanitarian Data Exchange，人道主义数据交换）](https://data.humdata.org/dataset/unesco-data-for-guinea-bissau) · **许可协议**：`cc-by-igo` · **最后更新时间**：2026-03-02

---

## 摘要

本数据集收录几内亚比绍的教育指标数据。
数据源自联合国教科文组织统计研究所（UNESCO Institute for Statistics）的[批量数据服务](http://data.uis.unesco.org)，涵盖以下类别：2026年2月编制的可持续发展目标4（SDG 4）全球与主题指标、2026年2月编制的其他政策相关指标、2026年2月编制的人口与社会经济指标。
本数据集的每一行均代表国家级汇总数据。数据最后于2026年3月2日在HDX平台更新。地理覆盖范围：**GNB（几内亚比绍国家代码）**。
*本数据集由[电羊非洲（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

| | |
|---|---|
| **领域** | 教育 |
| **观测单元** | 国家级汇总数据 |
| **总行数** | 2780 |
| **列数** | 6（2个数值型列，4个分类型列，0个日期型列） |
| **训练集样本数** | 2224 |
| **测试集样本数** | 556 |
| **地理覆盖范围** | GNB（几内亚比绍国家代码） |
| **发布方** | 联合国教科文组织（UNESCO） |
| **HDX平台最后更新时间** | 2026-03-02 |

---

## 变量说明

**地理类变量**：`country_id`（国家代码，取值为GNB）、`year`（年份范围：1976.0–2025.0）。
**结果/测量类变量**：`value`（指标数值范围：0.0–3520028.0）。
**标识符/元数据类变量**：`indicator_id`（指标ID，可选值为CR.MOD.1.F、CR.MOD.1、CR.MOD.1.GPIA）、`esa_source`（数据来源，取值为HDX）、`esa_processed`（数据处理时间，取值为2026-04-04）。

---

## 快速上手

python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-unesco-data-for-guinea-bissau")
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 |
|---|---|---|---|
| `indicator_id` | 字符串（object） | 0.0% | CR.MOD.1.F、CR.MOD.1、CR.MOD.1.GPIA |
| `country_id` | 字符串（object） | 0.0% | GNB |
| `year` | 64位整型（int64） | 0.0% | 1976.0 – 2025.0（平均值：2010.477） |
| `value` | 64位浮点型（float64） | 0.0% | 0.0 – 3520028.0（平均值：9243.7181） |
| `esa_source` | 字符串（object） | 0.0% | HDX |
| `esa_processed` | 字符串（object） | 0.0% | 2026-04-04 |

---

## 数值型变量统计摘要

| 列名 | 最小值 | 最大值 | 平均值 | 中位数 |
|---|---|---|---|---|
| `year` | 1976.0 | 2025.0 | 2010.477 | 2014.0 |
| `value` | 0.0 | 3520028.0 | 9243.7181 | 8.2583 |

---

## 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`。移除了2个缺失值占比超过80%的列：`magnitude`（量级）与`qualifier`（限定符）。采用固定随机种子（42）将数据集按80/20的比例划分为训练集与测试集，并保存为Snappy压缩的Parquet格式文件。

---

## 数据集局限性

- 数据源自联合国教科文组织，未经电羊非洲（Electric Sheep Africa）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。
- 如需查看发布方的方法说明与免责声明，请参阅[原始HDX数据集页面](https://data.humdata.org/dataset/unesco-data-for-guinea-bissau)。

---

## 引用格式

bibtex
@dataset{hdx_africa_unesco_data_for_guinea_bissau,
title = {Guinea-Bissau - Education Indicators},
author = {UNESCO},
year = {2026},
url = {https://data.humdata.org/dataset/unesco-data-for-guinea-bissau},
note = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}

---

*[电羊非洲（Electric Sheep Africa）](https://huggingface.co/electricsheepafrica)——非洲机器学习数据集基础设施，尼日利亚拉各斯。*