electricsheepafrica/africa-world-bank-private-sector-indicators-for-botswana

---
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
task_ids: []
tags:
- africa
- humanitarian
- hdx
- electric-sheep-africa
- economics
- indicators
- bwa
pretty_name: "Botswana - Private Sector"
dataset_info:
splits:
- name: train
num_examples: 2276
- name: test
num_examples: 569
---

# Botswana - Private Sector

**Publisher:** World Bank Group · **Source:** [HDX](https://data.humdata.org/dataset/world-bank-private-sector-indicators-for-botswana) · **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-botswana) on HDX.

Private markets drive economic growth, tapping initiative and investment to create productive jobs and raise incomes. Trade is also a driver of economic growth as it integrates developing countries into the world economy and generates benefits for their people. Data on the private sector and trade are from the World Bank Group's Private Participation in Infrastructure Project Database, Enterprise Surveys, and Doing Business Indicators, as well as from the International Monetary Fund's Balance of Payments database and International Financial Statistics, the UN Commission on Trade and Development, the World Trade Organization, and various other sources.

Each row in this dataset represents country-level aggregates. Data was last updated on HDX on 2026-03-27. Geographic scope: **BWA**.

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

---

## Dataset Characteristics

| | |
|---|---|
| **Domain** | Market and price monitoring |
| **Unit of observation** | Country-level aggregates |
| **Rows (total)** | 2,845 |
| **Columns** | 8 (2 numeric, 6 categorical, 0 datetime) |
| **Train split** | 2,276 rows |
| **Test split** | 569 rows |
| **Geographic scope** | BWA |
| **Publisher** | World Bank Group |
| **HDX last updated** | 2026-03-27 |

---

## Variables

**Geographic** — `country_name` (Botswana), `country_iso3` (BWA), `year` (range 1960.0–2024.0).

**Outcome / Measurement** — `value` (range -0.0–10293294986.0).

**Identifier / Metadata** — `indicator_name` (Merchandise imports (current US$), Merchandise exports (current US$), Merchandise trade (% of GDP)), `indicator_code` (TM.VAL.MRCH.CD.WT, TX.VAL.MRCH.CD.WT, TG.VAL.TOTL.GD.ZS), `esa_source` (HDX), `esa_processed` (2026-04-15).

---

## Quick Start

```python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-world-bank-private-sector-indicators-for-botswana")
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% | Botswana |
| `country_iso3` | object | 0.0% | BWA |
| `year` | int64 | 0.0% | 1960.0 – 2024.0 (mean 2005.8475) |
| `indicator_name` | object | 0.0% | Merchandise imports (current US$), Merchandise exports (current US$), Merchandise trade (% of GDP) |
| `indicator_code` | object | 0.0% | TM.VAL.MRCH.CD.WT, TX.VAL.MRCH.CD.WT, TG.VAL.TOTL.GD.ZS |
| `value` | float64 | 0.0% | -0.0 – 10293294986.0 (mean 237434905.939) |
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% | 2026-04-15 |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `year` | 1960.0 | 2024.0 | 2005.8475 | 2008.0 |
| `value` | -0.0 | 10293294986.0 | 237434905.939 | 17.16 |

---

## 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-private-sector-indicators-for-botswana) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_world_bank_private_sector_indicators_for_botswana,
title = {Botswana - Private Sector},
author = {World Bank Group},
year = {2026},
url = {https://data.humdata.org/dataset/world-bank-private-sector-indicators-for-botswana},
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:
- 无注释
language_creators:
- 现有公开资源
language:
- en
license: CC-BY 4.0
multilinguality:
- 单语言
size_categories:
- 1000 < n < 10000
source_datasets:
- 原生数据集
task_categories:
- 表格回归
task_ids: []
tags:
- 非洲
- 人道主义
- HDX
- Electric Sheep Africa
- 经济学
- 指标
- BWA（博茨瓦纳国家ISO3代码）
pretty_name: "博茨瓦纳——私营部门"
dataset_info:
splits:
- name: train
num_examples: 2276
- name: test
num_examples: 569

# 博茨瓦纳——私营部门

**发布方：** 世界银行集团 · **来源：** [HDX](https://data.humdata.org/dataset/world-bank-private-sector-indicators-for-botswana) · **许可证：** `CC-BY` · **最后更新：** 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/)的公开数据。HDX平台上还提供了一份[博茨瓦纳综合国家数据集](https://data.humdata.org/dataset/world-bank-combined-indicators-for-botswana)。

私营市场是经济增长的核心动力，通过激发创业活力与吸引投资创造生产性就业岗位并提升居民收入。贸易同样是经济增长的重要引擎，它将发展中国家融入全球经济体系，为当地民众创造福祉。本数据集涉及的私营部门与贸易数据来源于世界银行集团的《私营部门参与基础设施项目数据库》、《企业调查》与《营商环境指标》，同时还包含国际货币基金组织的《国际收支平衡数据库》、《国际金融统计》，联合国贸易和发展会议、世界贸易组织以及其他多个公开数据源的内容。

本数据集的每一行均代表国家层面的汇总统计数据。本数据集在HDX平台的最后更新时间为2026-03-27。地理覆盖范围：**BWA（博茨瓦纳）**。

*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。*

---

## 数据集特征

| | |
|---|---|
| **数据领域** | 市场与价格监测 |
| **观测单元** | 国家层面汇总数据 |
| **总样本行数** | 2845 |
| **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** | 2276行 |
| **测试集划分** | 569行 |
| **地理覆盖范围** | BWA（博茨瓦纳） |
| **发布方** | 世界银行集团 |
| **HDX平台最后更新时间** | 2026-03-27 |

---

## 变量说明

**地理类变量** — `country_name`（国家名称：博茨瓦纳）、`country_iso3`（国家ISO3代码：BWA）、`year`（年份范围：1960.0–2024.0）。

**结果/测量类变量** — `value`（指标数值，取值范围：-0.0–10293294986.0）。

**标识符/元数据类变量** — `indicator_name`（指标名称：商品进口额（现价美元）、商品出口额（现价美元）、商品贸易额（占GDP百分比））、`indicator_code`（指标代码：TM.VAL.MRCH.CD.WT、TX.VAL.MRCH.CD.WT、TG.VAL.TOTL.GD.ZS）、`esa_source`（数据来源：HDX）、`esa_processed`（数据处理日期：2026-04-15）。

---

## 快速入门

python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-world-bank-private-sector-indicators-for-botswana")
train = ds["train"].to_pandas()
test = ds["test"].to_pandas()

print(train.shape)
train.head()

---

## 数据架构

| 列名 | 数据类型 | 空值占比 | 取值范围/示例值 |
|---|---|---|---|
| `country_name` | 字符型 | 0.0% | 博茨瓦纳 |
| `country_iso3` | 字符型 | 0.0% | BWA |
| `year` | 64位整型 | 0.0% | 1960.0 – 2024.0（均值：2005.8475） |
| `indicator_name` | 字符型 | 0.0% | 商品进口额（现价美元）、商品出口额（现价美元）、商品贸易额（占GDP百分比） |
| `indicator_code` | 字符型 | 0.0% | TM.VAL.MRCH.CD.WT、TX.VAL.MRCH.CD.WT、TG.VAL.TOTL.GD.ZS |
| `value` | 64位浮点型 | 0.0% | -0.0 – 10293294986.0（均值：237434905.939） |
| `esa_source` | 字符型 | 0.0% | HDX |
| `esa_processed` | 字符型 | 0.0% | 2026-04-15 |

---

## 数值统计摘要

| 列名 | 最小值 | 最大值 | 均值 | 中位数 |
|---|---|---|---|---|
| `year` | 1960.0 | 2024.0 | 2005.8475 | 2008.0 |
| `value` | -0.0 | 10293294986.0 | 237434905.939 | 17.16 |

---

## 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。数据集的列名已统一转为小写并标准化为蛇形命名法。常见的缺失值标记（`N/A`、`null`、`none`、`-`、`unknown`、`no data`、`#N/A`）已统一替换为`NaN`。本数据集采用固定随机种子（42）按80/20的比例划分为训练集与测试集，并以Snappy压缩的Parquet格式存储。

---

## 局限性说明

- 本数据集的原始数据来源于世界银行集团，尚未由Electric Sheep Africa（ESA）进行独立验证。
- 自动化数据清洗流程无法修正原始数据集中的错报值、定义不一致问题或抽样偏差。
- 如需了解发布方的方法论说明与免责声明，请参阅[HDX平台原始数据集页面](https://data.humdata.org/dataset/world-bank-private-sector-indicators-for-botswana)。

---

## 引用格式

bibtex
@dataset{hdx_africa_world_bank_private_sector_indicators_for_botswana,
title = {Botswana - Private Sector},
author = {World Bank Group},
year = {2026},
url = {https://data.humdata.org/dataset/world-bank-private-sector-indicators-for-botswana},
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。尼日利亚拉各斯。*