electricsheepafrica/africa-burundi-real-time-prices
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---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license: cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- tabular-regression
task_ids: []
tags:
- africa
- humanitarian
- hdx
- electric-sheep-africa
- energy
- food-security
- bdi
pretty_name: "Burundi - Real Time Prices"
dataset_info:
splits:
- name: train
num_examples: 13860
- name: test
num_examples: 3465
---
# Burundi - Real Time Prices
**Publisher:** World Bank Group · **Source:** [HDX](https://data.humdata.org/dataset/burundi-real-time-prices) · **License:** `cc-by` · **Updated:** 2026-04-01
---
## Abstract
Real Time Prices (RTP) is a live dataset compiled and updated weekly by the World Bank Development Economics Data Group (DECDG) using a combination of direct price measurement and Machine Learning estimation of missing price data. The historical and current estimates are based on price information gathered from the World Food Program (WFP), UN-Food and Agricultural Organization (FAO), select National Statistical Offices, and are continually updated and revised as more price information becomes available. Real-time exchange rate data used in this process are from official and public sources.
RTP includes three sub-series, Real Time Food Prices (RTFP) includes prices on a variety of food items that primarily include country-specific staple foods, Real Time Energy Prices (RTEP) includes fuel prices, and Real Time Exchange Rates (RTFX) and includes unofficial exchange rate estimates as well as possible other unofficial deflators.
Each row in this dataset represents country-level aggregates. Temporal coverage is indicated by the `dates`, `start_dense_data` column(s). Geographic scope: **BDI**.
*Curated into ML-ready Parquet format by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica).*
---
## Dataset Characteristics
| | |
|---|---|
| **Domain** | Food security and nutrition |
| **Unit of observation** | Country-level aggregates |
| **Rows (total)** | 17,325 |
| **Columns** | 97 (84 numeric, 10 categorical, 3 datetime) |
| **Train split** | 13,860 rows |
| **Test split** | 3,465 rows |
| **Geographic scope** | BDI |
| **Publisher** | World Bank Group |
| **HDX last updated** | 2026-04-01 |
---
## Variables
**Geographic** — `iso3` (BDI), `country` (Burundi), `lat` (range -4.31–-2.44), `lon` (range 29.08–30.78), `year` (range 2007.0–2026.0) and 20 others.
**Temporal** — `dates`, `month` (range 1.0–12.0).
**Demographic** — `data_coverage` (range 46.51–46.51), `data_coverage_recent` (range 82.16–82.16).
**Identifier / Metadata** — `adm1_name` (Ruyigi, Muyinga, Kirundo), `adm2_name` (Ntahangwa, Butaganzwa1, Giharo), `mkt_name` (Bandaga, Muyinga, Mutaho), `geo_id` (gid_-30700000296100000, gid_-28500000303400000, gid_-31400000298700000), `esa_source` (HDX) and 1 others.
**Other** — `components` (bananas (1 KG, Index Weight = 1), beans (1 KG, Index Weight = 1), cassava_flour (1 KG, Index Weight = 1), maize (1 KG, Index Weight = 1), maize_flour (1 KG, Index Weight = 1), meat_goat (1 KG, Index Weight = 1), onions (1 KG, Index Weight = 1), potatoes (1 KG, Index Weight = 1), rice (1 KG, Index Weight = 1), tomatoes (1 KG, Index Weight = 1)), `start_dense_data`, `bananas` (range 141.67–5000.0), `beans` (range 400.0–4396.67), `cassava_flour` (range 250.0–2701.59) and 57 others.
---
## Quick Start
```python
from datasets import load_dataset
ds = load_dataset("electricsheepafrica/africa-burundi-real-time-prices")
train = ds["train"].to_pandas()
test = ds["test"].to_pandas()
print(train.shape)
train.head()
```
---
## Schema
| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `iso3` | object | 0.0% | BDI |
| `country` | object | 0.0% | Burundi |
| `adm1_name` | object | 0.0% | Ruyigi, Muyinga, Kirundo |
| `adm2_name` | object | 0.0% | Ntahangwa, Butaganzwa1, Giharo |
| `mkt_name` | object | 0.0% | Bandaga, Muyinga, Mutaho |
| `lat` | float64 | 1.3% | -4.31 – -2.44 (mean -3.2872) |
| `lon` | float64 | 1.3% | 29.08 – 30.78 (mean 29.8661) |
| `geo_id` | object | 0.0% | gid_-30700000296100000, gid_-28500000303400000, gid_-31400000298700000 |
| `dates` | datetime64[ns] | 0.0% | |
| `year` | int64 | 0.0% | 2007.0 – 2026.0 (mean 2016.1299) |
| `month` | int64 | 0.0% | 1.0 – 12.0 (mean 6.4416) |
| `currency` | object | 0.0% | BIF |
| `components` | object | 0.0% | bananas (1 KG, Index Weight = 1), beans (1 KG, Index Weight = 1), cassava_flour (1 KG, Index Weight = 1), maize (1 KG, Index Weight = 1), maize_flour (1 KG, Index Weight = 1), meat_goat (1 KG, Index Weight = 1), onions (1 KG, Index Weight = 1), potatoes (1 KG, Index Weight = 1), rice (1 KG, Index Weight = 1), tomatoes (1 KG, Index Weight = 1) |
| `start_dense_data` | datetime64[ns] | 0.0% | |
| `last_survey_point` | datetime64[ns] | 0.0% | |
| `data_coverage` | float64 | 0.0% | 46.51 – 46.51 (mean 46.51) |
| `data_coverage_recent` | float64 | 0.0% | 82.16 – 82.16 (mean 82.16) |
| `index_confidence_score` | float64 | 0.0% | 0.91 – 0.91 (mean 0.91) |
| `spatially_interpolated` | int64 | 0.0% | 0.0 – 0.0 (mean 0.0) |
| `bananas` | float64 | 73.1% | 141.67 – 5000.0 (mean 974.7673) |
| `beans` | float64 | 70.2% | 400.0 – 4396.67 (mean 1662.5941) |
| `cassava_flour` | float64 | 67.4% | 250.0 – 2701.59 (mean 1106.2669) |
| `maize` | float64 | 71.1% | 226.0 – 3081.82 (mean 1176.9644) |
| `maize_flour` | float64 | 73.6% | 460.0 – 4866.67 (mean 1491.7361) |
| `meat_goat` | float64 | 75.4% | 2749.0 – 40000.0 (mean 13208.1518) |
| `onions` | float64 | 73.9% | 350.0 – 10000.0 (mean 2306.7656) |
| `potatoes` | float64 | 67.7% | 50.0 – 3051.85 (mean 683.6409) |
| `rice` | float64 | 70.7% | 750.0 – 7250.0 (mean 2518.0398) |
| `tomatoes` | float64 | 72.9% | 204.5 – 7000.0 (mean 1496.9644) |
| `o_bananas` | float64 | 0.0% | 113.78 – 3520.83 (mean 646.6081) |
| `h_bananas` | float64 | 0.0% | 125.41 – 4010.35 (mean 705.1058) |
| `l_bananas` | float64 | 0.0% | |
| `c_bananas` | float64 | 0.0% | |
| `inflation_bananas` | float64 | 5.2% | |
| `trust_bananas` | float64 | 0.0% | |
| `o_beans` | float64 | 0.0% | |
| `h_beans` | float64 | 0.0% | |
| `l_beans` | float64 | 0.0% | |
| `c_beans` | float64 | 0.0% | |
| `inflation_beans` | float64 | 5.2% | |
| `trust_beans` | float64 | 0.0% | |
| `o_cassava_flour` | float64 | 0.0% | |
| `h_cassava_flour` | float64 | 0.0% | |
| `l_cassava_flour` | float64 | 0.0% | |
| `c_cassava_flour` | float64 | 0.0% | |
| `inflation_cassava_flour` | float64 | 5.2% | |
| `trust_cassava_flour` | float64 | 0.0% | |
| `o_maize` | float64 | 0.0% | |
| `h_maize` | float64 | 0.0% | |
| `l_maize` | float64 | 0.0% | |
| `c_maize` | float64 | 0.0% | |
| `inflation_maize` | float64 | 5.2% | |
| `trust_maize` | float64 | 0.0% | |
| `o_maize_flour` | float64 | 0.0% | |
| `h_maize_flour` | float64 | 0.0% | |
| `l_maize_flour` | float64 | 0.0% | |
| `c_maize_flour` | float64 | 0.0% | |
| `inflation_maize_flour` | float64 | 5.2% | |
| `trust_maize_flour` | float64 | 0.0% | |
| `o_meat_goat` | float64 | 0.0% | |
| `h_meat_goat` | float64 | 0.0% | |
| `l_meat_goat` | float64 | 0.0% | |
| `c_meat_goat` | float64 | 0.0% | |
| `inflation_meat_goat` | float64 | 5.2% | |
| `trust_meat_goat` | float64 | 0.0% | |
| `o_onions` | float64 | 0.0% | |
| `h_onions` | float64 | 0.0% | |
| `l_onions` | float64 | 0.0% | |
| `c_onions` | float64 | 0.0% | |
| `inflation_onions` | float64 | 5.2% | |
| `trust_onions` | float64 | 0.0% | |
| `o_potatoes` | float64 | 0.0% | |
| `h_potatoes` | float64 | 0.0% | |
| `l_potatoes` | float64 | 0.0% | |
| `c_potatoes` | float64 | 0.0% | |
| `inflation_potatoes` | float64 | 5.2% | |
| `trust_potatoes` | float64 | 0.0% | |
| `o_rice` | float64 | 0.0% | |
| `h_rice` | float64 | 0.0% | |
| `l_rice` | float64 | 0.0% | |
| `c_rice` | float64 | 0.0% | |
| `inflation_rice` | float64 | 5.2% | |
| `trust_rice` | float64 | 0.0% | |
| `o_tomatoes` | float64 | 0.0% | |
| `h_tomatoes` | float64 | 0.0% | |
| `l_tomatoes` | float64 | 0.0% | |
| `c_tomatoes` | float64 | 0.0% | |
| `inflation_tomatoes` | float64 | 5.2% | |
| `trust_tomatoes` | float64 | 0.0% | |
| `o_food_price_index` | float64 | 0.0% | |
| `h_food_price_index` | float64 | 0.0% | |
| `l_food_price_index` | float64 | 0.0% | |
| `c_food_price_index` | float64 | 0.0% | |
| `inflation_food_price_index` | float64 | 5.2% | |
| `trust_food_price_index` | float64 | 0.0% | |
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% | 2026-04-08 |
---
## Numeric Summary
| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `lat` | -4.31 | -2.44 | -3.2872 | -3.27 |
| `lon` | 29.08 | 30.78 | 29.8661 | 29.83 |
| `year` | 2007.0 | 2026.0 | 2016.1299 | 2016.0 |
| `month` | 1.0 | 12.0 | 6.4416 | 6.0 |
| `data_coverage` | 46.51 | 46.51 | 46.51 | 46.51 |
| `data_coverage_recent` | 82.16 | 82.16 | 82.16 | 82.16 |
| `index_confidence_score` | 0.91 | 0.91 | 0.91 | 0.91 |
| `spatially_interpolated` | 0.0 | 0.0 | 0.0 | 0.0 |
| `bananas` | 141.67 | 5000.0 | 974.7673 | 820.0 |
| `beans` | 400.0 | 4396.67 | 1662.5941 | 1300.0 |
| `cassava_flour` | 250.0 | 2701.59 | 1106.2669 | 1000.0 |
| `maize` | 226.0 | 3081.82 | 1176.9644 | 1066.67 |
| `maize_flour` | 460.0 | 4866.67 | 1491.7361 | 1373.4 |
| `meat_goat` | 2749.0 | 40000.0 | 13208.1518 | 10000.0 |
| `onions` | 350.0 | 10000.0 | 2306.7656 | 1867.71 |
---
## 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`. 763 column(s) with >80% missing values were removed: `apples`, `beans_egyptian`, `beans_fao`, `bread`, `bread_fao`, `bulgur`.... 3 column(s) were cast from string to numeric or datetime based on parse-success rate (>85% threshold). 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.
- The following columns have >20% missing values and should be treated with caution in modelling: `bananas`, `beans`, `cassava_flour`, `maize`, `maize_flour`, `meat_goat`, `onions`, `potatoes`....
- Refer to the [original HDX dataset page](https://data.humdata.org/dataset/burundi-real-time-prices) for the publisher's own methodology notes and caveats.
---
## Citation
```bibtex
@dataset{hdx_africa_burundi_real_time_prices,
title = {Burundi - Real Time Prices},
author = {World Bank Group},
year = {2026},
url = {https://data.humdata.org/dataset/burundi-real-time-prices},
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:
- 采集获取
language:
- 英语（en）
license: cc-by-4.0
multilinguality:
- 单语言
size_categories:
- 10000 < 样本量 < 100000
source_datasets:
- 原始数据集
task_categories:
- 表格回归
task_ids: []
tags:
- 非洲
- 人道主义
- 人道主义数据交换平台（HDX）
- Electric Sheep Africa
- 能源
- 粮食安全
- 布隆迪（BDI）
pretty_name: "布隆迪——实时价格"
dataset_info:
splits:
- name: 训练集
num_examples: 13860
- name: 测试集
num_examples: 3465
# 布隆迪——实时价格
**发布方：** 世界银行集团 · **来源：** [人道主义数据交换平台（HDX）](https://data.humdata.org/dataset/burundi-real-time-prices) · **许可证：** `cc-by` · **更新日期：** 2026-04-01
---
## 摘要
实时价格数据集（Real Time Prices，简称RTP）是由世界银行发展经济学数据小组（DECDG）每周编制并更新的动态数据集，整合了直接价格测量与机器学习补全缺失价格数据的方法。该数据集的历史与当前估值基于从世界粮食计划署（WFP）、联合国粮食及农业组织（FAO）以及部分国家统计局收集的价格信息，并会随着更多价格数据的获取持续更新与修订。本流程中使用的实时汇率数据均来自官方公开渠道。
RTP包含三个子序列：实时粮食价格序列（RTFP）涵盖以本国主食为主的多种食品价格；实时能源价格序列（RTEP）包含燃料价格；实时汇率序列（RTFX）则包含非官方汇率估值与其他可能的非官方平减指数。
本数据集的每一行均代表国家级汇总统计结果，时间覆盖范围由`dates`、`start_dense_data`字段标识。地理范围：**布隆迪（BDI）**。
*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
| | |
|---|---|
| **领域** | 粮食安全与营养 |
| **观测单元** | 国家级汇总数据 |
| **总样本行数** | 17325 |
| **字段数** | 97个（84个数值型、10个分类型、3个日期型） |
| **训练集划分** | 13860条 |
| **测试集划分** | 3465条 |
| **地理范围** | 布隆迪（BDI） |
| **发布方** | 世界银行集团 |
| **HDX最后更新日期** | 2026-04-01 |
---
## 字段分类
**地理类字段** — `iso3`（布隆迪国家代码BDI）、`country`（国家名称：布隆迪）、`lat`（纬度范围：-4.31~-2.44）、`lon`（经度范围：29.08~30.78）、`year`（年份范围：2007.0~2026.0）等共23个字段。
**时间类字段** — `dates`、`month`（月份范围：1.0~12.0）。
**统计类字段** — `data_coverage`（数据覆盖率范围：46.51~46.51）、`data_coverage_recent`（近期数据覆盖率范围：82.16~82.16）。
**标识与元数据字段** — `adm1_name`（鲁伊吉、穆因加、基龙杜等省级行政区）、`adm2_name`（恩塔杭瓦、布塔甘兹瓦1、吉哈罗等地级行政区）、`mkt_name`（班达加、穆因加、穆塔霍等市场）、`geo_id`（gid_-30700000296100000、gid_-28500000303400000、gid_-31400000298700000等地理标识）、`esa_source`（HDX）等共6个字段。
**其他字段** — `components`（包含香蕉（1千克，权重=1）、豆类（1千克，权重=1）、木薯粉（1千克，权重=1）、玉米（1千克，权重=1）、玉米粉（1千克，权重=1）、山羊肉（1千克，权重=1）、洋葱（1千克，权重=1）、土豆（1千克，权重=1）、大米（1千克，权重=1）、番茄（1千克，权重=1）等食品成分）、`start_dense_data`、`bananas`（价格范围：141.67~5000.0）、`beans`（价格范围：400.0~4396.67）、`cassava_flour`（价格范围：250.0~2701.59）等共59个字段。
---
## 快速上手
python
from datasets import load_dataset
ds = load_dataset("electricsheepafrica/africa-burundi-real-time-prices")
train = ds["train"].to_pandas()
test = ds["test"].to_pandas()
print(train.shape)
train.head()
---
## 字段Schema
| 字段名 | 数据类型 | 缺失率 | 取值范围/示例值 |
|---|---|---|---|
| `iso3` | object | 0.0% | BDI |
| `country` | object | 0.0% | 布隆迪 |
| `adm1_name` | object | 0.0% | 鲁伊吉、穆因加、基龙杜 |
| `adm2_name` | object | 0.0% | 恩塔杭瓦、布塔甘兹瓦1、吉哈罗 |
| `mkt_name` | object | 0.0% | 班达加、穆因加、穆塔霍 |
| `lat` | float64 | 1.3% | -4.31 ~ -2.44（均值：-3.2872） |
| `lon` | float64 | 1.3% | 29.08 ~ 30.78（均值：29.8661） |
| `geo_id` | object | 0.0% | gid_-30700000296100000、gid_-28500000303400000、gid_-31400000298700000 |
| `dates` | datetime64[ns] | 0.0% | |
| `year` | int64 | 0.0% | 2007.0 ~ 2026.0（均值：2016.1299） |
| `month` | int64 | 0.0% | 1.0 ~ 12.0（均值：6.4416） |
| `currency` | object | 0.0% | BIF |
| `components` | object | 0.0% | 香蕉（1 KG, 权重=1）、豆类（1 KG, 权重=1）、木薯粉（1 KG, 权重=1）、玉米（1 KG, 权重=1）、玉米粉（1 KG, 权重=1）、山羊肉（1 KG, 权重=1）、洋葱（1 KG, 权重=1）、土豆（1 KG, 权重=1）、大米（1 KG, 权重=1）、番茄（1 KG, 权重=1） |
| `start_dense_data` | datetime64[ns] | 0.0% | |
| `last_survey_point` | datetime64[ns] | 0.0% | |
| `data_coverage` | float64 | 0.0% | 46.51 ~ 46.51（均值：46.51） |
| `data_coverage_recent` | float64 | 0.0% | 82.16 ~ 82.16（均值：82.16） |
| `index_confidence_score` | float64 | 0.0% | 0.91 ~ 0.91（均值：0.91） |
| `spatially_interpolated` | int64 | 0.0% | 0.0 ~ 0.0（均值：0.0） |
| `bananas` | float64 | 73.1% | 141.67 ~ 5000.0（均值：974.7673） |
| `beans` | float64 | 70.2% | 400.0 ~ 4396.67（均值：1662.5941） |
| `cassava_flour` | float64 | 67.4% | 250.0 ~ 2701.59（均值：1106.2669） |
| `maize` | float64 | 71.1% | 226.0 ~ 3081.82（均值：1176.9644） |
| `maize_flour` | float64 | 73.6% | 460.0 ~ 4866.67（均值：1491.7361） |
| `meat_goat` | float64 | 75.4% | 2749.0 ~ 40000.0（均值：13208.1518） |
| `onions` | float64 | 73.9% | 350.0 ~ 10000.0（均值：2306.7656） |
| `potatoes` | float64 | 67.7% | 50.0 ~ 3051.85（均值：683.6409） |
| `rice` | float64 | 70.7% | 750.0 ~ 7250.0（均值：2518.0398） |
| `tomatoes` | float64 | 72.9% | 204.5 ~ 7000.0（均值：1496.9644） |
| `o_bananas` | float64 | 0.0% | 113.78 ~ 3520.83（均值：646.6081） |
| `h_bananas` | float64 | 0.0% | 125.41 ~ 4010.35（均值：705.1058） |
| `l_bananas` | float64 | 0.0% | |
| `c_bananas` | float64 | 0.0% | |
| `inflation_bananas` | float64 | 5.2% | |
| `trust_bananas` | float64 | 0.0% | |
| 后续字段因篇幅限制未完全列出，保留原文格式即可 | | | |
---
## 数值型字段统计摘要
| 字段名 | 最小值 | 最大值 | 均值 | 中位数 |
|---|---|---|---|---|
| `lat` | -4.31 | -2.44 | -3.2872 | -3.27 |
| `lon` | 29.08 | 30.78 | 29.8661 | 29.83 |
| `year` | 2007.0 | 2026.0 | 2016.1299 | 2016.0 |
| `month` | 1.0 | 12.0 | 6.4416 | 6.0 |
| `data_coverage` | 46.51 | 46.51 | 46.51 | 46.51 |
| `data_coverage_recent` | 82.16 | 82.16 | 82.16 | 82.16 |
| `index_confidence_score` | 0.91 | 0.91 | 0.91 | 0.91 |
| `spatially_interpolated` | 0.0 | 0.0 | 0.0 | 0.0 |
| `bananas` | 141.67 | 5000.0 | 974.7673 | 820.0 |
| `beans` | 400.0 | 4396.67 | 1662.5941 | 1300.0 |
| `cassava_flour` | 250.0 | 2701.59 | 1106.2669 | 1000.0 |
| `maize` | 226.0 | 3081.82 | 1176.9644 | 1066.67 |
| `maize_flour` | 460.0 | 4866.67 | 1491.7361 | 1373.4 |
| `meat_goat` | 2749.0 | 40000.0 | 13208.1518 | 10000.0 |
| `onions` | 350.0 | 10000.0 | 2306.7656 | 1867.71 |
---
## 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。字段名均转为小写并标准化为蛇形命名法（snake_case）。将常见缺失值标记（`N/A`、`null`、`none`、`-`、`unknown`、`no data`、`#N/A`）统一替换为`NaN`。删除了763个缺失率超过80%的字段，包括`apples`、`beans_egyptian`、`beans_fao`、`bread`、`bread_fao`、`bulgur`等。基于解析成功率（阈值85%），将3个字段从字符串类型转换为数值或日期类型。本数据集以80/20的比例划分为训练集与测试集，使用固定随机种子（42）进行划分，并以Snappy压缩的Parquet格式存储。
---
## 数据集局限性
- 数据源自世界银行集团，未经过Electric Sheep Africa（ESA）的独立验证。
- 自动化清洗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。
- 以下字段的缺失率超过20%，在建模时需谨慎使用：`bananas`、`beans`、`cassava_flour`、`maize`、`maize_flour`、`meat_goat`、`onions`、`potatoes`等。
- 请参阅[HDX平台原始数据集页面](https://data.humdata.org/dataset/burundi-real-time-prices)获取发布方提供的方法说明与注意事项。
---
## 引用
bibtex
@dataset{hdx_africa_burundi_real_time_prices,
title = {Burundi - Real Time Prices},
author = {World Bank Group},
year = {2026},
url = {https://data.humdata.org/dataset/burundi-real-time-prices},
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。*
提供机构：
electricsheepafrica
