COVID-19全球疫情统计数据
OWID COVID-19数据集提供全球疫情统计与疫苗接种信息,支持跨国家时间序列分析、公共卫生研究与政策评估。
基本信息
资源简介
Our World in Data 提供全球COVID-19疫情统计数据,包括确诊、死亡、检测率、疫苗接种等指标,支持跨国家时间序列分析与公共卫生研究。
下载信息
注册下载
Tips: 该数据集需要在对应的数据源网站注册通过后,才能进行数据下载,注册有对应要求,或者需要收费。
暂未开放公开下载
Tips: 该数据集属于公开下载,应该可以免费公开下载。
免登录有偿下载
Tips: 该数据集 Qianfanghub 可以协助提供有偿下载服务,注意,服务不针对数据相关产权,只是技术服务费。
提供高速下载与技术交付服务(收技术服务费,非数据销售)
暂未开放千方医数集,医疗数据集部分,是为社区服务的公开医疗数据集搜索引擎,并不存储或者下载原始的任何数据。 如果您有其他医疗数据需求,可以和客服联系,或者下工单。我们有强大的三甲医疗机构帮助您提供个性化的医疗数据定制、采集、标注服务。
使用方式
数据集获取
git clone https://github.com/owid/covid-19-data.git
curl -L -o repo.zip https://github.com/owid/covid-19-data/archive/refs/heads/master.zip
unzip repo.zip
源站 README 摘录(使用方式)
[!CAUTION]
This repository has been archived and is no longer maintained
The data in this repository was last updated on 19 August 2024 (see announcement).
Where to get latest COVID-19 data:
- CSV: catalog.ourworldindata.org/garden/covid/latest/compact/compact.csv
- Documentation & metadata: docs.owid.io/projects/etl/api/covid/
- All OWID data: https://ourworldindata.org/search via the ETL
For programmatic access to any OWID dataset, see our data access documentation.
COVID-19 Dataset by Our World in Data
Project structure
The project contains two independent directories:
public/data: Contains the final datasets. This is for people interested in consuming the data and
understanding all the caveats about it and its metrics.scripts: Contains all the code and intermediate files to produce the final dataset. This is for people interested in
contributing to the project or better understanding our internal technical processes.
Documentation
If you are interested in the final dataset file, refer to this document. If you want to
learn more about our processes, refer to our technical documentation.
Contribute
Thanks for considering contributing to this project! A good place to start is our contribution
guideline.
数据加载示例(表格/文本类)
import pandas as pd, glob, os
files = (glob.glob(os.path.join(path, "**", "*.csv"), recursive=True)
+ glob.glob(os.path.join(path, "**", "*.tsv"), recursive=True)
+ glob.glob(os.path.join(path, "**", "*.xlsx"), recursive=True))
print("数据文件:", files)
df = pd.read_csv(files[0])
print(df.shape); print(df.columns.tolist()); print(df.head(3))
精度瓶颈?数据缺失?
当前公开数据无法满足您的算法精度?千方提供针对 新冠病毒感染 的高质量、多模态真实临床数据定制解决方案。




