全球疫苗接种追踪数据集
全球各国疫苗接种数据，包含总接种量、接种人数等指标，用于追踪疫苗接种情况。
基本信息
资源简介
全球疫苗接种追踪数据集
【数据集背景】
本数据集源站标题为《jayant0702/global-vaccine-tracker》。源站首次发布：2025-04-26。
【数据内容】
源站原始描述：A data-driven project analyzing global COVID-19 vaccination trends using real-world datasets. The project includes data preprocessing, visualizations, and insights on vaccination rollouts across countries. # global-vaccine-tracker A data-driven project analyzing global COVID-19 vaccination trends using real-world datasets. The project includes data preprocessing, visualizations, and insights on vaccination rollouts across countries. 📁 Dataset The dataset contains country-wise vaccination data wi…
【适用方向】
结构化表格数据可直接用于统计建模、特征工程与队列分析。
【获取与许可】
源站页面：https://github.com/jayant0702/global-vaccine-tracker
下载信息
注册下载
需要注册 Kaggle 账号并登录后下载，适合需要跟踪下载记录和使用 API 的用户。
暂未开放公开下载
无需注册即可直接获取公开样本或文档，适合快速预览和评估数据集质量。
免登录有偿下载
公开数据集受托下载与技术交付服务。
提供高速下载与技术交付服务（收技术服务费，非数据销售）
暂未开放当前数据集主要来源为 Kaggle 公开托管，完整影像包建议通过原始链接或 Kaggle API 获取。
使用方式
数据集获取
git clone https://github.com/jayant0702/global-vaccine-tracker.git
curl -L -o repo.zip https://github.com/jayant0702/global-vaccine-tracker/archive/refs/heads/main.zip
unzip repo.zip
源站 README 摘录（使用方式）
global-vaccine-tracker
A data-driven project analyzing global COVID-19 vaccination trends using real-world datasets. The project includes data preprocessing, visualizations, and insights on vaccination rollouts across countries.
📁 Dataset
The dataset contains country-wise vaccination data with fields like:
location, iso_code, date
total_vaccinations, people_vaccinated, people_fully_vaccinated
daily_vaccinations, total_boosters, and related metrics per hundred/million
Source: (mention source if applicable)
Total Records: 196,000+
Time Span: Begins from early 2021
📊 Project Features
Data Cleaning and Preprocessing
Time-Series Analysis of Vaccination Trends
Country-wise Vaccination Comparison
Daily, Weekly, and Monthly Growth Insights
Visualization using Matplotlib / Seaborn / Plotly
Highlight on Booster Dose Trends
Dashboard-style Summary (optional if built)
📌 Objectives
Understand global vaccine distribution patterns.
Compare vaccination progress across countries.
Visualize trends over time for better insights.
Identify the impact of booster doses.
🚀 Technologies Used
Python (Pandas, NumPy)
Matplotlib / Seaborn / Plotly
Jupyter Notebook (for analysis)
Git & GitHub for version control
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))
数据缺失？
依托陕西、四川两大基地，我们与超过十家三甲医院建立直接合作关系，覆盖合规授权、采集治理、专业标注、数据交付的全流程，为AI医疗团队提供即拿即用的高质量临床数据。
⚡️ 需要数据支持或标注服务？立即联系我们获取专业方案。
