数据集获取

git clone https://github.com/JieYingWu/COVID-19_US_County-level_Summaries.git

curl -L -o repo.zip https://github.com/JieYingWu/COVID-19_US_County-level_Summaries/archive/refs/heads/master.zip
unzip repo.zip

源站 README 摘录（使用方式）

County-level Socioeconomic Data for Predictive Modeling of Epidemiological Effects

TL/DR: We gather a machine readable dataset related to socioeconomic factors that may affect the spread and/or consequences of epidemiological outbreaks, particularly the novel coronavirus (COVID-19). This dataset is envisioned to serve the data science, machine learning, and epidemiological modeling communities. If you want to contribute, please let us know!
Overview: Despite overoptimistic promises of an “American Resurrection” by Easter Sunday, many scientists and citizens fear that current mitigation strategies are likely insufficient to avert the collapse of the US healthcare system. Confirmed COVID-19 cases, hospitalizations, and - unfortunately - deaths are rapidly increasing; implementing an aggressive suppression strategy - “The Hammer” - seems to be the only viable option to buy time. How can we make best use of the time these measures buy?
The machine learning community should actively engage in these discussions and contribute possible solutions to actionable problems.
One interesting direction could be to identify the effect that different mitigation and suppression strategies have in terms of benefits and costs. “Benefits” in this case would correspond to reductions in the effective reproduction number R, potential lives saved and long-term socio-economic benefits, while “costs” could reflect the resulting burden on the healthcare system,short-term economic consequences and possible long-term economic restructuring.
Many of the recent epidemiological predictions and analyses are performed for the US as a whole. However, identifying relationships between “benefits” and “costs” will likely require a much higher granularity of analysis.
This is because highly localized contextual factors, such as population density, demographics or primary means of transportation, will affect critical parameters for computational epidemiological modeling, including the effective reproduction number R.
To facilitate research on such questions, we present a machine readable dataset that aggregates relevant data from around 10 governmental and academic sources on the county-level. In addition to county-level time-series data from the JHU CSSE COVID-19 Dashboard, our datas

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))

完整仓库：github.com/JieYingWu/COVID-19_US_County-level_Summaries