national.py

import json
import datetime
import pandas as pd
import numpy as np
import requests
from scipy import stats

df = pd.read_csv("covid-tracking.csv",
                 parse_dates=True, index_col='date')
df = df.sort_index(ascending=True)
df = df.groupby(by=['date']).sum()

rolling = df.rolling(window=7).mean()

export = df.join(rolling, rsuffix="_7day")
export['positivity_7day'] = 100*export['positiveIncrease_7day'] / \
    export['totalTestResultsIncrease_7day']
export.to_csv('us_daily.csv')
lastweek = export[['positivity_7day', 'deathIncrease_7day', 'hospitalized_7day',
                   'positiveIncrease_7day', 'totalTestResultsIncrease_7day']].tail(8)
lastweek['datestr'] = lastweek.index.strftime('%Y-%m-%d')
lastweek = lastweek.reset_index().set_index('datestr')
lastweek.drop(columns='date', inplace=True)
compare = lastweek.iloc[[0, 7]].transpose()
compare['diff'] = compare.iloc[:, 1] - compare.iloc[:, 0]
compare['change_pct'] = 100 * compare.iloc[:, 2] / compare.iloc[:, 0]
compare.to_csv('us_daily_7day_summary.csv')