***IMPORTANT NOTICE*** DATA REFORMATTED TO OLD DATA STRUCTURE ***IMPORTANT NOTICE***

[CWrecker]

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Hello all, I know they recently changed to the formatting of the data structure. I have recompiled it at shared it below as a google sheets and CSV file which both upload live as new information is collected. THIS USES THE OLD DATA STRUCTURE

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If you require the long and lat data, please let me know and I will add it

Ok here we go:

Link to a live google sheets file:
https://docs.google.com/spreadsheets/u/1/d/e/2PACX-1vQisxSucfFNfhB_KJg1WmXZW_9_VtW_WxAy4ReE7vsRKDUgg2XwhOO0k1yS4V6tZnwZziRETZ3s1WBP/pubhtml?gid=0&single=true

Link to a live CSV file:
https://docs.google.com/spreadsheets/d/e/2PACX-1vQisxSucfFNfhB_KJg1WmXZW_9_VtW_WxAy4ReE7vsRKDUgg2XwhOO0k1yS4V6tZnwZziRETZ3s1WBP/pub?output=csv

Link to a live JSON file:
https://script.google.com/macros/s/AKfycbzsyQNJwDvQc5SvNGEDZZOoNI3XxNar9PA9sRucZx7mgzfWpFQ/exec

[cortical-iv]

long and lat are important, and individual us state data

[CWrecker]

long and lat are important, and individual us state data

I’ll see what I can do

[cedvdb]

I need this as well only for the one of today. I can deal with the next ones..

[hippodribble]

Here's some Python code to reformat it yourself:

import os.path
import pandas as pd
def reformat(infile):
    (dir, file) = os.path.split(infile)
    f = file.replace(".csv","b.csv")
    outfile = os.path.join(dir,f)
    print(infile)
    print(outfile)
    df1 = pd.read_csv(infile, parse_dates=[4],skip_blank_lines=False )
    dic = {
        "Province/State":"Province_State",
        "Country/Region":"Country_Region",
        "Last Update":"Last_Update",
        "Confirmed":"Confirmed",
        "Deaths":"Deaths",
        "Recovered":"Recovered",
        "Latitude":"Lat",
        "Longitude":"Long_"
    }
    temp=df1[list(dic.values())]
    temp.columns=list(dic.keys())
    temp.reset_index(drop=True,inplace=True)
    temp.to_csv(outfile, index=False)