Plot reported cases since reaching 100 cases

plot.py

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.dates as mdates

#cutoff = 100

def flip(name):
    df = pd.read_csv(name)
    del df['Lat']
    del df['Long']
    df = df.groupby('Country/Region').sum().T
    dates = pd.to_datetime(df.index)
    df.index = dates#(dates-dates[-1]).days
    return df

df_c = flip("csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv")
del df_c['Holy See']
df_r = flip("csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_recovered_global.csv")
df_d = flip("csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv")
df = df_c-df_r-df_d

pop = pd.read_html("https://en.wikipedia.org/wiki/List_of_countries_by_population_%28United_Nations%29", attrs={'id': 'main'})[0]
popmap = {}
for idx, val in pop.iterrows():
    c = val['Country/Territory']
    if "[" in c:
        c = c[:-3]
    popmap[c] = val['Population(1 July 2019)']

reldf = df[:]
for idx in reldf:
    try:
        reldf[idx] /= popmap[idx]
    except KeyError:
        del reldf[idx]
        print(idx)

#for col in df:
#    days = df[col].gt(cutoff).idxmax()
    #df[col] = df[col].shift(-days)

#df = df.loc[:, df.max() >= cutoff]

#df = np.log(df).diff().rolling(window=7).mean().dropna(axis=1, thresh=3)

keys = ['Germany', 'Austria', 'Netherlands', 'France', 'Sweden', 'Denmark', 'Switzerland', 'Greece', 'Turkey', 'Russia']
#keys = ['Netherlands']

reldf[keys].plot()
#reldf[keys].diff().rolling(window=7).mean().plot()
#df.plot(logy=False, marker='.')
#plt.xlabel('Days')
plt.ylabel('Cases per inhabitant')# per inhabitant')
plt.grid(True)
#df.plot(logy=True)
#plt.xlim([-30, 0])
#plt.ylim([0, 0.5])
plt.show()