import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
from datetime import datetime, timedelta
 
FMT = '%Y-%m-%d'
get_ts = lambda dt: datetime.strftime(dt, FMT)
get_dt = lambda ts: datetime.strptime(ts, FMT)
 
DAYS = 366 + 63
TSFIRST = '2019-12-01'
AMAX = np.exp(DAYS / 31)
TSLAST = get_ts(get_dt(TSFIRST) + timedelta(DAYS - 1))
 
dat = [
  ['2020-01-28', 106],
  ['2020-01-29', 132],
  ['2020-01-30', 170],
  ['2020-01-31', 213],
  ['2020-02-01', 259],
  ['2020-02-02', 304],
  ['2020-02-03', 362],
  ['2020-02-04', 426],
  ['2020-02-05', 491],
  ['2020-02-06', 563],
  ['2020-02-07', 634],
  ['2020-02-08', 717]]
 
def new_corona(df):
  fig, ax = plt.subplots(nrows=1, ncols=1)
  df.dropna().plot(ax=ax, style='b.-')
  dfr = df.iloc[:,:]
  dfr.ix[TSFIRST] = (0.0, )
  dfr.ix[TSLAST] = (AMAX, )
  dfr.interpolate(method='quadratic').plot(ax=ax, style='r-')
  ax.set_xlim([TSFIRST, TSLAST])
  ax.set_ylim([0, AMAX])
  ax.legend(loc='best')
  plt.show()
 
idx = np.zeros(DAYS)
ts = pd.Series(idx, index=pd.date_range(TSFIRST, periods=DAYS))
val = np.full((DAYS, 1), np.nan)
df = pd.DataFrame(val, index=pd.date_range(TSFIRST, periods=DAYS), columns=['val'])
for d in dat:
  ymd, v = d[0], float(d[1])
  df.ix[ymd] = (v, )
 
new_corona(df)
 

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