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

df = pd.DataFrame(index=pd.date_range(start='2020-01-26 22:00:00+00:00', end='2020-02-03 14:00:00+00:00', freq='H'),
                 data=np.random.rand(185, 5), columns = ['code_200','code_201','code_500','mean_200','mean_201'] )
fig, ax = plt.subplots( figsize=(15,6))
# plt.figure()
dfd = df.resample('D')[['code_200','code_201','code_500']].sum().tail(10)
# plt.bar(dfd)
plt.bar(dfd.index, dfd.code_200, color='#008c6e', label='sum of 200s')
plt.bar(dfd.index, dfd.code_201, bottom=dfd.code_200, color='k', label='sum of 201s')
plt.bar(dfd.index, dfd.code_500, bottom=dfd.code_200+dfd.code_201, color='#872d32',label='sum of 500s')

ax.yaxis.grid('both',color='k', linestyle='-', alpha=0.2)
ax.set_ylabel('# responses')
dfm = df.resample('D')[['mean_200', 'mean_201']].mean().tail(10)
ax2 = ax.twinx() 
ax2.plot(dfm.index, dfm.mean_200.values, label='avg for 200s', color='#808080')
ax2.plot(dfm.index, dfm.mean_201.values, label='avg for 201s', color='#872d32')
ax.legend(loc='upper left')
ax2.legend(loc='lower right')
ax2.set_ylabel('average response times [ms]')
ax.xaxis.set_major_formatter( mdates.DateFormatter('%a, %d-%b'))
plt.show()