Q:

Update the high of the day column based on time series data vector in python

I am trying to update the highest price of the day into a new column named high.  If the lastPrice is > high than new high should be updated etc. 
I have this data:
SecurityID,dateTime,ask1,ask1Volume,bid1,bid1Volume,ask2,ask2Volume,bid2,bid2Volume,ask3,ask3Volume,bid3,bid3Volume,tradePrice,tradeVolume,isTrade

2318276,2017-11-2008:00:09.052240,12869.0,1,12868.0,3,12870.0,19,12867.5,2,12872.5,2,12867.0,1,0.0,0,0
2318276,2017-11-2008:00:09.052260,12869.0,1,12868.0,3,12870.0,19,12867.5,2,12872.5,2,12867.0,1,12868.0,1,1 
2318276,2017-11-2008:00:09.052260,12869.0,1,12868.0,2,12870.0,19,12867.5,2,12872.5,2,12867.0,1,12869.0,1,0 
2318276,2017-11-2008:00:09.052270,12869.0,1,12868.0,2,12870.0,19,12867.5,2,12872.5,2,12867.0,1,12870.0,1,1 
2318276,2017-11-2008:00:09.052270,12869.0,1,12868.0,1,12870.0,19,12867.5,2,12872.5,2,12867.0,1,12868.0,1,0 
2318276,2017-11-2008:00:09.052282,12869.0,1,12868.0,1,12870.0,19,12867.5,2,12872.5,2,12867.0,1,12871.0,1,1 
2318276,2017-11-2008:00:09.052282,12869.0,1,12867.5,2,12870.0,19,12867.0,1,12872.5,2,12865.5,1,12868.0,1,0 
2318276,2017-11-2008:00:09.052291,12869.0,1,12867.5,2,12870.0,19,12867.0,1,12872.5,2,12865.5,1,12867.5,1,1 
2318276,2017-11-2008:00:09.052291,12869.0,1,12867.5,1,12870.0,19,12867.0,1,12872.5,2,12865.5,1,12867.5,1,0 
2318276,2017-11-2008:00:09.052315,12869.0,1,12867.5,1,12870.0,19,12867.0,1,12872.5,2,12865.5,1,12867.5,1,1 
2318276,2017-11-2008:00:09.052315,12869.0,1,12867.5,1,12870.0,19,12867.0,1,12872.5,2,12865.5,1,12867.0,1,1 
2318276,2017-11-2008:00:09.052315,12869.0,1,12867.5,1,12870.0,19,12867.0,1,12872.5,2,12865.5,1,12865.5,1,1 
2318276,2017-11-2008:00:09.052315,12869.0,1,12867.5,1,12870.0,19,12867.0,1,12872.5,2,12865.5,1,13865.0,1,1 
2318276,2017-11-2008:00:09.052315,12869.0,1,12867.5,1,12870.0,19,12867.0,1,12872.5,2,12865.5,1,12864.0,1,1

I am trying to update the high of the day
from matplotlib import style
import pandas as pd
import numpy as np
style.use('ggplot')
desired_width = 320
pd.set_option('display.width', desired_width)

df = pd.read_csv('C:/test.csv')
df.set_index('dateTime', inplace=True)
df.drop(['SecurityID'],1,inplace=True)

df['high'] = 0

df.high = np.where((df.tradePrice.shift(1) < df.tradePrice),df.tradePrice,df.high.shift(1))

print(df.head(10))

The result is:
                            tradePrice     high
dateTime                                       
2017-11-20 08:00:09.052240         0.0      NaN
2017-11-20 08:00:09.052260     12868.0  12868.0
2017-11-20 08:00:09.052260     12869.0  12869.0
2017-11-20 08:00:09.052270     12870.0  12870.0
2017-11-20 08:00:09.052270     12868.0      0.0
2017-11-20 08:00:09.052282     12871.0  12871.0
2017-11-20 08:00:09.052282     12868.0      0.0
2017-11-20 08:00:09.052291     12867.5      0.0
2017-11-20 08:00:09.052291     12867.5      0.0
2017-11-20 08:00:09.052315     12867.5      0.0

My question is, according to the syntax, if the condition is not met, I should get the prior high achieved but I am getting 0 and not the prior high. 
Thanks! 

A:

IIUC, I think you want cummax:
df['high'] = df.tradePrice.cummax()

Output:
    SecurityID                   dateTime     ask1  ask1Volume     bid1  bid1Volume     ask2  ask2Volume     bid2  bid2Volume     ask3  ask3Volume     bid3  bid3Volume  tradePrice  tradeVolume  isTrade     high
0      2318276  2017-11-2008:00:09.052240  12869.0           1  12868.0           3  12870.0          19  12867.5           2  12872.5           2  12867.0           1         0.0            0        0      0.0
1      2318276  2017-11-2008:00:09.052260  12869.0           1  12868.0           3  12870.0          19  12867.5           2  12872.5           2  12867.0           1     12868.0            1        1  12868.0
2      2318276  2017-11-2008:00:09.052260  12869.0           1  12868.0           2  12870.0          19  12867.5           2  12872.5           2  12867.0           1     12869.0            1        0  12869.0
3      2318276  2017-11-2008:00:09.052270  12869.0           1  12868.0           2  12870.0          19  12867.5           2  12872.5           2  12867.0           1     12870.0            1        1  12870.0
4      2318276  2017-11-2008:00:09.052270  12869.0           1  12868.0           1  12870.0          19  12867.5           2  12872.5           2  12867.0           1     12868.0            1        0  12870.0
5      2318276  2017-11-2008:00:09.052282  12869.0           1  12868.0           1  12870.0          19  12867.5           2  12872.5           2  12867.0           1     12871.0            1        1  12871.0
6      2318276  2017-11-2008:00:09.052282  12869.0           1  12867.5           2  12870.0          19  12867.0           1  12872.5           2  12865.5           1     12868.0            1        0  12871.0
7      2318276  2017-11-2008:00:09.052291  12869.0           1  12867.5           2  12870.0          19  12867.0           1  12872.5           2  12865.5           1     12867.5            1        1  12871.0
8      2318276  2017-11-2008:00:09.052291  12869.0           1  12867.5           1  12870.0          19  12867.0           1  12872.5           2  12865.5           1     12867.5            1        0  12871.0
9      2318276  2017-11-2008:00:09.052315  12869.0           1  12867.5           1  12870.0          19  12867.0           1  12872.5           2  12865.5           1     12867.5            1        1  12871.0
10     2318276  2017-11-2008:00:09.052315  12869.0           1  12867.5           1  12870.0          19  12867.0           1  12872.5           2  12865.5           1     12867.0            1        1  12871.0
11     2318276  2017-11-2008:00:09.052315  12869.0           1  12867.5           1  12870.0          19  12867.0           1  12872.5           2  12865.5           1     12865.5            1        1  12871.0
12     2318276  2017-11-2008:00:09.052315  12869.0           1  12867.5           1  12870.0          19  12867.0           1  12872.5           2  12865.5           1     13865.0            1        1  13865.0
13     2318276  2017-11-2008:00:09.052315  12869.0           1  12867.5           1  12870.0          19  12867.0           1  12872.5           2  12865.5           1     12864.0            1        1  13865.0

