Select rows based on a criteria

[klllmmm]

# dataframe 

import pandas as pd

table = pd.DataFrame(data = {'DateTime':['01-01-17 16:30','01-01-17 16:31','02-01-17 08:45','02-01-17 08:45','02-01-17 10:40','02-01-17 16:40','02-01-17 16:41','02-01-17 16:42','03-01-17 08:45','03-01-17 08:45','03-01-17 10:48'],
                             'Amount':[1000,2000,1000,1000,50000,4000,5000,9000,4000,5000,20000],
                             'Ref':['Deduct','Deduct','Add','Add','Add','Transfer','Transfer','Deduct','Add','Add','Deduct'],
                             'DrCode':[1500,1400,9000,9000,9000,1600,1700,2000,9000,9000,4000],
                             'CrCode':[9000,9000,1500,1400,3000,2000,2000,9000,1600,1700,9000],})

# convert the DateTime field from an string/object to datetime 
table['DateTime']= pd.to_datetime(table['DateTime'])

i'm no where near the test of the crterias such as datetime & Amount, through a loop.

below code just gives me similar values in two coulmns.

table[table.CrCode.isin(table.DrCode) & table.DrCode.isin(table.CrCode)]
Out[210]: 
   Amount  CrCode            DateTime  DrCode       Ref
0    1000    9000 2017-01-01 16:30:00    1500    Deduct
1    2000    9000 2017-01-01 16:31:00    1400    Deduct
2    1000    1500 2017-02-01 08:45:00    9000       Add
3    1000    1400 2017-02-01 08:45:00    9000       Add
5    4000    2000 2017-02-01 16:40:00    1600  Transfer
6    5000    2000 2017-02-01 16:41:00    1700  Transfer
7    9000    9000 2017-02-01 16:42:00    2000    Deduct
8    4000    1600 2017-03-01 08:45:00    9000       Add
9    5000    1700 2017-03-01 08:45:00    9000       Add

I hope there should be a way to do this.

Thanks for your interest in this regard.