pandas dataframe add new column based on calulation on other column and avoid chained index

Question

I have a pandas dataframe and I need to add a new column, which would be based on calculation of specific columns,indicated by a column 'site'. I have found a way to do this with resort to numpy, but always it gives warning about chained index. I am sure there should be better solution, please help if you know.

df_num_bin1['Chip_id_3']=np.where(df_num_bin1[key_site_num]==1,df_num_bin1[WB_89_S1]*0x100+df_num_bin1[WB_78_S1],df_num_bin1[WB_89_S2]*0x100+df_num_bin1[WB_78_S2])
df_num_bin1['Chip_id_2']=np.where(df_num_bin1[key_site_num]==1,df_num_bin1[WB_67_S1]*0x100+df_num_bin1[WB_56_S1],df_num_bin1[WB_67_S2]*0x100+df_num_bin1[WB_56_S2])
df_num_bin1['Chip_id_1']=np.where(df_num_bin1[key_site_num]==1,df_num_bin1[WB_45_S1]*0x100+df_num_bin1[WB_34_S1],df_num_bin1[WB_45_S2]*0x100+df_num_bin1[WB_34_S2])
df_num_bin1['Chip_id_0']=np.where(df_num_bin1[key_site_num]==1,df_num_bin1[WB_23_S1]*0x100+df_num_bin1[WB_12_S1],df_num_bin1[WB_23_S2]*0x100+df_num_bin1[WB_12_S2])
df_num_bin1['mac_low']=(df_num_bin1['Chip_id_1'].map(int) % 0x10000) *0x100+df_num_bin1['Chip_id_0'].map(int) // 0x1000000

The code above have 2 issues:

1: Here the value of column [key_site_num] determines which columns I should extract chip id data from. In this example it is only of site 0 or 1, but actually it could be 2 or 3 as well. I would need a general solution.

2: it generates chained index warning;

C:\Anaconda2\lib\site-packages\ipykernel\__main__.py:35: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

Answer 1

Well, i´m not too sure about your first quest but i think that this will help you.

import pandas as pd
reader = pd.read_csv(path,engine='python')
reader['new'] = reader['treasury.maturity.rate']+reader['bond.yield']
reader.to_csv('test.csv',index=False)

As you can see,you don´t need get the values before operate with them only reference the column where they are; and to do the same for only a specific row you could filter the dataframe before create the new column.