如何在 df 上组合行？

Question

I have this dataframe(after doing one hot encoding) how do I combine it together?

    Code    Fund_Code_AADR  Fund_Code_AAXJ  Fund_Code_ABEQ  Fund_Code_ACSI  Fund_Code_ACVF  Fund_Code_ACWD  Fund_Code_ACWF  Fund_Code_ACWI  Fund_Code_ACWV  ... 
1625    MSFT    0   0   0   1   0   0   0   0   0   ... 
1635    MSFT    0   0   0   0   1   0   0   0   0   ... 
2067    MSFT    0   0   0   0   0   0   1   0   0   ... 
2423    MSFT    0   0   0   0   0   0   0   1   0   ... 
4517    IBM 0   0   0   0   0   0   0   0   1   ... 
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...

how do I combine all the values together? (ie one row for MFST and if it's 1 in any of those columns it's a 1 on the single row)?

Update - sorry I wasn't clear there is multiple items in code field. It's not all the same code but I want them merged as the same code

Answer 1

This should work all codes are different

new_df=df.drop(['Code'],axis=1)
vals=new_df.any(axis=1).map({True:1,False:0})
df['any']=vals

but if there is any duplicates use group by

new_df=df.groupby('Code').sum().any().map({True:1,False:0})

Answer 2

import numpy as np
df.replace(0, np.nan).ffill().bfill().iloc[:1,:]

or

df.groupby('Fund_Code_AADR').apply(lambda x: x.sum(0))

Answer 3

one solution would be to groupby sum and then convert all non zeroes to one:

df.groupby('code').sum().applymap(lambda x: 0 if x==0 else 1)