Replace non zero values in a single column, pandas dataframe

Question

I have this pandas dataframe: rls[20, 34, 21, 19] censored[260, 0, 0, 380] I am trying to replace non zero values in the censored column with 1s. I tried many different methods, but all of them either return the same copy of the dataframe or change both rls and censored colums which is not what I want. It feels that replace method should work, but I don't know how to specify non zero values data_rls.censored.replace([(!=0), 0], [1, 0], inplace=True)

Answer 1

Welcome to SO. Please review how to produce a reproducible example.

You can use the pd.where() function for your problem.

import pandas as pd
df = pd.DataFrame({'a':[1,3,4,0,0,4,5],'b':[1,3,4,0,0,4,5]})
df.head()

     a  b
0    1  1   
1    3  3   
2    4  4   
3    0  0   
4    0  0


df['a'] = df['a'].where(df['a'] == 0, 1)
df

    a   b
0   1   1
1   1   3
2   1   4
3   0   0
4   0   0
5   1   4
6   1   5