One way to use index with condition is first get the index of all the rows that satisfy your condition and then simply use those row indexes in a multiple of ways

conditional_index = df.loc[ df['col name'] <condition> ].index

Then you can use these row indexes in variety of ways like

1. Replace value of one column for conditional_index

df.loc[conditional_index , [col name]]= <new value>

2. Replace value of multiple column for conditional_index

df.loc[conditional_index, [col1,col2]]= <new value>

3. One benefit with saving the conditional_index is that you can assign value of one column to another column with same row index

df.loc[conditional_index, [col1,col2]]= df.loc[conditional_index,'col name']

This is all possible because .index returns a array of index which .loc can use with direct addressing so it avoids traversals again and again.

One way to use index with condition is first get the index of all the rows that satisfy your condition and then simply use those row indexes in a multiple of ways

conditional_index = df.loc[ df['col name'] <condition> ].index

Example condition is like

==5, >10 , =="Any string", >= DateTime

Then you can use these row indexes in variety of ways like

1. Replace value of one column for conditional_index

df.loc[conditional_index , [col name]]= <new value>

2. Replace value of multiple column for conditional_index

df.loc[conditional_index, [col1,col2]]= <new value>

3. One benefit with saving the conditional_index is that you can assign value of one column to another column with same row index

df.loc[conditional_index, [col1,col2]]= df.loc[conditional_index,'col name']

This is all possible because .index returns a array of index which .loc can use with direct addressing so it avoids traversals again and again.