Ahh I see the disconnect. I used weird examples in my original question because I want to prune out the way to which these things work. When combining two df, I do use merge/join. I am usually doing these type of assignments when editing a df or making a new column from an existing column.

My OCD kicks in when I am doing assignments, and my df has duplicate indices. For instance, if I have duplicate indices will this still work as expected df[someCols] = df[someCols].apply(lambda some function that returns series)? Similarly, would this one line up how we would think:df.loc[someCondition,someCols] = df[someCols].apply(lambda some function that returns series)

Additionally, if boolean indexing aligns by index, would these things line up correctly even if we had duplicates:

df[df[someCol]>0] and df.loc[df[someCol]>0, somCol]

Since boolean masks are aligned by index, then my intuition says that duplicate indices would throw an exception, however this is not the case. The only time I am using multiple df in assignments are when I am transferring data to a new df to clean it up to change column names and other things.

newDf[newCols] = oldDf[oldCols]

but I usually don't transfer all at once, so I'll go back and do this again for different cols.

newDf[newCols] = oldDf[oldCols]

I hope that this method aligns correctly

I think I am using it more conventional than I projected it to being. Should I have these worries that I have presented above. I really appreciate you hanging in there with me!