Pandas turn last N columns into NA based on another dataframe

Question

I have the following dataframes:

df1 = pd.DataFrame(data={'col1': ['a', 'd', 'g', 'j'], 
                        'col2': ['b', 'c', 'i', np.nan], 
                        'col3': ['c', 'f', 'i', np.nan],
                        'col4': ['x', np.nan, np.nan, np.nan]},
                index=pd.Series(['ind1', 'ind2', 'ind3', 'ind4'], name='index'))

index	col1	col2	col3	col4
ind1	a	b	c	x
ind2	d	c	f	NaN
ind3	g	i	i	NaN
ind4	j	NaN	NaN	NaN

df2 = pd.Series(data=[True, False, True, False],
                index=pd.Series(['ind1', 'ind2', 'ind3', 'ind4']))

ind1	True
ind2	False
ind3	True
ind4	False

How do I make the last 2 values for each row in df1 into NA, based on the boolean values of df2 ?

In this case, since ind1 and ind3 are True, it would impact the same indices in df1 .

index	col1	col2	col3	col4
ind1	a	b	NaN	NaN
ind2	d	c	f	NaN
ind3	g	i	NaN	NaN
ind4	j	NaN	NaN	NaN

Answer 1

A possible solution, based on pandas.DataFrame.mask :

df1[['col3', 'col4']] = df1[['col3', 'col4']].mask(df2)

Output:

      col1 col2 col3 col4
index                    
ind1     a    b  NaN  NaN
ind2     d    c    f  NaN
ind3     g    i  NaN  NaN
ind4     j  NaN  NaN  NaN

Answer 2

You can use boolean indexing :

N = 2
df1.iloc[df2, -N:] = np.nan

NB. what you call df2 is actually a Series, s / ser might be more appropriate as a name.

output:

      col1 col2 col3 col4
index                    
ind1     a    b  NaN  NaN
ind2     d    c    f  NaN
ind3     g    i  NaN  NaN
ind4     j  NaN  NaN  NaN