Turn cell into False based another row/column Pandas

I have the following table of boolean values:

df = pd.DataFrame(data={'val1': [True, False, False, True], 
                        'val2': [False, True, False, True], 
                        'val3': [True, True, False, True], 
                        'val4': [True, False, True, False], 
                        'val5': [True, True, False, False],
                        'val6': [False, False, True, True]},
                        index=pd.Series([1, 2, 3, 4], name='index'))

index	val1	val2	val3	val4	val5	val6
1	True	False	True	True	True	False
2	False	True	True	False	True	False
3	False	False	False	True	False	True
4	True	True	True	False	False	True

I also have the following dictionary:

dict = {'val1': ['val2', 'val3'], 'val4': ['val5', 'val6']}

How do I change the table so for every key column in dict, if that row has a True value, the value columns turn to False?

index	val1	val2	val3	val4	val5	val6
1	True	False	False	True	False	False
2	False	True	True	False	True	False
3	False	False	False	True	False	False
4	True	False	False	False	False	True

For example, since val1 is True at index 1, val2 and val3 turned to False

I’m doing something like this:

for k, v in dict.items():
    if df[k] == True:
        df[v] = False

but I get the following error:

ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all()

Answer

You can’t use if with Series, which is only meant for scalar boolean check. Use conditional update with boolean indexing instead:

for k, v in dict.items():
    df.loc[df[k], v] = False  # update columns v where df[k] is True

df
        val1   val2   val3   val4   val5   val6
index                                          
1       True  False  False   True  False  False
2      False   True   True  False   True  False
3      False  False  False   True  False  False
4       True  False  False  False  False   True