How to aggregate a boolean field with null values with pandas?

Convert them to numeric columns. The None will become NaN, Trues become 1, and Falses become 0. A convenient way to convert the whole dataframe is to use pd.to_numeric with the errors parameter set to ignore. This will leave the grouping column alone because it will error out on move on.

Consider the dataframe df

df = pd.DataFrame(dict(
        gcol=list('aaaabbbb'),
        clc1=[True, False, True, None] * 2,
        clc2=[True, False, True, False] * 2,
        clc3=[True, True, True, True] * 2,
        clc4=[False, None, None, True]* 2
    ))

This is what converting to numeric looks like

df.apply(pd.to_numeric, errors='ignore')

   clc1   clc2  clc3  clc4 gcol
0   1.0   True  True   0.0    a
1   0.0  False  True   NaN    a
2   1.0   True  True   NaN    a
3   NaN  False  True   1.0    a
4   1.0   True  True   0.0    b
5   0.0  False  True   NaN    b
6   1.0   True  True   NaN    b
7   NaN  False  True   1.0    b

Using this with the subsequent groupby should get you what you want.

df.apply(pd.to_numeric, errors='ignore').groupby('gcol').mean()

          clc1  clc2  clc3  clc4
gcol                            
a     0.666667   0.5   1.0   0.5
b     0.666667   0.5   1.0   0.5