Calculate all missing values for specific data using pivot tables in pandas

I am working on this dataset called titanic.csv Let's simplify the problem and include some data here:

I need to calculate all missing values for child, as you see it is a value under who column. This should be done using a pivot table.

I have tried this solution:

pd.pivot_table(df[df['who'] == 'child'], 
index='sex', 
aggfunc=lambda x: x.isnull().sum(), 
 margins=True) # to sum all missing values based on gender

But I get this output: in which as you also notice, ALL row doesn't sum all missing values per gender.

Where is the problem in my code? Should I use another way to create the pivot table?

EDIT:

If you prefer to use a pivot table, just add the parameter dropna=False to get the result you want.

First answer:

If you want the number of missing values per features for only the child you can use isna/isnull directly after filtering:

data = {'survived': [0, 1, 1, 1, 0], 
        'pclass': [3, 1, None, 1, 3], 
        'sex': ['male', 'female', 'female', 'female', 'male'], 
        'age': [22, 38, None, None, 35], 
        'class_': ['Third', 'First', None, 'First', 'Third'], 
        'who': ['man', 'woman', 'child', 'child', 'man'], 
        'deck': [None, 'C', None, 'C', None], 
        'alive': ['no', 'yes', 'yes', 'yes', 'no'], 
        'alone': [False, False, True, False, True] } 
df = pd.DataFrame(data)

display(df[df["who"] == "child"].isna().sum())

survived    0
pclass      1
sex         0
age         2
class_      1
who         0
deck        1
alive       0
alone       0