Pandas 计算 groupby 函数中的空值

Question

df = pd.DataFrame({'A' : ['foo', 'bar', 'foo', 'bar', 'foo', 'bar', 'foo', 'foo'],
               'B' : ['one', 'one', 'two', 'three', 'two', 'two', 'one', 'three'],
               'C' : [np.nan, 'bla2', np.nan, 'bla3', np.nan, np.nan, np.nan, np.nan]})

Output:

     A      B     C
0  foo    one   NaN
1  bar    one  bla2
2  foo    two   NaN
3  bar  three  bla3
4  foo    two   NaN
5  bar    two   NaN
6  foo    one   NaN
7  foo  three   NaN

I would like to use groupby in order to count the number of NaN's for the different combinations of foo.

Expected Output (EDIT):

     A      B     C    D
0  foo    one   NaN    2
1  bar    one  bla2    0
2  foo    two   NaN    2
3  bar  three  bla3    0
4  foo    two   NaN    2
5  bar    two   NaN    1
6  foo    one   NaN    2
7  foo  three   NaN    1

Currently I am trying this:

df['count']=df.groupby(['A'])['B'].isnull().transform('sum')

But this is not working...

Thank You

Answer 1

I think you need groupby with sum of NaN values:

df2 = df.C.isnull().groupby([df['A'],df['B']]).sum().astype(int).reset_index(name='count')
print(df2)
     A      B  count
0  bar    one      0
1  bar  three      0
2  bar    two      1
3  foo    one      2
4  foo  three      1
5  foo    two      2

If need filter first add boolean indexing :

df = df[df['A'] == 'foo']
df2 = df.C.isnull().groupby([df['A'],df['B']]).sum().astype(int)
print(df2)
A    B    
foo  one      2
     three    1
     two      2

Or simpler:

df = df[df['A'] == 'foo']
df2 = df['B'].value_counts()
print(df2)
one      2
two      2
three    1
Name: B, dtype: int64

EDIT: Solution is very similar, only add transform :

df['D'] = df.C.isnull().groupby([df['A'],df['B']]).transform('sum').astype(int)
print(df)
     A      B     C  D
0  foo    one   NaN  2
1  bar    one  bla2  0
2  foo    two   NaN  2
3  bar  three  bla3  0
4  foo    two   NaN  2
5  bar    two   NaN  1
6  foo    one   NaN  2
7  foo  three   NaN  1

Similar solution:

df['D'] = df.C.isnull()
df['D'] = df.groupby(['A','B'])['D'].transform('sum').astype(int)
print(df)
     A      B     C  D
0  foo    one   NaN  2
1  bar    one  bla2  0
2  foo    two   NaN  2
3  bar  three  bla3  0
4  foo    two   NaN  2
5  bar    two   NaN  1
6  foo    one   NaN  2
7  foo  three   NaN  1

Answer 2

df[df.A == 'foo'].groupby('b').agg({'C': lambda x: x.isnull().sum()})

returns:

=>        C
B       
one    2
three  1
two    2