Pandas groupby and filter

Question

I have dataframe:

df = pd.DataFrame({'ID':[1,1,2,2,3,3], 
                   'YEAR' : [2011,2012,2012,2013,2013,2014], 
                   'V': [0,1,1,0,1,0],
                   'C':[00,11,22,33,44,55]})

I would like to group by ID, and select the row with V = 0 within each group.

This doesn't seem to work:

print(df.groupby(['ID']).filter(lambda x: x['V'] == 0)) 

Got an error:

TypeError: filter function returned a Series, but expected a scalar bool

How can I use filter to achieve the goal? Thank you.

EDIT: The condition on V may vary for each group, e.g., it could be V==0 for ID 1, V==1 for ID 2, and this info can be available through another DF:

df = pd.DataFrame({'ID':[1,2,3], 
                   'V': [0,1,0])

So how to do row filtering within each group?

Answer 1

I think groupby is not necessary, use boolean indexing only if need all rows where V is 0:

print (df[df.V == 0])
    C  ID  V  YEAR
0   0   1  0  2011
3  33   2  0  2013
5  55   3  0  2014

But if need return all groups where is at least one value of column V equal 0 add any, because filter need True or False for filtering all rows in group:

print(df.groupby(['ID']).filter(lambda x: (x['V'] == 0).any())) 
    C  ID  V  YEAR
0   0   1  0  2011
1  11   1  1  2012
2  22   2  1  2012
3  33   2  0  2013
4  44   3  1  2013
5  55   3  0  2014

Better for testing is change column for groupby - row with 2012 is filter out because no V==0:

print(df.groupby(['YEAR']).filter(lambda x: (x['V'] == 0).any())) 
    C  ID  V  YEAR
0   0   1  0  2011
3  33   2  0  2013
4  44   3  1  2013
5  55   3  0  2014

If performance is important use GroupBy.transform with boolean indexing:

print(df[(df['V'] == 0).groupby(df['YEAR']).transform('any')]) 
   ID  YEAR  V   C
0   1  2011  0   0
3   2  2013  0  33
4   3  2013  1  44
5   3  2014  0  55

Detail:

print((df['V'] == 0).groupby(df['YEAR']).transform('any')) 
0     True
1    False
2    False
3     True
4     True
5     True
Name: V, dtype: bool