Here is the Dataframe I am working with:

You can create it using the snippet:

my_dict = {'id': [1,2,1,2,1,2,1,2,1,2,3,1,3, 3], 
           'category':['a', 'a',  'b', 'b', 'b', 'b',  'a', 'a',  'b', 'b', 'b', 'a', 'a', 'a'], 
           'value' : [1, 12, 34, 12, 12 ,34, 12, 35, 34, 45, 65, 55, 34, 25]
          }
x = pd.DataFrame(my_dict)
x

I want to filter IDs based on the condition: for category a, the count of values should be 2 and for category b, the count of values should be 3. Therefore, I would remove id 1 from category a and id 3 from category b from my original dataset x.

I can write the code for individual categories and start removing id's manually by using the code:

x.query('category == "a"').groupby('id').value.count().loc[lambda x: x != 2]
x.query('category == "b"').groupby('id').value.count().loc[lambda x: x != 3]

But, I don't want to do it manually since there are multiple categories. Is there a better way of doing it by considering all the categories at once and remove id's based on the condition listed in a list/dictionary?

CodePudding user response：

If need filter MultiIndex Series - s by dictionary use Index.get_level_values with Series.map and get equal values per groups in boolean indexing:

s = x.groupby(['category','id']).value.count()

d = {'a': 2, 'b': 3}
print (s[s.eq(s.index.get_level_values(0).map(d))])
category  id
a         2     2
          3     2
b         1     3
          2     3
Name: value, dtype: int64

If need filter original DataFrame:

s = x.groupby(['category','id'])['value'].transform('count')
print (s)
0     3
1     2
2     3
3     3
4     3
5     3
6     3
7     2
8     3
9     3
10    1
11    3
12    2
13    2
Name: value, dtype: int64

d = {'a': 2, 'b': 3}
print (x[s.eq(x['category'].map(d))])
    id category  value
1    2        a     12
2    1        b     34
3    2        b     12
4    1        b     12
5    2        b     34
7    2        a     35
8    1        b     34
9    2        b     45
12   3        a     34
13   3        a     25