New in 0.12, groupby objects have a filter
method, allowing you to do these types of operations:
In [11]: g = data.groupby('tag')
In [12]: g.filter(lambda x: len(x) > 1) # pandas 0.13.1
Out[12]:
pid tag
1 1 45
2 1 62
4 2 45
7 3 62
The function (the first argument of filter) is applied to each group (subframe), and the results include elements of the original DataFrame belonging to groups which evaluated to True.
Note: in 0.12 the ordering is different than in the original DataFrame, this was fixed in 0.13+:
In [21]: g.filter(lambda x: len(x) > 1) # pandas 0.12
Out[21]:
pid tag
1 1 45
4 2 45
2 1 62
7 3 62
Edit: Thanks to @WesMcKinney for showing this much more direct way:
data[data.groupby('tag').pid.transform(len) > 1]
import pandas
import numpy as np
data = pandas.DataFrame(
{'pid' : [1,1,1,2,2,3,3,3],
'tag' : [23,45,62,24,45,34,25,62],
})
bytag = data.groupby('tag').aggregate(np.count_nonzero)
tags = bytag[bytag.pid >= 2].index
print(data[data['tag'].isin(tags)])
yields
pid tag
1 1 45
2 1 62
4 2 45
7 3 62