Pandas groupby then drop groups below specified size

Question

I'm trying to separate a DataFrame into groups and drop groups below a minimum size (small outliers).

Here's what I've tried:

df.groupby(['A']).filter(lambda x: x.count() > min_size)
df.groupby(['A']).filter(lambda x: x.size() > min_size)
df.groupby(['A']).filter(lambda x: x['A'].count() > min_size)
df.groupby(['A']).filter(lambda x: x['A'].size() > min_size)

But these either throw an exception or return a different table than I'm expecting. I'd just like to filter, not compute a new table.

Answer 1

You can use len:

In [11]: df = pd.DataFrame([[1, 2], [1, 4], [5, 6]], columns=['A', 'B'])

In [12]: df.groupby('A').filter(lambda x: len(x) > 1)
Out[12]:
   A  B
0  1  2
1  1  4

Answer 2

The number of rows is in the attribute .shape[0]:

df.groupby('A').filter(lambda x: x.shape[0] >= min_size)

NB: If you want to remove the groups below the minimum size, keep those that are above or at the minimum size (>=, not >).