Looking for simpler solution to group by and select rows in pandas

Question

df:

id c1 c2 c3
101  a b c
102  b c d
103  d e f
101  h i j
102  k l m

I want to select rows based on grouping on id column where count > 1

The result should be all rows whose id had more than 1 entry

Expected result:

df:

id c1 c2 c3
101  a b c
102  b c d
101  h i j
102  k l m

I am able to achieve this with below code I wrote.

g = df.groupby('id').size().reset_index(name='counts')
filt = g.query('counts > 1')
m_filt = df.id.isin (filt.id)
df_filtered= df[m_filt]

Wanted to check if there is a better way of doing this.

Answer 1

Use GroupBy.transform with GroupBy.size for Series with same size like original DataFrame, so possible filter by boolean indexing:

df[df.groupby('id').transform('size')['id'].gt(1)]

Or if need all duplicated rows use DataFrame.duplicated with keep=False:

df[df.duplicated('id', keep=False)]

Or similar:

df[df['id'].duplicated(keep=False)]