Group by column on basis of occurrences-CodePudding

This is my dataframe:

df = pd.DataFrame({
    'User': ["alex", "alex", "ravi", "dodo", "dodo", "dodo", "cokie","dodo","nemo","ravi"],
    'Id': ['a', 'b', 'b', 'a', 'b', 'b', 'c','a','e','b']
})

This is how my dataframe looklike:

    User Id
0   alex  a
1   alex  b
2   ravi  b
3   dodo  a
4   dodo  b
5   dodo  b
6  cokie  c
7   dodo  a
8   nemo  e
9   ravi  b

I first counted the no of items for each user using the following code:

df_group = df.groupby(['User', 'Id'])

# size of group to count observations
df_group = df_group.size()
  
# make a column name 
df_group = df_group.reset_index(name='Observation')

This is how it looks:

  User  Id Observation
0   alex    a   1
1   alex    b   1
2   cokie   c   1
3   dodo    a   2
4   dodo    b   2
5   nemo    e   1
6   ravi    b   2

I want to remove a user who is coming 1 time and also observation is 1. For example user nemo and cokie. But I don't want to remove user alex, even though items a and b are coming 1 time or user ravi.

How can I do it?

My end goal:

    User    Id Observation
0   alex    a   1
1   alex    b   1

3   dodo    a   2
4   dodo    b   2

6   ravi    b   2

CodePudding user response：

To do it in an automated way I suggest to use a merge after using .groupby(['User']).filter(lambda x: len(x) to filter those Users that show up only once.

df_group = df.groupby(['User', 'Id'])

# size of group to count observations
df_group = df_group.size()
  
# make a column name 
df_group = df_group.reset_index(name='Observation').merge(df.groupby(['User']).filter(lambda x: len(x) > 1)).drop_duplicates()

Which outputs:

   User Id  Observation
0  alex  a            1
1  alex  b            1
2  dodo  a            2
4  dodo  b            2
6  ravi  b            2

Alternatively without using merge:

df_group = df.groupby(['User', 'Id'],as_index=False)

# size of group to count observations
df_group = df_group.size()
  
# make a column name 
df_group = df_group.loc[df_group['User'].isin(df.groupby(['User']).filter(lambda x: len(x) > 1)['User'].unique())]

CodePudding user response：

This works:

s = df.groupby('User').size() > 1
df = df.merge(df_group, how='left')[df['User'].isin(s[s].index)].drop_duplicates()