How to find the number of not unique rows after groupby()

Question

I have a data frame df with two features: ID_owner , ID_phone , I have to find:

1. How many people have more than n phones.
2. Phones shared among more owners, ID_phone having one or more ID_owner .

In order to answer the first question, I have tried:

df.groupby('`ID_owner`')['`ID_phone'].nunique().to_frame()

It seems doesn't work because I need to count the number of duplicates rows per ID_owner after the grouping. I have encountered the same issue in the second question.

I would like to know if exist a specific method or function in pandas for this kind of issues.

The output, for the first question, should be a dataframe with two columns: one showing the ID_owner and the second with the number of smartphones that ID_owner owns.

Answer 1

It looks like you were slicing your table prematurely though it seems like you want to keep the aggregated table. To answer your first question the following code would work.

n = 2

(df.groupby('ID_owner').agg({'ID_phone': pd.Series.nunique}).query('ID_phone > @n').shape[0]

To answer your second question you can reverse the IDs in the above query, change n, and select the "ID_phone" column.

Answer 2

df1.groupby('ID_owner').agg({'ID_phone': 'unique'}).reset_index()

or you can use the following way

df1.groupby('User_owner').apply(lambda x:x.zipcode.unique()).reset_index()

this will give you the output:

    User_owner  zipcode
0   Dave        [34567]
1   Donald      [34353]
2   Jae         [12345]
3   Shankar     [23456, 22222]

but for count you can use, nunique function:

df1.groupby('ID_owner').agg({'ID_phone': 'nunique'}).reset_index().rename(columns = {'zipcode':'count'})

or

df1.groupby('User_owner').apply(lambda x:x.zipcode.nunique()).reset_index(name ='count')

which will result in

    User_owner  count
0   Dave        1
1   Donald      1
2   Jae         1
3   Shankar     2