Pandas Merge row data with multiple values to Python list for a column

Question

I have a data-frame that looks like

DATA

*id*,             *name*,                      *URL*,                 *Type*  
    2,             birth_f         


        

Answer 1

I think you need groupby and aggregate tuple and then convert to list:

df = df.groupby(['id','name']).agg(lambda x: tuple(x)).applymap(list).reset_index()

print (df)
   id                    name  \
0   2  birth_france_by_region   
1   3                long_lat   
2   4      random_time_series   
3   5             birth_names   

                                                 URL          Type  
0                 [http://abc.cm, http://pt.python]      [T1, T2]  
1                  [http://abc.cm, http://pqur.com]      [T3, T1]  
2            [http://sadsdc.com, http://sadcadf.com]      [T2, T3]  
3  [http://google.;com, http://helloworld.com, ht...  [T1, T2, T3] 

Because in version 0.20.3 raise error:

df = df.groupby(['id','name']).agg(lambda x: x.tolist())

ValueError: Function does not reduce

Answer 2

This will give you the expected result for the "URL" column:

test.groupby(["id", "name"])['URL'].apply(list)

id  name                  
2   birth_france_by_region                 [http://abc. com, http://pt. python]
3   long_lat                                [http://abc. com, http://pqur. com]
4   random_time_series                [http://sadsdc. com, http://sadcadf. com]
5   birth_names               [http://google. com, http://helloworld. com, h...

However, I can't find a solution for both URL and Type columns.

I could propose to do it in 2 steps:

• temp_table1 = test.groupby(["id", "name"])['URL'].apply(list)
• temp_table2 = test.groupby(["id", "name"])['Type'].apply(list)
• Merge temp_table1 & temp_table2