Python: transform data set

Question

I have the following data set

            id  type  value
0             1   A    10
1             1   C   120
2             2   B    20
3             2   C    40
4             3   A    10
5             3   B    50

I want in python to transform it to be like (1,A,10,C,120) (2,B,20,C,40) (3,A,10,B,50)

Any suggestion would be much appreciated

Start from here, Read about this and this. Finally use this and solve your problem. As for the question it is too broad for Stack Overflow. See How to Ask and minimal reproducible example. — Bhargav Rao
– Bhargav Rao, Commented Nov 10, 2016 at 19:09

Nickil Maveli · Accepted Answer · 2016-11-10 19:44:53Z

2

Perform groupby w.r.t id column. Iterate over each group by converting the other two columns to a list and add the unique value corresponding to the id per group number alongside. Finally, convert them into a tuple and append these to a list.

grouped = df.groupby('id')
L = []
for _, grp in grouped:
    L.append(tuple(grouped.get_group(_)['id'].unique().tolist() + grp[['type','value']].values.ravel().tolist()))
print(L)
#[(1, 'A', 10, 'C', 120), (2, 'B', 20, 'C', 40), (3, 'A', 10, 'B', 50)]

answered Nov 10, 2016 at 19:44

Nickil Maveli

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2 Comments

Marc Zaharescu Over a year ago

thanks @Nickil Maveli, however after I run your implementation after the values another 'L' is being appended, is there anyway I can get rid of it?

Nickil Maveli Over a year ago

You mean you do not want to keep them as a list? How do you want to store them? You could also try editing the question to show what is going wrong.

jezrael · Accepted Answer · 2016-11-10 19:50:56Z

2

You can use:

L = df.groupby('id').apply(lambda x: tuple([x['id'].iat[0]] + 
                                          x[['type','value']].values.flatten().tolist()))
                    .tolist()
print (L)
[(1, 'A', 10, 'C', 120), (2, 'B', 20, 'C', 40), (3, 'A', 10, 'B', 50)]

answered Nov 10, 2016 at 19:50

jezrael

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