Convert a pandas dataframe in a transactional data format to a list - Python

Question

I have a pandas dataframe in a transactional format:

id  purchased_item
1   apple
1   banana
1   carrot
2   banana
3   apple
4   apple
4   carrot
4   diet coke
5   banana
5   carrot
6   banana
6   carrot

I would like to convert this to the following:

[['apple', 'banana', 'carrot'],
 ['banana'],
 ['apple'],
 ['apple', 'carrot', 'diet coke'],
 ['banana', 'carrot'],
 ['banana', 'carrot']]

I have tried this:

df.groupby(['id'])['purchased_item'].apply(list)

The output looks like:

customer_id
1                 [apple, banana, carrot]
2                                [banana]
3                                 [apple]
4              [apple, carrot, diet coke]
5                        [banana, carrot]
6                        [banana, carrot]

What to do next? Or is there a different approach? Thanks a lot for help.

This question is answered here:stackoverflow.com/questions/34080979/… — Joe T. Boka
– Joe T. Boka, Commented Dec 4, 2015 at 6:20
To me, this is a different question because the original data are in a different format. So I am looking for a different solution. — kevin
– kevin, Commented Dec 4, 2015 at 6:23
I found a solution from here stackoverflow.com/questions/15112234/… — kevin
– kevin, Commented Dec 4, 2015 at 6:36

Community · Accepted Answer · 2017-05-23 12:23:41Z

3

The solution which you mentioned in a comment from answer to question:

df.groupby(['id'])['purchased_item'].apply(list).values.tolist()

In [434]: df.groupby(['id'])['purchased_item'].apply(list).values.tolist()
Out[434]:
[['apple', 'banana', 'carrot'],
 ['banana'],
 ['apple'],
 ['apple', 'carrot', 'diet_coke'],
 ['banana', 'carrot'],
 ['banana', 'carrot']]

EDIT

Some test performance to compare with @Colonel Beauvel solution:

In [472]: %timeit [gr['purchased_item'].tolist() for n, gr in df.groupby('id')]
100 loops, best of 3: 2.1 ms per loop

In [473]: %timeit df.groupby(['id'])['purchased_item'].apply(list).values.tolist()
1000 loops, best of 3: 1.36 ms per loop

edited May 23, 2017 at 12:23

CommunityBot

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answered Dec 4, 2015 at 7:48

Anton Protopopov

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Comments

Colonel Beauvel · Accepted Answer · 2015-12-04 08:10:37Z

1

I would rather employ a different solution using comprehension list:

[gr['purchased_item'].tolist() for n, gr in df.groupby('id')]

Out[9]:
[['apple', 'banana', 'carrot'],
 ['banana'],
 ['apple'],
 ['apple', 'carrot', 'dietcoke'],
 ['banana', 'carrot'],
 ['banana', 'carrot']]

answered Dec 4, 2015 at 8:10

Colonel Beauvel

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