Pandas dataframe grouping values

Question

I have a pandas dataframe like this,

dd = pd.DataFrame(
{'name': ['abc','bcd','abc'],
 'seconds': [75,77,90],
})

I need to combine the seconds column into a single list for rows with same name.

I am able to do this using for loop,

names= list(set(dd['name']))
counter=[]
for a in names:
    counter.append(list(dd[dd['name'] == a]['seconds']))
end
seconds_list = pd.DataFrame(
{'name': names,
'seconds': counter,
})

Output:

But this takes a lot of time on a big dataframe. Any simple way to achieve this without a for loop?

Thanks!

jezrael · Accepted Answer · 2017-09-06 13:59:35Z

2

Use groupby with apply list:

df = dd.groupby('name')['seconds'].apply(list).reset_index()
print (df)

  name   seconds
0  abc  [75, 90]
1  bcd      [77]

answered Sep 6, 2017 at 13:59

jezrael

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Comments

Scott Boston · Accepted Answer · 2017-09-06 14:05:31Z

1

Use groupby, agg, and tolist:

 dd.groupby('name')['seconds'].agg(lambda x: x.tolist()).reset_index(name='seconds')

Output:

  name   seconds
0  abc  [75, 90]
1  bcd      [77]

answered Sep 6, 2017 at 14:05

Scott Boston

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