Suppose we take a pandas dataframe...
name age family
0 john 1 1
1 jason 36 1
2 jane 32 1
3 jack 26 2
4 james 30 2
Then do a groupby() ...
group_df = df.groupby('family')
group_df = group_df.aggregate({'name': name_join, 'age': pd.np.mean})
Then do some aggregate/summarize operation (in my example, my function name_join aggregates the names):
def name_join(list_names, concat='-'):
return concat.join(list_names)
The grouped summarized output is thus:
age name
family
1 23 john-jason-jane
2 28 jack-james
Is there a quick, efficient way to get to the following from the aggregated table?
name age family
0 john 23 1
1 jason 23 1
2 jane 23 1
3 jack 28 2
4 james 28 2
(Note: the age column values are just examples, I don't care for the information I am losing after averaging in this specific example)
The rough equivalent is .reset_index(), but it may not be helpful to think of it as the "opposite" of groupby().
You are splitting a string in to pieces, and maintaining each piece's association with 'family'. This old answer of mine does the job.
Just set 'family' as the index column first, refer to the link above, and then reset_index() at the end to get your desired result.