How to put groupby result into the same row - pandas

Question

I have the following dataframe:
import pandas as pd
df = pd.DataFrame({'id' :["c1","c1","c1","c2","c2","c3","c3","c3","c3","c4","c4","c5","c6","c6","c6","c7","c7"],'store' : ["first","second","second","first",
"second","first","third","fourth",
"fifth","second","fifth","first",
"first","second","third","fourth","fifth"],
'purchase': [10,10,10,20,20,30,30,30,30,40,40,50,60,60,60,70,70]})
after you do groupby:
df_group= df.groupby(['id','store']).agg({'purchase': ["sum"]})
Result of df_group
I want to let each card have all the purchases in the different stores appear in the same row, for example:
id 1_store 1_sum 2_store 2_sum 3_store 3_sum 4_store 4_sum...
0 c1 first 10 second 20
1 C2 first 20 second 20
2 c3 fifth 30 first 30 fourth 30 third 30
I don't want to use unstack on store, the reason behind it is there are so many stores, it will cause too much columns for stores and most of them are empty.
How can I achieve the above result?
Thanks

Answer 1

Need to create a cumcount variable to get the column labels, then this becomes a .pivot_table problem: You get quite the MultiIndex on the columns, which we can collapse:
df_group['idx'] = df_group.groupby(level=0).cumcount()+1
df_res = (df_group.reset_index()
.pivot_table(index='id',
columns='idx',
values=['store', 'purchase'],
aggfunc='first')
.sort_index(level=2, axis=1))
Output:
purchase store purchase store purchase store purchase store
sum sum sum sum
idx 1 1 2 2 3 3 4 4
id
c1 10.0 first 20.0 second NaN NaN NaN NaN
c2 20.0 first 20.0 second NaN NaN NaN NaN
c3 30.0 fifth 30.0 first 30.0 fourth 30.0 third
c4 40.0 fifth 40.0 second NaN NaN NaN NaN
c5 50.0 first NaN NaN NaN NaN NaN NaN
c6 60.0 first 60.0 second 60.0 third NaN NaN
c7 70.0 fifth 70.0 fourth NaN NaN NaN NaN
If need to collapse the columns (probably a good idea since it's not lexsorted anymore):
df_res.columns = ['_'.join(map(str, [y for y in x[::-1] if y != ''])) for x in df_res.columns]
1_sum_purchase 1_store 2_sum_purchase 2_store 3_sum_purchase 3_store 4_sum_purchase 4_store
id
c1 10.0 first 20.0 second NaN NaN NaN NaN
c2 20.0 first 20.0 second NaN NaN NaN NaN
c3 30.0 fifth 30.0 first 30.0 fourth 30.0 third
c4 40.0 fifth 40.0 second NaN NaN NaN NaN
c5 50.0 first NaN NaN NaN NaN NaN NaN
c6 60.0 first 60.0 second 60.0 third NaN NaN
c7 70.0 fifth 70.0 fourth NaN NaN NaN NaN