Panda'lar dateframe yeniden biçimlendirmek

Question

Aşağıda gibi görünen bir tarihle oynuyorum. Acaba

song_id      action_type ds 
0 effb071415be51f11e845884e67c0f8c 1 14 days 
1 f87ff481d85d2f95335ab602f38a7655 1 13 days 
2 8a27d9a6c59628c991c154e8d93f412e 2 12 days 
3 ecea5fe33e6817d09c395f2910479728 1 11 days 
4 31a3d0420d89c9b121bb55dbdbbeda6b 1 13 days 
5 096e604f7b152fad0246ae731ed8ca73 1 15 days 
6 5a7d9d75b898cd1b19ef6941cc1ddccf 1 12 days 
7 8a103bd3a3295fbf9b3c3bf7972db299 2 12 days 
8 17e90c7b3b7ebbe4b47344fcfab2fa7a 1 11 days 
9 8a27d9a6c59628c991c154e8d93f412e 2 13 days 

bunu nasıl yeniden şekillendirmek ve böyle bir şey olsun: Artık

song_id day1_type1 day1_type2 day1_type3 day2_type1 ......... dayn_typen 
(songid) (count of type1 on day1) (Nan if no count)...... (count of typen on dayn) 

Ben

action.groupby(['song_id','ds','action_type']).action_type.sum() 

kullanmak ve benzeri bir şeyle karşılaşırsınız:

song_id       ds  action_type 
00088cb1e6d740fcd42bc8ff2673c805 3 days 1    1 
            4 days 1    1 
            13 days 2    2 
            27 days 1    1 
            41 days 1    1 
              2    2 
            42 days 1    1 
            67 days 2    2 
            68 days 1    1 
            75 days 2    2 
0008de587f84d8c9491502c5a5c8b466 0 days 1    4 
              2    4 
            1 days 1    17 
            4 days 1    6 
            7 days 1    10 
            8 days 1    5 

nasıl aldığım şeyi yeniden inşa edebilir veya yeniden şekillendirebilir miyim? istediğim ?

Şimdiden teşekkürler. Nihai örnek üzerinde

Answer 1

>>> (df.groupby(['song_id', 'ds', 'action_type']) 
     .action_type 
     .sum() 
     .unstack(['action_type', 'ds']) 
     .fillna(0) 
     .sortlevel(level=[0, 1], axis=1)) 

action_type       1          2   
ds        11 days 12 days 13 days 14 days 15 days 12 days 13 days 
song_id                     
096e604f7b152fad0246ae731ed8ca73  0  0  0  0  1  0  0 
17e90c7b3b7ebbe4b47344fcfab2fa7a  1  0  0  0  0  0  0 
31a3d0420d89c9b121bb55dbdbbeda6b  0  0  1  0  0  0  0 
5a7d9d75b898cd1b19ef6941cc1ddccf  0  1  0  0  0  0  0 
8a103bd3a3295fbf9b3c3bf7972db299  0  0  0  0  0  2  0 
8a27d9a6c59628c991c154e8d93f412e  0  0  0  0  0  2  2 
ecea5fe33e6817d09c395f2910479728  1  0  0  0  0  0  0 
effb071415be51f11e845884e67c0f8c  0  0  0  1  0  0  0 
f87ff481d85d2f95335ab602f38a7655  0  0  1  0  0  0  0