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This topic created in 3692 days ago, the information mentioned may be changed or developed.
我现在有个 DateFrame
index song_id action_type ds
0 f87ff481d85d2f95335ab602f38a7655 1 20150315
1 f87ff481d85d2f95335ab602f38a7655 2 20150315
2 f87ff481d85d2f95335ab602f38a7655 1 20150315
3 f87ff481d85d2f95335ab602f38a7655 3 20150316
4 096e604f7b152fad0246ae731ed8ca73 1 20150315
5 096e604f7b152fad0246ae731ed8ca73 1 20150315
6 096e604f7b152fad0246ae731ed8ca73 2 20150315
7 096e604f7b152fad0246ae731ed8ca73 1 20150316
8 096e604f7b152fad0246ae731ed8ca73 2 20150316
9 096e604f7b152fad0246ae731ed8ca73 1 20150316
怎样才能让他变成
song_id 20150315 20150316
f87ff481d85d2f95335ab602f38a7655 [1:2,2:1,3:0] [1:0,2:0,3:1]
096e604f7b152fad0246ae731ed8ca73 [1:2,2:1,3:0] [1:2,2:1,3:0]
这样的呢？
提前感谢大家。
index song_id action_type ds
0 f87ff481d85d2f95335ab602f38a7655 1 20150315
1 f87ff481d85d2f95335ab602f38a7655 2 20150315
2 f87ff481d85d2f95335ab602f38a7655 1 20150315
3 f87ff481d85d2f95335ab602f38a7655 3 20150316
4 096e604f7b152fad0246ae731ed8ca73 1 20150315
5 096e604f7b152fad0246ae731ed8ca73 1 20150315
6 096e604f7b152fad0246ae731ed8ca73 2 20150315
7 096e604f7b152fad0246ae731ed8ca73 1 20150316
8 096e604f7b152fad0246ae731ed8ca73 2 20150316
9 096e604f7b152fad0246ae731ed8ca73 1 20150316
怎样才能让他变成
song_id 20150315 20150316
f87ff481d85d2f95335ab602f38a7655 [1:2,2:1,3:0] [1:0,2:0,3:1]
096e604f7b152fad0246ae731ed8ca73 [1:2,2:1,3:0] [1:2,2:1,3:0]
这样的呢？
提前感谢大家。
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1
JamesRuan Mar 31, 2016
这不是反范式的吗？现在转换麻烦，日后要提取也麻烦。
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2
KIDJourney OP |
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3
timeship Mar 31, 2016
银古桑
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4
JamesRuan Mar 31, 2016  1
@KIDJourney
拆表啊，现在这个表是多对多的关系，你拆成多对一的关系用就好了。 |
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5
ruoyu0088 Apr 1, 2016 1
df.groupby(["song_id", "ds", "action_type"]).action_type.count().unstack().fillna(0).astype(int)
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6
KIDJourney OP |
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7
KIDJourney OP @ruoyu0088 十分感谢！
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