使用Pandas标记groupby DataFrame中的更改列 [英] Flag changing column in a groupby DataFrame using Pandas

问题描述

这里是数据集：

>>> df = pd.DataFrame({'id_police':['p123','p123','p123','b123','b123'],
                   'date':['24/01/2017','24/11/2017','25/02/2018','24/02/2018','24/03/2018'],
                   'prime':[0,0,10,20,30],
                   'prime2':[0,30,10,20,0],
})
###
  id_police        date  prime  prime2
0      p123  24/01/2017      0       0
1      p123  24/11/2017      0      30
2      p123  25/02/2018     10      10
3      b123  24/02/2018     20      20
4      b123  24/03/2018     30       0

这是我使用有效解决方案时得到的结果来自@Erfan：

This is the result I got when I used the working solution from @Erfan:

  id_police        date  prime  prime2  changed
0      p123  24/01/2017      0       0<-      0
1      p123  24/11/2017      0<-    30<-      1
2      p123  25/02/2018     10<-    10        1
3      b123  24/02/2018     20      20        0
4      b123  24/03/2018     30       0        0

命令：

df['changed'] = (df[['prime', 'prime2']].shift().eq(0).any(axis=1) & df[['prime', 'prime2']].ne(0).any(axis=1)).astype(int)

现在，我想为每个 id_police 应用此功能，例如添加groupby或其他东西...谢谢您的帮助！

Now I want to apply this for each id_police like add a groupby or something... Thanks for help!

推荐答案

基于您的命令：

cols = ['prime', 'prime2']

df['changed'] = (df.groupby('id_police', sort=False, as_index=False)
                   .apply(lambda x: (x[cols].ne(0) & x[cols].shift(1).eq(0))
                   .any(axis=1).astype(int))
                   .reset_index(drop=True))
df

  id_police        date  prime  prime2  changed
0      p123  24/01/2017      0       0        0
1      p123  24/11/2017      0      30        1
2      p123  25/02/2018     10      10        1
3      b123  24/02/2018     20      20        0
4      b123  24/03/2018     30       0        0

使用 groupby 并应用 t o在每个组上应用功能。并设置 sort = False 使其与主df顺序相同。

Use groupby with apply to apply function on each group. and set sort=False to make it order the same with your main df.

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