我是一个新的Python转换器(来自Matlab)。我正在使用pandas groupby
函数,我被一个看似简单的问题绊倒了。我已经编写了一个自定义函数,我apply
到分组的df,返回4个不同的值。其中三个值很有效,但另一个值给我一个错误。这是原始df
:
Index,SN,Date,City,State,ID,County,Age,A,B,C
0,32,9/1/16,X,AL,360,BB County,29.0,negative,positive,positive
1,32,9/1/16,X,AL,360,BB County,1.0,negative,negative,negative
2,32,9/1/16,X,AL,360,BB County,10.0,negative,negative,negative
3,32,9/1/16,X,AL,360,BB County,11.0,negative,negative,negative
4,35,9/1/16,X,AR,718,LL County,67.0,negative,negative,negative
5,38,9/1/16,X,AR,728-13,JJ County,3.0,negative,negative,negative
6,38,9/1/16,X,AR,728-13,JJ County,8.0,negative,negative,negative
7,30,9/1/16,X,AR,728-13,JJ County,8.0,negative,negative,negative
8,30,9/1/16,X,AR,728-13,JJ County,14.0,negative,negative,negative
9,30,9/1/16,X,AR,728-13,JJ County,5.0,negative,negative,negative
...
这是转换数据的功能。基本上,它计算的是“正面”的数量。值和组中的观察总数。我还希望它返回ID值,这就是问题所在:
def _ct_id_pos(grp):
return grp['ID'][0], grp[grp.A == 'positive'].shape[0], grp[grp.B == 'positive'].shape[0], grp.shape[0]
我apply
_ct_id_pos
函数对按Date
和SN
分组的数据:
FullMx_prime = FullMx.groupby(['Date', 'SN']).apply(_ct_id_pos).reset_index()
因此,该方法应返回如下内容:
Date SN ID 0
0 9/1/16 32 360 (360,2,1,4)
1 9/1/16 35 718 (718,0,0,1)
2 9/2/16 38 728 (728,1,0,2)
3 9/3/16 30 728 (728,2,0,3)
但是,我一直收到以下错误:
...
KeyError: 0
显然,它不喜欢这部分功能:grp['ID'][0]
。我只想取grp['ID']
的第一个值,因为 - 如果有多个值 - 它们应该都是相同的(即,我可以取最后一个,无关紧要)。我已尝试过其他方式进行索引,但无济于事。
答案 0 :(得分:2)
将public class ApplicationUser : IdentityUser {
// some custom fields
}
public class Comment{
public int Id {get;set;}
public string Message{get;set;}
public DateTime Time{get;set;}
public string AuthorId {get;set}
[ForeignKey("AuthorId")]
public virtual ApplicationUser Author {get;set;}
}
public class MyContext :IdentityDbContext<ApplicationUser> {
public MyContext(): base("DefaultConnection", false){ }
public DbSet<Comment> Comments { get; set; }
//... other DbSets
}
更改为grp['ID'][0]
您遇到的问题是grp.iloc[0]['ID']
选择了一列并返回grp['ID']
。哪个是直截了当的,你可以合理地期望pandas.Series
会选择第一个元素。但[0]
实际上是根据Series的索引进行选择的,在这种情况下,索引来自分组的数据帧。因此,[0]
并不总是有效的索引。
<强>代码:强>
0
测试代码:
def _ct_id_pos(grp):
id = grp.iloc[0]['ID']
a = grp[grp.A == 'positive'].shape[0]
b = grp[grp.B == 'positive'].shape[0]
sz = grp.shape[0]
return id, a, b, sz
<强>结果:强>
df = pd.read_csv(StringIO(u"""
Index,SN,Date,City,State,ID,County,Age,A,B,C
0,32,9/1/16,X,AL,360,BB County,29.0,negative,positive,positive
1,32,9/1/16,X,AL,360,BB County,1.0,negative,negative,negative
2,32,9/1/16,X,AL,360,BB County,10.0,negative,negative,negative
3,32,9/1/16,X,AL,360,BB County,11.0,negative,negative,negative
4,35,9/1/16,X,AR,718,LL County,67.0,negative,negative,negative
5,38,9/1/16,X,AR,728-13,JJ County,3.0,negative,negative,negative
6,38,9/1/16,X,AR,728-13,JJ County,8.0,negative,negative,negative
7,30,9/1/16,X,AR,728-13,JJ County,8.0,negative,negative,negative
8,30,9/1/16,X,AR,728-13,JJ County,14.0,negative,negative,negative
9,30,9/1/16,X,AR,728-13,JJ County,5.0,negative,negative,negative
"""), header=0, index_col=0)
print(df.groupby(['Date', 'SN']).apply(_ct_id_pos).reset_index())