pandas :查找第二高值的行的索引 [英] Pandas: Find index of the row with second highest value
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问题描述
我试图在执行groupby之后获取具有第二高值的行的索引,但是我没有得到正确的结果
I am trying to get the index of the row with the second highest value after doing groupby but I am not getting the right result
df = pd.DataFrame({'Sp':['a','b','c','d','e','f'], 'Mt':['s1', 's1', 's2','s2','s2','s3'], 'Value':[1,2,3,4,5,6], 'count':[3,2,5,10,10,6]})
这样做
df.iloc[df.groupby(['Mt'])['Value'].apply(lambda x: (x!=max(x)).idxmax())]
正在返回
Mt Sp Value count
0 s1 a 1 3
2 s2 c 3 5
5 s3 f 6 6
对于组s2,应返回原始数据帧的索引3.
For group s2 , index 3 of the original dataframe should be returned.
推荐答案
Since 'Value' is already sorted you can use nth
:
In [11]: g = df.groupby("Mt", as_index=False)
In [12]: g.nth(-2)
Out[12]:
Mt Sp Value count
0 s1 a 1 3
3 s2 d 4 10
否则,我将首先按值df = df.sort_values("Value")
排序.
Otherwise I'd first sort by Value, df = df.sort_values("Value")
.
如果您想要最后一个(如果给定的组中少于两个),您也可以抓住它
If you want the last (if there are fewer than two in a given group), you could grab that too
In [21]: g = df.groupby("Mt")
In [22]: res = g.nth(-1)
In [23]: res.update(g.nth(-2))
In [24]: res
Out[24]:
Sp Value count
Mt
s1 a 1 3
s2 d 4 10
s3 f 6 6
A related function is tail
(to get the last two elements):
In [31]: g.tail(2)
Out[31]:
Mt Sp Value count
0 s1 a 1 3
1 s1 b 2 2
3 s2 d 4 10
4 s2 e 5 10
5 s3 f 6 6
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