pandas 找到两列之间差异最大的行
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Find the row which has the maximum difference between two columns
提问by ayhan
I have a DataFrame with columns Goldand Gold.1. I want to find the row where the difference of these two columns is the maximum.
我有一个带有列Gold和Gold.1. 我想找到这两列的差异最大的行。
For the following DataFrame, this should return me row 6.
对于以下 DataFrame，这应该返回第 6 行。
df
Out:
Gold Gold.1
0 2 1
1 1 4
2 6 9
3 4 4
4 4 8
5 5 5
6 5 2 ---> The difference is maximum (3)
7 5 9
8 5 3
9 5 6
I tried using the following:
我尝试使用以下方法：
df.where(max(df['Gold']-df['Gold.1']))
However that raised a ValueError:
然而，这引发了一个 ValueError：
df.where(max(df['Gold']-df['Gold.1']))
Traceback (most recent call last):
File "", line 1, in
df.where(max(df['Gold']-df['Gold.1']))
File "../python3.5/site-packages/pandas/core/generic.py", line 5195, in where
raise_on_error)
File "../python3.5/site-packages/pandas/core/generic.py", line 4936, in _where
raise ValueError('Array conditional must be same shape as '
ValueError: Array conditional must be same shape as self
How can I find the row that satisfies this condition?
如何找到满足此条件的行？
回答by ayhan
Instead of .where, you can use .idxmax:
代替.where，您可以使用.idxmax：
(df['Gold'] - df['Gold.1']).idxmax()
Out: 6
This will return the index where the difference is maximum.
这将返回差异最大的索引。
If you want to find the row with the maximum absolutedifference, then you can call .abs()first.
如果要找到绝对差最大的行，那么可以.abs()先调用。
(df['Gold'] - df['Gold.1']).abs().idxmax()
Out: 4
回答by Loochie
Though my method is a longer than the above one, people who are comfortable working with lists may find this useful.
虽然我的方法比上面的方法长，但习惯使用列表的人可能会发现这很有用。
x= list((df['col1']-df['col2']).abs())
x.index(max(x))
