[英]Pandas Groupby Max of Multiple Columns
Given this data frame:给定这个数据框:
import pandas as pd
df = pd.DataFrame({'group':['a','a','b','c','c'],'strings':['ab',' ',' ','12',' '],'floats':[7.0,8.0,9.0,10.0,11.0]})
group strings floats
0 a ab 7.0
1 a 8.0
2 b 9.0
3 c 12 10.0
4 c 11.0
I want to group by "group" and get the max value of strings and floats.我想按“组”分组并获得字符串和浮点数的最大值。
Desired result:期望的结果:
strings floats
group
a ab 8.0
b 9.0
c 12 11.0
I know I can just do this:我知道我可以这样做:
df.groupby(['group'], sort=False)['strings','floats'].max()
But in reality, I have many columns so I want to refer to all columns (save for "group") in one go.但实际上,我有很多列,所以我想引用一个 go 中的所有列(“组”除外)。
I wish I could just do this:我希望我可以这样做:
df.groupby(['group'], sort=False)[x for x in df.columns if x != 'group'].max()
But, alas, "invalid syntax".但是,唉,“无效的语法”。
If need max
of all columns without group
is possible use:如果需要没有group
的所有列的max
,可以使用:
df = df.groupby('group', sort=False).max()
print (df)
strings floats
group
a ab 8.0
b 9.0
c 12 11.0
Your second solution working if add next []
:如果添加 next []
,您的第二个解决方案将起作用:
df = df.groupby(['group'], sort=False)[[x for x in df.columns if x != 'group']].max()
print (df)
strings floats
group
a ab 8.0
b 9.0
c 12 11.0
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