从组内的最大值中减去值

Question

Trying to take a df and create a new column thats based on the difference between the Value in a group and that groups max:

Group Value
A     4
A     6
A     10   
B     5
B     8
B     11

End up with a new column "from_max"

from_max
6
4
0
6
3
0

I tried this but a ValueError:

df['from_max'] = df.groupby(['Group']).apply(lambda x: x['Value'].max() - x['Value'])

Thanks in Advance

Answer 1

Option 1
vectorised groupby + transform

df['from_max'] = df.groupby('Group').Value.transform('max') - df.Value

df
  Group  Value  from_max
0     A      4         6
1     A      6         4
2     A     10         0
3     B      5         6
4     B      8         3
5     B     11         0

---

Option 2
index aligned subtraction

df['from_max'] = (df.groupby('Group').Value.max() - df.set_index('Group').Value).values

df
  Group  Value  from_max
0     A      4         6
1     A      6         4
2     A     10         0
3     B      5         6
4     B      8         3
5     B     11         0

Answer 2

I think need GroupBy.transform for return Series with same size as original DataFrame :

df['from_max'] = df.groupby(['Group'])['Value'].transform(lambda x: x.max() - x)

Or:

df['from_max'] = df.groupby(['Group'])['Value'].transform(max) - df['Value']

Alternative is Series.map by aggregate max :

df['from_max'] = df['Group'].map(df.groupby(['Group'])['Value'].max()) - df['Value']

---

print (df)
  Group  Value  from_max
0     A      4         6
1     A      6         4
2     A     10         0
3     B      5         6
4     B      8         3
5     B     11         0

Answer 3

Using reindex

df['From_Max']=df.groupby('Group').Value.max().reindex(df.Group).values-df.Value.values
df
Out[579]: 
  Group  Value  From_Max
0     A      4         6
1     A      6         4
2     A     10         0
3     B      5         6
4     B      8         3
5     B     11         0