pandas - groupby 和重新调整值 [英] pandas - groupby and re-scale values

问题描述

我想向这个数据框添加一个重新缩放的列:

I would like to add a rescaled column to this dataframe:

I,Value
A,1
A,4
A,2
A,5
B,1
B,2
B,1

以便新列(我们称之为scale)在每个I 组的value 列上遵循一个函数.该函数只是对每个组的范围进行归一化:

so that the new column (let's call it scale), follows a function over the value column for each group of I. The function is just a normalization over the range for each group:

lambda x: (x-min(x))/(max(x)-min(x))

到目前为止我尝试过:

d = df.groupby('I').apply(lambda x: (x-min(x))/(max(x)-min(x)))

收到以下类型错误:

TypeError: Could not operate array(['A'], dtype=object) with block values index 1 is out of bounds for axis 1 with size 1

推荐答案

如果您将值"列添加到您的代码中，那么它会起作用:

If you added the 'Value' column to your code then it would work:

In [69]:
df.groupby('I')['Value'].apply(lambda x: (x-min(x))/(max(x)-min(x)))

Out[69]:
0    0.00
1    0.75
2    0.25
3    1.00
4    0.00
5    1.00
6    0.00
dtype: float64

pandas 方法版本如下，结果相同:

The pandas method version is the following which produces the same result:

In [67]:
df['Normalised'] = df.groupby('I')['Value'].apply(lambda x: (x-x.min())/(x.max()-x.min()))
df

Out[67]:
   I  Value  Normalised
0  A      1        0.00
1  A      4        0.75
2  A      2        0.25
3  A      5        1.00
4  B      1        0.00
5  B      2        1.00
6  B      1        0.00

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