按组规范化DataFrame [英] Normalize DataFrame by group

问题描述

比方说，我生成了一些数据，如下所示:

Let's say that I have some data generated as follows:

N = 20
m = 3
data = np.random.normal(size=(N,m)) + np.random.normal(size=(N,m))**3

然后创建一些分类变量:

and then I create some categorization variable:

indx = np.random.randint(0,3,size=N).astype(np.int32)

并生成一个DataFrame:

and generate a DataFrame:

import pandas as pd
df = pd.DataFrame(np.hstack((data, indx[:,None])), 
             columns=['a%s' % k for k in range(m)] + [ 'indx'])

我可以得到每个组的平均值:

I can get the mean value, per group as:

df.groubpy('indx').mean()

我不确定该怎么做，然后减去原始数据中每个列的每个组的均值，以使每列中的数据通过组中的均值进行归一化.任何建议，将不胜感激.

What I'm unsure of how to do is to then subtract the mean off of each group, per-column in the original data, so that the data in each column is normalized by the mean within group. Any suggestions would be appreciated.

推荐答案

In [10]: df.groupby('indx').transform(lambda x: (x - x.mean()) / x.std())

应该这样做.

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