使用sklearn缩放的pandas数据框列 [英] pandas dataframe columns scaling with sklearn

问题描述

我有一个带有混合类型列的pandas数据框，我想将sklearn的min_max_scaler应用于某些列.理想情况下，我想就地进行这些转换，但还没有找到一种方法来进行.我编写了以下有效的代码:

I have a pandas dataframe with mixed type columns, and I'd like to apply sklearn's min_max_scaler to some of the columns. Ideally, I'd like to do these transformations in place, but haven't figured out a way to do that yet. I've written the following code that works:

import pandas as pd
import numpy as np
from sklearn import preprocessing

scaler = preprocessing.MinMaxScaler()

dfTest = pd.DataFrame({'A':[14.00,90.20,90.95,96.27,91.21],'B':[103.02,107.26,110.35,114.23,114.68], 'C':['big','small','big','small','small']})
min_max_scaler = preprocessing.MinMaxScaler()

def scaleColumns(df, cols_to_scale):
    for col in cols_to_scale:
        df[col] = pd.DataFrame(min_max_scaler.fit_transform(pd.DataFrame(dfTest[col])),columns=[col])
    return df

dfTest

    A   B   C
0    14.00   103.02  big
1    90.20   107.26  small
2    90.95   110.35  big
3    96.27   114.23  small
4    91.21   114.68  small

scaled_df = scaleColumns(dfTest,['A','B'])
scaled_df

A   B   C
0    0.000000    0.000000    big
1    0.926219    0.363636    small
2    0.935335    0.628645    big
3    1.000000    0.961407    small
4    0.938495    1.000000    small

我很好奇这是否是进行此转换的首选/最有效的方法.有没有一种方法可以使用df.apply更好呢?

I'm curious if this is the preferred/most efficient way to do this transformation. Is there a way I could use df.apply that would be better?

我也很惊讶我无法使用以下代码:

I'm also surprised I can't get the following code to work:

bad_output = min_max_scaler.fit_transform(dfTest['A'])

如果我将整个数据帧传递给缩放器，它将起作用:

If I pass an entire dataframe to the scaler it works:

dfTest2 = dfTest.drop('C', axis = 1) good_output = min_max_scaler.fit_transform(dfTest2) good_output

dfTest2 = dfTest.drop('C', axis = 1) good_output = min_max_scaler.fit_transform(dfTest2) good_output

我很困惑为什么将系列传递给定标器会失败.在上面的完整工作代码中，我希望只将一个系列传递给缩放器，然后将dataframe column =设置为缩放后的序列.我已经看到这个问题在其他几个地方问过，但找不到一个好的答案.任何帮助了解这里发生的事情将不胜感激！

I'm confused why passing a series to the scaler fails. In my full working code above I had hoped to just pass a series to the scaler then set the dataframe column = to the scaled series. I've seen this question asked a few other places, but haven't found a good answer. Any help understanding what's going on here would be greatly appreciated!

推荐答案

我不确定pandas的先前版本是否阻止了此操作，但是现在以下代码段对我而言非常有效，并且可以精确地生成您想要的内容而无需使用apply

I am not sure if previous versions of pandas prevented this but now the following snippet works perfectly for me and produces exactly what you want without having to use apply

>>> import pandas as pd
>>> from sklearn.preprocessing import MinMaxScaler


>>> scaler = MinMaxScaler()

>>> dfTest = pd.DataFrame({'A':[14.00,90.20,90.95,96.27,91.21],
                           'B':[103.02,107.26,110.35,114.23,114.68],
                           'C':['big','small','big','small','small']})

>>> dfTest[['A', 'B']] = scaler.fit_transform(dfTest[['A', 'B']])

>>> dfTest
          A         B      C
0  0.000000  0.000000    big
1  0.926219  0.363636  small
2  0.935335  0.628645    big
3  1.000000  0.961407  small
4  0.938495  1.000000  small

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