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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

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?

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

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

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

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!

1 Answer

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by (19.9k points)

This following snippet works perfectly and produces exact output 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

...