Apply StandardScaler to parts of a data set

问题

I want to use sklearn's StandardScaler. Is it possible to apply it to some feature columns but not others?

For instance, say my data is:

data = pd.DataFrame({'Name' : [3, 4,6], 'Age' : [18, 92,98], 'Weight' : [68, 59,49]})

   Age  Name  Weight
0   18     3      68
1   92     4      59
2   98     6      49


col_names = ['Name', 'Age', 'Weight']
features = data[col_names]

I fit and transform the data

scaler = StandardScaler().fit(features.values)
features = scaler.transform(features.values)
scaled_features = pd.DataFrame(features, columns = col_names)

       Name       Age    Weight
0 -1.069045 -1.411004  1.202703
1 -0.267261  0.623041  0.042954
2  1.336306  0.787964 -1.245657

But of course the names are not really integers but strings and I don't want to standardize them. How can I apply the fit and transform methods only on the columns Age and Weight?

回答1:

Update:

Currently the best way to handle this is to use ColumnTransformer as explained here.

---

First create a copy of your dataframe:

scaled_features = data.copy()

Don't include the Name column in the transformation:

col_names = ['Age', 'Weight']
features = scaled_features[col_names]
scaler = StandardScaler().fit(features.values)
features = scaler.transform(features.values)

Now, don't create a new dataframe but assign the result to those two columns:

scaled_features[col_names] = features
print(scaled_features)


        Age  Name    Weight
0 -1.411004     3  1.202703
1  0.623041     4  0.042954
2  0.787964     6 -1.245657

回答2:

Introduced in v0.20 is ColumnTransformer which applies transformers to a specified set of columns of an array or pandas DataFrame.

import pandas as pd
data = pd.DataFrame({'Name' : [3, 4,6], 'Age' : [18, 92,98], 'Weight' : [68, 59,49]})

col_names = ['Name', 'Age', 'Weight']
features = data[col_names]

from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import StandardScaler

ct = ColumnTransformer([
        ('somename', StandardScaler(), ['Age', 'Weight'])
    ], remainder='passthrough')

ct.fit_transform(features)

NB: Like Pipeline it also has a shorthand version make_column_transformer which doesn't require naming the transformers

Output

-1.41100443,  1.20270298,  3.       
 0.62304092,  0.04295368,  4.       
 0.78796352, -1.24565666,  6.       

回答3:

Another option would be to drop Name column before scaling then merge it back together:

data = pd.DataFrame({'Name' : [3, 4,6], 'Age' : [18, 92,98], 'Weight' : [68, 59,49]})
from sklearn.preprocessing import StandardScaler

# Save the variable you don't want to scale
name_var = data['Name']

# Fit scaler to your data
scaler.fit(data.drop('Name', axis = 1))

# Calculate scaled values and store them in a separate object
scaled_values = scaler.transform(data.drop('Name', axis = 1))

data = pd.DataFrame(scaled_values, index = data.index, columns = data.drop('ID', axis = 1).columns)
data['Name'] = name_var

print(data)

回答4:

A more pythonic way to do this -

from sklearn.preprocessing import StandardScaler
data[['Age','Weight']] = data[['Age','Weight']].apply(
                           lambda x: StandardScaler().fit_transform(x))
data 

Output -

         Age  Name    Weight
0 -1.411004     3  1.202703
1  0.623041     4  0.042954
2  0.787964     6 -1.245657

来源：https://stackoverflow.com/questions/38420847/apply-standardscaler-to-parts-of-a-data-set