Apply StandardScaler to parts of a data set

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.       

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