Apply StandardScaler to parts of a data set

Question

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?

Answer 1

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.