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

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