How to normalize just one feature by scikit-learn? Wanna apply a specific scaler, say StandardScaler, on a specific feature, keeping other features intact. 

the dataset format is something like:
[
[1, 0.2, 1000],
[2, 0.1, 2400],
[3, 0.9, 7620]
]

I need to transform only one column, the third in this example.

I don't want to use pandas.
As mentioned, the easiest way is to apply the StandardScaler to only the subset of features that need to be scaled, and then concatenate the result with the remaining features.

Alternatively, scikit-learn also offers (a still experimental, i.e. subject to change) ColumnTransformer API. It works similar to a pipline:

from sklearn.compose import ColumnTransformer 
from sklearn.preprocessing import  StandarScaler 
column_trans = ColumnTransformer(
    [('scaler', StandardScaler(),2],
    remainder='passthrough') 
column_trans.fit_transform(X)


The third entry of the tuple (here : 2) can be a single or a list of column indices (or column names when working with a pandas DataFrame).