Feature preprocessing of both continuous and categorical variables (of integer type) with scikit-learn

Question

The main goals are as follows:

1) Apply StandardScaler to continuous variables

2) Apply LabelEncoder and OnehotEncoder to

Answer 1

Check out the sklearn_pandas.DataFrameMapper meta-transformer. Use it as the first step in your pipeline to perform column-wise data engineering operations:

mapper = DataFrameMapper(
  [(continuous_col, StandardScaler()) for continuous_col in continuous_cols] +
  [(categorical_col, LabelBinarizer()) for categorical_col in categorical_cols]
)
pipeline = Pipeline(
  [("mapper", mapper),
  ("estimator", estimator)]
)
pipeline.fit_transform(df, df["y"])

Also, you should be using sklearn.preprocessing.LabelBinarizer instead of a list of [LabelEncoder(), OneHotEncoder()].