How to use sklearn fit_transform with pandas and return dataframe instead of numpy array?

Question

I want to apply scaling (using StandardScaler() from sklearn.preprocessing) to a pandas dataframe. The following code returns a numpy array, so I lose all the column names a

Answer 1

You can mix multiple data types in scikit-learn using Neuraxle:

Option 1: discard the row names and column names

from neuraxle.pipeline import Pipeline
from neuraxle.base import NonFittableMixin, BaseStep

class PandasToNumpy(NonFittableMixin, BaseStep):
    def transform(self, data_inputs, expected_outputs): 
        return data_inputs.values

pipeline = Pipeline([
    PandasToNumpy(),
    StandardScaler(),
])

Then, you proceed as you intended:

features = df[["col1", "col2", "col3", "col4"]]  # ... your df data
pipeline, scaled_features = pipeline.fit_transform(features)

Option 2: to keep the original column names and row names

You could even do this with a wrapper as such:

from neuraxle.pipeline import Pipeline
from neuraxle.base import MetaStepMixin, BaseStep

class PandasValuesChangerOf(MetaStepMixin, BaseStep):
    def transform(self, data_inputs, expected_outputs): 
        new_data_inputs = self.wrapped.transform(data_inputs.values)
        new_data_inputs = self._merge(data_inputs, new_data_inputs)
        return new_data_inputs

    def fit_transform(self, data_inputs, expected_outputs): 
        self.wrapped, new_data_inputs = self.wrapped.fit_transform(data_inputs.values)
        new_data_inputs = self._merge(data_inputs, new_data_inputs)
        return self, new_data_inputs

    def _merge(self, data_inputs, new_data_inputs): 
        new_data_inputs = pd.DataFrame(
            new_data_inputs,
            index=data_inputs.index,
            columns=data_inputs.columns
        )
        return new_data_inputs

df_scaler = PandasValuesChangerOf(StandardScaler())

Then, you proceed as you intended:

features = df[["col1", "col2", "col3", "col4"]]  # ... your df data
df_scaler, scaled_features = df_scaler.fit_transform(features)