class_docstr: A custom processing pipeline for the phishing URLs dataset.
class_docstr_tok: ["A","custom","processing","pipeline","for","the","phishing","URLs","dataset","."]
docstr: Transform the input dataframe using this fitted preprossor.
docstr_tok: ["Transform","the","input","dataframe","using","this","fitted","preprossor","."]
code: def transform(self, df: pd.DataFrame) -> pd.DataFrame:
      """Transform the input dataframe using this fitted preprossor."""
      df = self._shared_preprocess(df)
      try:
          df[self.scale_cols] = self.scaler.transform(
              df[self.scale_cols]
          )
          return df
      except AttributeError as e:
          raise Exception(
              "UrlDatasetProcessor is unfitted! Call fit_transform() first!"
          ) from e
  
file_path: deepchecks/deepchecks/tabular/datasets/classification/phishing.py
imports: import typing
  from urllib.request import urlopen
  import joblib
  import pandas
  import sklearn
  from category_encoders import OneHotEncoder
  from sklearn.compose import ColumnTransformer
  from sklearn.ensemble import RandomForestClassifier
  from sklearn.impute import SimpleImputer
  from sklearn.pipeline import Pipeline
  from deepchecks.tabular.dataset import Dataset
label_desc: Use image node_id 4 for calling the UrlDatasetProcessor obj's underlying member method code with example usage: obj.transform(df) and returns: df