class_docstr: A custom processing pipeline for the phishing URLs dataset.
class_docstr_tok: ["A","custom","processing","pipeline","for","the","phishing","URLs","dataset","."]
docstr: Fit this preprossor on the input dataframe and transform it.
docstr_tok: ["Fit","this","preprossor","on","the","input","dataframe","and","transform","it","."]
code: def fit_transform(self, df: pd.DataFrame) -> pd.DataFrame:
      """Fit this preprossor on the input dataframe and transform it."""
      df = self._shared_preprocess(df)
      self.scaler = sklearn.preprocessing.StandardScaler()
      self.scale_cols = self._cols_to_scale(df)
      df[self.scale_cols] = self.scaler.fit_transform(
          df[self.scale_cols]
      )
      return df
  
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 3 for calling the UrlDatasetProcessor obj's underlying member method code with example usage: obj.fit_transform(df) and returns: df