class_docstr: A custom processing pipeline for the phishing URLs dataset.
class_docstr_tok: ["A","custom","processing","pipeline","for","the","phishing","URLs","dataset","."]
code: def _shared_preprocess(self, df: pd.DataFrame) -> pd.DataFrame:
      df = df.copy()
      df[_DATE_COL] = pd.to_datetime(df[_DATE_COL], format="%Y-%m-%d")
      df = df.set_index(keys=_DATE_COL, drop=True)
      df = df.drop(_NON_FEATURES, axis=1)
      df = pd.get_dummies(df, columns=["ext"])
      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 2 for calling the UrlDatasetProcessor obj's underlying member method code with example usage: obj._shared_preprocess(df) and returns: df