code: def standardize(table, with_std=True):
      """
      Perform Z-Normalization on each numeric column of the given table.
  
      If `table` is a pandas.DataFrame, only numeric columns are modified,
      all other columns remain unchanged. If `table` is a numpy.ndarray,
      it is only modified if it has numeric dtype, in which case the returned
      array will have floating point dtype.
  
      Parameters
      ----------
      table : pandas.DataFrame or numpy.ndarray
          Data to standardize.
  
      with_std : bool, optional, default: True
          If ``False`` data is only centered and not converted to unit variance.
  
      Returns
      -------
      normalized : pandas.DataFrame
          Table with numeric columns normalized.
          Categorical columns in the input table remain unchanged.
      """
      if isinstance(table, pd.DataFrame):
          cat_columns = table.select_dtypes(
              include=["category"]
          ).columns
      else:
          cat_columns = []
  
      new_frame = _apply_along_column(
          table, standardize_column, with_std=with_std
      )
  
      # work around for apply converting category dtype to object
      # https://github.com/pydata/pandas/issues/9573
      for col in cat_columns:
          new_frame[col] = table[col].copy()
  
      return new_frame
  
file_path: scikit-survival/sksurv/column.py
imports: import logging
  import numpy
  import pandas
  from pandas.api.types import CategoricalDtype, is_object_dtype
label_desc: Use image node_id 3 for calling a global function with example usage: standardize(table, with_std) and returns: new_frame