class_docstr: Subset a pandas.DataFrame by passing a series of steps
  
  Parameters
  ----------
  *steps
      Steps to apply to the data sequentially (order matters). Each step
      must be a dictionary with a key "kind" whose value must be one of
      "column_drop", "row_drop" or "column_keep". The rest of the key-value
      pairs must match the signature for the corresponding Step objects
class_docstr_tok: ["Subset","a","pandas.DataFrame","by","passing","a","series","of","steps","Parameters","--","--","--","--","--","*","steps","Steps","to","apply","to","the","data","sequentially","(","order","matters",")",".","Each","step","must","be","a","dictionary","with","a","key","``","kind","''","whose","value","must","be","one","of","``","column_drop","''",",","``","row_drop","''","or","``","column_keep","''",".","The","rest","of","the","key-value","pairs","must","match","the","signature","for","the","corresponding","Step","objects"]
code: def _get_table(self):
      return Table(
          [
              (type(step).__name__, step.get_args(), step.get_params())
              for step in self.steps
          ],
          header=["Step", "Args", "Params"],
      )
  
imports: from copy import copy, deepcopy
  import abc
  import inspect
  import importlib
  import itertools
  from collections.abc import Mapping
  import pandas
  from decorator import decorator
  from sklearn_evaluation.exceptions import DataSelectorError
  from sklearn_evaluation.util import map_parameters_in_fn_call
  from sklearn_evaluation.table import Table
label_desc: Use image node_id 3 for calling the DataSelector obj's underlying member method code with example usage: obj._get_table() and returns: Table