docstr: This function converts a DataFrame type to a custom type used within dowhy.
  We make use of the following mapping
  int -> 'c'
  float -> 'c'
  binary -> 'b'
  category -> 'd'
  Currently we have not added support for time.
  :param input_type: str: The datatype of a column within a DataFrame
docstr_tok: ["This","function","converts","a","DataFrame","type","to","a","custom","type","used","within","dowhy",".","We","make","use","of","the","following","mapping","int","-",">","'","c'","float","-",">","'","c'","binary","-",">","'","b'","category","-",">","'d'","Currently","we","have","not","added","support","for","time",".",":","param","input_type",":","str",":","The","datatype","of","a","column","within","a","DataFrame"]
code: def convert_to_custom_type(self, input_type):
      """
      This function converts a DataFrame type to a custom type used within dowhy.
      We make use of the following mapping
      int -> 'c'
      float -> 'c'
      binary -> 'b'
      category -> 'd'
      Currently we have not added support for time.
      :param input_type: str: The datatype of a column within a DataFrame
      """
      if "int" in input_type:
          return "c"
      elif "float" in input_type:
          return "c"
      elif "bool" in input_type:
          return "b"
      elif "category" in input_type:
          return "d"
      else:
          raise Exception(
              "{} format is not supported".format(input_type)
          )
  
file_path: dowhy/dowhy/api/causal_data_frame.py
imports: import networkx
  import pandas
  import dowhy.do_samplers
  from dowhy import EstimandType
  from dowhy.graph import build_graph
  from dowhy.utils.api import parse_state
label_desc: Use image node_id 4 for calling the CausalAccessor obj's underlying member method code with example usage: obj.convert_to_custom_type(input_type) and returns: str, str, str, str