class_docstr: Class which stores vertex and edge properties that can be used to construct
  Graphs from individual property selections and used later to annotate graph
  algorithm results with corresponding properties.
class_docstr_tok: ["Class","which","stores","vertex","and","edge","properties","that","can","be","used","to","construct","Graphs","from","individual","property","selections","and","used","later","to","annotate","graph","algorithm","results","with","corresponding","properties","."]
docstr: Returns a list containing tuples of (column name, dtype) for each
  column in from_df that is not present in to_df.
docstr_tok: ["Returns","a","list","containing","tuples","of","(","column","name",",","dtype",")","for","each","column","in","from_df","that","is","not","present","in","to_df","."]
code: def __get_new_column_dtypes(from_df, to_df):
      """
      Returns a list containing tuples of (column name, dtype) for each
      column in from_df that is not present in to_df.
      """
      new_cols = set(from_df.columns) - set(to_df.columns)
      return [(col, from_df.dtypes[col]) for col in new_cols]
  
imports: import cudf
  import cupy
  import cugraph
  import dask_cudf
  import cugraph.dask
  from cugraph.utilities.utils import import_optional, create_list_series_from_2d_ar
  from typing import Union
label_desc: Use image node_id 43 for calling the EXPERIMENTAL__MGPropertyGraph obj's underlying member method code with example usage: obj.__get_new_column_dtypes(from_df, to_df) and returns: unknown