docstr: Test that `convert_df_to_data_format` correctly converts
  all types of missing values to None.
docstr_tok: ["Test","that","`","convert_df_to_data_format","`","correctly","converts","all","types","of","missing","values","to","None","."]
code: def test_convert_df_with_missing_values(self):
      """Test that `convert_df_to_data_format` correctly converts
      all types of missing values to None.
      """
  
      # Add dataframe with different missing values:
      df = pd.DataFrame(
          {
              "missing": [None, pd.NA, np.nan, pd.NaT],
          }
      )
  
      self.assertEqual(
          type_util.convert_df_to_data_format(
              df, type_util.DataFormat.LIST_OF_VALUES
          ),
          [None, None, None, None],
      )
      self.assertEqual(
          type_util.convert_df_to_data_format(
              df, type_util.DataFormat.TUPLE_OF_VALUES
          ),
          (None, None, None, None),
      )
      self.assertEqual(
          type_util.convert_df_to_data_format(
              df, type_util.DataFormat.SET_OF_VALUES
          ),
          {None},
      )
      self.assertEqual(
          type_util.convert_df_to_data_format(
              df, type_util.DataFormat.LIST_OF_ROWS
          ),
          [
              [None],
              [None],
              [None],
              [None],
          ],
      )
      self.assertEqual(
          type_util.convert_df_to_data_format(
              df, type_util.DataFormat.LIST_OF_RECORDS
          ),
          [
              {"missing": None},
              {"missing": None},
              {"missing": None},
              {"missing": None},
          ],
      )
      self.assertEqual(
          type_util.convert_df_to_data_format(
              df, type_util.DataFormat.COLUMN_VALUE_MAPPING
          ),
          {
              "missing": [None, None, None, None],
          },
      )
      self.assertEqual(
          type_util.convert_df_to_data_format(
              df, type_util.DataFormat.COLUMN_INDEX_MAPPING
          ),
          {"missing": {0: None, 1: None, 2: None, 3: None}},
      )
      self.assertEqual(
          type_util.convert_df_to_data_format(
              df, type_util.DataFormat.KEY_VALUE_DICT
          ),
          {0: None, 1: None, 2: None, 3: None},
      )
  
imports: import enum
  import unittest
  from collections import namedtuple
  from datetime import date
  from decimal import Decimal
  from typing import Any
  from unittest.mock import patch
  import numpy
  import pandas
  import plotly.graph_objs
  import pytest
  from pandas.api.types import infer_dtype
  from parameterized import parameterized
  from streamlit import errors, type_util
  from streamlit.errors import StreamlitAPIException
  from tests.streamlit.data_mocks import BASE_TYPES_DF, DATETIME_TYPES_DF, INTERVAL_TYPES_DF, LIST_TYPES_DF, NUMBER_TYPES_DF, PERIOD_TYPES_DF, SHARED_TEST_CASES, SPECIAL_TYPES_DF, UNSUPPORTED_TYPES_DF, TestCaseMetadata, TestObject
  from tests.streamlit.snowpark_mocks import DataFrame
  from tests.streamlit.snowpark_mocks import Row
  from tests.testutil import create_snowpark_session, patch_config_options
label_desc: Use image node_id 30 for calling the TypeUtilTest obj's underlying member method code with example usage: obj.test_convert_df_with_missing_values() without return types