Failure on cursor.fetchall()

Start with integration_test/utils/databricks/databricks_sql.py at line 89, then follow cursor.fetchall through databricks/sql/client.py and the pandas conversion shown in the traceback. Reproduce the integration case and determine why the one-row COUNT result with nullable Int64 data raises TypeError; done means fetchall returns the count without that exception.

Recently started having issues with the fetchall() method. This exact code was working fine last week, but now this same query statement is throwing the errors seen below.

def databricks_sql_count(column, catalog, schema, table, where=""):
    connection_cursor = open_connection()
    cursor = connection_cursor["cursor"]
    connection = connection_cursor["connection"]
    query = f'SELECT COUNT({column}) FROM `{catalog}`.`{schema}`.`{table}` WHERE  {where};'
    cursor.execute(query)
    response = cursor.fetchall()
    close_connection(cursor, connection)
    return response[0][0]

initial_count = databricks_sql_count('visitor_id',
                                               catalog,
                                               schema,
                                               table,
                                               f"created_at >= '{TEST_START_DATE}'")

---

integration_test/utils/databricks/databricks_sql.py:89: in databricks_sql_count
response = cursor.fetchall()
/usr/local/lib/python3.10/dist-packages/databricks/sql/client.py:670: in fetchall
return self.active_result_set.fetchall()
/usr/local/lib/python3.10/dist-packages/databricks/sql/client.py:944: in fetchall
return self._convert_arrow_table(self.fetchall_arrow())
/usr/local/lib/python3.10/dist-packages/databricks/sql/client.py:884: in _convert_arrow_table
res = df.to_numpy(na_value=None)
/usr/local/lib/python3.10/dist-packages/pandas/core/frame.py:1981: in to_numpy
result = self._mgr.as_array(dtype=dtype, copy=copy, na_value=na_value)

---

self = BlockManager
Items: Index(['0'], dtype='object')
Axis 1: RangeIndex(start=0, stop=1, step=1)
ExtensionBlock: slice(0, 1, 1), 1 x 1, dtype: Int64
dtype = None, copy = True, na_value = None

  def as_array(
      self,
      dtype: np.dtype | None = None,
      copy: bool = False,
      na_value: object = lib.no_default,
  ) -> np.ndarray:
      """
      Convert the blockmanager data into an numpy array.
  
      Parameters
      ----------
      dtype : np.dtype or None, default None
          Data type of the return array.
      copy : bool, default False
          If True then guarantee that a copy is returned. A value of
          False does not guarantee that the underlying data is not
          copied.
      na_value : object, default lib.no_default
          Value to be used as the missing value sentinel.
  
      Returns
      -------
      arr : ndarray
      """
      passed_nan = lib.is_float(na_value) and isna(na_value)
  
      if len(self.blocks) == 0:
          arr = np.empty(self.shape, dtype=float)
          return arr.transpose()
  
      if self.is_single_block:
          blk = self.blocks[0]
  
          if na_value is not lib.no_default:
              # We want to copy when na_value is provided to avoid
              # mutating the original object
              if lib.is_np_dtype(blk.dtype, "f") and passed_nan:
                  # We are already numpy-float and na_value=np.nan
                  pass
              else:
                  copy = True
  
          if blk.is_extension:
              # Avoid implicit conversion of extension blocks to object
  
              # error: Item "ndarray" of "Union[ndarray, ExtensionArray]" has no
              # attribute "to_numpy"
              arr = blk.values.to_numpy(  # type: ignore[union-attr]
                  dtype=dtype,
                  na_value=na_value,
                  copy=copy,
              ).reshape(blk.shape)
          else:
              arr = np.array(blk.values, dtype=dtype, copy=copy)
  
          if using_copy_on_write() and not copy:
              arr = arr.view()
              arr.flags.writeable = False
      else:
          arr = self._interleave(dtype=dtype, na_value=na_value)
          # The underlying data was copied within _interleave, so no need
          # to further copy if copy=True or setting na_value
  
      if na_value is lib.no_default:
          pass
      elif arr.dtype.kind == "f" and passed_nan:
          pass
      else:
          arr[isna(arr)] = na_value

E TypeError: int() argument must be a string, a bytes-like object or a real number, not 'NoneType'

/usr/local/lib/python3.10/dist-packages/pandas/core/internals/managers.py:1701: TypeError