class_docstr: Accepts or displays 2D input through a spreadsheet-like component for dataframes.
  Preprocessing: passes the uploaded spreadsheet data as a {pandas.DataFrame}, {numpy.array}, or {List[List]} depending on `type`
  Postprocessing: expects a {pandas.DataFrame}, {pandas.Styler}, {numpy.array}, {List[List]}, {List}, a {Dict} with keys `data` (and optionally `headers`), or {str} path to a csv, which is rendered in the spreadsheet.
  Examples-format: a {str} filepath to a csv with data, a pandas dataframe, or a list of lists (excluding headers) where each sublist is a row of data.
  Demos: filter_records, matrix_transpose, tax_calculator
code: def postprocess(
      self,
      value: pd.DataFrame
      | Styler
      | np.ndarray
      | list
      | list[list]
      | dict
      | str
      | None,
  ) -> DataframeData:
      if value is None:
          return self.postprocess(self.empty_input)
      if isinstance(value, dict):
          return DataframeData(
              headers=value.get("headers", []),
              data=value.get("data", [[]]),
          )
      if isinstance(value, (str, pd.DataFrame)):
          if isinstance(value, str):
              value = pd.read_csv(value)  # type: ignore
          return DataframeData(
              headers=list(value.columns),  # type: ignore
              data=value.to_dict(orient="split")["data"],  # type: ignore
          )
      elif isinstance(value, Styler):
          if semantic_version.Version(
              pd.__version__
          ) < semantic_version.Version("1.5.0"):
              raise ValueError(
                  "Styler objects are only supported in pandas version 1.5.0 or higher. Please try: `pip install --upgrade pandas` to use this feature."
              )
          if self.interactive:
              warnings.warn(
                  "Cannot display Styler object in interactive mode. Will display as a regular pandas dataframe instead."
              )
          df: pd.DataFrame = value.data  # type: ignore
          return DataframeData(
              headers=list(df.columns),
              data=df.to_dict(orient="split")["data"],  # type: ignore
              metadata=self.__extract_metadata(value),  # type: ignore
          )
      elif isinstance(value, (str, pd.DataFrame)):
          df = pd.read_csv(value) if isinstance(value, str) else value  # type: ignore
          return DataframeData(
              headers=list(df.columns),
              data=df.to_dict(orient="split")["data"],  # type: ignore
          )
      elif isinstance(value, (np.ndarray, list)):
          if len(value) == 0:
              return self.postprocess([[]])
          if isinstance(value, np.ndarray):
              value = value.tolist()
          if not isinstance(value, list):
              raise ValueError("output cannot be converted to list")
  
          _headers = self.headers
          if len(self.headers) < len(value[0]):
              _headers: list[str] = [
                  *self.headers,
                  *[
                      str(i)
                      for i in range(
                          len(self.headers) + 1, len(value[0]) + 1
                      )
                  ],
              ]
          elif len(self.headers) > len(value[0]):
              _headers = self.headers[: len(value[0])]
  
          return DataframeData(headers=_headers, data=value)
      else:
          raise ValueError("Cannot process value as a Dataframe")
  
imports: from __future__ import annotations
  import warnings
  from typing import Any, Callable, Dict, List, Literal, Optional
  import numpy
  import pandas
  import semantic_version
  from gradio_client.documentation import document, set_documentation_group
  from pandas.io.formats.style import Styler
  from gradio.components import Component
  from gradio.data_classes import GradioModel
  from gradio.events import Events
label_desc: Use image node_id 3 for calling the Dataframe obj's underlying member method code with example usage: obj.postprocess(value) and returns: self, DataframeData, DataframeData, DataframeData, DataframeData, DataframeData, self