code: def split_result_of_axis_func_pandas(axis, num_splits, result, length_list=None):
      
      if num_splits == 1:
          return result
      if length_list is not None:
          length_list.insert(0, 0)
          sums = np.cumsum(length_list)
          if axis == 0:
              return [result.iloc[sums[i] : sums[i + 1]] for i in range(len(sums) - 1)]
          else:
              return [result.iloc[:, sums[i] : sums[i + 1]] for i in range(len(sums) - 1)]
      # We do this to restore block partitioning
      chunksize = compute_chunksize(result, num_splits, axis=axis)
      if axis == 0:
          return [
              result.iloc[chunksize * i : chunksize * (i + 1)] for i in range(num_splits)
          ]
      else:
          return [
              result.iloc[:, chunksize * i : chunksize * (i + 1)]
              for i in range(num_splits)
          ]
docs: Split the Pandas result evenly based on the provided number of splits.
  
      Args:
          axis: The axis to split across.
          num_splits: The number of even splits to create.
          result: The result of the computation. This should be a Pandas
              DataFrame.
          length_list: The list of lengths to split this DataFrame into. This is used to
              return the DataFrame to its original partitioning schema.
  
      Returns:
          A list of Pandas DataFrames.