code: def get_skew_result_dataframe(
      skew_results: Iterable[feature_skew_results_pb2.FeatureSkew],
  ) -> pd.DataFrame:
      """Formats FeatureSkew results as a pandas dataframe."""
      result = []
      for feature_skew in skew_results:
          result.append(
              (
                  feature_skew.feature_name,
                  feature_skew.base_count,
                  feature_skew.test_count,
                  feature_skew.match_count,
                  feature_skew.base_only,
                  feature_skew.test_only,
                  feature_skew.mismatch_count,
                  feature_skew.diff_count,
              )
          )
      # Preserve deterministic order from the proto.
      columns = [
          "feature_name",
          "base_count",
          "test_count",
          "match_count",
          "base_only",
          "test_only",
          "mismatch_count",
          "diff_count",
      ]
      return (
          pd.DataFrame(result, columns=columns)
          .sort_values("feature_name")
          .reset_index(drop=True)
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import base64
  import collections
  import sys
  from typing import Dict, Iterable, List, Optional, Text, Tuple, Union
  import pandas
  from tensorflow_data_validation import types
  from tensorflow_data_validation.skew.protos import feature_skew_results_pb2
  from tensorflow_data_validation.utils import stats_util
  from tensorflow_metadata.proto.v0 import anomalies_pb2
  from tensorflow_metadata.proto.v0 import schema_pb2
  from tensorflow_metadata.proto.v0 import statistics_pb2
label_desc: Use image node_id 17 for calling a global function with example usage: get_skew_result_dataframe(skew_results) and returns: pd