code: def get_drift_skew_dataframe(anomalies):
      """Get drift_skew_info as a Pandas dataframe."""
      result = []
      for info in anomalies.drift_skew_info:
          for measurement in info.drift_measurements:
              result.append(
                  (
                      str(types.FeaturePath.from_proto(info.path)),
                      anomalies_pb2.DriftSkewInfo.Measurement.Type.Name(
                          measurement.type
                      ),
                      measurement.value,
                      measurement.threshold,
                  )
              )
      return pd.DataFrame(
          result, columns=["path", "type", "value", "threshold"]
      ).set_index("path")
  
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 5 for calling a global function with example usage: get_drift_skew_dataframe(anomalies) and returns: pd