code: def calculate_1d_changes(explainer, new_observation):
      yhats = np.empty_like(explainer.data.columns)
      for i, col in enumerate(explainer.data.columns):
          current_data = deepcopy(explainer.data)
          current_data.loc[:, col] = new_observation.loc[:, col].iloc[0]
          yhats[i] = explainer.predict(current_data).mean()
  
      return pd.Series(yhats, index=explainer.data.columns)
  
file_path: DALEX/python/dalex/dalex/predict_explanations/_break_down/utils.py
imports: from copy import deepcopy
  import numpy
  import pandas
label_desc: Use image node_id 2 for calling a global function with example usage: calculate_1d_changes(explainer, new_observation) and returns: pd