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def find_stats(pre_train_df, fine_tune_df, n = 5): emotions = list(pre_train_df) emotions.remove("group") difference = pre_train_df.loc[:, pre_train_df.columns != 'group'] - fine_tune_df.loc[:, fine_tune_df.columns != 'group'] difference["group"] = pre_train_df["group"] dic = {} for emotion in emotions: max_change = difference[emotion].nlargest(n) min_change = difference[emotion].nsmallest(n) max_groups = list(difference["group"].loc[max_change.index]) min_groups = list(difference["group"].loc[min_change.index]) max_min = {"max_change":max_groups,"max_change_values":list(max_change) ,"min_change":min_groups, "min_change_values":list(min_change)} dic[emotion] = max_min return dic