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