fe9.py

train["User_ID_MinPrice"] = train.groupby(['User_ID'])['Purchase'].transform('min')
userID_min_dict = train.groupby(['User_ID'])['Purchase'].min().to_dict()
test['User_ID_MinPrice'] = test['User_ID'].apply(lambda x:userID_min_dict.get(x,0))
 
train["User_ID_MaxPrice"] = train.groupby(['User_ID'])['Purchase'].transform('max')
userID_max_dict = train.groupby(['User_ID'])['Purchase'].max().to_dict()
test['User_ID_MaxPrice'] = test['User_ID'].apply(lambda x:userID_max_dict.get(x,0))
 
train["Product_ID_MinPrice"] = train.groupby(['Product_ID'])['Purchase'].transform('min')
productID_min_dict = train.groupby(['Product_ID'])['Purchase'].min().to_dict()
test['Product_ID_MinPrice'] = test['Product_ID'].apply(lambda x:productID_min_dict.get(x,0))

train["Product_ID_MaxPrice"] = train.groupby(['Product_ID'])['Purchase'].transform('max')
productID_max_dict = train.groupby(['Product_ID'])['Purchase'].max().to_dict()
test['Product_ID_MaxPrice'] = test['Product_ID'].apply(lambda x:productID_max_dict.get(x,0))