# 02-pandas-restaurant ## 2. 数据分析实际案例之：pandas在餐厅评分数据中的使用 ## 简介 为了更好的熟练掌握pandas在实际数据分析中的应用，今天我们再介绍一下怎么使用pandas做美国餐厅评分数据的分析。 ## 餐厅评分数据简介 数据的来源是UCI ML Repository，包含了一千多条数据，有5个属性，分别是： userID： 用户ID placeID：餐厅ID rating：总体评分 food\_rating：食物评分 service\_rating：服务评分 我们使用pandas来读取数据： ``` import numpy as np path = '../data/restaurant_rating_final.csv' df = pd.read_csv(path) df ``` | | userID | placeID | rating | food\_rating | service\_rating | | ---: | -----: | ------: | -----: | -----------: | --------------- | | 0 | U1077 | 135085 | 2 | 2 | 2 | | 1 | U1077 | 135038 | 2 | 2 | 1 | | 2 | U1077 | 132825 | 2 | 2 | 2 | | 3 | U1077 | 135060 | 1 | 2 | 2 | | 4 | U1068 | 135104 | 1 | 1 | 2 | | ... | ... | ... | ... | ... | ... | | 1156 | U1043 | 132630 | 1 | 1 | 1 | | 1157 | U1011 | 132715 | 1 | 1 | 0 | | 1158 | U1068 | 132733 | 1 | 1 | 0 | | 1159 | U1068 | 132594 | 1 | 1 | 1 | | 1160 | U1068 | 132660 | 0 | 0 | 0 | 1161 rows × 5 columns ## 分析评分数据 如果我们关注的是不同餐厅的总评分和食物评分，我们可以先看下这些餐厅评分的平均数，这里我们使用pivot\_table方法： ``` mean_ratings = df.pivot_table(values=['rating','food_rating'], index='placeID', aggfunc='mean') mean_ratings[:5] ``` | | food\_rating | rating | | ------: | -----------: | -----: | | placeID | | | | 132560 | 1.00 | 0.50 | | 132561 | 1.00 | 0.75 | | 132564 | 1.25 | 1.25 | | 132572 | 1.00 | 1.00 | | 132583 | 1.00 | 1.00 | 然后再看一下各个placeID，投票人数的统计： ``` ratings_by_place = df.groupby('placeID').size() ratings_by_place[:10] ``` ``` placeID 132560 4 132561 4 132564 4 132572 15 132583 4 132584 6 132594 5 132608 6 132609 5 132613 6 dtype: int64 ``` 如果投票人数太少，那么这些数据其实是不客观的，我们来挑选一下投票人数超过4个的餐厅： ``` active_place = ratings_by_place.index[ratings_by_place >= 4] active_place ``` ``` Int64Index([132560, 132561, 132564, 132572, 132583, 132584, 132594, 132608, 132609, 132613, ... 135080, 135081, 135082, 135085, 135086, 135088, 135104, 135106, 135108, 135109], dtype='int64', name='placeID', length=124) ``` 选择这些餐厅的平均评分数据： ``` mean_ratings = mean_ratings.loc[active_place] mean_ratings ``` | | food\_rating | rating | | ------: | -----------: | -------: | | placeID | | | | 132560 | 1.000000 | 0.500000 | | 132561 | 1.000000 | 0.750000 | | 132564 | 1.250000 | 1.250000 | | 132572 | 1.000000 | 1.000000 | | 132583 | 1.000000 | 1.000000 | | ... | ... | ... | | 135088 | 1.166667 | 1.000000 | | 135104 | 1.428571 | 0.857143 | | 135106 | 1.200000 | 1.200000 | | 135108 | 1.181818 | 1.181818 | | 135109 | 1.250000 | 1.000000 | 124 rows × 2 columns 对rating进行排序，选择评分最高的10个： ``` top_ratings = mean_ratings.sort_values(by='rating', ascending=False) top_ratings[:10] ``` | | food\_rating | rating | | ------: | -----------: | -------: | | placeID | | | | 132955 | 1.800000 | 2.000000 | | 135034 | 2.000000 | 2.000000 | | 134986 | 2.000000 | 2.000000 | | 132922 | 1.500000 | 1.833333 | | 132755 | 2.000000 | 1.800000 | | 135074 | 1.750000 | 1.750000 | | 135013 | 2.000000 | 1.750000 | | 134976 | 1.750000 | 1.750000 | | 135055 | 1.714286 | 1.714286 | | 135075 | 1.692308 | 1.692308 | 我们还可以计算平均总评分和平均食物评分的差值，并以一栏diff进行保存： ``` mean_ratings['diff'] = mean_ratings['rating'] - mean_ratings['food_rating'] sorted_by_diff = mean_ratings.sort_values(by='diff') sorted_by_diff[:10] ``` | | food\_rating | rating | diff | | ------: | -----------: | -------: | --------: | | placeID | | | | | 132667 | 2.000000 | 1.250000 | -0.750000 | | 132594 | 1.200000 | 0.600000 | -0.600000 | | 132858 | 1.400000 | 0.800000 | -0.600000 | | 135104 | 1.428571 | 0.857143 | -0.571429 | | 132560 | 1.000000 | 0.500000 | -0.500000 | | 135027 | 1.375000 | 0.875000 | -0.500000 | | 132740 | 1.250000 | 0.750000 | -0.500000 | | 134992 | 1.500000 | 1.000000 | -0.500000 | | 132706 | 1.250000 | 0.750000 | -0.500000 | | 132870 | 1.000000 | 0.600000 | -0.400000 | 将数据进行反转，选择差距最大的前10： ``` sorted_by_diff[::-1][:10] ``` | | food\_rating | rating | diff | | ------: | -----------: | -------: | -------: | | placeID | | | | | 134987 | 0.500000 | 1.000000 | 0.500000 | | 132937 | 1.000000 | 1.500000 | 0.500000 | | 135066 | 1.000000 | 1.500000 | 0.500000 | | 132851 | 1.000000 | 1.428571 | 0.428571 | | 135049 | 0.600000 | 1.000000 | 0.400000 | | 132922 | 1.500000 | 1.833333 | 0.333333 | | 135030 | 1.333333 | 1.583333 | 0.250000 | | 135063 | 1.000000 | 1.250000 | 0.250000 | | 132626 | 1.000000 | 1.250000 | 0.250000 | | 135000 | 1.000000 | 1.250000 | 0.250000 | 计算rating的标准差，并选择最大的前10个： ``` # Standard deviation of rating grouped by placeID rating_std_by_place = df.groupby('placeID')['rating'].std() # Filter down to active_titles rating_std_by_place = rating_std_by_place.loc[active_place] # Order Series by value in descending order rating_std_by_place.sort_values(ascending=False)[:10] ``` ``` placeID 134987 1.154701 135049 1.000000 134983 1.000000 135053 0.991031 135027 0.991031 132847 0.983192 132767 0.983192 132884 0.983192 135082 0.971825 132706 0.957427 Name: rating, dtype: float64 ``` --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. 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