目录
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
