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
