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

本文已收录于 http://www.flydean.com/02-pandas-restaurant/

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