如何计算每个用户的评分平均值？ [英] How to calculate the mean of ratings of each user?

问题描述

假设我有一个像这样的数据集：

  userID productID等级
ai 5 
bi 4 
ci 4 
aj 3 
bj 5 
  

问题是，我该如何计算每个用户的平均评分？
我看到了

由用户分组并计算每个的均值：

 在[2]中：df.groupby（'userID'）。mean（）
  

您还可以在 df 命名为 user_avg_rating 并为其分配每个用户的平均分数：

 在[3]中：df ['user_avg_rating'] = df.groupby（'userID'）['ratin g']。transform（'mean'）
 df 
  

方法 transform 接受分组的对象并创建一个序列：

 在[4]中：df.groupby（'userID'）['rating']。transform（'mean'）
 
 0 4.0 
 1 4.5 
 2 4.0 
 3 4.0 
 4 4.5 
 dtype：float64 
  

此系列已分配给 user_avg_rating 列。

Assume I have a dataset like this:

userID productID rating
a          i       5
b          i       4
c          i       4 
a          j       3
b          j       5

The question is, how can I calculate the mean rating of each user? I saw this answer, but I didn't quite understand it. I would really appreciate your time, if you show some guidance.

解决方案

I work in an IPython Notebook.

Let's assume you have this file user_ratings.csv:

userID productID rating
a          i       5
b          i       4
c          i       4
a          j       3
b          j       5

The example in the link uses pandas. So import pandas:

In [1]: import pandas as pd

Read your file into a dataframe:

In [2]: df = pd.read_csv('user_ratings.csv', delim_whitespace=True)
df 

Group by the user and calculate the mean for each:

In [2]: df.groupby('userID').mean()

You can also create a new column in df named user_avg_rating an assign the mean score of each user to it:

In [3]:  df['user_avg_rating'] = df.groupby('userID')['rating'].transform('mean')
         df

The method transform takes your grouped object and creates a series:

In [4]:  df.groupby('userID')['rating'].transform('mean')

    0    4.0
    1    4.5
    2    4.0
    3    4.0
    4    4.5
    dtype: float64

This series is assigned to the column user_avg_rating.

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