data1 = pd.read_csv('rating.csv')
时间: 2023-11-19 12:54:56 浏览: 72
data1 = pd.read_csv('rating.csv')是读取名为rating.csv的文件并将其存储在名为data1的变量中。这个文件可能包含用户对书籍的评分数据。但是,由于没有指定列名或索引,因此需要使用默认值。这意味着第一行将被视为列名,第一列将被视为索引。如果文件中没有列名,则默认情况下列名将被分配为0,1,2等。如果文件中没有索引,则默认情况下索引将被分配为0,1,2等。
相关问题
# 读取评分数据文件 ratings = pd.read_csv("E:\\Recommender-System\\!Final\\ml-100k\\u.data", sep="\t", names=["user_id", "movie_id", "rating", "timestamp"]) # print(ratings) # 创建用户-电影评分矩阵 user_movie_matrix = pd.pivot_table(ratings, index="user_id", columns="movie_id", values="rating") print(user_movie_matrix.user_id) AttributeError: 'DataFrame' object has no attribute 'user_id'
根据您提供的代码,`user_movie_matrix`是一个以电影ID为列索引,以用户ID为行索引的DataFrame对象,因此无法访问`user_id`属性。如果您想访问行索引,可以使用`index`属性,例如:
```python
import pandas as pd
# 读取评分数据文件
ratings = pd.read_csv("E:\\Recommender-System\\!Final\\ml-100k\\u.data", sep="\t", names=["user_id", "movie_id", "rating", "timestamp"])
# 创建用户-电影评分矩阵
user_movie_matrix = pd.pivot_table(ratings, index="user_id", columns="movie_id", values="rating")
# 访问行索引
print(user_movie_matrix.index)
```
上述代码中,`user_movie_matrix.index`将返回用户ID的行索引。如果您要访问列索引,可以使用`columns`属性,例如:
```python
import pandas as pd
# 读取评分数据文件
ratings = pd.read_csv("E:\\Recommender-System\\!Final\\ml-100k\\u.data", sep="\t", names=["user_id", "movie_id", "rating", "timestamp"])
# 创建用户-电影评分矩阵
user_movie_matrix = pd.pivot_table(ratings, index="user_id", columns="movie_id", values="rating")
# 访问列索引
print(user_movie_matrix.columns)
```
上述代码中,`user_movie_matrix.columns`将返回电影ID的列索引。
Rating = pd.read_csv('data/BX-Book-Ratings.csv', sep=None, error_bad_lines=False) Rating=Rating[:10000]
Great, it looks like you have loaded in the user ratings data from the BX-Book-Ratings.csv file. The [:10000] syntax suggests that you are only working with the first 10,000 rows of the data.
To calculate similarity between users, we will need to pivot the data so that each row represents a user and each column represents a book. The values in the table will be the ratings that each user has given to each book. Here's some code to pivot the data:
```
ratings_pivot = Rating.pivot(index='User-ID', columns='ISBN', values='Book-Rating')
```
This will create a new dataframe where each row represents a user and each column represents a book. The values in the table will be the ratings that each user has given to each book. Note that we are assuming that the column containing book ratings is called 'Book-Rating'. If it has a different name in your data, you will need to change the code accordingly.
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