data[rating[0]-1][rating[1]-1] = rating[2]
时间: 2024-04-19 18:20:13 浏览: 111
根据你提供的代码片段,data是一个二维数组,rating是一个包含三个元素的列表。根据代码逻辑,data[rating-1][rating-1] = rating的作用是将rating[2]的值赋给data二维数组中索引为rating-1和rating-1的元素。
具体来说,rating表示行索引,rating表示列索引,rating表示要赋给该位置的值。通过rating-1和rating-1可以将索引转换为数组的实际索引。
相关问题
data1 = pd.read_csv('rating.csv')
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。
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.