输入数据类型为sklearn SVD fit_transform函数(Input data type for sklearn SVD fit_transform function)

 我已经在CSV文件中处理了文档数据，我在pandas DataFrame中读到了这个文件： 
 
+----------+------+------------+
| document | term | count      |
+----------+------+------------+
| 1        | 126  | 1          |
| 1        | 80   | 1          |
| 1        | 1221 | 2          |
| 2        | 2332 | 1          |
 
 所以它由document_id，term和term frequency组成。 
 
 我没有原始文档，只有这个处理过的数据，我想用sklearn应用SVD，但我无法想象如何为SVD fit_transform（）准备这个DataFrame，它期望： 
 
 
 
 X：{array-like，sparse matrix}，shape（n_samples，n_features） 
 

I have already processed document data in CSV file, which I read in pandas DataFrame:
 
+----------+------+------------+
| document | term | count      |
+----------+------+------------+
| 1        | 126  | 1          |
| 1        | 80   | 1          |
| 1        | 1221 | 2          |
| 2        | 2332 | 1          |
 
So it consists of document_id, term, and term frequency.
 
I don't have original documents, but just this processed data, and I want to apply SVD with sklearn, but I can not figure how to prepare this DataFrame for SVD fit_transform(), which expects:
 
 
 
X : {array-like, sparse matrix}, shape (n_samples, n_features)
 

原文：https://stackoverflow.com/questions/39393994