ValueError：使用 pandas hub_table 不允许使用负尺寸

我正在尝试制作项目-项目协作推荐代码。我的完整数据集可以找到here https://drive.google.com/open?id=0B7KsvcvcTJ4DeU1IRE93ZHVnRGM。我希望用户成为行，项目成为列，评级成为值。

我的代码如下：

import pandas as pd     
import numpy as np   
file = pd.read_csv("data.csv", names=['user', 'item', 'rating', 'timestamp'])
table = pd.pivot_table(file, values='rating', index=['user'], columns=['item'])

我的数据如下：

             user        item  rating   timestamp
0  A2EFCYXHNK06IS  5555991584       5   978480000  
1  A1WR23ER5HMAA9  5555991584       5   953424000
2  A2IR4Q0GPAFJKW  5555991584       4  1393545600
3  A2V0KUVAB9HSYO  5555991584       4   966124800
4  A1J0GL9HCA7ELW  5555991584       5  1007683200

错误是：

Traceback (most recent call last):  
  File "D:\python\reco.py", line 9, in <module>   
    table=pd.pivot_table(file,values='rating',index=['user'],columns=['item'])  
  File "C:\python35\lib\site-packages\pandas\tools\pivot.py", line 133, in   pivot_table     
        table = agged.unstack(to_unstack)   
  File "C:\python35\lib\site-packages\pandas\core\frame.py", line 4047, in       unstack  
    return unstack(self, level, fill_value)
  File "C:\python35\lib\site-packages\pandas\core\reshape.py", line 402, in   unstack      
    return _unstack_multiple(obj, level)    
  File "C:\python35\lib\site-packages\pandas\core\reshape.py", line 297, in   _unstack_multiple  
    unstacked = dummy.unstack('__placeholder__')  
  File "C:\python35\lib\site-packages\pandas\core\frame.py", line 4047, in   unstack  
    return unstack(self, level, fill_value)  
  File "C:\python35\lib\site-packages\pandas\core\reshape.py", line 406, in   unstack  
    return _unstack_frame(obj, level, fill_value=fill_value)  
  File "C:\python35\lib\site-packages\pandas\core\reshape.py", line 449, in   _unstack_frame  
    fill_value=fill_value)  
  File "C:\python35\lib\site-packages\pandas\core\reshape.py", line 103, in   __init__  
    self._make_selectors()  
  File "C:\python35\lib\site-packages\pandas\core\reshape.py", line 137, in   _make_selectors  
    mask = np.zeros(np.prod(self.full_shape), dtype=bool)  
ValueError: negative dimensions are not allowed

我不能保证这会完成（我厌倦了等待它计算），但这里有一种创建稀疏数据帧的方法，希望能够最大限度地减少内存并提供帮助。

import pandas as pd
import numpy as np
file=pd.read_csv("data.csv",names=['user','item','rating','timestamp'])

from scipy.sparse import csr_matrix

user_u = list(sorted(file.user.unique()))
item_u = list(sorted(file.item.unique()))

row = file.user.astype('category', categories=user_u).cat.codes
col = file.item.astype('category', categories=item_u).cat.codes

data = file['rating'].tolist()

sparse_matrix = csr_matrix((data, (row, col)), shape=(len(user_u), len(item_u)))

df = pd.SparseDataFrame([ pd.SparseSeries(sparse_matrix[i].toarray().ravel(), fill_value=0) 
                              for i in np.arange(sparse_matrix.shape[0]) ], 
                       index=user_u, columns=item_u, default_fill_value=0)

See 这个问题 https://stackoverflow.com/questions/31661604/efficiently-create-sparse-pivot-tables-in-pandas以获得更多选择。