小编use*_*980的帖子

假设我有两个具有多索引的数据帧，其中一个索引比另一个更深。现在我只想从一个（更深的）数据框中选择那些行，其中它们的部分索引包含在另一个数据框中。

输入示例：

df = pandas.DataFrame(
    {
        "A": ["a1", "a1", "a1", "a2", "a2", "a2"],
        "B": ["b1", "b1", "b2", "b1", "b2", "b2"],
        "C": ["c1", "c2", "c1", "c1", "c1", "c2"],
        "V": [1, 2, 3, 4, 5, 6],
     }
).set_index(["A", "B", "C"])
df2 = pandas.DataFrame(
    {
        "A": ["a1", "a1", "a2", "a2"],
        "B": ["b1", "b3", "b1", "b3"],
        "X": [1, 2, 3, 4]
     }
).set_index(["A", "B"])

视觉的：

          V
A  B  C
a1 b1 c1  1
      c2  2
   b2 c1  3
a2 b1 c1  4
   b2 c1 …

我正在计算scipy.sparse矩阵(CSC)和numpy ndarray向量之间的点积:

>>> print type(np_vector), np_vector.shape
<type 'numpy.ndarray'> (200,)
>>> print type(sp_matrix), sparse.isspmatrix(sp_matrix), sp_matrix.shape
<class 'scipy.sparse.csc.csc_matrix'> True (200, 200)
>>> dot_vector = dot(np_vector, sp_matrix)

结果似乎是一个新的ndarray矢量,正如我所料:

>>> print type(dot_vector), dot_vector.shape
<type 'numpy.ndarray'> (200,)

但是当我尝试向该向量添加标量时,我收到异常:

>>> scalar = 3.0
>>> print dot_vector + scalar 
C:\Python27\lib\site-packages\scipy\sparse\compressed.pyc in __add__(self, other)
    173                 return self.copy()
    174             else: # Now we would add this scalar to every element.
--> 175                 raise NotImplementedError('adding a nonzero scalar to a '
    176                                           'sparse matrix is not supported')
    177         elif isspmatrix(other): …