What Is The Use Case Of Numpy Array Of Scalar Value?

In the latest scipy version, I found: >>> import numpy as np >>> from scipy.sparse import csr_matrix >>> a = csr_matrix((3, 4), dtype=np.int8) >>&g

Solution 1:

You've created a sparse matrix, shape (3,4), but no elements:

In [220]: a = sparse.csr_matrix((3, 4), dtype=np.int8)                                                       
In [221]: a                                                                                                  
Out[221]: 
<3x4 sparse matrix of type'<class 'numpy.int8'>'
    with 0 stored elements in Compressed Sparse Row format>
In [222]: a.toarray()                                                                                        
Out[222]: 
array([[0, 0, 0, 0],
       [0, 0, 0, 0],
       [0, 0, 0, 0]], dtype=int8)

Selecting one element:

In[223]: a[0,0]Out[223]: array(0, dtype=int8)

Converting it to a dense np.matrix:

In[224]: a.todense()                                                                                        
Out[224]: 
matrix([[0, 0, 0, 0],
        [0, 0, 0, 0],
        [0, 0, 0, 0]], dtype=int8)
In[225]: a.todense()[0,0]Out[225]: 0

and to other sparse formats:

In[226]: a.tolil()[0,0]Out[226]: 0In[227]: a.todok()[0,0]Out[227]: 0

It looks like csr is some what unique in returning a scalar array like this. I'm not sure if it's intentional, a feature, or a bug. I haven't noticed it before. Usually we work with the whole matrix, rather than specific elements.

But a 0d array is allowed, even if in most cases it isn't useful. If we can have 2d or 1d arrays, why not 0?

There are a couple of ways of extracting that element from a 0d array:

In[233]: np.array(0, 'int8')                                                                                
Out[233]: array(0, dtype=int8)
In[234]: _.shapeOut[234]: ()
In[235]: __.item()                                                                                          
Out[235]: 0In[236]: ___[()]       # indexwithanemptytupleOut[236]: 0

Scipy version 1.3.0 release notes includes:

CSR and CSC sparse matrix fancy indexing performance has been improved substantially

https://github.com/scipy/scipy/pull/7827 - looks like this pull request was a long time in coming, and had a lot of faults (and may still). If this behavior is a change from previous scipy releases, we need to see if there's a related issue (and possibly create one).

https://github.com/scipy/scipy/pull/10207 BUG: Compressed matrix indexing should return a scalar

Looks like it will be fixed in 1.4.

Solution 2:

What are they?

They're seemingly an single-element array, like an array with one element.

How do I get the value out of it?

By using:

>>>np.array(0).item()
0
>>>