Indexing to access elements of a matrix

Question

In the example below, I am creating idxL and I want to loop through its elements to carry out other operations. I'm trying to understand why idxL[0][0] returns [[ True False False False False]] instead of returning just True. idxL.item(0) seems to work. I think I could loop through the entire number of items in idxL using it. However, for some reason, I think it would not be as efficient when I start handling bigger arrays.

from scipy.sparse import csr_matrix
a=['foo','panda','donkey','bird','egg']
b='foo'
idxL=csr_matrix((1,5), dtype=bool)
idxTemp=np.array(list(map(lambda x: x in b, a)))
idxL = idxL + idxTemp
print(idxL[0][0])
print(idxL.item(0))

Answer 1

In [193]: from scipy import sparse                                              
In [194]: a=['foo','panda','donkey','bird','egg'] 
     ...: b='foo' 
     ...: idxL=sparse.csr_matrix((1,5), dtype=bool) 
     ...: idxTemp=np.array(list(map(lambda x: x in b, a)))  

The sparse matrix:

In [195]: idxL                                                                  
Out[195]: 
<1x5 sparse matrix of type '<class 'numpy.bool_'>'
    with 0 stored elements in Compressed Sparse Row format>
In [196]: idxL.A                                                                
Out[196]: array([[False, False, False, False, False]])

the dense array; note that it is 1d

In [197]: idxTemp                                                               
Out[197]: array([ True, False, False, False, False])

Indexing the sparse matrix:

In [198]: idxL[0,0]                                                             
Out[198]: False

The addition - it is a dense matrix now:

In [199]: idxLL = idxL + idxTemp                                                
In [200]: idxLL                                                                 
Out[200]: matrix([[ True, False, False, False, False]])
In [201]: idxLL[0,0]                                                            
Out[201]: True

[0] of a matrix selects the first row, but the result will still be 2d. [0][0] indexing doesn't help. This style of indexing works with 2d ndarray, but the [0,0] is generally better.

In [202]: idxLL[0]                                                              
Out[202]: matrix([[ True, False, False, False, False]])
In [203]: idxTemp[0]                                                            
Out[203]: True

edit

We can make a sparse matrix from idxTemp directly:

In [257]: M = sparse.csr_matrix(idxTemp)                                        
In [258]: M                                                                     
Out[258]: 
<1x5 sparse matrix of type '<class 'numpy.bool_'>'
    with 1 stored elements in Compressed Sparse Row format>
In [259]: M.A                                                                   
Out[259]: array([[ True, False, False, False, False]])
In [260]: print(M)                                                              
  (0, 0)    True

There's no need to add it to idxL. It could be added:

In [261]: idxL+M                                                                
Out[261]: 
<1x5 sparse matrix of type '<class 'numpy.bool_'>'
    with 1 stored elements in Compressed Sparse Row format>

I wouldn't recommend building a spare matrix by adding matrices.