In [1]:

    

import numpy as np
from scipy.sparse import csr_matrix, csc_matrix, coo_matrix, lil_matrix


    

In [2]:

    

l = [[0, 1, 2],
     [3, 0, 4],
     [0, 0, 0]]


    

In [3]:

    

csr = csr_matrix(l)


    

In [4]:

    

print((csr + csr).toarray())


    

    




[[0 2 4]
 [6 0 8]
 [0 0 0]]

In [5]:

    

print(type((csr + csr)))


    

    




<class 'scipy.sparse.csr.csr_matrix'>

In [6]:

    

print((csr - csr).toarray())


    

    




[[0 0 0]
 [0 0 0]
 [0 0 0]]

In [7]:

    

print((csr * csr).toarray())


    

    




[[3 0 4]
 [0 3 6]
 [0 0 0]]

In [8]:

    

print((csr.multiply(csr)).toarray())


    

    




[[ 0  1  4]
 [ 9  0 16]
 [ 0  0  0]]

In [9]:

    

print((csr.dot(csr)).toarray())


    

    




[[3 0 4]
 [0 3 6]
 [0 0 0]]

In [10]:

    

print(csr / csr)


    

    




[[nan  1.  1.]
 [ 1. nan  1.]
 [nan nan nan]]

In [11]:

    

print(type(csr / csr))


    

    




<class 'numpy.matrix'>

In [12]:

    

print(np.array(csr / csr))


    

    




[[nan  1.  1.]
 [ 1. nan  1.]
 [nan nan nan]]

In [13]:

    

print(type(np.array(csr / csr)))


    

    




<class 'numpy.ndarray'>

In [14]:

    

csr_full = csr_matrix([[1, 2, 3], [4, 5, 6], [7, 8, 9]])


    

In [15]:

    

print(csr_full / csr_full)


    

    




[[1. 1. 1.]
 [1. 1. 1.]
 [1. 1. 1.]]

In [16]:

    

print(type(csr_full / csr_full))


    

    




<class 'numpy.matrix'>

In [17]:

    

# print(csr + 10)
# NotImplementedError: adding a nonzero scalar to a sparse matrix is not supported


    

In [18]:

    

# print(csr - 10)
# NotImplementedError: subtracting a nonzero scalar from a sparse matrix is not supported


    

In [19]:

    

# print(csr_full + 10)
# NotImplementedError: adding a nonzero scalar to a sparse matrix is not supported


    

In [20]:

    

# print(csr_full - 10)
# NotImplementedError: subtracting a nonzero scalar from a sparse matrix is not supported


    

In [21]:

    

print((csr * 10).toarray())


    

    




[[ 0 10 20]
 [30  0 40]
 [ 0  0  0]]

In [22]:

    

print((csr / 10).toarray())


    

    




[[0.  0.1 0.2]
 [0.3 0.  0.4]
 [0.  0.  0. ]]

In [23]:

    

print((csr ** 2).toarray())


    

    




[[3 0 4]
 [0 3 6]
 [0 0 0]]

In [24]:

    

print((csr ** 3).toarray())


    

    




[[ 0  3  6]
 [ 9  0 12]
 [ 0  0  0]]

In [25]:

    

print((csr * csr * csr).toarray())


    

    




[[ 0  3  6]
 [ 9  0 12]
 [ 0  0  0]]

In [26]:

    

# print((csr ** -1).toarray())
# alueError: exponent must be >= 0


    

In [27]:

    

# print((csr ** 0.5).toarray())
# ValueError: exponent must be an integer


    

In [28]:

    

csc = csc_matrix(l)
coo = coo_matrix(l)
lil = lil_matrix(l)


    

In [29]:

    

print(type(csc + csc))


    

    




<class 'scipy.sparse.csc.csc_matrix'>

In [30]:

    

print(type(csr + csc))


    

    




<class 'scipy.sparse.csr.csr_matrix'>

In [31]:

    

print(type(csc + csr))


    

    




<class 'scipy.sparse.csc.csc_matrix'>

In [32]:

    

print(type(coo + coo))


    

    




<class 'scipy.sparse.csr.csr_matrix'>

In [33]:

    

print(type(lil + lil))


    

    




<class 'scipy.sparse.csr.csr_matrix'>

In [34]:

    

a = np.array(l)
print(a)


    

    




[[0 1 2]
 [3 0 4]
 [0 0 0]]

In [35]:

    

print(type(a))


    

    




<class 'numpy.ndarray'>

In [36]:

    

print(a + csr)


    

    




[[0 2 4]
 [6 0 8]
 [0 0 0]]

In [37]:

    

print(type(a + csr))


    

    




<class 'numpy.matrix'>

In [38]:

    

print(type(csr - a))


    

    




<class 'numpy.matrix'>

In [39]:

    

# print(type(a / csr))
# TypeError: unsupported operand type(s) for /: 'numpy.ndarray' and 'csr_matrix'


    

In [40]:

    

print(csr / a)


    

    




[[nan  1.  1.]
 [ 1. nan  1.]
 [nan nan nan]]






    




/usr/local/lib/python3.7/site-packages/scipy/sparse/base.py:596: RuntimeWarning: invalid value encountered in true_divide
  return np.true_divide(self.todense(), other)

In [41]:

    

print(type(csr / a))


    

    




<class 'numpy.matrix'>






    




/usr/local/lib/python3.7/site-packages/scipy/sparse/base.py:596: RuntimeWarning: invalid value encountered in true_divide
  return np.true_divide(self.todense(), other)

In [42]:

    

print(csr * a)


    

    




[[3 0 4]
 [0 3 6]
 [0 0 0]]

In [43]:

    

print(type(csr * a))


    

    




<class 'numpy.ndarray'>

In [44]:

    

print(type(a * csr))


    

    




<class 'numpy.ndarray'>

In [45]:

    

print(type(csr.dot(a)))


    

    




<class 'numpy.ndarray'>

In [46]:

    

print(a.dot(csr))


    

    




[[<3x3 sparse matrix of type '<class 'numpy.int64'>'
	with 4 stored elements in Compressed Sparse Row format>
  <3x3 sparse matrix of type '<class 'numpy.int64'>'
	with 4 stored elements in Compressed Sparse Row format>
  <3x3 sparse matrix of type '<class 'numpy.int64'>'
	with 4 stored elements in Compressed Sparse Row format>]
 [<3x3 sparse matrix of type '<class 'numpy.int64'>'
	with 4 stored elements in Compressed Sparse Row format>
  <3x3 sparse matrix of type '<class 'numpy.int64'>'
	with 4 stored elements in Compressed Sparse Row format>
  <3x3 sparse matrix of type '<class 'numpy.int64'>'
	with 4 stored elements in Compressed Sparse Row format>]
 [<3x3 sparse matrix of type '<class 'numpy.int64'>'
	with 4 stored elements in Compressed Sparse Row format>
  <3x3 sparse matrix of type '<class 'numpy.int64'>'
	with 4 stored elements in Compressed Sparse Row format>
  <3x3 sparse matrix of type '<class 'numpy.int64'>'
	with 4 stored elements in Compressed Sparse Row format>]]

In [47]:

    

print(csr.multiply(a))


    

    




  (0, 1)	1
  (0, 2)	4
  (1, 0)	9
  (1, 2)	16

In [48]:

    

print(type(csr.multiply(a)))


    

    




<class 'scipy.sparse.coo.coo_matrix'>

In [49]:

    

print(csr.multiply(a).toarray())


    

    




[[ 0  1  4]
 [ 9  0 16]
 [ 0  0  0]]

In [50]:

    

print(np.multiply(a, csr))


    

    




[[<3x3 sparse matrix of type '<class 'numpy.int64'>'
	with 4 stored elements in Compressed Sparse Row format>
  <3x3 sparse matrix of type '<class 'numpy.int64'>'
	with 4 stored elements in Compressed Sparse Row format>
  <3x3 sparse matrix of type '<class 'numpy.int64'>'
	with 4 stored elements in Compressed Sparse Row format>]
 [<3x3 sparse matrix of type '<class 'numpy.int64'>'
	with 4 stored elements in Compressed Sparse Row format>
  <3x3 sparse matrix of type '<class 'numpy.int64'>'
	with 4 stored elements in Compressed Sparse Row format>
  <3x3 sparse matrix of type '<class 'numpy.int64'>'
	with 4 stored elements in Compressed Sparse Row format>]
 [<3x3 sparse matrix of type '<class 'numpy.int64'>'
	with 4 stored elements in Compressed Sparse Row format>
  <3x3 sparse matrix of type '<class 'numpy.int64'>'
	with 4 stored elements in Compressed Sparse Row format>
  <3x3 sparse matrix of type '<class 'numpy.int64'>'
	with 4 stored elements in Compressed Sparse Row format>]]