How to slice a csr_matrix by certain list of rows/columns?





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Suppose my csr_matrix is 



I = [0, 1, 3 ,4 ,5, 0, 3]

J = [1, 3 , 4, 9 ,1 , 0, 8]

V = [1]*6

V.append(0)

data=sparse.coo_matrix((V,(I,J)),shape=(7,10)).tocsr()


which outputs



(0, 0)  1

(0, 1)  1

(1, 3)  1

(3, 4)  1

(3, 8)  0

(4, 9)  1

(5, 1)  1


So,I want to get entries with specific list of rows/columns. For example:



print(data[:,[3,5,8]])

(1, 0) 1


But I expected to get, 



(1, 3)    1

(3, 8)    0


Similarly I want to get specific rows, but data[list:] does not give desired output. How can I solve this?






python numpy scipy sparse-matrix







share|improve this question




















  • 1





    If you want to preserve explicit zeros, be prepared to do a lot of things manually. Most scipy.sparse operations make no guarantees about explicit zeros.

    – user2357112
    Nov 29 '18 at 5:25











  • The result of that indexing is a (7,3) matrix, not the original (7,10). Indexing reflects that.

    – hpaulj
    Nov 29 '18 at 5:52











  • yea, but how I can preserve the indexing tho?

    – user3086871
    Nov 29 '18 at 5:57


















3















Suppose my csr_matrix is 



I = [0, 1, 3 ,4 ,5, 0, 3]

J = [1, 3 , 4, 9 ,1 , 0, 8]

V = [1]*6

V.append(0)

data=sparse.coo_matrix((V,(I,J)),shape=(7,10)).tocsr()


which outputs



(0, 0)  1

(0, 1)  1

(1, 3)  1

(3, 4)  1

(3, 8)  0

(4, 9)  1

(5, 1)  1


So,I want to get entries with specific list of rows/columns. For example:



print(data[:,[3,5,8]])

(1, 0) 1


But I expected to get, 



(1, 3)    1

(3, 8)    0


Similarly I want to get specific rows, but data[list:] does not give desired output. How can I solve this?






python numpy scipy sparse-matrix







share|improve this question




















  • 1





    If you want to preserve explicit zeros, be prepared to do a lot of things manually. Most scipy.sparse operations make no guarantees about explicit zeros.

    – user2357112
    Nov 29 '18 at 5:25











  • The result of that indexing is a (7,3) matrix, not the original (7,10). Indexing reflects that.

    – hpaulj
    Nov 29 '18 at 5:52











  • yea, but how I can preserve the indexing tho?

    – user3086871
    Nov 29 '18 at 5:57














3












3








3








Suppose my csr_matrix is 



I = [0, 1, 3 ,4 ,5, 0, 3]

J = [1, 3 , 4, 9 ,1 , 0, 8]

V = [1]*6

V.append(0)

data=sparse.coo_matrix((V,(I,J)),shape=(7,10)).tocsr()


which outputs



(0, 0)  1

(0, 1)  1

(1, 3)  1

(3, 4)  1

(3, 8)  0

(4, 9)  1

(5, 1)  1


So,I want to get entries with specific list of rows/columns. For example:



print(data[:,[3,5,8]])

(1, 0) 1


But I expected to get, 



(1, 3)    1

(3, 8)    0


Similarly I want to get specific rows, but data[list:] does not give desired output. How can I solve this?






python numpy scipy sparse-matrix







share|improve this question
















Suppose my csr_matrix is 



I = [0, 1, 3 ,4 ,5, 0, 3]

J = [1, 3 , 4, 9 ,1 , 0, 8]

V = [1]*6

V.append(0)

data=sparse.coo_matrix((V,(I,J)),shape=(7,10)).tocsr()


which outputs



(0, 0)  1

(0, 1)  1

(1, 3)  1

(3, 4)  1

(3, 8)  0

(4, 9)  1

(5, 1)  1


So,I want to get entries with specific list of rows/columns. For example:



print(data[:,[3,5,8]])

(1, 0) 1


But I expected to get, 



(1, 3)    1

(3, 8)    0


Similarly I want to get specific rows, but data[list:] does not give desired output. How can I solve this?






python numpy scipy sparse-matrix




python numpy scipy sparse-matrix






share|improve this question















share|improve this question













share|improve this question




share|improve this question








edited Nov 29 '18 at 7:50









hpaulj

117k786160




117k786160










asked Nov 29 '18 at 5:19









user3086871user3086871

113114




113114








  • 1





    If you want to preserve explicit zeros, be prepared to do a lot of things manually. Most scipy.sparse operations make no guarantees about explicit zeros.

    – user2357112
    Nov 29 '18 at 5:25











  • The result of that indexing is a (7,3) matrix, not the original (7,10). Indexing reflects that.

    – hpaulj
    Nov 29 '18 at 5:52











  • yea, but how I can preserve the indexing tho?

    – user3086871
    Nov 29 '18 at 5:57














  • 1





    If you want to preserve explicit zeros, be prepared to do a lot of things manually. Most scipy.sparse operations make no guarantees about explicit zeros.

    – user2357112
    Nov 29 '18 at 5:25











  • The result of that indexing is a (7,3) matrix, not the original (7,10). Indexing reflects that.

    – hpaulj
    Nov 29 '18 at 5:52











  • yea, but how I can preserve the indexing tho?

    – user3086871
    Nov 29 '18 at 5:57








1




1





If you want to preserve explicit zeros, be prepared to do a lot of things manually. Most scipy.sparse operations make no guarantees about explicit zeros.

– user2357112
Nov 29 '18 at 5:25





If you want to preserve explicit zeros, be prepared to do a lot of things manually. Most scipy.sparse operations make no guarantees about explicit zeros.

– user2357112
Nov 29 '18 at 5:25













The result of that indexing is a (7,3) matrix, not the original (7,10). Indexing reflects that.

– hpaulj
Nov 29 '18 at 5:52





The result of that indexing is a (7,3) matrix, not the original (7,10). Indexing reflects that.

– hpaulj
Nov 29 '18 at 5:52













yea, but how I can preserve the indexing tho?

– user3086871
Nov 29 '18 at 5:57





yea, but how I can preserve the indexing tho?

– user3086871
Nov 29 '18 at 5:57












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