SciPy Sparse Array: Get index for a data point

Question

I am creating a csr sparse array (because I have a lot of empty elements/cells) that I need to use forwards and backwards. That is, I need to input two indices and get the element that corresponds to it ( matrix[0][9]=34) but I also need to be able to get the indices upon just knowing the value is 34. The elements in my array will all be unique. I have looked all over for an answer regarding this, but have not found one, or may have not understood it was what I was looking for if I did! I'm quite new to python, so if you could make sure to let me know what the functions you find do and the steps to retrieve the indices for the element, I would very much appreciate it!

Thanks in advance!

First, the correct way to index an array, and especially a sparse matrix is matrix[0,9]. Don't use the [][] syntax unless you know what you are doing. — hpaulj
– hpaulj, Commented Jun 29, 2015 at 16:15

hpaulj · Accepted Answer · 2015-06-29 16:19:55Z

1

Here's a way of finding a specific value that is applicable to both numpy arrays and sparse matrices

In [119]: A=sparse.csr_matrix(np.arange(12).reshape(3,4))

In [120]: A==10
Out[120]: 
<3x4 sparse matrix of type '<class 'numpy.bool_'>'
    with 1 stored elements in Compressed Sparse Row format>

In [121]: (A==10).nonzero()
Out[121]: (array([2], dtype=int32), array([2], dtype=int32))

In [122]: (A.A==10).nonzero()
Out[122]: (array([2], dtype=int32), array([2], dtype=int32))

answered Jun 29, 2015 at 16:19

hpaulj

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ev-br · Accepted Answer · 2015-06-29 16:17:13Z

You can use the nonzero method:

In [44]: from scipy.sparse import csr_matrix

In [45]: a = np.arange(50).reshape(5, 10)

In [46]: a
Out[46]: 
array([[ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9],
       [10, 11, 12, 13, 14, 15, 16, 17, 18, 19],
       [20, 21, 22, 23, 24, 25, 26, 27, 28, 29],
       [30, 31, 32, 33, 34, 35, 36, 37, 38, 39],
       [40, 41, 42, 43, 44, 45, 46, 47, 48, 49]])

In [47]: s = csr_matrix(a)

In [48]: s
Out[48]: 
<5x10 sparse matrix of type '<type 'numpy.int64'>'
    with 49 stored elements in Compressed Sparse Row format>

In [49]: (s == 36).nonzero()
Out[49]: (array([3], dtype=int32), array([6], dtype=int32))

In general, it often works to try a method which worked on a numpy array. This does not always work, but at least here it just did (and I learned something new today).

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