chebyshev - 9 months ago 195

Python Question

In working with some text data, I'm trying to join an np array(from a pandas series) to a csr matrix.

I've done the below.

`#create a compatible sparse matrix from my np.array.`

#sparse.csr_matrix(X['link'].values) returns array size (1,7395)

#transpose that array for (7395,1)

X = sparse.csr_matrix(X['link'].values.transpose)

#bodies is a sparse.csr_matrix with shape (7395, 20000)

bodies = sparse.hstack((bodies,X))

However, this line gives the error

`no supported conversion for types: (dtype('O'),)`

Thanks.

Answer

Here's Saullo Castro's comment cast as an answer:

```
x = np.arange(12).reshape(1,12) # ndarray
sparse.csr_matrix(x)
Out[14]: <1x12 sparse matrix of type '<type 'numpy.int32'>'
with 11 stored elements in Compressed Sparse Row format>
x.transpose # function, not ndarray
Out[15]: <function transpose>
X = sparse.csr_matrix(x.transpose)
TypeError: no supported conversion for types: (dtype('O'),)
```

The error occurs before using `hstack`

, trying to make a sparse matrix from a function rather than an ndarray. The error was omitting the `()`

.

```
# x.transpose() == x.T # ndarray
sparse.csr_matrix(x.transpose())
Out[17]: <12x1 sparse matrix of type '<type 'numpy.int32'>'
with 11 stored elements in Compressed Sparse Row format>
sparse.csr_matrix(x.T)
Out[18]: <12x1 sparse matrix of type '<type 'numpy.int32'>'
with 11 stored elements in Compressed Sparse Row format>
bodies = sparse.rand(12,3,format='csr',density=.1)
sparse.hstack((bodies,X))
Out[32]: <12x4 sparse matrix of type '<type 'numpy.float64'>'
with 14 stored elements in COOrdinate format>
```

`csr_matrix`

works fine if it is given the transposed array.

Source (Stackoverflow)