I noticed Pandas now has support for Sparse Matrices and Arrays. Currently, I create DataFrame()
s like this:
return DataFrame(matrix.toarray(), columns=features, index=observations)
Is there a way to create a SparseDataFrame()
with a scipy.sparse.csc_matrix()
or csr_matrix()
? Converting to dense format kills RAM badly. Thanks!
A direct conversion is not supported ATM. Contributions are welcome!
Try this, should be ok on memory as the SpareSeries is much like a csc_matrix (for 1 column) and pretty space efficient
In [37]: col = np.array([0,0,1,2,2,2])
In [38]: data = np.array([1,2,3,4,5,6],dtype='float64')
In [39]: m = csc_matrix( (data,(row,col)), shape=(3,3) )
In [40]: m
Out[40]:
<3x3 sparse matrix of type '<type 'numpy.float64'>'
with 6 stored elements in Compressed Sparse Column format>
In [46]: pd.SparseDataFrame([ pd.SparseSeries(m[i].toarray().ravel())
for i in np.arange(m.shape[0]) ])
Out[46]:
0 1 2
0 1 0 4
1 0 0 5
2 2 3 6
In [47]: df = pd.SparseDataFrame([ pd.SparseSeries(m[i].toarray().ravel())
for i in np.arange(m.shape[0]) ])
In [48]: type(df)
Out[48]: pandas.sparse.frame.SparseDataFrame