Populate a Pandas SparseDataFrame from a SciPy Sparse Matrix

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
<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]) ])
   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

As of pandas v 0.20.0 you can use the SparseDataFrame constructor.

An example from the pandas docs:

import numpy as np
import pandas as pd
from scipy.sparse import csr_matrix

arr = np.random.random(size=(1000, 5))
arr[arr < .9] = 0
sp_arr = csr_matrix(arr)
sdf = pd.SparseDataFrame(sp_arr)

A much shorter version:

df = pd.DataFrame(m.toarray())

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