Pandas sparse dataFrame to sparse matrix, without generating a dense matrix in memory

Question

Is there a way to convert from a pandas.SparseDataFrame to scipy.sparse.csr_matrix, without generating a dense matrix in memory?

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Answer 1

Here's a solution that fills the sparse matrix column by column (assumes you can fit at least one column to memory).

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

def sparse_df_to_array(df):
    """ Convert sparse dataframe to sparse array csr_matrix used by
    scikit learn. """
    arr = lil_matrix(df.shape, dtype=np.float32)
    for i, col in enumerate(df.columns):
        ix = df[col] != 0
        arr[np.where(ix), i] = df.ix[ix, col]

    return arr.tocsr()