How to pre-process a very large data in python

I have a couple of files 100 MB each. The format for those files looks like this:

0  1  2  5  8  67  9  122
1  4  5  2  5  8
0  2  1  5  6
.....

(note the actual file does not have the alignment spaces added in, only one space separates each element, added alignment for aesthetic effect)

this first element in each row is it's binary classification, and the rest of the row are indices of features where the value is 1. For instance, the third row says the row's second, first, fifth and sixth features are 1, the rest are zeros.

I tried to read each line from each file, and use sparse.coo_matrix create a sparse matrix like this:

for train in train_files:  
    with open(train) as f:
        row = []
        col = []
        for index, line in enumerate(f):
            record = line.rstrip().split(' ')
            row = row+[index]*(len(record)-4)
            col = col+record[4:]
        row = np.array(row)
        col = np.array(col)
        data = np.array([1]*len(row))
        mtx = sparse.coo_matrix((data, (row, col)), shape=(n_row, max_feature))
        mmwrite(train+'trans',mtx)

but this took forever to finish. I started reading the data at night, and let the computer run after I went to sleep, and when I woke up, it still haven't finish the first file!

What are the better ways to process this kind of data?