Memory runs out when creating a numpy array from a list of lists in python

I want to read in a tab separated value file and turn it into a numpy array. The file has 3 lines. It looks like this:

Ann1    Bill1   Chris1   Dick1
Ann2    Bill2   Chris2  "Dick2
Ann3    Bill3   Chris3   Dick3

So, I used this simple line of code:

new_list = []
with open('/home/me/my_tsv.tsv') as tsv:
    for line in csv.reader(tsv, delimiter="\t"):
        new_list.append(line)

new = np.array(job_posts)
print new.shape

And because of that pesky " character, the shape of my fancy new numpy array is

(2,4)

That's not right! So, the solution is to include the argument, quoting, in the csv.reader call, as follows:

for line in csv.reader(tsv, delimiter="\t", quoting=csv.QUOTE_NONE):

This is great! Now my dimension is

(3,4)  

as I hoped.

Now comes the real problem -- in all actuality, I have a 700,000 X 10 .tsv file, with long fields. I can read the file into Python with no problems, just like in the above situation. But, when I get to the step where I create new = np.array(job_posts), my wimpy 16 GB laptop cries and says...

MEMORY ERROR  

Obviously, I cannot simultaneously have these 2 object in memory -- the Python list of lists, and the numpy array.

My question is therefore this: how can I read this file directly into a numpy array, perhaps using genfromtxt or something similar...yet also achieve what I've achieved by using the quoting=csv.QUOTE_NONE argument in the csv.reader?

So far, I've found no analogy to the quoting=csv.QUOTE_NONE option available anywhere in the standard ways to read in a tsv file using numpy.

This is a tough little problem. I though about iteratively building the numpy array during the reading in process, but I can't figure it out.

I tried

nparray = np.genfromtxt("/home/me/my_tsv.tsv", delimiter="/t")
print obj.shap

and got

(3,0)

If anyone has any advice I'd be greatly appreciative. Also, I know the real answer is probably to use Pandas...but at this point I'm committed to using numpy for a lot of compelling reasons...

Thanks in advance.