Question

I’m trying to process data obtained from a csv file using csv module in python. there are about 50 columns & 401125 rows in this. I used the following code chunk to put that data into a list

csv_file_object = csv.reader(open(r'some_path\Train.csv','rb'))
header = csv_file_object.next()
data = []
for row in csv_file_object:
    data.append(row)

I can get length of this list using len(data) & it returns 401125. I can even get each individual record by calling list indices.
But when I try to get the size of the list by calling np.size(data) (I imported numpy as np) I get the following stack trace.

MemoryError Traceback (most recent call
last) in ()
—-> 1 np.size(data)

C:\Python27\lib\site-packages\numpy\core\fromnumeric.pyc in size(a,
axis) 2198 return a.size 2199 except
AttributeError:
-> 2200 return asarray(a).size 2201 else: 2202 try:

C:\Python27\lib\site-packages\numpy\core\numeric.pyc in asarray(a,
dtype, order)
233
234 “””
–> 235 return array(a, dtype, copy=False, order=order)
236
237 def asanyarray(a, dtype=None, order=None):

MemoryError:

I can’t even divide that list into a multiple parts using list indices or convert this list into a numpy array. It give this same memory error.

how can I deal with this kind of big data sample. Is there any other way to process large data sets like this one.

I’m using ipython notebook in windows 7 professional.

Answer 1

As noted by @DSM in the comments, the reason you’re getting a memory error is that calling np.size on a list will copy the data into an array first and then get the size.

If you don’t need to work with it as a numpy array, just don’t call np.size. If you do want numpy-like indexing options and so on, you have a few options.

You could use pandas, which is meant for handling big not-necessarily-numerical datasets and has some great helpers and stuff for doing so.

If you don’t want to do that, you could define a numpy structure array and populate it line-by-line in the first place rather than making a list and copying into it. Something like:

fields = [('name1', str), ('name2', float), ...]
data = np.zeros((num_rows,), dtype=fields)

csv_file_object = csv.reader(open(r'some_path\Train.csv','rb'))
header = csv_file_object.next()
for i, row in enumerate(csv_file_object):
    data[i] = row

You could also define fields based on header so you don’t have to manually type out all 50 column names, though you’d have to do something about specifying the data types for each.