Iterate from a certain row of a csv file in Python

Question

I have a csv file with many millions of rows. I want to start iterating from the 10,000,000 row. At the moment I have the code:

    with open(csv_file, encoding='UTF-8') as f: 
        r = csv.reader(f)
        for row_number, row in enumerate(r):    
            if row_number < 10000000:
                continue
            else:
                process_row(row)      

This works, however take several seconds to run before the rows of interest appear. Presumably all the unrequired rows are loaded into python unnecessarily, slowing it down. Is there a way of starting the iteration process on a certain row - ie without the start of the data read in.

Answer 1

You could use islice :

from itertools import islice

with open(csv_file, encoding='UTF-8') as f:
    r = csv.reader(f)
    for row in islice(r,  10000000, None):
            process_row(row)  

It still iterates over all the rows but does it a lot more efficiently.

You could also use the consume recipe which calls functions that consume iterators at C speed , calling it on the file object before you pass it to the csv.reader , so you also avoid needlessly processing those lines with the reader:

import collections
from itertools import islice
def consume(iterator, n):
    "Advance the iterator n-steps ahead. If n is none, consume entirely."
    # Use functions that consume iterators at C speed.
    if n is None:
        # feed the entire iterator into a zero-length deque
        collections.deque(iterator, maxlen=0)
    else:
        # advance to the empty slice starting at position n
        next(islice(iterator, n, n), None)


with open(csv_file, encoding='UTF-8') as f:
    consume(f, 9999999)
    r = csv.reader(f)
    for row  in r:
          process_row(row)  

As Shadowranger commented, if a file could conatin embedded newlines then you would have to consume the reader and pass newline="" but if that is not the case then use do consume the file object as the performance difference will be considerable especially if you have a lot of columns.