memory error when skipping rows

[nkulki]

I have a file with over 100Million rows. When I do
pd.read_csv(filename, skiprows=100000000, iterator=True)
python crashes with a memory error. I have 32 gigs of memory and python eats up all that memory!!

[jreback]

if you specify chunksize=1000000 (or something I think it might help).

That said, still could be a bug. Would appreciate you having a deeper look if you can.

[jreback]

prob related #8661, #8679

cc @mdmueller

[nkulki]

Hi,
I tried your suggestion. Unfortunately it did not really work. I dont have a dev version of pandas installed. However I was reading through the code, and I suspect the issue could be the use of lrange(skiprows)
if com.is_integer(skiprows):
skiprows = lrange(skiprows)
if skiprows is 100M, then lrange will generate a very large array.

[nkulki]

I know that skiprows can be a array of ints or a int. I think that if its simply an int, then we should use a more efficient code path to skip the rows instead of generating an 100M size array.

[jreback]

yep that sounds right
but would need some modifications in the code to deal with that

have a go!

[nkulki]

Do you have any suggestions on how best to tackle this issue

Sent from my iPhone

On Oct 30, 2014, at 9:03 AM, jreback notifications@github.com wrote:

yep that sounds right
but would need some modifications in the code to deal with that

have a go!

—
Reply to this email directly or view it on GitHub.

[jreback]

so currently the skipped rows are transformed to a list of rows with range (this is why this blows memory up). You could instead change the impl a bit to pass a list of a list was original passed (e.g. skiprows=[0,3,5], and then pass say slice to represent the interval. This object appears as .skiprows in parser.pyx and then is directly passed to the tokenizer.c

You prob need to change the c-impl as well (so maybe make the original skiprows_list, and the new one skiprows_slice). The original is handled just as it was, the new can do a range change (this is all done in c-land). to just skip if the start >= slice.start and end < slice.end.

So a bit involved, but would be a nice change.

[jreback]

@cpcloud

[guyrt]

This bug is listed in the latest release notes as closed:
http://pandas.pydata.org/pandas-docs/stable/whatsnew.html#whatsnew-0152-performance

Is it actually fixed?