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Message from discussion Efficient processing of large nuumeric data file
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Jorgen Grahn  
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 More options Jan 20 2008, 8:53 am
Newsgroups: comp.lang.python
From: Jorgen Grahn <grahn+n...@snipabacken.dyndns.org>
Date: 20 Jan 2008 13:53:54 GMT
Local: Sun, Jan 20 2008 8:53 am
Subject: Re: Efficient processing of large nuumeric data file
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On Fri, 18 Jan 2008 09:15:58 -0800 (PST), David Sanders <dpsand...@gmail.com> wrote:
> Hi,

> I am processing large files of numerical data.  Each line is either a
> single (positive) integer, or a pair of positive integers, where the
> second represents the number of times that the first number is
> repeated in the data -- this is to avoid generating huge raw files,
> since one particular number is often repeated in the data generation
> step.

> My question is how to process such files efficiently to obtain a
> frequency histogram of the data (how many times each number occurs in
> the data, taking into account the repetitions).  My current code is as
> follows:

...

> The data files are large (~100 million lines), and this code takes a
> long time to run (compared to just doing wc -l, for example).

I don't know if you are in control of the *generation* of data, but
I think it's often better and more convenient to pipe the raw data
through 'gzip -c' (i.e. gzip-compress it before it hits the disk)
than to figure out a smart application-specific compression scheme.

Maybe if you didn't have a homegrown file format, there would have
been readymade histogram utilities?  Or at least a good reason to
spend the time writing an optimized C version.

/Jorgen

--
  // Jorgen Grahn <grahn@        Ph'nglui mglw'nafh Cthulhu
\X/     snipabacken.se>          R'lyeh wgah'nagl fhtagn!


 
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