[Numpy-discussion] convert csv file into recarray without pre-specifying dtypes and variable names

Torgil Svensson torgil.svensson@gmail....
Sun Jul 8 05:52:35 CDT 2007


Given that both your script and the mlab version preloads the whole
file before calling numpy constructor I'm curious how that compares in
speed to using numpy's fromiter function on your data. Using fromiter
should improve on memory usage (~50% ?).

The drawback is for string columns where we don't longer know the
width of the largest item. I made it fall-back to "object" in this
case.

Attached is a fromiter version of your script. Possible speedups could
be done by trying different approaches to the "convert_row" function,
for example using "zip" or "enumerate" instead of "izip".

Best Regards,

//Torgil


On 7/8/07, Vincent Nijs <v-nijs@kellogg.northwestern.edu> wrote:
> Thanks for the reference John! csv2rec is about 30% faster than my code on
> the same data.
>
> If I read the code in csv2rec correctly it converts the data as it is being
> read using the csv modules. My setup reads in the whole dataset into an
> array of strings and then converts the columns as appropriate.
>
> Best,
>
> Vincent
>
>
> On 7/6/07 8:53 PM, "John Hunter" <jdh2358@gmail.com> wrote:
>
> > On 7/6/07, Vincent Nijs <v-nijs@kellogg.northwestern.edu> wrote:
> >> I wrote the attached (small) program to read in a text/csv file with
> >> different data types and convert it into a recarray without having to
> >> pre-specify the dtypes or variables names. I am just too lazy to type-in
> >> stuff like that :) The supported types are int, float, dates, and strings.
> >>
> >> I works pretty well but it is not (yet) as fast as I would like so I was
> >> wonder if any of the numpy experts on this list might have some suggestion
> >> on how to speed it up. I need to read 500MB-1GB files so speed is important
> >> for me.
> >
> > In matplotlib.mlab svn, there is a function csv2rec that does the
> > same.  You may want to compare implementations in case we can
> > fruitfully cross pollinate them.  In the examples directy, there is an
> > example script examples/loadrec.py
> > _______________________________________________
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> >
>
>
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