CSV methodology

[Cameron Simpson]

On 13Sep2014 21:34, jetrn at newsguy.com <jetrn at newsguy.com> wrote:
>Hello.  Back in the '80s, I wrote a fractal generator, [...]
>Anyway, something I thought would be interesting, would be to export
>some data from my fractal program (I call it MXP), and write something
>in Python and its various scientific data analysis and plotting modules,
>and... well, see what's in there.
>
>An example of the data:
>1.850358651774470E-0002
>32
>22
>27
>... (this format repeats)
>
>So, I wrote a procedure in MXP which converts "the data" and exports
>a csv file.  So far, here's what I've started with:

Normally a CSV file will have multiple values per row. Echoing Terry, what 
shape did you intend your CSV data to be? i.e. what values appear on a row?

>import csv
>fname = 'E:/Users/jayte/Documents/Python Scripts/XportTestBlock.csv'
>f = open(fname)
>reader = csv.reader(f)
>for flt in reader:
>    x = len(flt)
>file.close(f)
>
>This will get me an addressable array, as:
>
>flt[0], flt[1], flt[350], etc...  from which values can be assigned to
>other variables, converted...
>
>My question:  Is there a better way?  Do I need to learn more about
>how csv file are organized?  Perhaps I know far too little of Python
>to be attempting something like this, just yet.

If you have a nice regular CSV file, with say 3 values per row, you can go:

   reader = csv.reader(f)
   for row in reader:
       a, b, c - row

and proceed with a, b and c directly from there. But of course, that requires 
your export format to be usable that way.

Cheers,
Cameron Simpson <cs at zip.com.au>

For a good prime, call:  391581 * 2^216193 -1