converting strings to most their efficient types '1' --> 1, 'A' ---> 'A', '1.2'---> 1.2

[Paddy]

On May 20, 1:12 pm, John Machin <sjmac... at lexicon.net> wrote:
> On 20/05/2007 8:52 PM, Paddy wrote:
>
>
>
> > On May 20, 2:16 am, John Machin <sjmac... at lexicon.net> wrote:
> >> On 19/05/2007 3:14 PM, Paddy wrote:
>
> >>> On May 19, 12:07 am, py_genetic <conor.robin... at gmail.com> wrote:
> >>>> Hello,
> >>>> I'm importing large text files of data using csv.  I would like to add
> >>>> some more auto sensing abilities.  I'm considing sampling the data
> >>>> file and doing some fuzzy logic scoring on the attributes (colls in a
> >>>> data base/ csv file, eg. height weight income etc.) to determine the
> >>>> most efficient 'type' to convert the attribute coll into for further
> >>>> processing and efficient storage...
> >>>> Example row from sampled file data: [ ['8','2.33', 'A', 'BB', 'hello
> >>>> there' '100,000,000,000'], [next row...] ....]
> >>>> Aside from a missing attribute designator, we can assume that the same
> >>>> type of data continues through a coll.  For example, a string, int8,
> >>>> int16, float etc.
> >>>> 1. What is the most efficient way in python to test weather a string
> >>>> can be converted into a given numeric type, or left alone if its
> >>>> really a string like 'A' or 'hello'?  Speed is key?  Any thoughts?
> >>>> 2. Is there anything out there already which deals with this issue?
> >>>> Thanks,
> >>>> Conor
> >>> You might try investigating what can generate your data. With luck,
> >>> it could turn out that the data generator is methodical and column
> >>> data-types are consistent and easily determined by testing the
> >>> first or second row. At worst, you will get to know how much you
> >>> must check for human errors.
> >> Here you go, Paddy, the following has been generated very methodically;
> >> what data type is the first column? What is the value in the first
> >> column of the 6th row likely to be?
>
> >> "$39,082.00","$123,456.78"
> >> "$39,113.00","$124,218.10"
> >> "$39,141.00","$124,973.76"
> >> "$39,172.00","$125,806.92"
> >> "$39,202.00","$126,593.21"
>
> >> N.B. I've kindly given you five lines instead of one or two :-)
>
> >> Cheers,
> >> John
>
> > John,
> > I've had cases where some investigation of the source of the data has
> > completely removed any ambiguity. I've found that data was generated
> > from one or two sources and been able to know what every field type is
> > by just examining a field that I have determined wil tell me the
> > source program that generated the data.
>
> The source program that produced my sample dataset was Microsoft Excel
> (or OOo Calc or Gnumeric); it was induced to perform a "save as CSV"
> operation. Does that help you determine the true nature of the first column?
>
>
>
> > I have also found that the flow generating some data is subject to
> > hand editing so have had to both put in extra checks in my reader, and
> > on some occasions created specific editors to replace hand edits by
> > checked assisted hand edits.
> > I stand by my statement; "Know the source of your data", its less
> > likely to bite!
>
> My dataset has a known source, and furthermore meets your "lucky"
> criteria (methodically generated, column type is consistent) -- I'm
> waiting to hear from you about the "easily determined" part :-)
>
> Cheers,
> John

John,
Open up your Excel spreadsheet and check what the format is for the
column. It's not a contest. If you KNOW what generated the data then
USE that knowledge. It would be counter-productive to do otherwise
surely?

(I know, don't call you Shirley :-)

- Paddy.