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

[py_genetic]

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