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 -- http://mail.python.org/mailman/listinfo/python-list
