On May 18, 6:07 pm, py_genetic <[EMAIL PROTECTED]> 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?
given the string s: try: integerValue = int(s) except ValueError, e: try: floatValue = float(s) except ValueError: pass else: s = floatValue else: s = integerValue I believe it will automatically identify base 8 and base 16 integers (but not base 8/16 floats). > 2. Is there anything out there already which deals with this issue? > > Thanks, > Conor -- http://mail.python.org/mailman/listinfo/python-list