python - ValueError: Error parsing datetime string NumPy -

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i trying convert string date date format in numpy array. using datetime64 datatype cast off seconds , receive error. code below. write numpy datatype date type database.

import json import jsonpickle import requests import arcpy import numpy np    #note import random import timestring     fc = "c:\mylatesting.gdb\myla311copy" if arcpy.exists(fc):   arcpy.delete_management(fc)      f2 = open('c:\users\administrator\desktop\detailview.json', 'r') data2 = jsonpickle.encode( jsonpickle.decode(f2.read()) )  url2 = "myurl" headers2 = {'content-type': 'text/plain', 'accept': '/'}  r2 = requests.post(url2, data=data2, headers=headers2) decoded2 = json.loads(r2.text)   dt = np.dtype([('sraddress', 'u40'),                 ('latitudeshape', '<f8'),                 ('longitudeshape', '<f8'),                 ('latitude', '<f8'),                 ('longitude', '<f8'),                 ('type', 'u40'),                 ('srnumber', 'u40'),                 ('firstname', 'u40'),                ('lastname', 'u40'),                ('homephone', 'u40'),                 ('createdate', 'datetime64[s]'),                ('comment', 'u128'),                 ('iteminfo', 'u128'),                 ('daytest', 'u128'),                 ('districtname', 'u128'),                 ('shortday', 'u128'),                 ('parentnumber', 'u128'),                 ('a_call_no','u128'),                 ('area', 'u128'),                 ('directionsuffix','u128'),                 ('districtabbr', 'u128'),                 ('districtnumber', 'u128'),                 ('districtoffice', 'u128'),                 ('fraction', 'u128'),                 ('r_call_no', 'u128'),                 ('sectionid', 'u128'),                 ('streetto', 'u128'),                 ('streetfrom', 'u128'),                 ('streetlightid', 'u128'),                 ('streetlightstatus', 'u128'),                 ('y_call_no', 'u128'),                 ('communityplanningarea', 'u128'),                 ('lastupdatedby', 'u128'),                 ('bosradioholdername', 'u128'),                 ])        items = [] sr in decoded2['response']['listofservicerequest']['servicerequest']:     sraddress = sr['sraddress']     latitude = sr['latitude']     longitude = sr['longitude']     srnumber = sr['srnumber']     firstname = sr['firstname']     lastname = sr['lastname']     homephone = sr['homephone']     createddate = sr['createddate']       print createddate       iteminfo = " "      ew in sr["listofla311electronicwaste"][u"la311electronicwaste"]:             commoditytype = ew['type']             itemtype = ew['electronicwesttype']             itemcount = ew['itemcount']             iteminfo += '{0},  {1}, '.format(itemtype, itemcount)             parentnumber = ew['name']         gis in sr["listofla311gislayer"][u"la311gislayer"]:             day = gis['day']             districtname = gis['districtname']             shortday = gis['shortday']             a_call_no = gis['a_call_no']             area = gis['area']             directionsuffix = gis['directionsuffix']             districtabbr = gis['districtabbr']             districtnumber = gis['districtnumber']             districtoffice = gis['districtoffice']             fraction = gis['fraction']             r_call_no = gis['r_call_no']             sectionid = gis['sectionid']             streetfrom = gis ['streetfrom']             streetto = gis ['streetto']             streetlightid = gis ['streetlightid']             streetlightstatus = gis['streetlightstatus']             y_call_no = gis ['y_call_no']             communityplanningarea = gis['communityplanningarea']             lastupdatedby = gis['lastupdatedby']             bosradioholdername = gis['bosradioholdername']         comments =  [ cl['comment'] cl in sr["listofla311servicerequestnotes"][u"la311servicerequestnotes"]]     print comments     comment = ' '.join(comments)   items.append((sraddress,                           latitude,                          longitude,                           latitude,                           longitude,                           commoditytype,                           srnumber,                          firstname,                           lastname,                           homephone,                           createddate,                           comment,                           iteminfo,                           day,                           districtname,                           shortday,                           parentnumber,                          a_call_no,                         area,                         directionsuffix,                         districtabbr,                         districtnumber,                         districtoffice,                         fraction,                         r_call_no,                         sectionid,                         streetfrom,                         streetto,                         streetlightid,                         streetlightstatus,                         y_call_no,                         communityplanningarea,                         lastupdatedby,                         bosradioholdername ))   arr = np.array(items,dtype=dt) sr = arcpy.spatialreference(4326)   arcpy.da.numpyarraytofeatureclass(arr, fc, ['longitudeshape', 'latitudeshape'], sr )   print json.dumps(decoded2, sort_keys=true, indent=4)    file "c:/users/administrator/desktop/devsummitjson_pyseminar.py", line 166, in <module>     arr = np.array(items,dtype=dt) valueerror: error parsing datetime string "02/17/2015 16:53:25" @ position 2

just looking @ error message get, you've input datetime in wrong string format

numpy.datetime64("02/17/2015 16:53:25") traceback (most recent call last):   file "<interactive input>", line 1, in <module> valueerror: error parsing datetime string "02/17/2015 16:53:25" @ position 2  numpy.datetime64("2015-02-17t16:53:25") >>> numpy.datetime64('2015-02-17t16:53:25+0100')

beware numpy assumes time given in local timezone (here utc+1). append "z" , interpreted utc.

so have either change format of string, or try solution using pandas presented here seems more flexible in interpreting string formats


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