ValueError：解析日期时间字符串 NumPy 时出错

Question

我正在尝试将此字符串日期转换为 NumPy 数组中的日期格式。 我正在使用 datetime64 数据类型转换为秒并收到此错误。 我的代码如下。 我想将 numpy 数据类型作为日期类型写入我的数据库。

import json
import jsonpickle
import requests
import arcpy
import numpy as np    #NOTE THIS
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 = []
for 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 = " "

    for ew in sr["ListOfLa311ElectronicWaste"][u"La311ElectronicWaste"]:
            CommodityType = ew['Type']
            ItemType = ew['ElectronicWestType']
            ItemCount = ew['ItemCount']
            ItemInfo += '{0},  {1}, '.format(ItemType, ItemCount)
            ParentNumber = ew['Name']




    for 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'] for 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" at position 2

Answer 1

np.datetime64使用格式yyyy-mm-dd hh:mm:ss

在pandas使用to_datetime()方法，因为它更灵活：

import pandas as pd
pd.to_datetime("02/17/2015 16:53:25")

或者

如果你仍然想使用np.datetime64那么：

将日期格式更改为yyyy-mm-dd hh:mm:ss

例如：

numpy.datetime64("02/17/2015 16:53:25")
#change above format to:
numpy.datetime64("2015-02-17 16:53:25")

Answer 2

仅查看您收到的错误消息，您就以错误的字符串格式输入了日期时间

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" at position 2

numpy.datetime64("2015-02-17T16:53:25")
>>> numpy.datetime64('2015-02-17T16:53:25+0100')

请注意，numpy 假定时间是在您的本地时区（此处为 UTC+1）中给出的。 附加“Z”，它将被解释为 UTC。

因此，您必须更改字符串的格式，或者您可以尝试使用此处提供的Pandas 的解决方案，这在解释字符串格式时似乎更灵活