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。我使用datetime64数据类型强制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

Just looking at the error message you get, you've input your datetime in the 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" at position 2

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

Beware that numpy assumes that the time is given in your local timezone (here UTC+1). Append "Z" and it will be interpreted as UTC.

So you have to either change the format of your string, or you could try the solution using pandas presented here which seems is more flexible in interpreting string formats

Answer 2

np.datetime64 使用格式 yyyy-mm-dd hh:mm:ss

在 to_datetime 中使用 pandas 方法，因为它更灵活：-

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

或者如果您只想使用 np.datetime64 则：-

更改日期的format(yyyy-mm-dd hh:mm:ss)

例如：-

numpy.datetime64("02/17/2015 16:53:25")

到

numpy.datetime64("2015-02-17 16:53:25")

ValueError：解析日期时间字符串NumPy时出错

2 个答案: