Latest Contributions by RandyBurton

I believe the request should go to: https://www.arcgis.com/sharing/rest/generateToken (word "rest" between sharing and generateToken). ... View more

Xander Bakker‌ has a blog post on this topic with a good code example: Implementing cascading drop down lists in a toolbox using validation with Python. ... View more

My recommendation would be to put a field in the feature table.  I would also use a domain with values that would suggest what types of maintenance is required: "None, Inspection, Service, Replacement, etc."  You can then symbolize on this field. When the crew has completed the inspection, they select "None" and the symbol changes to the default. ... View more

After you run a tool  (like Points to line) in the ArcMap and get results you like, click on Geoprocessing >> Results and you should see the results of that tool.  When you right click, you can "Copy as Python Snippet".  This will show you the parameters used when the tool was run, and makes it easy to paste into a Python script. ... View more

Since you are using lat/lon, try "WGS 1984" as your spatial reference instead of "NAD 1983 2011 Contiguous US Albers". ... View more

Perhaps export hosted feature layer using python would be of interest. ... View more

Glad to help, and good luck with your project. ... View more

See Dan Patterson‌'s comment in this thread: numbering polygon in clockwise.  Although the message doesn't have a code example, it gives a suggestion on how this problem might be approached. ... View more

Not knowing your intended use of feature2, I thought having an option to average x,y coordinates might be of interest although keeping the point of the matched community in the Mutual field would probably be your preferred choice.  I agree with rfairhur24‌'s comments on aggregating data.  Creating the second feature can help you understand the data in feature1, it may help you find data errors, but it should not be a replacement.  Hope this helps. ... View more

Here is another version that may give you some additional ideas.  It populates an empty feature2, but you could add code to create an empty feature. I think it is simpler and probably more accurate to populate an empty feature. It groups on your "Mutual" field and ignores rows where the value is null. The "Mutual" field is also carried over to feature2, so it could be used for a label.  Or the concatenated community names can be the label. An ordered dictionary is used so you can have the communities listed in order, or you can have the codes in order (see photo).  By uncommenting/commenting some lines in the code you have the option to use a regular dictionary and omit the order by sql.  The x,y point of the community in the "Mutual" field is used, although if desired, it can average the x,y coordinates and produce a point somewhere near the center of the group of points of the joined communities. Richard Fairhurst‌ has given some good ideas regarding data rules, etc.  For my part I have not provided any error checking should the data have issues.  import arcpy import itertools from collections import OrderedDict as OD mapData = OD([]) # empty ordered dictionary for cities and codes # you can also use a regular dictionary if sort order is not an issue # mapData = {} # empty dictionary fields = ['Community', 'Ref_C_MOLG', 'Mutual', 'SHAPE@XY' ] fc1 = r'C:\Path\To\Test.gdb\Feature1' fc2 = r'C:\Path\To\Test.gdb\Feature2' # read fc1 : an ordered dictionary and sql order by may be of interest # for row in arcpy.da.SearchCursor(fc1,fields): # if using a regular dictionary and not using order by # for row in arcpy.da.SearchCursor(fc1,fields,sql_clause=(None,"ORDER BY Ref_C_MOLG")): # to order by Code for row in arcpy.da.SearchCursor(fc1,fields,sql_clause=(None,"ORDER BY Community")): # to order by Community     if row[2] is not None: # Mutual (for grouping) : ignore rows where this field is null         # mapData { governorate_mutual: [ 'city': 'cityname', 'code': 'numberCode', 'xy': (0.0, 0.0) ] }         item = ["".join(x) for _, x in itertools.groupby(str(row[1]), key=str.isdigit)]         dKey = "{}_{}".format(item[0],row[2])         if dKey in mapData:             mapData[dKey].append({'city': row[0], 'code': item[1], 'xy' : row[3] })         else:             mapData[dKey] = [{'city': row[0], 'code': item[1], 'xy' : row[3] }]             # insert cursor for "empty" fc2 cursor = arcpy.da.InsertCursor(fc2, fields) for k1, v1 in mapData.iteritems():     city = []     cityCode = []     cityXY =[] # for averaging x,y option     for lst in v1:         city.append(lst['city'])         cityCode.append(lst['code'])         # if averaging x,y for new point         # cityXY.append(lst['xy'])         # if using city in Mutual field         if k1.split('_')[1] == lst['city']:             newXY = lst['xy']     # if averaging x,y for new point            # newXY = [sum(x) / len(x) for x in zip(*cityXY)]     # cursor.insertRow(( ", ".join(city), k1.split('_')[0]+"_".join(cityCode), k1.split('_')[1], tuple(newXY) ))     # if using city in Mutual field     cursor.insertRow(( ", ".join(city), k1.split('_')[0]+"_".join(cityCode), k1.split('_')[1], newXY ))    del cursor print "Done."‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍ ... View more

And in my URL request (way back in my first code block)  I like to add "outSR" : "4326" so that I get the geometry in latitude and longitude.  I find this is more meaningful than meters when working with Collector and Survey123 data. query_dict = {     "where" : "EditDate >= DATE '2017-04-29 09:00:00'",     "outFields" : "*",     "orderByFields" : "EditDate",     "returnGeometry" : "true",     "outSR" : "4326", # get geometry in lat/lon     "f" : "json", "token" : token['token'] } ‍‍‍‍‍‍‍ ... View more

Here's a version of the stackoverflow code that will include the geometry.  (Also changed it to tab delimited.) import csv from datetime import datetime def timestamp_to_date(t):     return datetime.fromtimestamp(t / 1e3).strftime('%Y-%m-%d %H:%M') sheetname = 'test' with open('{}.csv'.format(sheetname), 'wb') as outf:     dw = csv.DictWriter(         outf,         delimiter="\t", # for tab delimited; omit for csv         quotechar="|",         fieldnames=['objectid', 'globalid', 'SurveyDate', 'Ingress1Arrive', 'PointX', 'PointY']     )     dw.writeheader()     for row in gdata2:         row['attributes']['PointX'] = row['geometry']['x']         row['attributes']['PointY'] = row['geometry']['y']         current = row['attributes']         times = {             'Ingress1Arrive': timestamp_to_date(current['Ingress1Arrive']),             'SurveyDate': timestamp_to_date(current['SurveyDate'])         }         current.update(times)         dw.writerow(current)‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍ ... View more

Looks like this is best answer. You should mark it as correct. ... View more

I can see how DictWriter would be helpful with lots of columns.  I suppose you could loop through gdata2 and edit  u'SurveyDate': 1519102800000L so that it contains the time/date data in the desired format before passing it to DictWriter. ... View more