Keep smallest value for each unique ID with arcpy/numpy

Question

I've got a ESRI Point Shape file with (amongst others) a nMSLINK field and a DIAMETER field. The MSLINK is not unique, because of a spatial join. What I want to achieve is to keep only the features in the shapefile that have a unique MSLINK and the smallest DIAMETER value, together with the corresponding values in the other fields. I can use a searchcursor to achieve this (looping through all features and removing each feature that does not comply, but this takes ages (> 75000 features). I was wondering if eg. numpy could do the trick faster in ArcMap/arcpy.

Answer 1

I think, making that kind of processing would definitely be a lot faster if you work on memory instead of interacting with arcgis. For example, by putting all the rows first into a python object (probably a namedtuple would be a good option here). Then you can find out which rows you want to delete or insert.

The fastest approach depends on a) if you have a lot of (MSLINK) repeated rows, then the fastest would be inserting just the ones you need in a new layer. Or b) if the rows to be deleted are just a few compared to the total of rows, then deleting is faster.

For a) you'll need to fetch all fields into the tuple, including the point coordinates, so that you can just create a new feature class and insert the new rows.

# Example of Variant a:

from collections import namedtuple

# assuming the following:
source_fc # contains name of the fclass
the_path # contains path to the shape
cleaned_fc # the name of the cleaned fclass


# use all fields of source_fc plus the shape token to get a touple with xy
# coordinates (using 'mslink' and 'diam' here to simplify the example)
fields = ['mslink', 'diam', 'field3', ... ]
all_fields = fields + ['SHAPE@XY']

# define a namedtuple to hold and work with the rows, use the name 'point' to
# hold the coordinates-tuple
Row = namedtuple('Row', fields + ['point'])
data = []
with arcpy.da.SearchCursor(source_fc, fields) as sc:
    for r in sc:
        # unzip the values from each row into a new Row (namedtuple) and append
        # to data
        data.append(Row(*r))

# now just delete the rows we don't want, for this, the easiest way, is probably
# to order the tuple first after MSLINK and then after the diamater...
data = sorted(data, key = lambda x : (x.mslink, x.diam))

# ... now just keep the first ones for each mslink
to_keep = []
last_mslink = None
for d in data:
    if last_mslink != d.mslink:
        last_mslink = d.mslink
        to_keep.append(d)

# create a new feature class with the same fields as the source_fc
arcpy.CreateFeatureclass_management(
        out_path=the_path, out_name=cleaned_fc, template=source_fc)
with arcpy.da.InsertCursor(cleaned_fc, all_fields) as ic:
    for r in to_keep:
        ic.insertRow(*r)

And for alternative b) I would just fetch 3 fields, a unique ID, MSLINK and the diameter. Then make a delete list (here you only need the unique ids). Then loop again through the feature class and delete the rows with the id on your delete-list. Just to be sure, I would duplicate the feature class first, and work on a copy.

Answer 2

There are a few steps you can take to accomplish this task more efficiently. First and foremost, making use of the data analyst cursor as opposed to the older version of cursor will increase the speed of your process. This assumes you are working in 10.1 or beyond. Then you can employ summary statistics, namely its ability to find a minimum value based off a case field. For yours, the case field would be nMSLINK.

The code below first creates a statistics table with all unique 'nMSLINK' values, and its corresponding minimum 'DIAMETER' value. I then use a table select to select out only rows in the table whose 'FREQUENCY' field is not 1. From here I iterate through my new table and start to build a list of strings that will make up a final sql statement. After this iteration, I use the python join function to create an sql string that looks something like this:

("nMSLINK" = 'value1' AND "DIAMETER" <> 624.0) OR ("nMSLINK" = 'value2' AND "DIAMETER" <> 1302.0) OR ("nMSLINK" = 'value3' AND "DIAMETER" <> 1036.0) ...

The sql selects rows where nMSLINK values are not unique and where DIAMETER values are not the minimum. Using this SQL, I select by attribute and delete selected rows.

This SQL statement is written assuming your feature class is in a file geodatabase and that 'nMSLINK' is a string field and 'DIAMETER' is a numeric field.

The code has the following inputs:

Feature: The feature to be analyzed

Workspace: A folder that will store a couple intermediate tables temporarily

TempTableName1: A name for one temporary table.

TempTableName2: A name for a second temporary table

Field1 = The nonunique field

Field2 = The field with the numeric values that you wish to find the lowest of

Code:

# Import modules
from arcpy import *
import os
# Local variables

#Feature to analyze
Feature = r"C:\E1B8\ScriptTesting\Workspace\Workspace.gdb\testfeatureclass"
#Workspace to export table of identicals
Workspace = r"C:\E1B8\ScriptTesting\Workspace"
#Name of temp DBF table file
TempTableName1 = "Table1"
TempTableName2 = "Table2"

#Field names
Field1 = "nMSLINK" #nonunique
Field2 = "DIAMETER" #field with numeric values

#Make layer to allow selection
MakeFeatureLayer_management (Feature, "lyr")

#Path for first temp table
Table = os.path.join (Workspace, TempTableName1)

#Create statistics table with min value
Statistics_analysis (Feature, Table, [[Field2, "MIN"]], [Field1])

#SQL Select rows with frequency not equal to one
sql = '"FREQUENCY" <> 1'
# Path for second temp table
Table2 = os.path.join (Workspace, TempTableName2)
# Select rows with Frequency not equal to one
TableSelect_analysis (Table, Table2, sql)

#Empty list for sql bits
li = []

# Iterate through second table
cursor = da.SearchCursor (Table2, [Field1, "MIN_" + Field2])
for row in cursor:
    # Add SQL bit to list
    sqlbit = '("' + Field1 + '" = \'' + row[0] + '\' AND "' + Field2 + '" <> ' + str(row[1]) + ")"
    li.append (sqlbit)
del row
del cursor

#Create SQL for selection of unwanted features
sql = " OR ".join (li)
print sql
#Select based on SQL
SelectLayerByAttribute_management ("lyr", "", sql)

#Delete selected features
DeleteFeatures_management ("lyr")

#delete temp files
Delete_management ("lyr")
Delete_management (Table)
Delete_management (Table2)

This should be quicker than a straight-up cursor. Let me know if this makes sense. Good luck!