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I have an attribute table with 100 columns (values are float and not NULL; more than 3000 rows). Using ArcGIS 10.6

  • I want to calculate the mean value of every field in a shapefile separately (like summary statistic works);
  • and use the resulting value for deleting the field: if the Mean value of the field lower than 0.6- delete the field from the table.

How can I use the Mean values of the fields in a for loop in order to delete the unnecessary fields from a table with python?

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    What have you tried? What are your python skills like? This would be s relatively simple coding exercise.
    – Hornbydd
    Jan 23 at 16:41
  • Basic python skill. I work with model builder mainly, but for this I can not find any solution.
    – GisT
    Jan 23 at 17:24
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This would be difficult and I suspect overtly complex if implemented in modelbuilder, a python solution would be much easier to run.

Here is the code that will achieve what you need:

import arcpy
    
table = r"C:\Scratch\fGDB_Scratch.gdb\test"

# Creates an array using all fields in table
array = arcpy.da.TableToNumPyArray(table,"*")

# Create a list of field names
lstFields = list(array.dtype.names)

# Remove OBJECTID from list
# You add other fields to remove here, eg. text fields
lstFields.remove("OBJECTID")

# Compute mean and if it falls below limit add to delete list
print("identifying fields to delete...")
toDelete = list()
for fn in lstFields:
    if array[fn].mean() < 3.6:
        toDelete.append(fn)

# Drop fields
print("Deleting fields...")
arcpy.DeleteField_management(table,toDelete)

print("Done!")

Once you have modified this code to your case drop this code into the Python console window in ArcMap or run it from an IDE.

In my test scenario I have a table and I'm checking the mean of the fields to see if they are below the value of 3.6, obviously adapt it to your scenario. I would recommend you do this from a backed up version in case your logic was wrong. My test table was this:

Test table

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    Thank you!!! It works fine!
    – GisT
    Jan 23 at 21:26

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