Accepted Solutions
There are many ways to solve this, but do you really need a table for each landuse type or could the result be a single table and one row for each landuse type? In case a single table would do, you could do this:
import arcpy
import os
fc = r'C:\Users\xxx\Desktop\GIS_Training\NonpntGrndwaterContCaves_2013\NonpntGrndwaterContCaves_2013\Student\merged_caves_dissolved_identifed.shp'
fld_luse = 'LEVEL1'
fld_length = 'Length_m'
out_tbl = os.path.join(os.path.dirname(fc), 'LanduseStats.dbf')
arcpy.Statistics_analysis(in_table=fc, out_table=out_tbl,
statistics_fields="{0} SUM;{0} MEAN;{0} MIN;{0} MAX;{0} RANGE;{0} COUNT;{0} STD".format(fld_length),
case_field=fld_luse)The result would will be a single table like this:
Adjust the statistic type as you please. The case field in this case will tell the tool to generate a record for each unique value in the specified field. In this case the landuse type.
If you need a table per landuse type, you would have to create a list of unique values (land use types) and loop through them create a feature layer with the where clause, define the output table and perform the summary statistics. Something like this, although I did not test it:
import arcpy
import os
fc = r'C:\Users\xxx\Desktop\GIS_Training\NonpntGrndwaterContCaves_2013\NonpntGrndwaterContCaves_2013\Student\merged_caves_dissolved_identifed.shp'
fld_luse = 'LEVEL1'
fld_length = 'Length_m'
landuses = sorted(list(set([r[0] for r in arcpy.da.SearchCursor(fc, fld_luse)])))
# [u'AGRICULTURE', u'RANGELAND', u'TRANSPORTATION, COMMUNICATION AND UTILITIES', u'UPLAND FORESTS', u'URBAN AND BUILT-UP', u'WATER', u'WETLANDS']
for landuse in landuses:
where = '"{0}" = \'{1}\''.format(fld_luse, landuse)
layer = arcpy.MakeFeatureLayer_management(fc, "lay", where)
out_tbl = os.path.join(os.path.dirname(fc), landuse.split(' ')[0]+'.dbf')
arcpy.Statistics_analysis(in_table=fc, out_table=out_tbl,
statistics_fields="{0} SUM;{0} MEAN;{0} MIN;{0} MAX;{0} RANGE;{0} COUNT;{0} STD".format(fld_length))
Another way is to not create an output table and calculate the values yourself:
import arcpy
fc = r'C:\Users\xxx\Desktop\GIS_Training\NonpntGrndwaterContCaves_2013\NonpntGrndwaterContCaves_2013\Student\merged_caves_dissolved_identifed.shp'
fld_luse = 'LEVEL1'
fld_length = 'Length_m'
data = [[r[0], r[1]] for r in arcpy.da.SearchCursor(fc, (fld_luse, fld_length))]
landuses = sorted(list(set([r[0] for r in data])))
for landuse in landuses:
data = [r[1] for r in data if r[0] == landuse]
print '\n', landuse
print 'items:', len(data)
print 'sum :', sum(data)
print 'mean :', sum(data) / float(len(data))
print 'min :', min(data)
print 'max :', max(data)
... which would print out:
AGRICULTURE
items: 10
sum : 3130.88187758
mean : 313.088187758
min : 16.8203685714
max : 1684.22223123
RANGELAND
items: 4
sum : 1456.44815747
mean : 364.112039367
min : 18.1880738434
max : 845.280368334
TRANSPORTATION, COMMUNICATION AND UTILITIES
items: 7
sum : 673.49199935
mean : 96.2131427643
min : 1.0823500953
max : 268.780346657
UPLAND FORESTS
items: 37
sum : 14051.0723213
mean : 379.758711388
min : 12.5138760298
max : 1579.5682325
URBAN AND BUILT-UP
items: 18
sum : 7045.18274852
mean : 391.399041584
min : 12.541521943
max : 2871.78168613
WATER
items: 1
sum : 280.813009629
mean : 280.813009629
min : 280.813009629
max : 280.813009629
WETLANDS
items: 4
sum : 743.244800894
mean : 185.811200224
min : 20.7191473933
max : 369.700021771
There are many ways to solve this, but do you really need a table for each landuse type or could the result be a single table and one row for each landuse type? In case a single table would do, you could do this:
import arcpy
import os
fc = r'C:\Users\xxx\Desktop\GIS_Training\NonpntGrndwaterContCaves_2013\NonpntGrndwaterContCaves_2013\Student\merged_caves_dissolved_identifed.shp'
fld_luse = 'LEVEL1'
fld_length = 'Length_m'
out_tbl = os.path.join(os.path.dirname(fc), 'LanduseStats.dbf')
arcpy.Statistics_analysis(in_table=fc, out_table=out_tbl,
statistics_fields="{0} SUM;{0} MEAN;{0} MIN;{0} MAX;{0} RANGE;{0} COUNT;{0} STD".format(fld_length),
case_field=fld_luse)The result would will be a single table like this:
Adjust the statistic type as you please. The case field in this case will tell the tool to generate a record for each unique value in the specified field. In this case the landuse type.
If you need a table per landuse type, you would have to create a list of unique values (land use types) and loop through them create a feature layer with the where clause, define the output table and perform the summary statistics. Something like this, although I did not test it:
import arcpy
import os
fc = r'C:\Users\xxx\Desktop\GIS_Training\NonpntGrndwaterContCaves_2013\NonpntGrndwaterContCaves_2013\Student\merged_caves_dissolved_identifed.shp'
fld_luse = 'LEVEL1'
fld_length = 'Length_m'
landuses = sorted(list(set([r[0] for r in arcpy.da.SearchCursor(fc, fld_luse)])))
# [u'AGRICULTURE', u'RANGELAND', u'TRANSPORTATION, COMMUNICATION AND UTILITIES', u'UPLAND FORESTS', u'URBAN AND BUILT-UP', u'WATER', u'WETLANDS']
for landuse in landuses:
where = '"{0}" = \'{1}\''.format(fld_luse, landuse)
layer = arcpy.MakeFeatureLayer_management(fc, "lay", where)
out_tbl = os.path.join(os.path.dirname(fc), landuse.split(' ')[0]+'.dbf')
arcpy.Statistics_analysis(in_table=fc, out_table=out_tbl,
statistics_fields="{0} SUM;{0} MEAN;{0} MIN;{0} MAX;{0} RANGE;{0} COUNT;{0} STD".format(fld_length))
Another way is to not create an output table and calculate the values yourself:
import arcpy
fc = r'C:\Users\xxx\Desktop\GIS_Training\NonpntGrndwaterContCaves_2013\NonpntGrndwaterContCaves_2013\Student\merged_caves_dissolved_identifed.shp'
fld_luse = 'LEVEL1'
fld_length = 'Length_m'
data = [[r[0], r[1]] for r in arcpy.da.SearchCursor(fc, (fld_luse, fld_length))]
landuses = sorted(list(set([r[0] for r in data])))
for landuse in landuses:
data = [r[1] for r in data if r[0] == landuse]
print '\n', landuse
print 'items:', len(data)
print 'sum :', sum(data)
print 'mean :', sum(data) / float(len(data))
print 'min :', min(data)
print 'max :', max(data)
... which would print out:
AGRICULTURE
items: 10
sum : 3130.88187758
mean : 313.088187758
min : 16.8203685714
max : 1684.22223123
RANGELAND
items: 4
sum : 1456.44815747
mean : 364.112039367
min : 18.1880738434
max : 845.280368334
TRANSPORTATION, COMMUNICATION AND UTILITIES
items: 7
sum : 673.49199935
mean : 96.2131427643
min : 1.0823500953
max : 268.780346657
UPLAND FORESTS
items: 37
sum : 14051.0723213
mean : 379.758711388
min : 12.5138760298
max : 1579.5682325
URBAN AND BUILT-UP
items: 18
sum : 7045.18274852
mean : 391.399041584
min : 12.541521943
max : 2871.78168613
WATER
items: 1
sum : 280.813009629
mean : 280.813009629
min : 280.813009629
max : 280.813009629
WETLANDS
items: 4
sum : 743.244800894
mean : 185.811200224
min : 20.7191473933
max : 369.700021771
