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You can use pyqgis to create a function which creates a temporary vector layer of your one selected polygon, executes zonal statistics and print the results if the python console. Adjust the input lay …

For each polygon: Clip the raster Convert the pixels to points Extract the raster values from all points Then calculate mean value per polygon id with percentile_cont: SELECT polyid, PERCENTILE_CONT …

Try to split you layer into subsets then zonal stats using each split layer and merge all the outputs. (I'm using a point layer just to show the workflow, you need to use your polygon layer) Field ca …

You can use a generator to yield chunks of 100 items at a time from a list of all rows. Then use each chunk to build sql query using the IN operator: ... def chunks(l, n): """Yield successive n-s …

rasterstats zonal_stats with categorical=True. For example with pandas so you can manipulate the data as you like: import geopandas as gpd import pandas as pd from rasterstats import zonal_stats lots …

Zonal histogram Multiply each output column/pixel count with the pixel size (can be found under layer properties) using Field Calcualtor or Refactor Fields

You dont need to split your raster. Zonal Histogram will create a column for each pixel value with the pixel count. Appends fields representing counts of each unique value from a raster layer contain …

Input in Select Layer by Attribute must be a feature layer or a table view. The input cannot be a feature class or table. Make Feature Layer Your input is "shp" which may be a feature class.

Use da.SearchCursor(): import arcpy zonetable = r'C:\folder\data.gdb\zonstats' #Change fieldnames = ['field90', 'field75','field50'] #Change PER_90, PER_75, PER_50 = [row for row in arcpy.da.SearchC …

You could try resampling the raster to a finer resolution. Not sure why this works but accourding to the help section for version 10.3 (not present in 10.6): If the zone input is a feature dataset …

I recommend using rasterstats.zonal_stats: import geopandas as gpd import rasterio from rasterstats import zonal_stats rasterfile = r"/home/bera/GIS/data/nh/2021/Jamtland/nh_23_Jamtlands_lan_Sweref_9 …

Use Zonal histogram tool: Appends fields representing counts of each unique value from a raster layer contained within polygon features.

You dont need batch: 1 Zonal statistics to find the majority value for each polygon: 2 Zonal histogram with the zonal statistics as input to count the number of pixels of each raster class, for each …

I believe Zonal Statistics is calculating weighted average. Are you sure it doesnt when you use it? I have a raster with values from 0-100 and a polygon layer with two polygons in it. The first polygo …

Try zonal_stats with categorial and count, then multiply by pixel area: import geopandas as gpd import pandas as pd from rasterstats import zonal_stats import rasterio states = gpd.read_file(r'/home/ …