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I want to count the no data values, because when I loaded them with rioxarray they seemed to be oddly many pixel to be non-valid.

When I open the given file in qgis the valid pixel percentage is 89.6 %.

My attempt to measure it with raster was:

import rioxarray as rxa
import numpy as np

raster = rxa.open_rasterio("Z:/path/to/test.tif")
data = raster.data
print(np.sum(data == raster.rio.nodata) /data.size * 100)

output: 10.122128919010978

Background: I wanted to use numpy masked arrays and mask the e.g. the nodata, black pixels, or values above a threshold. But when I use this method, to mask the nodata pixels, almost everypixel is getting masked.

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Using the following you can check if this is what you want:

# Import Libraries
import rasterio as rio
import numpy as np
import pandas as pd

# Open Raster Dataset with Rasterio
with rio.open('path to your raster') as src:
    no_data = src.nodata # Get Raster No-Data value
    img = src.read(1) # Read Image as Numpy Array

# Get unique Pixel Values & their Count in numpy array    
unique, count = np.unique(img, return_counts = True)

# Defining a new pandas dataframe and adding the Numpy Array to the dataframe    
raster_dataframe = pd.DataFrame()
raster_dataframe['Pixel Value'] = unique
raster_dataframe['Count'] = count

# Compute sum of all values in the 'Count' Column of the dataframe 
sum_count = raster_dataframe['Count'].sum()

# Get the dataframe row where 'Pixel Value' is 'no_data'
search = raster_dataframe.loc[raster_dataframe['Pixel Value'] == no_data]

# Caculating the percentage of No-Data Pixels
percentage_no_data = (search['Count'].values[0]/sum_count) * 100
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  • Could you add some comments and explanations? That will help beginners a lot :) THanks! Dec 17 '20 at 11:04
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For rioxarray you would want something like this:

import rioxarray as rxa

rds = rxa.open_rasterio("Z:/path/to/test.tif", masked=True)

valid_px_count = int(rds.count().compute())
total_px_count = rds.size

nodata_px_count = total_px_count - valid_px_count

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