Calculate mean values for all bands and date to a dataframe

Question

I have 5 rasters, each having 10 bands, where each band corresponds to a specific date.

How do I calculate mean values for all 10 bands and save them?

I need to save the mean values in a dataframe like this example:

   rasters   bands       mean         date             years 
    raster1   band1       1234         24/01/2000       2000
              band2       45213        01/03/2000       2000
              band3        4221        12/04/2000       2000 
              band4         ...         ...              ....
              band10
   raster 2   band 1       4521        01/02/2001      2001
              band2        1234        04/05/2001      2001
              ...           ....         ....

This script calculates mean value for each band in my raster:

raster = gdal.Open("D:/script/NDVI2000.tif")
bands = raster.RasterCount

for band in range(1, bands+1):
    data = raster.GetRasterBand(band).ReadAsArray().astype('float')
    mean = np.mean(data[data != 0]) #calculate mean without value 0
    print("Band %s: Mean = %s" % (band, round(mean, 2)))
Out[2]:
Band 1: Mean = 1712.83
Band 2: Mean = 1803.14
Band 3: Mean = 1662.33
Band 4: Mean = 1868.77
Band 5: Mean = 1900.97
Band 6: Mean = 2031.13
Band 7: Mean = 1847.89
Band 8: Mean = 2185.66
Band 9: Mean = 2081.14
Band 10: Mean = 8248.7

How can I save the mean result stats in the dataframe using gdal python 3 after I have the results for all rasters?

I need this dataframe to plot times series for NDVI.

Answer 1

Something like this, append dicts to an empty list. It seems from your example output, you may want to do this inside a function that you can pass the path of the target raster. Note the ??? means you will have to implement something. Maybe your year comes from the filename?

import pandas as pd

raster_filename = "D:/script/NDVI2000.tif"
raster = gdal.Open(raster_filename)
bands = raster.RasterCount

row_list = []

for band in range(1, bands+1):
    data = raster.GetRasterBand(band).ReadAsArray().astype('float')
    mean = np.mean(data[data != 0]) #calculate mean without value 0
    row_list.append({
        'raster': os.path.basename(raster_filename),
        'band': band,
        'mean': mean,
        'year': ???,
        'date': ???,
        })
    print("Band %s: Mean = %s" % (band, round(mean, 2)))

# create DataFrame with columns from the dict keys
df = pd.DataFrame(row_list)
df.to_csv('band_means.csv')

Good luck!