This is what i would suggest, load the dataframe, piece by piece, do not load the entirety of it at the same time, this might actually exceed your RAM, hence the dying kernel.
Then iterate through the dataframe line by line, take the 32332 columns, and reshape them into an image of 137x236 and save them to disk with a the appropriate name in to the folder train_data/class_number/, you can then use keras ImageDataGenerator().flowfromDirectory()
the issue is that the 32332 columns dont make sense to me, if the image was a single channel 137x236 image, then the number of columns would be 137*236 = 29972.So theres like 2k columns unaccounted for. Are you sure of the format of the data?
