I m using python to analyse some large files and I m running into memory issues so I ve been using sys getsizeof to try and keep track of the usage but it s behaviour with numpy arrays is bizarre Here s an example involving a map of albedos that I m having to open gt gt gt import numpy as np gt gt gt import struct gt gt gt from sys import getsizeof gt gt gt f open Albedo map assoc rb gt gt gt getsizeof f 144 gt gt gt albedo struct unpack df 7200 3600 f read 7200 3600 4 gt gt gt getsizeof albedo 207360056 gt gt gt albedo np array albedo reshape 3600 7200 gt gt gt getsizeof albedo 80 Well the data s still there but the size of the object a 3600x7200 pixel map has gone from 200 Mb to 80 bytes I d like to hope that my memory issues are over and just convert everything to numpy arrays but I feel that this behaviour if true would in some way violate some law of information theory or thermodynamics or something so I m inclined to believe that getsizeof doesn t work with numpy arrays Any ideas

You can use array nbytes for numpy arrays for example gt gt gt import numpy as np gt gt gt from sys import getsizeof gt gt gt a 0 1024 gt gt gt b np array a gt gt gt getsizeof a 8264 gt gt gt b nbytes 8192

Python memory usage of numpy arrays

The Solution to Python memory usage of numpy arrays is

You can use array.nbytes for numpy arrays, for example:

>>> import numpy as np
>>> from sys import getsizeof
>>> a = [0] * 1024
>>> b = np.array(a)
>>> getsizeof(a)
8264
>>> b.nbytes
8192

~ Answered on 2012-08-02 19:24:14

Python memory usage of numpy arrays

The Solution to Python memory usage of numpy arrays is

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