Python: How to estimate / calculate memory footprint of data structures?

What's a good way to estimate the memory footprint of an object?

Conversely, what's a good way to measure the footprint?

For example, say I have a dictionary whose values are lists of integer,float tuples:

d['key'] = [ (1131, 3.11e18), (9813, 2.48e19), (4991, 9.11e18) ]

I have 4G of physical memory and would like to figure out approximately how many rows (key:values) I can store in memory before I spill into swap. This is on linux/ubuntu 8.04 and OS X 10.5.6 .

Also, what's the best way to figure out the actual in-memory footprint of my program? How do I best figure out when it's exhausting physical memory and spilling?

From stackoverflow

• You can do this with a memory profiler, of which there are a couple I'm aware of:

  1. PySizer - poissibly obsolete, as the homepage now recommends:

  2. Heapy.

  This is possibly a duplicate of this question.

• Guppy has a nice memory profiler (Heapy):

  >>> from guppy import hpy
  >>> hp = hpy()
  >>> hp.setrelheap() # ignore all existing objects
  >>> d = {}
  >>> d['key'] = [ (1131, 3.11e18), (9813, 2.48e19), (4991, 9.11e18) ]
  >>> hp.heap()
   Partition of a set of 24 objects. Total size = 1464 bytes.
   Index  Count   %     Size   % Cumulative  % Kind (class / dict of class)
       0      2   8      676  46       676  46 types.FrameType
       1      6  25      220  15       896  61 str
       2      6  25      184  13      1080  74 tuple
   ...
  

  Heapy is a little underdocumented, so you might have to dig through the web page or source code a little, but it's very powerful. There are also some articles which might be relevant.