在这里已经回答了这个问题：Python memory profiler
基本上，你可以执行以下操作（引用自Guppy-PE）：
>>> from guppy import hpy; h=hpy()>>> h.heap()Partition of a set of 48477 objects. Total size = 3265516 bytes. Index Count % Size % Cumulative % Kind (class / dict of class) 0 25773 53 1612820 49 1612820 49 str 1 11699 24 483960 15 2096780 64 tuple 2 174 0 241584 7 2338364 72 dict of module 3 3478 7 222592 7 2560956 78 types.CodeType 4 3296 7 184576 6 2745532 84 function 5 401 1 175112 5 2920644 89 dict of class 6 108 0 81888 3 3002532 92 dict (no owner) 7 114 0 79632 2 3082164 94 dict of type 8 117 0 51336 2 3133500 96 type 9 667 1 24012 1 3157512 97 __builtin__.wrapper_descriptor<76 more rows. Type e.g. '_.more' to view.>>>> h.iso(1,[],{})Partition of a set of 3 objects. Total size = 176 bytes. Index Count % Size % Cumulative % Kind (class / dict of class) 0 1 33 136 77 136 77 dict (no owner) 1 1 33 28 16 164 93 list 2 1 33 12 7 176 100 int>>> x=[]>>> h.iso(x).sp 0: h.Root.i0_modules['__main__'].__dict__['x']>>> 
