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Memory Inspection

Profiling:​

python -m cProfile foo.py

Memory Observation​

Garbage Collector:

import gc
gc.get_objects()
import tracemalloc

tracemalloc.start()

a = [str(i) for i in range(10000)]

snapshot = tracemalloc.take_snapshot()
top_stats = snapshot.statistics('lineno')

for stat in top_stats[:10]:
    print(stat)

Deep object memory analysis​

pympler​

This is one of the best practical libraries.

Install:

pip install pympler

Use:

from pympler import asizeofasizeof.asizeof(obj)

It gives deep size including referenced objects.

Also useful:

from pympler import muppy, summaryall_objects = muppy.get_objects()sum1 = summary.summarize(all_objects)summary.print_(sum1)

This shows memory grouped by object type (very useful for class behavior).

objgraph (understanding object relationships)​

This is the “Cheat Engine feeling” but for Python object graphs.

Install:

pip install objgraph

Examples:

import objgraphobjgraph.show_most_common_types()objgraph.show_backrefs([some_object], filename='graph.png')

This shows who is holding references to what.

Memory allocation at interpreter level​

Python doesn’t give raw memory addresses in a useful way, but you can inspect identity:

id(obj)

And sometimes:

import ctypesctypes.addressof(ctypes.py_object(obj))

⚠️ This is low-level and not very useful for learning memory behavior beyond curiosity.

profiling tools​

memory_profiler​

Line-by-line memory usage.

pip install memory_profiler

Usage:

from memory_profiler import profile@profiledef my_func():    x = [i for i in range(100000)]    return x

Run:

python -m memory_profiler your_script.py

guppy3 (Heapy)​

Very powerful heap analysis:

pip install guppy3
from guppy import hpyh = hpy()print(h.heap())

This gives you a heap breakdown similar to memory inspectors in other languages.

System-level profiling​

If you want real mastery, you also step outside Python:

Linux tools:​

  • valgrind + massif (heap profiler)
  • /proc/<pid>/status
  • pmap
  • smem

Example:

valgrind --tool=massif python script.py

Then visualize memory growth.