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profile Module Complexity

The profile module is a deterministic profiler written in Python. It installs a function with sys.setprofile(), and the interpreter calls that function on every call and return event, where it reads a timer and updates a table of per-function totals. The table holds one entry per function, not per call, so the memory a run needs follows the program's shape rather than its length.

The unit of cost is the event. Every event is one Python-level call of the profiler's dispatcher, which makes profile far slower per event than cProfile, which does the same bookkeeping in C behind the same functions and report format. Use profile when you need to extend the profiler in Python; measure with cProfile.

Deprecated in Python 3.15

Deprecated in Python 3.15 by PEP 799 and scheduled for removal in Python 3.17; importing it issues a DeprecationWarning. profiling.tracing, which cProfile re-exports, provides the same interface.

c is the calls the profiled code makes: calls of Python functions, each resumption of a generator, and calls of built-in functions and methods; each costs two events. f is the distinct functions among them, keyed by file, first line and name, e the distinct caller-callee pairs, and d the deepest nesting of calls. m is the argument to calibrate(). Every Time bound for a profiling run is overhead on top of what the profiled code costs unprofiled, and lookups in the profiler's table are treated as O(1).

Complexity Reference

Module functions

Operation	Time	Space	Notes
profile.run(statement, filename=None, sort=-1)	O(c + f log f + e)	O(f + e + d)	Runs statement in __main__'s namespace, then prints the report sorted by sort, or writes it to filename; returns None
profile.runctx(statement, globals, locals, filename=None, sort=-1)	O(c + f log f + e)	O(f + e + d)	Same, in the namespaces you pass

Profile

Operation	Time	Space	Notes
profile.Profile(timer=None, bias=None)	O(1)	O(1)	Calls timer at construction to see what it returns, then twice per event; the default, time.process_time, counts the process's CPU time, so sleeping and waiting on I/O do not show, but CPU used meanwhile by other threads does
Profile.runcall(func, /, *args, **kwargs)	O(c)	O(f + e + d)	Returns what func returns; Profile has no enable() or disable(), so this is how to profile one call. A second runcall() on the same instance fails unless create_stats() ran in between, and then the totals accumulate: use a new Profile per call to keep them apart
Profile.runctx(cmd, globals, locals)	O(c)	O(f + e + d)	Runs cmd with exec(); returns the profiler
Profile.run(cmd)	O(c)	O(f + e + d)	runctx() in __main__'s namespace
Profile.create_stats()	O(f + e)	O(f + e)	Snapshots the table into .stats, one entry per function with its callers
Profile.print_stats(sort=-1)	O(f log f + e)	O(f + e)	Builds a pstats.Stats, sorts it and prints one line per function
Profile.dump_stats(filename)	O(f + e)	O(f + e)	Writes the snapshot with marshal, for pstats.Stats(filename) to load

Calibration

Operation	Time	Space	Notes
Profile.calibrate(m, verbose=0)	O(m)	O(1)	Runs a fixed workload of m calls unprofiled and profiled, and returns the per-event time the profiler failed to subtract
Profile.bias	O(1)	O(1)	Subtracted from the time of every event; pass bias=, or set Profile.bias before creating profilers

Where the Overhead Goes

One Dispatch per Event

The profiler sees calls, not work. A function that adds a million numbers in a loop produces two events, its own call and return; a loop that calls a tiny function a million times produces two million, and each is a Python-level dispatch. Code made of many short calls is where profile slows down most.

import profile
import pstats

def leaf():
    pass

def many_short_calls(n):
    for _ in range(n):
        leaf()

def one_long_call(n):
    total = 0
    for i in range(n):
        total += i
    return total

calls = profile.Profile()
calls.runcall(many_short_calls, 10_000)  # O(c) - 10,001 Python calls
by_name = pstats.Stats(calls).get_stats_profile().func_profiles
assert by_name['leaf'].ncalls == '10000'

loop = profile.Profile()
assert loop.runcall(one_long_call, 10_000) == 49_995_000  # O(1) - one call, whatever n is
by_name = pstats.Stats(loop).get_stats_profile().func_profiles
assert by_name['one_long_call'].ncalls == '1'
assert 'leaf' not in by_name

What Counts as a Call

Built-in functions count, and so does every resumption of a generator, so iterating a Python generator costs two events per item. A recursive function is one entry however deep it goes; the report shows its calls as total/primitive.

import profile
import pstats

def numbers(k):
    for i in range(k):
        yield i

def lengths(n):
    return [len('x') for _ in range(n)]

def depth(d):
    return 0 if d == 0 else depth(d - 1)

def mixed():
    sum(numbers(50))  # 51 resumptions, one per item plus the last
    lengths(100)      # 100 calls of len()
    depth(99)         # 100 calls, one of them from outside

profiler = profile.Profile()
profiler.runcall(mixed)

by_name = pstats.Stats(profiler).get_stats_profile().func_profiles
assert by_name['numbers'].ncalls == '51'
assert by_name['len'].ncalls == '100'
assert by_name['depth'].ncalls == '100/1'

Memory Follows Distinct Functions

The table grows by one entry the first time a function is called, and by one caller record the first time a given caller calls it. Calling the same function more often only updates counters.

import profile

def leaf():
    pass

def repeat(n):
    for _ in range(n):
        leaf()

def entries(n):
    profiler = profile.Profile()
    profiler.runcall(repeat, n)  # O(c) time, O(f + e + d) memory
    profiler.create_stats()      # O(f + e)
    return len(profiler.stats)

assert entries(10) == entries(10_000)  # 1,000x the calls, the same table

One Profiler, One Thread

sys.setprofile() installs its function for the calling thread only, so a thread started during the run is not profiled. And a run cannot start while any profile function is already installed, cProfile included: it fails rather than nesting.

import cProfile
import profile
import threading

def worker():
    return sum(range(10))

def spawn():
    thread = threading.Thread(target=worker)
    thread.start()
    thread.join()

profiler = profile.Profile()
profiler.runcall(spawn)
profiler.create_stats()
names = {name for _, _, name in profiler.stats}
assert 'spawn' in names and 'worker' not in names  # the new thread ran unprofiled

outer = cProfile.Profile()
outer.enable()
try:
    profile.Profile().runcall(worker)
except (AssertionError, TypeError):
    pass
else:
    raise AssertionError('profile started under another profiler')
finally:
    outer.disable()

Calibration

The dispatcher's own time between reading the timer and returning leaks into every event, so a function making many short calls is charged for work that is the profiler's. calibrate(m) runs a fixed workload of m calls with and without profiling and returns that leak per event; setting it as bias subtracts it from every event. The value depends on the machine and the moment it is measured, and a bias set too high makes some times come out negative.

import profile

bias = profile.Profile().calibrate(1_000)  # O(m) - runs the workload three times
assert isinstance(bias, float)

calibrated = profile.Profile(bias=bias)  # O(1) - this profiler only
assert calibrated.runcall(sum, range(10)) == 45

Common Patterns

Profiling One Call and Reading the Top Entries

import io
import profile
import pstats
import time

def leaf(x):
    return x * 2

def work(n):
    return sum(leaf(i) for i in range(n))

profiler = profile.Profile(timer=time.perf_counter)  # a fine-grained clock on every platform
assert profiler.runcall(work, 1_000) == 999_000  # O(c)

report = io.StringIO()
stats = pstats.Stats(profiler, stream=report)  # O(f + e)
stats.sort_stats('tottime').print_stats(5)    # O(f log f), five lines printed
assert 'leaf' in report.getvalue()

Saving a Report and Loading It Later

import os
import profile
import pstats
import tempfile

def work():
    return sorted(range(100), reverse=True)

profiler = profile.Profile()
profiler.runcall(work)

with tempfile.TemporaryDirectory() as directory:
    path = os.path.join(directory, 'work.prof')
    profiler.dump_stats(path)  # O(f + e)
    loaded = pstats.Stats(path)  # O(f + e)
    assert any(name == 'work' for _, _, name in loaded.stats)

Performance Best Practices

✅ Do:

• Measure with cProfile; reach for profile only to subclass or change the profiler in Python, and not in code that must run on Python 3.17
• Profile one call with runcall() rather than a whole program, so c counts only the calls you care about
• Create a new Profile for each runcall() whose report should stand alone
• Calibrate once per machine and pass the result as bias, if you compare functions that make many short calls
• Sort the report and read the top entries; the report is one line per function, not per call

❌ Avoid:

• Trusting absolute times for code made of many tiny calls: each call adds two dispatches, and that overhead dominates
• Starting profile while cProfile or another profile function is active - it fails instead of nesting
• Expecting sleeps or I/O waits in the report under the default timer, which counts CPU time

Version Notes

• Python 3.13+: Profile.print_stats() accepts a tuple of sort keys, and so does the sort argument of run() and runctx()
• Python 3.15+: Deprecated by PEP 799, for removal in Python 3.17; importing the module issues a DeprecationWarning

Related Modules

• cProfile - the same interface and report, with the per-event work done in C
• profiling - profiling.tracing, the home of cProfile from Python 3.15, and a sampling profiler
• pstats - loads, sorts and prints what a profiler recorded
• timeit - times a snippet with no profiler in the way
• sys - sys.setprofile(), the hook profile is built on