tprof 1.2.0

A targeting profiler.

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A targeting profiler.

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Requirements

Python 3.12 to 3.14 supported.

Installation

1. Install with pip:

   python -m pip install tprof

Usage

tprof measures the time spent in specified target functions when running a script or module. Unlike a full program profiler, it only tracks the specified functions using sys.monitoring (new in Python 3.12), reducing overhead and helping you focus on the bits you’re changing. Timing is done in C to further reduce overhead.

tprof supports usage as a CLI and with a Python API.

CLI

Specify one or more target functions with -t, then what to run: a script file by filename, or a module with -m then its name. Any unrecognized arguments are passed to the script or module.

Use the format <module>:<function> to specify target functions. When using -m with a module, you can skip the <module> part and it will be inferred from the module name.

$ tprof -t lib:maths ./example.py
...
🎯 tprof results:
 function    calls total  mean ± σ     min … max
 lib:maths()     2 610ms 305ms ± 2ms 304ms … 307ms

Full help:

usage: tprof [-h] -t target [-x] (-m module | script) ...

positional arguments:
  script         Python script to run
  args           Arguments to pass to the script or module

options:
  -h, --help     show this help message and exit
  -t target      Target callable to profile (format: module:function).
  -x, --compare  Compare performance of targets, with the first as baseline.
  -m module      Run library module as a script (like python -m)

Comparison mode

Pass -x (--compare) to compare the performance of multiple target functions, with the first as the baseline, in an extra “delta” column. For example, given this code:

def before():
    total = 0
    for i in range(100_000):
        total += i
    return total


def after():
    return sum(range(100_000))


for _ in range(100):
    before()
    after()

…you can run tprof like this to compare the two functions:

$ tprof -x -t before -t after -m example
🎯 tprof results:
 function         calls total  mean ± σ      min … max   delta
 example:before()   100 227ms   2ms ± 34μs   2ms … 2ms   -
 example:after()    100  86ms 856μs ± 15μs 835μs … 910μs -62.27%

API

tprof(*targets, label: str | None = None, compare: bool = False)

Use this context manager / decorator within your code to perform profiling in a specific block. The report is printed when the block ends, each time it ends.

Each item in targets may be a callable to profile, or a string reference to one that will be resolved with pkgutil.resolve_name().

label is an optional string to add to the report heading to distinguish multiple reports.

Set compare to True to enable comparison mode, as documented above in the CLI section.

For example, given this code:

from lib import maths

from tprof import tprof

print("Doing the maths…")
with tprof(maths):
    maths()
print("The maths has been done!")

…running it would produce output like:

$ python example.py
Doing the maths…
🎯 tprof results:
 function    calls total  mean ± σ   min … max
 lib:maths()     1 305ms 305ms     305ms … 305ms
The maths has been done!

Another example using comparison mode:

from tprof import tprof


def before():
    total = 0
    for i in range(100_000):
        total += i
    return total


def after():
    return sum(range(100_000))


with tprof(before, after, compare=True):
    for _ in range(100):
        before()
        after()

…which produces output like:

$ python example.py
🎯 tprof results:
 function          calls total  mean ± σ      min … max delta
 __main__:before()   100 227ms   2ms ± 83μs   2ms … 3ms -
 __main__:after()    100  85ms 853μs ± 22μs 835μs … 1ms -62.35%

History

When optimizing Python code, I found I was using this workflow:

1. Profile the whole program with a tool like cProfile or py-spy to find slow functions.

2. Pick a function to optimize.

3. Make a change.

4. Re-profile the whole program to see if the changes helped.

This works fined but profiling the whole program again adds overhead, and picking out the one function’s stats from the report is extra work. When I saw that Python 3.12’s sys.monitoring allows tracking specific functions with low overhead, I created tprof to streamline this workflow, allowing the final step to re-profile just the target function.

It also seemed a natural extension that tprof could compare multiple functions, supporting a nice microbenchmarking workflow.

Output inspired by poop and formatted nicely with Rich.