rkern

line_profiler

Python2015–2020

rkern / line_profiler · Archived

line_profiler

(OLD REPO) Line-by-line profiling for Python - Current repo ->

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6Mentions

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3.6KGitHub stars

REPOSITORY AT A GLANCE

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What it is

(OLD REPO) Line-by-line profiling for Python - Current repo ->

GitHub

Where it is used

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What people are saying

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This week on Hacker News

Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 3:56:07 PM UTC.

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Mentions over time

HN mentions by year6 dated mentions

Observed mentions: 2015-07-28 19:39:43 UTC to 2020-01-01 20:56:23 UTC.

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

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Period	Start (UTC)	End (exclusive, UTC)	Mentions	Submissions	Comments
2015	2015-01-01 00:00:00 UTC	2016-01-01 00:00:00 UTC	1	0	1
2016	2016-01-01 00:00:00 UTC	2017-01-01 00:00:00 UTC	1	0	1
2017	2017-01-01 00:00:00 UTC	2018-01-01 00:00:00 UTC	3	1	2
2018	2018-01-01 00:00:00 UTC	2019-01-01 00:00:00 UTC	0	0	0
2019	2019-01-01 00:00:00 UTC	2020-01-01 00:00:00 UTC	0	0	0
2020	2020-01-01 00:00:00 UTC	2021-01-01 00:00:00 UTC	1	0	1

What Hacker News says

6 sources

eesmith·Jan 1, 2020

Should also use kernprof - https://github.com/rkern/line_profiler - to see which lines in that function take the most time. Oddly, none of the techniques listed would help that slow_program.py function since kernprof says that 99.8% of the time is spent in: s += num / fact One comment about "# Fast:" / "from re import findall" -- That's not fast. Sure, it reduces a getattr lookup, but it's much better to pre-compile the regex than to pass it in to re.findall() each time. Now, re.findall() does cache the last 100 or so regexps, so it probably won't re-evaluate the regex each time. But really, pre-compute that regex with "_my_pattern = re.compile(regex) ... _my_pattern.findall()" and avoid even that cache lookup.

In Making Python Programs Blazingly Fast

git-pull·Jun 20, 2017

This is my first time hearing about Silk. Thanks for mentioning that, looks like a dream for profiling. GitHub for clicking: https://github.com/django-silk/silk To add, if using Django Debug Toolbar, there is a line_profiler [1] extension for it: https://github.com/dmclain/django-debug-toolbar-line-profile... [1] https://github.com/rkern/line_profiler

In Django project optimization guide – Part 1

hamid914·Jun 15, 2017Submission

Rkern/line_profiler: Line-by-line profiling for Python

1 points · Open discussion

tgb·Jun 1, 2017

I really like the simplicity of your solution, but you might be interested in kernprof: https://github.com/rkern/line_profiler It seems to be the nearly-canonical line-by-line profiler for Python, or was some years ago.

In Hunting Performance in Python Code: Measuring Memory Consumption

dr_zoidberg·Nov 12, 2016

> We changed timeit to now report average +/- standard deviation, which is better than the misleading minimum value reported in CPython. While I don't agree with many of the CPython devs ideas/decisions/crawling-pace-decision-making, I don't like the tone with which PyPy devs refer to them most of the time. It's like a rivalry which is ignored from the CPython side, even unacknowledged. And PyPy is not the Holy Grail of Python, it's just another implementation which runs into its own problems on some places[0], so it feels weird seeing them with kind of constant badgering of "the other guys". The feature looks interesting, still if you don't like a particular feature/profiler/timer for Python, there's nothing stoping you from making your own[1], like kernprof which is amazing. [0] for example, if you don't follow RPythons "way of doing th

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ings" you can end up with even slower performance than CPython -- I know because I've done it! [1] https://github.com/rkern/line_profiler#id7

In PyPy2.7 v5.6 released – stdlib 2.7.12 support, C-API improvements, and more

sown·Jul 28, 2015

I'm a fan of line_profiler: https://github.com/rkern/line_profiler % cat > l.py @profile def slow_func(): for i in xrange(1048576): continue return -1 @profile def fast_func(): for i in xrange(10): continue return -1 @profile def the_func(): for i in xrange(10): slow_func() fast_func() if __name__ == "__main__": the_func() % kernprof -l l.py % python -m line_profiler l.py.lprof Timer unit: 1e-06 s Total time: 5.21814 s File: l.py Function: slow_func at line 1 Line # Hits

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Time Per Hit % Time Line Contents ============================================================== 1 @profile 2 def slow_func(): 3 10485770 2644263 0.3 50.7 for i in xrange(1048576): 4 10485760 2573875 0.2 49.3 continue 5 6 10 5 0.5 0.0 return -1 Total time: 8.9e-05 s File: l.py Function: fast_func at line 8 Line # Hits Time Per Hit % Time Line Contents ============================================================== 8 @profile 9 def fast_func(): 10 110 61 0.6 68.5 for i in xrange(10): 11 100 26 0.3 29.2 continue 12 13 10 2 0.2 2.2 return -1 Total time: 10.5501 s File: l.py Function: the_func at line 14 Line # Hits Time Per Hit % Time Line Contents ============================================================== 14 @profile 15 def the_func(): 16 11 3 0.3 0.0 for i in xrange(10): 17 10 10549853 1054985.3 100.0 slow_func() 18 10 203 20.3 0.0 fast_func()

In Profiling in Python