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line_profiler reviews and mentions
- Ask HN: C/C++ developer wanting to learn efficient Python
- New version of line_profiler: 4.1.0
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Making Python 100x faster with less than 100 lines of Rust
LineProfiler is the best tool to learn how to write performant Python and code optimization.
https://github.com/pyutils/line_profiler
You can literally see the hot spot of your code, then you can grind different algorithms or change the whole architecture to make it faster.
For example replace short for loops to list comprehensions, vectorize all numpy operations (only vectorize partially do not help the issue), using 'not any()' instead or 'all()' for boolean, etc.
Doing this for like 2 weeks, basically you can automatically recognized most bad code patterns in a glance.
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Why is my Pubmed plant search app so slow?
You may want to try using a package like line_profiler to narrow down where the time is spent.
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How to make nested for loops run faster
When tuning for performance, always measure. Never assume you know where the slow parts are. Run a line profiler and see where all the time is actually going.
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I'm working on a world map generator, but I have one function in particular that is very slow and keeping me from being able to scale my maps to as large as I'd like... is there a way that I can optimize this depth first search function, or another way of grouping contiguous cells based on criteria?
Either way I would highly recommend running a profiler on your code to see where the program is spending most of its time. line_profiler is a very nice one, as it shows you execution time for each line.
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Is it possible to make a function to check how many lines of code have been executed in the program so far (including said function’s lines)?
There are dedicated tools like line_profiler for python - if this doesn't do exactly what you need it can be easily modified.
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Why does sklearn.Pipeline with regex outperform spacy for text preprocessing?
It's surprising to me that an sklearn pipeline and a spacy pipeline both doing simple regexing are vastly different in performance. I would go one layer deeper with measurement with something like line_profiler, which I've used to great effect to get line-by-line perf stats. This should illuminate why.
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Hot profiling for Python
This looks really nice! Does it use line_profiler or is it a different implementation for the profiling? Either way the interface is fantastic!
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Profiling and Analyzing Performance of Python Programs
# https://github.com/pyutils/line_profiler pip install line_profiler kernprof -l -v some-code.py # This might take a while... Wrote profile results to some-code.py.lprof Timer unit: 1e-06 s Total time: 13.0418 s File: some-code.py Function: exp at line 3 Line # Hits Time Per Hit % Time Line Contents ============================================================== 3 @profile 4 def exp(x): 5 1 4.0 4.0 0.0 getcontext().prec += 2 6 1 0.0 0.0 0.0 i, lasts, s, fact, num = 0, 0, 1, 1, 1 7 5818 4017.0 0.7 0.0 while s != lasts: 8 5817 1569.0 0.3 0.0 lasts = s 9 5817 1837.0 0.3 0.0 i += 1 10 5817 6902.0 1.2 0.1 fact *= i 11 5817 2604.0 0.4 0.0 num *= x 12 5817 13024902.0 2239.1 99.9 s += num / fact 13 1 5.0 5.0 0.0 getcontext().prec -= 2 14 1 2.0 2.0 0.0 return +s
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A note from our sponsor - InfluxDB
www.influxdata.com | 26 Apr 2024
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pyutils/line_profiler is an open source project licensed under GNU General Public License v3.0 or later which is an OSI approved license.
The primary programming language of line_profiler is Python.
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