Learning cProfile

Let’s say you have an algorithm that takes a lot of time to run. And you want to reduce the code run time. The first question that might crop up is:

Why does my code take so long to run?

Python Profilers can answer that question. It tells you which part of the code took how long to run. This lets you focus on that particular part and achieve efficiency.

Imports

import cProfile
import pstats

Simple Usage

import cProfile
cProfile.run("20+10")

Function Inside Function Example

def create_array():
    arr = []
    for i in range(0, 400000):
        arr.append(i)


def print_statement():
    print('Array created successfully')


def main():
    create_array()
    print_statement()


if __name__ == '__main__':
    cProfile.run('main()')

Using classes of cProfile and Stats

import cProfile
import pstats

def create_array():
    arr = []
    for i in range(0, 400000):
        arr.append(i)

def print_statement():
    print('Array created successfully')

def main():
    create_array()
    print_statement()

if __name__ == '__main__':
    profiler = cProfile.Profile()
    profiler.enable()
    main()
    profiler.disable()
    # stats = pstats.Stats(profiler).sort_stats('ncalls')
    stats = pstats.Stats(profiler).sort_stats('cumtime')
    stats.print_stats()

To Export Data

stats = pstats.Stats(profiler).sort_stats('cumtime')
stats.dump_stats('stats/results.pstat')

To remove dir prefixes from the output

stats.strip_dirs()

To visualize

snakeviz stats/results.pstat

Credits

• blog | cProfile – How to profile your python code
• snakeviz
• The Python Profilers