[Tutor] Run Time Analysis With Python

[Ricardo Grant]

I am trying to learn more about performance analysis using python. I 
made a small script to extract titles and uri's from Firefox's bookmark 
JSON dump. Here is the script:

#!/usr/bin/python3

import json

bookmarks = json.load(open('/home/ricardo/bookmarks-2019-08-20.json'))

def descend(dict):
     if 'children' in dict:
         for child in dict['children']:
             if 'uri' in child:
                 print('{} {}'.format(child['title'], child['uri']))
             else:
                 descend(child)

and the profiling information

          103 function calls (92 primitive calls) in 0.000 seconds

    Ordered by: standard name

    ncalls  tottime  percall  cumtime  percall filename:lineno(function)
         1    0.000    0.000    0.000    0.000 <string>:1(<module>)
      12/1    0.000    0.000    0.000    0.000 bookmarks.py:8(descend)
         1    0.000    0.000    0.000    0.000 {built-in method 
builtins.exec}
        44    0.000    0.000    0.000    0.000 {built-in method 
builtins.print}
         1    0.000    0.000    0.000    0.000 {method 'disable' of 
'_lsprof.Profiler' objects}
        44    0.000    0.000    0.000    0.000 {method 'format' of 'str' 
objects}

Due to it's recursive nature and my limited understanding of big O 
analysis, I can't really find the steps to define a function that 
describes the performance of this script. I would appreciate some hints 
or even an explanation. Can you explain the profiling information as well?

I am also curious about my implementation. Does this code suffice, or is 
there other ways to recurse into dictionaries?