How Can Python\'s `timeit` Module Help Compare Algorithm Performance?

Comparing Algorithm Performance with Timeit

The timeit module provides a convenient way to measure and compare the execution time of different functions or code snippets. Here's how you can use this module to compare the performance of your own algorithms, such as "insertion_sort" and "tim_sort":

Interactive Python Session

For an interactive Python session, you can utilize either IPython or the standard Python interpreter.

Using IPython Shell

IPython offers the %timeit function:

def insertion_sort(arr):
    # Your implementation of insertion sort

def tim_sort(arr):
    # Your implementation of tim sort

%timeit for x in range(100): insertion_sort(x)
%timeit for x in range(100): tim_sort(x)

This displays the execution time for each algorithm in microseconds.

Using Standard Python Interpreter

Import your functions from __main__ in the setup statement:

def insertion_sort(arr):
    # Your implementation of insertion sort

def tim_sort(arr):
    # Your implementation of tim sort

import timeit
timeit.repeat("for x in range(100): insertion_sort(x)", "from __main__ import insertion_sort",
              number=100000)
timeit.repeat("for x in range(100): tim_sort(x)", "from __main__ import tim_sort",
              number=100000)

This returns a list of execution times for each algorithm.

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