An Introduction to Code Profiling in Python with `time`, `timeit`

Determining where your code is spending its time is an important step in making your program more efficient. In Python, you can do this using the built-in time and timeit modules. So let’s have a look at how you can use these packages to pinpoint the bottlenecks in your code.

The time module

The time module in Python is base on the Unix timestamp, which represents the number of seconds since the start of January 1, 1970 (also known as the epoch). An example of its usage:

import time

def your_function():
    start = time.time()
     # Steps in your function
    end = time.time()
    print('Time spent:', end - start)

In this example, time.time() returns the current system time, and then the difference between the end and start times gives the total time spent for executing the function.

The timeit module

The timeit module provides a simple interface for timing small bits of Python code. It has both a command-line interface and a callable one. It avoids a number of common traps for measuring execution times. Here’s how you can use it

import timeit

start = timeit.default_timer()

# Your code here

end = timeit.default_timer()

print('Time taken:', end - start)

In this snippet, timeit.default_timer() is used which is a higher precision clock and gives better measure of time intervals in the code.

You can also use timeit function to run a particular function multiple times.

import timeit

def function():
    # Your code here
    pass

print(timeit.timeit(function, number=1000))

In this style, timeit() runs the function 1000 times and then gives the total time taken for those 1000 executions.

Conclusion

time and timeit are powerful tools for basic timing and profiling in Python. But it’s always important to remember that many factors can influence timing results, including other processes running on your system, disk I/O, and more. For more robust profiling, you might want to consider using more advanced tools like profile or cProfile, which can give detailed reports about time spent in each function call, or other third-party libraries like line_profiler for line-by-line analyses