Book Image

Modern Python Standard Library Cookbook

By : Alessandro Molina
Book Image

Modern Python Standard Library Cookbook

By: Alessandro Molina

Overview of this book

The Python 3 Standard Library is a vast array of modules that you can use for developing various kinds of applications. It contains an exhaustive list of libraries, and this book will help you choose the best one to address specific programming problems in Python. The Modern Python Standard Library Cookbook begins with recipes on containers and data structures and guides you in performing effective text management in Python. You will find Python recipes for command-line operations, networking, filesystems and directories, and concurrent execution. You will learn about Python security essentials in Python and get to grips with various development tools for debugging, benchmarking, inspection, error reporting, and tracing. The book includes recipes to help you create graphical user interfaces for your application. You will learn to work with multimedia components and perform mathematical operations on date and time. The recipes will also show you how to deploy different searching and sorting algorithms on your data. By the end of the book, you will have acquired the skills needed to write clean code in Python and develop applications that meet your needs.
Table of Contents (21 chapters)
Title Page
Copyright and Credits
Packt Upsell
Contributors
Preface
Index

Profiling


When you need to speed up your code or understand where a bottleneck is, profiling is one of the most effective techniques.

The Python standard library provides a built-in profiler that traces the execution and timing for each function and allows you to spot the functions that are more expensive or that run too many times, consuming most of the execution time.

How to do it...

For this recipe, the following steps are to be performed:

  1. We can take any function we want to profile (which can even be the main entry point of the program):
import time

def slowfunc(goslow=False):
    l = []
    for i in range(100):
        l.append(i)
        if goslow:
            time.sleep(0.01)
    return l
  1. We can profile it using the cProfile module:
from cProfile import Profile

profiler = Profile()
profiler.runcall(slowfunc, True)
profiler.print_stats()
  1. That will print the timing for the function and the slowest functions called by the profiled one:
202 function calls in 1.183 seconds

Ordered by: standard...