Book Image

Learning Python for Forensics

By : Chapin Bryce
Book Image

Learning Python for Forensics

By: Chapin Bryce

Overview of this book

This book will illustrate how and why you should learn Python to strengthen your analysis skills and efficiency as you creatively solve real-world problems through instruction-based tutorials. The tutorials use an interactive design, giving you experience of the development process so you gain a better understanding of what it means to be a forensic developer. Each chapter walks you through a forensic artifact and one or more methods to analyze the evidence. It also provides reasons why one method may be advantageous over another. We cover common digital forensics and incident response scenarios, with scripts that can be used to tackle case work in the field. Using built-in and community-sourced libraries, you will improve your problem solving skills with the addition of the Python scripting language. In addition, we provide resources for further exploration of each script so you can understand what further purposes Python can serve. With this knowledge, you can rapidly develop and deploy solutions to identify critical information and fine-tune your skill set as an examiner.
Table of Contents (24 chapters)
Learning Python for Forensics
Credits
About the Authors
Acknowledgments
About the Reviewer
www.PacktPub.com
Preface
Index

TQDM – a simpler progress bar


In Chapter 7, Fuzzy Hashing, we used the progressbar module to track program progress for the user. And while the progressbar module allows us to create a finely tuned progress bar, we can accomplish the same task in one line of code with tqdm. The tqdm module (version 3.4.0) can create a progress bar with any Python iterator.

In the preceding example, we wrap an iterator created by range(100) around tqdm. That alone creates the progress bar displayed in the image. An alternative method, using the trange() function, makes our task even simpler. We will use this module to create a progress bar for processing each WAL frame.

The following code creates the same progress bar as shown in the previous image. The trange() is an alias for tqdm(xrange()) and makes creating a progress bar even simpler.

>>>from tqdm import trange
>>> from time import sleep
>>> for x in trange(100):
...     
sleep(1)