1. The code in the book will require you to run on Python 3.7 or higher.
2. The Python interactive environment can also be used to run the code snippets.
3. Readers are advised to learn the algorithms and concepts by executing the codes provided in the book that are designed to facilitate understanding of the algorithms.
4. The book aims to give readers practical exposure, so it is recommended that you carry out programming for all the algorithms in order to get the maximum out of this book.

You can download the example code files for this book from your account at www.packt.com. If you purchased this book elsewhere, you can visit www.packt.com/support and register to have the files emailed directly to you.

You can download the code files by following these steps:

1. Log in or register at www.packt.com.
2. Select the SUPPORT tab.
3. Click on Code Downloads & Errata.
4. Enter the name of the book in the Search box and follow the onscreen instructions.

Once the file is downloaded, please make sure that you unzip or extract the folder using the latest version of:

• WinRAR/7-Zip for Windows
• Zipeg/iZip/UnRarX for Mac
• 7-Zip/PeaZip for Linux

The code bundle for the book is also hosted on GitHub at https://github.com/PacktPublishing/Hands-On-Data-Structures-and-Algorithms-with-Python-Second-Edition. In case there's an update to the code, it will be updated on the existing GitHub repository.

We also have other code bundles from our rich catalog of books and videos available at https://github.com/PacktPublishing/. Check them out!

We also provide a PDF file that has color images of the screenshots/diagrams used in this book. You can download it here: https://www.packtpub.com/sites/default/files/downloads/9781788995573_ColorImages.pdf

There are a number of text conventions used throughout this book.

CodeInText: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: "We instantiate the CountVectorizer class and pass training_data.data to the fit_transform method of the count_vect object."

A block of code is set as follows:

class Node: 
    def __init__(self, data=None): 
        self.data = data 
        self.next = None

When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:

def dequeue(self):  
    if not self.outbound_stack: 
        while self.inbound_stack: 
            self.outbound_stack.append(self.inbound_stack.pop()) 
    return self.outbound_stack.pop()

Any command-line input or output is written as follows:

0     1      2
0   4.0  45.0  984.0
1   0.1   0.1    5.0
2  94.0  23.0   55.0

Bold: Indicates a new term, an important word, or words that you see on screen.