All the EDA activities in this book are based on Python 3.x. So, the first and foremost requirement to run any code from this book is for you to have Python 3.x installed on your computer irrespective of the operating system. Python can be installed on your system by following the documentation on its official website: https://www.python.org/downloads/.

Here is the software that needs to be installed in order to execute the code:

We primarily used Python notebooks to execute our code. One of the reasons for that is, with them, it is relatively easy to break code into a clear structure and see the output on the fly. It is always safer to install a notebook locally. The official website holds great information on how they can be installed. However, if you do not want the hassle and simply want to start learning immediately, then Google Colab provides a great platform where you can code and execute code using both Python 2.x and Python 3.x with support for Graphics Processing Units (GPUs) and Tensor Processing Units (TPUs).

If you are using the digital version of this book, we advise you to type the code yourself or access the code via the GitHub repository (link available in the next section). Doing so will help you avoid any potential errors related to the copying and pasting of code.

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.packtpub.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.
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-exploratory-data-analysis-with-python. 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://static.packt-cdn.com/downloads/9781789537253_ColorImages.pdf.

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

CodeInText: Indicates code words in the text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: "we visualized a time series dataset using the
matplotlib and seaborn libraries."

A block of code is set as follows:

import os
import numpy as np
%matplotlib inline from matplotlib
import pyplot as plt
import seaborn as sns

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

> pip install virtualenv
> virtualenv Local_Version_Directory -p Python_System_Directory

Bold: Indicates a new term, an important word, or words that you see onscreen. For example, words in menus or dialog boxes appear in the text like this. Here is an example: "Time series data may contain a notable amount of outliers."