To get the most out of this book

The chapters in this book require a PC or Mac with 8GB or 16GB of RAM (the higher, the better). Your machine should have at least a 2.2 GHz Core i3/i5 processor or an AMD equivalent.

Download the example code files

You can download the example code files for this book from your account at www.packtpub.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:

Log in or register at www.packtpub.com.
Select the SUPPORT tab.
Click on Code Downloads & Errata.
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-Science-with-Anaconda. 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!

Download the color images

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/HandsOnDataSciencewithAnaconda_ColorImages.pdf.

Conventions used

In this book, you will find a number of styles of text that distinguish between different kinds of information. Here are some examples of these styles, and an explanation of their meaning.

Code words in text, database table names, folder names, filenames, file extensions, path names, dummy URLs, user input, and Twitter handles are shown as follows: "The most widely used Python package for graphs and images is called matplotlib."

A block of code is set as follows:

import matplotlib.pyplot as plt  
plt.plot([2,3,8,12]) 
plt.show()

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

import matplotlib.pyplot as plt  
plt.plot([2,3,8,12]) 
plt.show()

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

install.packages("rattle")

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: "For the sources of data, we choose from seven potential formats, such as File, ARFF, ODBC, R Dataset, RData File, and we can load our data from there."

Warnings or important notes appear like this.

Tips and tricks appear like this.