Book Image

Python Data Science Essentials

By : Alberto Boschetti, Luca Massaron
Book Image

Python Data Science Essentials

By: Alberto Boschetti, Luca Massaron

Overview of this book

The book starts by introducing you to setting up your essential data science toolbox. Then it will guide you across all the data munging and preprocessing phases. This will be done in a manner that explains all the core data science activities related to loading data, transforming and fixing it for analysis, as well as exploring and processing it. Finally, it will complete the overview by presenting you with the main machine learning algorithms, the graph analysis technicalities, and all the visualization instruments that can make your life easier in presenting your results. In this walkthrough, structured as a data science project, you will always be accompanied by clear code and simplified examples to help you understand the underlying mechanics and real-world datasets.
Table of Contents (13 chapters)

Chapter 1. First Steps

Whether you are an eager learner of data science or a well-grounded data science practitioner, you can take advantage of this essential introduction to Python for data science. You can use it to the fullest if you already have at least some previous experience in basic coding, writing general-purpose computer programs in Python, or some other data analysis-specific language, such as MATLAB or R.

The book will delve directly into Python for data science, providing you with a straight and fast route to solve various data science problems using Python and its powerful data analysis and machine learning packages. The code examples that are provided in this book don't require you to master Python. However, they will assume that you at least know the basics of Python scripting, data structures such as lists and dictionaries, and the working of class objects. If you don't feel confident about this subject or have minimal knowledge of the Python language, we suggest that before you read this book, you should take an online tutorial, such as the Code Academy course at or Google's Python class at Both the courses are free, and in a matter of a few hours of study, they should provide you with all the building blocks that will ensure that you enjoy this book to the fullest. We have also prepared a tutorial of our own, which you can download from the Packt Publishing website, in order to provide an integration of the two aforementioned free courses.

In any case, don't be intimidated by our starting requirements; mastering Python for data science applications isn't as arduous as you may think. It's just that we have to assume some basic knowledge on the reader's part because our intention is to go straight to the point of using data science without having to explain too much about the general aspects of the language that we will be using.

Are you ready, then? Let's start!

In this short introductory chapter, we will work out the basics to set off in full swing and go through the following topics:

  • How to set up a Python Data Science Toolbox

  • Using IPython

  • An overview of the data that we are going to study in this book