Publishing - Python Pandas for Beginners: Pandas Specialization for Data Scientist (Python for Beginners in Data Science and Data Analysis Book 2)

Copyright 2021 by AI Publishing

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First Printing, 2021

Edited by AI Publishing

eBook Converted and Cover by AI Publishing Studio

Published by AI Publishing LLC

ISBN-13: 978-1-956591-10-1

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Table of Contents

Preface

With the rise of data science and high-performance computing hardware, programming languages have evolved as well. Various libraries in different programming languages have been developed that provide a layer of abstraction over complex data science tasks. Python programming language has taken the lead in this regard. More than 50 percent of all data science-related projects are being developed using Python programming.

If you ask a data science expert what the two most common and widely used Python libraries for data science are, the answer would almost invariably be the NumPy library and the Pandas library. And this is what the focus of this book is. It introduces you to the NumPy and Pandas libraries with the help of different use cases and examples.

Thank you for your decision to purchase this book. I can assure you that you will not regret your decision.

The book follows a very simple approach. The 1st chapter is introductory and provides information about setting up the installation environment. The 1st chapter also contains a brief crash course on Python, which you can skip if you are already familiar with Python.

Chapter 2 provides a brief introduction to Pandas. Chapter 3 explains how you can manipulate Pandas dataframes, while the 4th chapter focuses on grouping, aggregating, and merging data with Pandas. Finally, the 5th and 6th chapters focus on data visualization and time-series handling with Pandas, respectively.

Each chapter explains the concepts theoretically, followed by practical examples. Each chapter also contains exercises that students can use to evaluate their understanding of the concepts explained in the chapter. The Python notebook for each chapter is provided in the Codes Folder that accompanies this book. It is advised that instead of copying the code from the book, you write the code yourself, and in case of an error, you match your code with the corresponding Python notebook, find and then correct the error. The datasets used in this book are either downloaded at runtime or are available in the Resources folder.

Do not copy and paste the code from the PDF notebook, as you might face an indentation issue. However, if you have to copy some code, copy it from the Python Notebooks.

The book is aimed ideally at absolute beginners to data science in specific and Python programming in general. If you are a beginner-level data scientist, you can use this book as a first introduction to NumPy and dataframes. If you are already familiar with Python and data science, you can also use this book for general reference to perform common tasks with NumPy and Pandas.

Since this book is aimed at absolute beginners, the only prerequisites to efficiently use this book are access to a computer with the internet and basic knowledge of programming. All the codes and datasets have been provided. However, you will need the internet to download the data preparation libraries.

In each chapter, try to understand the usage of a specific concept first and then execute the example code. I would again stress that rather than copying and pasting code, try to write codes yourself. Then, in case of any error, you can match your code with the source code provided in the book as well as in the Python Notebooks in the Resources folder.

Finally, answer the questions asked in the exercises at the end of each chapter. The solutions to the exercises have been given at the end of the book.