Hands-on Matplotlib

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Hands-on Matplotlib: Learn Plotting and Visualizations with Python 3 by Ashwin Pajankar

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I have been working in the domain of data science for more than a decade now, and I was introduced to Python more than 15 years ago. When I first worked with libraries such as NumPy, Matplotlib, and Pandas, I found it a bit tedious to comb through all the available literature in the form of printed books, video tutorials, and online articles, as most of them lacked comprehensive steps for beginners. It was then that I resolved to write a book, and I am glad that I could bring my resolution to life with the help of Apress.


This book is the result of thousands of hours (in addition to the ones spent writing the actual book) going through technical documentation, watching training videos, writing code with the help of different tools, debugging faulty code snippets, posting questions and participating in discussions on various technical forums, and referring to various code repositories for pointers. I have written the book in such a way that beginners will find it easy to understand the topics. The book has hundreds of code examples and images of code output so that you can fully understand each concept introduced. All the code examples are explained in detail.


The book begins with a general discussion of Python and a small guide explaining how to install it on various computing platforms such as the Windows OS and Linux computers (like the Raspberry Pi). We then move on to discussing the scientific ecosystem. Then we focus on NumPy, which is the fundamental library for numerical computing. We specifically focus on the multidimensional, array-like data structure of NumPy, called the Ndarray. We then explore data visualization libraries, such as Matplotlib and Plotly, to learn how to plot Ndarrays.


Most of the chapters explore the data visualization library Matplotlib. You will learn a lot of data visualization tips and techniques in these chapters.


Then we dive into Pandas so you can learn about its important data structures, called the series and dataframe. Midway through the book, you will also learn how to read data from various data sources using Python, NumPy, Matplotlib, and Pandas. You will also learn how to visualize Pandas data with popular visualization libraries such as Matplotlib and Seaborn, as well as how to work with time-indexed data.


On an ending note, we started and finished working on this project at a very turbulent time (the beginning of 2021) marked by hardships such as hospitalization and deaths of friends, social unrest, lockdowns, curfews, economic slowdown, and a host of other sociopolitical problems brought about in part by the COVID-19 pandemic. I myself was hospitalized due to severe complications from pneumonia and breathing troubles caused by a COVID-19 infection. It delayed the launch of this long-planned project. To be frank, working on this book with the help of my longtime mentors at Apress (Celestin, James, and Aditee) offered me a sense of purpose in these troubled times. I end this introduction with a note of hope and positive words that we, as a society and a global civilization, will overcome these turbulent times with the help of each other, and I look at the future with a lot of hope and bright eyes.


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