Python 3 and Data Analytics Pocket Primer: A Quick Guide to NumPy, Pandas, and Data Visualization

PREFACE

WHAT IS THE PRIMARY VALUE PROPOSITION FOR THIS BOOK?

This book contains a fast-paced introduction to as much relevant information about data analytics as possible in a book of this size. At the same time, please keep in mind: you will not become an expert in data analytics by reading this book.

However, you will be exposed to a variety of features of NumPy and Pandas, how to write regular expressions (with the accompanying appendix), and how to perform many data cleaning tasks. Keep in mind that some topics are presented in a cursory manner for two main reasons. First, it’s important that you be exposed to these concepts. In some cases, you will find topics that might pique your interest, and hence motivate you to learn more about them through self-study; in other cases, you will probably be satisfied with a brief introduction. In other words, you will decide whether or not to delve into more detail regarding the topics in this book. Second, a full treatment of all the topics that are covered in this book would significantly increase its length. This is contrary to the series design as primers. It’s important for you to decide if this approach is suitable for your needs and learning style: if not, you can select one or more of the plethora of data analytics books that are available.

THE TARGET AUDIENCE

The book is intended primarily for people who have worked with Python and are interested in learning about several important Python libraries, such as NumPy and Pandas.

It is also intended to reach an international audience of readers with highly diverse backgrounds in various age groups. While many readers know how to read English, their native spoken language is not English (which could be their second, third, or even fourth language). Consequently, this book uses standard English rather than colloquial expressions that might be confusing to those readers. As you know, many people learn by different types of imitation, which includes reading, writing, or hearing new material. The book takes these points into consideration in order to provide a comfortable and meaningful learning experience for the intended readers.

WHAT WILL I LEARN FROM THIS BOOK?

The first chapter contains a quick tour of basic Python 3, followed by a chapter which introduces you to data types and data cleaning tasks, such as working with datasets that contain different types of data, and how to handle missing data. The third and fourth chapters introduce you to NumPy and Pandas (and many code samples).

The fifth chapter contains fundamental concepts in probability and statistics, such as mean, mode, and variance and correlation matrices. You will also learn about Gini impurity, entropy, and KL-divergence. The book covers eigenvalues, eigenvectors, and PCA (principal component analysis).

The sixth and final chapter of this book delves into data visualization with Matplotlib, Seaborn, and an example of a rendering of graphics effects in Bokeh. Finally, there is an appendix for regular expressions, with enough examples so you can understand most regular expressions that you will encounter in your code.

WHY ARE THE CODE SAMPLES PRIMARILY IN PYTHON?

Most of the code samples are short (usually less than one page and sometimes less than half a page), and if need be, you can easily and quickly copy/paste the code into a new Jupyter notebook. For the Python code samples that reference a CSV file, you do not need any additional code in the corresponding Jupyter notebook to access the CSV file. Moreover, the code samples execute quickly, so you won’t need to avail yourself of the free GPU that is provided in Google Colaboratory.

If you do decide to use Google Colaboratory, you can easily copy/paste the Python code into a notebook, and also use the upload feature to upload existing Jupyter notebooks. Keep in mind the following point: if the Python code references a CSV file, make sure that you include the appropriate code snippet (as explained in Chapter 1) to access the CSV file in the corresponding Jupyter notebook in Google Colaboratory.

DO I NEED TO LEARN THE THEORY PORTIONS OF THIS BOOK?

Once again, the answer depends on the extent to which you plan to become involved in data analytics. For example, if you plan to study machine learning, then you will probably learn how to create and train a model, which is a task that is performed after data cleaning tasks. In general, you will probably need to learn everything that you encounter in this book if you are planning to become a machine learning engineer.

WHY DOES THIS BOOK INCLUDE SKLEARN MATERIAL?

First, keep in mind that the Sklearn material in this book is minimalistic because this book is not about machine learning. Second, the Sklearn material is located in Chapter 6 where you will learn about some of the Sklearn built-in datasets. If you decide to delve into machine learning, you will have already been introduced to some aspects of Sklearn.

WHY IS A REGEX APPENDIX INCLUDED IN THIS BOOK?

Regular expressions are supported in multiple languages (including Java and JavaScript) and they enable you to perform complex tasks with very compact, regular expressions. Alas, regular expressions can seem arcane and too complex to learn in a reasonable amount of time. Fortunately, there is good news: Chapter 2 contains some Pandas-based code samples that use regular expressions to perform tasks that might otherwise be more complicated.

If you plan to use Pandas extensively or you plan to work on NLP-related tasks, then the code samples in the appendix will be very useful for you because they are more than adequate for solving certain types of tasks, such as removing HTML tags. Moreover, the knowledge you gain will transfer instantly to other languages that support regular expressions.

GETTING THE MOST FROM THIS BOOK

Some programmers learn well from prose, others learn well from sample code (and lots of it), which means that there’s no single style that can be used for everyone.

Moreover, some programmers want to run the code first, see what it does, and then return to the code to delve into the details (and others use the opposite approach).

Consequently, there are various types of code samples in this book: some are short, some are long, and other code samples build from earlier code samples.

WHAT DO I NEED TO KNOW FOR THIS BOOK?

Current knowledge of Python 3.x is the most helpful skill. Knowledge of other programming languages (such as Java) can also be helpful because of the exposure to programming concepts and constructs. The less technical knowledge that you have, the more diligence will be required in order to understand the various topics that are covered.

If you want to be sure that you can grasp the material in this book, glance through some of the code samples to get an idea of how much is familiar to you and how much is new for you.

DON’T THE COMPANION FILES OBVIATE THE NEED FOR THIS BOOK?

The companion files contain all the code samples to save you time and effort from the error-prone process of manually typing code into a text file. There are situations, however, in which you might not have easy access to the companion files. Furthermore, the code samples in the book provide explanations that are not available on the companion files.

DOES THIS BOOK CONTAIN PRODUCTION-LEVEL CODE