Python For Beginners.Learn Data Science in 5 Days the Smart Way and Remember it Longer. With Easy Step by Step Guidance & Hands on Examples. (Python Crash Course-Programming for Beginners): Python for Beginners

By Arthur T. Brooks

Have you always wanted to learn computer programming but thought it was too difficult or would take too long?

Do you want to know the secret to learning Python Data Science the easy way and start programming today?

This book is for you.

You don't need to waste your time and money learning Python the hard way through tiresome technical books, expensive online courses and difficult Python tutorials.

This non-technical book will gently guide you through…

The Python Data Science Language.

You will learn the most concise methods to get you coding on day one-the smart way.

Python for Beginners.

Beginner friendly hands on examples of practical and usable projects.

The most useful Python examples.

Each example is specifically designed to give you a progressive and thorough understanding of key concepts and all answers are provided.

Strategic Python Data Science topics.

The topics are presented in user friendly bite sized chunks to optimize a quick learning style which will also make it easy for you to remember.

This book is different in that it's primary focus it to teach you Python Data Science in a simple and concise format and in the quickest time frame possible. Each short chapter has exercises at the end which summarize what you have learned in a progressive manner to avoid overloading you with information. Each exercise has been carefully chosen to enable you to master the language and retain what you have learned. No technical skills, previous knowledge or experience is required.

Download it now buy clicking the BUY button.

You'll also learn:

• Exactly what is IPython?

• What are the IPython keyboard shortcuts?

• What are the IPython magic commands?

• What is Numpy and how can I use it?

• Numpy array attributes?

• Advanced Ufunc Features?

• Working with Boolean Arrays?

• Boolean Operators?

• Data Manipulation with Pandas?

• Constructing Data Frame objects?

• Data Selection in DataFrame?

• Handling Missing Data?

• Operations with Matplotlib?

And more!

Finally, you will be gently guided on how to put everything that you have learned together so that you can immediately start your own Python coding in your chosen real-world scenarios.

• Language: English
• Publisher: Arthur T. Brooks
• Release date: Mar 10, 2020
• ISBN: 9781393721154

Book preview

Python for Beginners

Learn Data Science the Smart Way and remember it longer

––––––––

Arthur T Brooks

Other Python books by Arthur T Brooks

Python for Beginners

A Smarter Way to Learn Python Progrmming in 5 Days and Remember it longer

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Contents

INTRODUCTION

CHAPTER ONE

IPYTHON: A NOTCH HIGHER THAN NORMAL PYTHON

A Few Helpful Tips

Exploring Modules with Tab Completion

Keyboard Shortcuts in the IPython Shell

IPython Magic Commands

Input and Output History

Errors and Debugging

Partial List of Debugging Commands

Chapter Summary

CHAPTER TWO

INTRODUCTION TO NUMPY

NumPy Array Attributes

Computation on NumPy Arrays: Universal Functions

Advanced Ufunc Features

Aggregations: Min, Max, and Everything in Between

Comparisons and Boolean Logic

Working with Boolean Arrays

Boolean Operators

Structured Data: NumPy’s Structured Arrays

Chapter Summary

CHAPTER THREE

DATA MANIPULATION WITH PANDAS

Introducing Pandas Objects

The Pandas DataFrame Object

Constructing DataFrame Objects

Data Selection in Series

Data Selection in DataFrame

Additional Indexing Conventions

Ufuncs: Index Preservation

UFuncs: Index Alignment

Handling Missing Data

Combining Datasets: Merge and Join

Specification of the Merge Key

List of Pandas String Methods Similar to Python

Chapter Summary

CHAPTER FOUR

OPERATIONS WITH MATPLOTLIB

Plotting from an IPython Shell

Simple Scatter Plots

Density and Contour Plots

Histograms, Binnings, and Density

Multiple Subplots

Customizing Ticks

Three-Dimensional Plotting in Matplotlib

Visualization with Seaborn

Exploring Seaborn Plots

Chapter Summary

CHAPTER FIVE

MACHINE LEARNING

Feature Engineering

Example 5: Feature Pipelines

In Depth: Naive Bayes Classification

In Depth: Linear Regression

Basis Function Regression

In-Depth: Support Vector Machines

In-Depth: Manifold Learning

Chapter Summary

CONCLUSION

ANSWERS

INDEX

Copyright 2020 by Arthur T. Brooks - All rights reserved.The following book is reproduced with the goal of providing information that is as accurate and reliable as possible. The recommendations suggestions contained in these pages are solely for entertainment purposes. This declaration is deemed fair and valid by both the American Bar Association and the Committee of Publishers Association and is legally binding throughout the United States.

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INTRODUCTION

Python was created in the 1980s by Guido van Rossum and since then, notable companies like Yahoo, Facebook and Google have been using the language extensively. Its advantage over other programming languages is its simplicity which enables new programmers to learn it quickly and execute their own projects effectivley. In addition, highly experienced programmers also find the Python language handy and versatile in the daily practice of their art.

Who this Book is for and Those Who Will Benefit from It

This is the second book in the highly acclaimed ‘Python for Beginners’ series by Arthur T Brooks and it covers the more advanced aspects of the Python language. It is aimed at beginners and intermediates. The fact that you are holding this book in your hands alone suggests that you are not totally new to Python. Although it is entirely possible to read this book as a complete novice and still learn the lessons very well, it will perhaps be a bit easier to follow to those who already have a basic understanding of Python fundimentals. Those with a little prior experience may also feel more at home with the interface as your mind and fingers will already be used to the behaviour of Python codes. Fear not in any case because this book will take you by the hand and gently guide you through the concepts in an easy to digest and progressive manner, whatever your experience or skill level.

Basic Python is used in programming mobile applications, designing computer games, creating interactive websites and interactive devices that can be tailored to perform any automated tasks in just about any industry of human endeavour. The advanced Python, which is called Interactive Python or simply IPython, is more commonly used in operations concerning data. It is a very useful tool for data scientists. As a matter of fact, it is built with the aim of aiding the task of data manipulation and presentation. As expected, it will benefit academic researchers, industry researchers, educational institutions, crime-fighting agencies, administrative organs, business organizations, and every other area that deals with some kind of data.

IPython’s usefulness stems from the fact that it has many inbuilt methods and functions that automate and simplify otherwise complex mathematical operations on data. It also helps with the organization, cleaning, processing, and presentation of data. When large datasets are involved, it does not only save time but also reduces the occurrence of errors. You can use a small dataset to write and test a code. The moment the code is correct and works as expected, it is safe to be applied to large datasets, which will be manipulated with minimal input requirements.

This publication is a clear and concise non-technical guidebook on how to learn data science using IPython in 5 days or less. The goal for this book is not just for you to learn and memorize codes, but how to understand actual Python itself. As well as knowing the code, you will be able to understand the way Python works which will enable you to use your creativity to achieve unlimited innovative tasks. It is written in a very simple language which makes it easy for anyone who understands English to read, follow the instructions and understand. It is structured and based to reflect real world situations.

The aim is to provide just the required amount of information at the right stage , so that the reader does not get overwhelmed by an endless sea of code with little or no immediate relevance to his or her life. Therefore, the book is structured from the simplest and shortest operations, introducing the more advanced parts of the language only after you have become familiar with the basic concepts. At the end of the book, you will realize that you have, without any stressful self-exertion, become able to not only code in Python, but also think the way Python thinks.

How to Benefit from Using this Book

This book is designed to enable you to solve your data science problems within 5 days. It is deliberately kept short and concise. If you are an avid reader, you could finish it quickly however it is recommended that you follow the 5-day plan in order to properly digest what you are learning. Since there are only five chapters, you could read and absorb the contents of one chapter each day. Every chapter begins with a brief general overview of what it contains and how it will benefit you when you understand it. The earlier part of each concept is usually the most basic and precise. After the principle is adequately emphasized, it is further illustrated using real life situations in which it may be applicable.

At the end of each chapter, there is a chapter summary which recaps what you have learned in that chapter. More importantly, there are exercises at the end of each chapter to enable you try your hand at what you have just learned and to help make it stick in your mind. The examples and exercises in this book are set in real-world work situations. These senarios are designed to inspire and encourage you to see yourself using the language to offer innovative solutions to other challenges.

I’d like to clarify that this is not a book ABOUT data science. Instead, it is a data science book. What is the difference? A book ABOUT data science will tell you a lot about how to use advanced Python and may contain everything that exists to know about Python. But you can finish a book about data science and not be able to use the language. On the contrary, this is a Python book because you will not just read it, but jump into its very pages and codes. It is a practical manual for learning how to do data science in Python. Irrespective of whether you are reading this book on your device or in a hard copy, where you are sitting should be littered with the codes. I mean, you should be so into the book that some of the code jumps out and rubs on your clothes, fingers and litter your desk. You need to allow the code to get into you. To achieve this goal, it is strongly advised that you download the required software, and install them on your system before proceeding to the rest of the book. How to do this is covered in the early part of chapter one of the book.

The aim is to enable you not just read along, but work along with the book. You can read the codes and just believe that they will do what the book says the codes will do. But a better way to learn is to actually write out the code in your IPython shell or Jupyter Notebook and run it to see whether what the book says about it is true. Secondly, as you write the code yourself, you will realize that there are certain aspects of the code like spacing, indentation, punctuation and so on, that you did not really notice at a glance but which can affect your outcome tremendously. You should, therefore, apply your hand to the task yourself.

At the end of this book, there are answers to the exercises found in each chapter. Do not rush to go behind and check the answers. Instead, try hard to get the required result. The beautiful thing