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Python NumPy for Beginners NumPy Specialization for Data Science (AI Publishing) (z-library.sk, 1lib.sk, z-lib.sk)
PythonAuthor：AI Publishing
Python NumPy for Beginners Python Libraries Textbook for Beginners with Codes Folder Python is doubtless the most versatile programming language. But are you serious enough about becoming proficient in Python? If yes, then you need to become a master in the two essential Python libraries—NumPy and Pandas. You simply can’t overlook this truth. In data science, NumPy and Pandas are by far the most widely used Python libraries. The main features of these libraries are powerful data analysis tools and easy-to-use structures. Python NumPy for Beginnerspresents you with a hands-on, simple approach to learning Python fast. This book is refreshingly different, as there’s a lot for you to do than mere reading. Each theoretical concept you cover is followed by practical examples, making it easier to master the concept. The step-by-step layout of this book simplifies your learning. The author has gone to great lengths to ensure what you learn sticks. You have short exercises at the end of each one of the 11 chapters to test your knowledge of the theoretical concepts you have learned. This book presents you with: A strong foundation in NumPy. A deep understanding of fundamental and intermediate topics. The essentials of coding in Python. Links to reference materials related to the topics you study. Quick access to external files to practice and learn advanced concepts of NumPy. AResources folder containing all the datasets used in the book. The Focus of the Book Is on Learning by Doing In thislearning by doing book, you start with Python installation in the very first chapter. Then there’s a crash course in Python in the second half of the first chapter. In the second chapter, you jump straight to NumPy. Right through the book, you’ll useJupyter Notebookto write code. You can also get fast access to the datasets used in this book. The book is loaded with self-explanatory scripts, graphs, and images. They have been meticulously designed to help you understand new concepts eas
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© Copyright 2021 by AI Publishing All rights reserved. First Printing, 2021 Edited by AI Publishing eBook Converted and Cover by AI Publishing Studio Published by AI Publishing LLC ISBN-13: 978-1-956591-09-5 The contents of this book may not be copied, reproduced, duplicated, or transmitted without the direct written permission of the author. Under no circumstances whatsoever will any legal liability or blame be held against the publisher for any compensation, damages, or monetary loss due to the information contained herein, either directly or indirectly. Legal Notice: You are not permitted to amend, use, distribute, sell, quote, or paraphrase any part of the content within this book without the specific consent of the author. Disclaimer Notice: Kindly note that the information contained within this document is solely for educational and entertainment purposes. No warranties of any kind are indicated or expressed. Readers accept that the author is not providing any legal, professional, financial, or medical advice. Kindly consult a licensed professional before trying out any techniques explained in this book. By reading this document, the reader consents that under no circumstances is the author liable for any losses, direct or indirect, that are incurred as a consequence of the use of the information contained within this document, including, but not restricted to, errors, omissions, or inaccuracies.
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How to Contact Us If you have any feedback, please let us know by emailing contact@aipublishing.io. Your feedback is immensely valued, and we look forward to hearing from you. It will be beneficial for us to improve the quality of our books. To get the Python codes and materials used in this book, please click the link below: www.aipublishing.io/book-Numpy-python The order number is required.
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About the Publisher At AI Publishing Company, we have established an international learning platform specifically for young students, beginners, small enterprises, startups, and managers who are new to data science and artificial intelligence. Through our interactive, coherent, and practical books and courses, we help beginners learn skills that are crucial to developing AI and data science projects. Our courses and books range from basic introduction courses to language programming and data science to advanced courses for machine learning, deep learning, computer vision, big data, and much more, using programming languages like Python, R, and some data science and AI software. AI Publishing’s core focus is to enable our learners to create and try proactive solutions for digital problems by leveraging the power of AI and data science to the maximum extent. Moreover, we offer specialized assistance in the form of our free online content and eBooks, providing up-to-date and useful insight into AI practices and data science subjects, along with eliminating the doubts and misconceptions about AI and programming. Our experts have cautiously developed our online courses and kept them concise, short, and comprehensive so that you can understand everything clearly and effectively and start practicing the applications right away. We also offer consultancy and corporate training in AI and data science for enterprises so that their staff can navigate through the workflow efficiently. With AI Publishing, you can always stay closer to the innovative world of AI and data science.
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If you are eager to learn the A to Z of AI and data science but have no clue where to start, AI Publishing is the finest place to go. Please contact us by email at contact@aipublishing.io.
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AI Publishing Is Searching for Authors Like You Interested in becoming an author for AI Publishing? Please contact us at author@aipublishing.io. We are working with developers and AI tech professionals just like you to help them share their insights with global AI and Data Science lovers. You can share all your knowledge about hot topics in AI and Data Science.
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Table of Contents How to Contact Us About the Publisher AI Publishing Is Searching for Authors Like You Preface Book Approach Who Is This Book For? How to Use This Book? About the Author Get in Touch With Us Download the PDF version Warning Chapter 1: Introduction 1.1. What Is NumPy? 1.2. Environment Setup and Installation 1.2.1. Windows Setup 1.2.2. Mac Setup 1.2.3. Linux Setup 1.2.4. Using Google Colab Cloud Environment 1.2.5. Writing Your First Program 1.3. Python Crash Course 1.3.1. Python Syntax
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1.3.2. Python Variables and Data Types 1.3.3. Python Operators 1.3.4. Conditional Statements 1.3.5. Iteration Statements 1.3.6. Functions 1.3.7. Objects and Classes Exercise 1.1 Exercise 1.2 Chapter 2: NumPy Basics 2.1. Introduction to NumPy Arrays 2.2. NumPy Data Types 2.3. Creating NumPy Arrays 2.3.1. Using Array Method 2.3.2. Using Arrange Method 2.3.3. Using Ones Method 2.3.4. Using Zeros Method 2.3.5. Using Eyes Method 2.3.6. Using Random Method 2.4. Printing NumPy Arrays 2.5. Adding Items in a NumPy Array 2.6. Removing Items from a NumPy Array Exercise 2.1 Exercise 2.2 Chapter 3: NumPy Array Manipulation 3.1. Sorting NumPy Arrays 3.1.1. Sorting Numeric Arrays
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3.1.2. Sorting Text Arrays 3.1.3. Sorting Boolean Arrays 3.1.4. Sorting 2-D Arrays 3.1.5. Sorting in Descending Order 3.2. Reshaping NumPy Arrays 3.2.1. Reshaping from Lower to Higher Dimensions 3.2.2. Reshaping from Higher to Lower Dimensions 3.3. Indexing and Slicing NumPy Arrays 3.4. Broadcasting NumPy Arrays 3.5. Copying NumPy Arrays 3.6. NumPy I/O Operations 3.6.1. Saving a NumPy Array 3.6.2. Loading a NumPy Array Exercise 3.1 Exercise 3.2 Chapter 4: NumPy Tips and Tricks 4.1. Statistical Operations with NumPy 4.1.1. Finding the Mean 4.1.2. Finding the Median 4.1.3. Finding the Max and Min Values 4.1.4. Finding Standard Deviation 4.1.5. Finding Correlations 4.2. Getting Unique Items and Counts 4.3. Reversing a NumPy Array 4.4. Importing and Exporting CSV Files 4.4.1. Saving a NumPy File as CSV
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4.4.2. Loading CSV Files into NumPy Arrays 4.5. Plotting NumPy Arrays with Matplotlib Exercise 4.1 Exercise 4.2 Chapter 5: Arithmetic and Linear Algebra Operations with NumPy 5.1. Arithmetic Operations with NumPy 5.1.1. Finding Square Roots 5.1.2. Finding Logs 5.1.3. Finding Exponents 5.1.4. Finding Sine and Cosine 5.2. NumPy for Linear Algebra Operations 5.2.1. Finding the Matrix Dot Product 5.2.2. Element-wise Matrix Multiplication 5.2.3. Finding the Matrix Inverse 5.2.4. Finding the Matrix Determinant 5.2.5. Finding the Matrix Trace 5.2.6. Solving a System of Linear Equations with Python Exercise 5.1 Exercise 5.2 Chapter 6: Implementing a Deep Neural Network with NumPy 6.1. Neural Network with a Single Output 6.1.1. Feed Forward 6.1.2. Backpropagation 6.1.3. Implementation with NumPy Library 6.2. Neural Network with Multiple Outputs
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6.2.1. Feed Forward 6.2.2. Backpropagation 6.2.3. Implementation with NumPy Library Exercise 6.1 Exercise 6.2 Appendix: Working with Jupyter Notebook Exercise Solutions Exercise 1.1 Exercise 1.2 Exercise 2.1 Exercise 2.2 Exercise 3.1 Exercise 3.2 Exercise 4.1 Exercise 4.2 Exercise 5.1 Exercise 5.2 Exercise 6.1 Exercise 6.2 From the Same Publisher
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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.
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§ Book Approach 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. The rest of the book contain five chapters. Chapter 2 provides a brief introduction to the NumPy array. You will study how to create NumPy arrays and add, remove, and print items in NumPy arrays. Chapter 3 focuses on NumPy arrays manipulation concepts such as sorting, reshaping, and indexing. Chapter 4 provides miscellaneous tips and tricks for the NumPy library. The 5th chapter explains how you can perform mathematical operations with NumPy, while the 6th chapter explains the process of creating an artificial neural network with NumPy from scratch. 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 or Kindle version, as you might face an indentation issue. However, if you have to copy some code, copy it from the Python Notebooks.
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§ Who Is This Book For? 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. 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. 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.
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§ How to Use This Book? 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. To facilitate the reading process, occasionally, the book presents three types of box-tags in different colors: Requirements, Further Readings, and Hands-on Time. Examples of these boxes are shown below. Requirements This box lists all requirements needed to be done before proceeding to the next topic. Generally, it works as a checklist to see if everything is ready before a tutorial. Further Readings Here, you will be pointed to some external reference or source that will serve as additional content about the specific Topic being studied. In general, it consists of packages, documentations, and cheat sheets. Hands-on Time Here, you will be pointed to an external file to train and test all the knowledge acquired about a Tool that has been studied. Generally, these files are Jupyter notebooks (.ipynb), Python (.py) files, or documents (.pdf).
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The box-tag Requirements lists the steps required by the reader after reading one or more topics. Further Readings provides relevant references for specific topics to get to know the additional content of the topics. Hands-on Time points to practical tools to start working on the specified topics. Follow the instructions given in the box-tags to better understand the topics presented in this book.
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About the Author M. Usman Malik holds a Ph.D. in Computer Science from Normandy University, France, with Artificial Intelligence and Machine Learning being his main areas of research. Muhammad Usman Malik has over five years of industry experience in Data Science and has worked with both private and public sector organizations. He likes to listen to music and play snooker in his free time. You can follow his Twitter handle: @usman_malikk.
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Get in Touch With Us Feedback from our readers is always welcome. For general feedback, please send us an email at contact@aipublishing.io and mention the book title in the subject line. Although we have taken extraordinary care to ensure the accuracy of our content, errors do occur. If you have found an error in this book, we would be grateful if you could report this to us as soon as you can. If you are interested in becoming an AI Publishing author and if you have expertise in a topic and you are interested in either writing or contributing to a book, please send us an email at author@aipublishing.io.
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Download the PDF version We request you to download the PDF file containing the color images of the screenshots/diagrams used in this book here: www.aipublishing.io/book-Numpy-python The order number is required.
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