Practical Python Programming for Data Scientists

Data Science plays a very vital role in shaping up the process of transitioning data into information and into knowledge. As business enterprises, organizations, governments, IT companies, and service providers are keenly becoming data-driven, the role and responsibility of data scientists are bound to go up significantly. Python is emerging as the leading programming language for Data Science projects. The aim of the book is to clearly explain how Python simplifies and speeds up the realization of next-generation Data Science applications. Data Science (DS) is a fast-emerging field of study and research. It leverages integrated data analytics (big, fast, and streaming analytics) platforms and Artificial Intelligence (AI) (machine and deep learning (ML/DL), computer vision (CV), and natural language processing (NLP)) algorithms extensively to extract actionable insights out of burgeoning data volumes in time. Due to the ready availability of several libraries for facilitating the development of data science services, Python is turning out the programming language of choice for data science. The following libraries are enabling data science applications and are made available in Python: 1. NumPy: This is a library that makes a variety of mathematical and statistical operations easier and faster. This is also the basis for many features of the Pandas library. 2. Pandas: Pandas is a fast, powerful, flexible and easy to use open source data analysis and manipulation tool, built on top of the Python programming language. This is one of the gamechangers for the tremendous success of data science projects. 3. Matplotlib: This is a visualization library that makes it quick and easy to generate charts from data. 4. Scikit-Learn: This is the most popular library for machine learning (ML) work in Python. The book starts with a couple of chapters on data science and machine learning (ML) topics. Thereafter, the chapters are focusing on the fundamental and foundational aspects of Python programming language. All kinds of language constructs are accentuated and articulated for the benefit of programmers with all the practical details. There are dedicated chapters for producing machine learning applications. The gist of the book is to clearly explain how Python simplifies and speeds up the realization of next-generation data science applications. All the specific libraries towards data science are given the required thrust in order to empower our esteemed readers with all the right and relevant information. This book is being prepared with the intention of empowering data scientists with all the vital details about programming using the Python language. Cover 1 Title Page 5 Copyright 6 ABOUT THE AUTHORS 7 TABLE OF CONTENTS 11 List of Figures 17 List of Tables 19 List of Abbreviations 21 Preface 23 Chapter 1 The Distinctions of Python Language 25 1.1. Introduction 26 1.2. Web Application Development 27 1.3. Game Development 27 1.4. Artificial Intelligence (AI) Applications 27 1.5. Graphical User Interfaces (GUIS) 28 1.6. Computer Vision (CV) Applications 28 1.7. Audio And Video Applications 28 1.8. Knowledge Visualization Applications 29 1.9. Scientific and Numeric Applications 29 1.10. IoT and CPS Applications 29 1.11. Data Analytics 30 1.12. Python For Blockchain Apps 30 1.13. Conclusion 31 Chapter 2 Demystifying the Data Science Paradigm 33 2.1. Introduction 34 2.2. Briefing Data Analysis 35 2.3. Entering Into Data Science (DS) 35 2.4. The Lifecycle of a Data Science (DS) Project 39 2.5. The Prominent Use Cases of Data Science (DS) 41 2.6. Machine Learning (Ml) Algorithms 45 2.7. Key Machine Learning (Ml) Algorithms 52 2.8. Ensemble Learning Algorithms 55 2.9. Steps to Build a Random Forest (RF) 56 2.10. Time Series Forecasting 57 2.11. Time Series Forecasting Methods 58 2.12. Time Series Forecasting Applications 59 2.13. Clustering Algorithms 59 2.14. Case Study: Diabetes Prevention 64 2.15. Conclusion 66 Chapter 3 Python for Data Analysis 67 3.1. Python for Data Analysis 68 3.2. Python Libraries 68 3.3. Scientific Libraries in Python-Numpy, Scipy, Matplotlib, and Pandas 70 3.4. Machine Learning (Ml) 81 3.5. Machine Learning (Ml) With Internet of Things (IoT) 93 3.6. Machine Learning (Ml) Application With IoT 95 3.7. Algorithm 96 3.8. Building Blocks of Algorithms (Instructions/Statements, State, Control Flow, Functions) 97 3.9. Notation (Pseudocode, Flow Chart, Programming Language) 101 3.10. Algorithmic Problem Solving 111 3.11. Flow of Control 115 3.12. Illustrative Program 120 Chapter 4 Python Programming: An Introduction 127 4.1. Introduction to Python 128 4.2. Downloading and Installing Python 3.6.2 130 4.3. Python Interpreter and Interactive Mode 134 4.4. Values and Types: Int, Float, Boolean, String, and List 138 4.5. Variables 143 4.6. Keywords 143 4.7. Statements and Expressions 144 4.8. Comments 145 4.9. Input and Output 145 4.10. Operators 146 Chapter 5 Functions 159 5.1. Function Definition 160 5.2. Built-In Functions 160 5.3. Math Functions 164 5.4. User Defined Function 166 5.5. Function Prototypes 168 5.6. Return Statement 172 5.7. Modules 172 Chapter 6 Control Structures 181 6.1. Boolean Values 182 6.2. Conditional Statements 183 6.3. Iteration/Control Statements 190 6.4. Loop Control Statements 198 6.5. Fruitful Functions 203 6.6. Local and Global Scope 204 6.7. Function Composition 205 6.8. Recursion 206 Chapter 7 Strings 209 7.1. String Definition 210 7.2. Operations On String 210 7.3. String Methods 212 7.4. String Module 219 7.5. List As Array 221 7.6. Searching 223 Chapter 8 Lists 231 8.1. Lists 232 8.2. List Operations 233 8.3. List Slices 233 8.4. List Methods 234 8.5. List Loop 239 8.6. Mutability 240 8.7. List Aliasing 241 8.8. Cloning Lists 243 8.9. List Parameters 245 8.10. Deleting List Elements 247 8.11. Python Functions For List Operations 247 8.12. List Comprehension 248 Chapter 9 Tuples 251 9.1. Tuples 252 9.2. Tuple Methods 259 9.3. Other Tuple Operations 260 9.4. Tuples As Return Values 261 9.5. Built-In Functions With Tuple 262 9.6. Variable-Length Argument Tuples 262 9.7. Comparing Tuples 263 Chapter 10 Dictionaries 265 10.1. Dictionaries 266 10.2. Built-In Dictionary Functions and Methods 268 10.3. Access, Update, and Add Elements in Dictionary 269 10.4. Delete or Remove Elements From a Dictionary 270 10.5. Sorting a Dictionary 271 10.6. Iterating Through a Dictionary 271 10.7. Reverse Lookup 271 10.8. Inverting a Dictionary 272 10.9. Memoization (MEMOS) 273 Chapter 11 Files 287 11.1. Files 288 11.2. Errors and Exception 301 Chapter 12 Modules and Packages 311 12.1. Modules 312 12.2. Packages 318 Chapter 13 Classes in Python 329 13.1. Introducing the Concept of Classes in Python 330 13.2. Object 330 13.3. Methods 331 13.4. Inheritance 332 13.5. Encapsulation 333 13.6. Polymorphism 334 Index 341 Back Cover 346